(v3.0.1.9059) Update taxonomy of microorganisms

data-raw/_pre_commit_checks.R

@@ -109,11 +109,11 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
       which(MO_staph$species %in% c(
         "coagulase-negative", "argensis", "arlettae",
         "auricularis", "borealis", "caeli", "capitis", "caprae",
-        "carnosus", "casei", "caseolyticus", "chromogenes", "cohnii", "condimenti",
+        "carnosus", "casei", "caseorum", "caseolyticus", "chromogenes", "cohnii", "condimenti",
         "croceilyticus",
         "debuckii", "devriesei", "edaphicus", "epidermidis",
-        "equorum", "felis", "fleurettii", "gallinarum",
-        "haemolyticus", "hominis", "jettensis", "kloosii",
+        "equorum", "felis", "fleurettii", "gallinarum", "halotolerans",
+        "haemolyticus", "hominis", "hsinchuensis", "jettensis", "kloosii",
         "lentus", "lugdunensis", "massiliensis", "microti",
         "muscae", "nepalensis", "pasteuri", "petrasii",
         "pettenkoferi", "piscifermentans", "pragensis", "pseudoxylosus",
@@ -142,7 +142,8 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
         "pseudintermedius", "pseudointermedius",
         "schweitzeri", "simiae",
         "roterodami",
-        "singaporensis"
+        "singaporensis",
+        "ursi"
       ) |
         # old, now renamed to S. coagulans (but still as synonym in our data of course):
         (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
@@ -280,6 +281,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
   "Malbranchea",
   "Metagonimus",
   "Meyerozyma",
+  "Microascus",
   "Microsporidium",
   "Microsporum",
   "Millerozyma",
@@ -306,6 +308,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
   "Piedraia",
   "Pithomyces",
   "Pityrosporum",
+  "Plasmodium",
   "Pneumocystis",
   "Pseudallescheria",
   "Pseudoscopulariopsis",
@@ -323,6 +326,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
   "Sarcoptes",
   "Scedosporium",
   "Schistosoma",
+  "Schizophyllum",
   "Schizosaccharomyces",
   "Scolecobasidium",
   "Scopulariopsis",

data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints.R

@@ -293,7 +293,7 @@ breakpoints_new <- breakpoints |>
     host = ifelse(BREAKPOINT_TYPE == "ECOFF", "ECOFF", tolower(HOST)),
     method = TEST_METHOD,
     site = SITE_OF_INFECTION,
-    mo,
+    mo = as.mo(mo),
     rank_index = case_when(
       is.na(mo_rank(mo, keep_synonyms = TRUE)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
       mo_rank(mo, keep_synonyms = TRUE) %like% "(infra|sub)" ~ 1,
@@ -453,6 +453,9 @@ breakpoints_new$breakpoint_R[breakpoints_new$guideline %like% "EUCAST" & breakpo
 breakpoints_new <- breakpoints_new |>
   filter(!(guideline %like% "EUCAST (2024|2025|2026)" & ref_tbl == "PK/PD"))
 
+# WHONET still contains generic anaerobic rules for EUCAST >= 2021, but this was ended from v12 (2022) on
+breakpoints_new <- breakpoints_new |>
+  filter(!(guideline %like% "EUCAST (2022|2023|2024|2025|2026)" & ref_tbl %like% "anaerob"))
 
 # WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
 # EUCAST 2023 guideline: S <= 8 and R > 8

data-raw/_reproduction_scripts/reproduction_of_intrinsic_resistant.R

@@ -35,13 +35,13 @@ for (i in seq_len(nrow(antimicrobials))) {
   colnames(int_resis)[ncol(int_resis)] <- antimicrobials$ab[i]
 }
 
-int_resis <- eucast_rules(int_resis,
-  eucast_rules_df = subset(
-    AMR:::EUCAST_RULES_DF,
-    is.na(have_these_values) & reference.rule_group == "Expected phenotypes" & reference.version == 1.2
-  ),
-  overwrite = TRUE,
-  info = FALSE
+int_resis <- interpretive_rules(int_resis,
+                                interpretive_rules_df = subset(
+                                  AMR:::INTERPRETIVE_RULES_DF,
+                                  is.na(have_these_values) & reference.rule_group == "Expected phenotypes" & reference.version == 1.2
+                                ),
+                                overwrite = TRUE,
+                                info = FALSE
 )
 
 int_resis2 <- int_resis[, sapply(int_resis, function(x) any(!is.sir(x) | x == "R")), drop = FALSE] %>%

data-raw/clin_break.md5

@@ -1 +1 @@
-45068afc4cd9770dea329782c1aed045
+7bcb6eaf7e2da23ac552acbfd12b3e62

data-raw/datasets/microorganisms.groups.txt

@@ -298,7 +298,6 @@
 "B_HACEK"	"B_HMPHL_PSCM"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus piscium"
 "B_HACEK"	"B_HMPHL_PTTM"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus pittmaniae"
 "B_HACEK"	"B_ACTNB_PLRP"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus pleuropneumoniae"
-"B_HACEK"	"B_HMPHL_QNTN"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus quentini"
 "B_HACEK"	"B_AGGRG_SGNS"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus segnis"
 "B_HACEK"	"B_HMPHL_SMNL"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus seminalis"
 "B_HACEK"	"B_HMPHL_SPTR"	"Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)"	"Haemophilus sputorum"
@@ -319,7 +318,6 @@
 "B_KLBSL_PNMN-C"	"B_KLBSL_QSPN"	"Klebsiella pneumoniae complex"	"Klebsiella quasipneumoniae"
 "B_KLBSL_PNMN-C"	"B_KLBSL_QSPN_QSPN"	"Klebsiella pneumoniae complex"	"Klebsiella quasipneumoniae quasipneumoniae"
 "B_KLBSL_PNMN-C"	"B_KLBSL_QSPN_SMLP"	"Klebsiella pneumoniae complex"	"Klebsiella quasipneumoniae similipneumoniae"
-"B_KLBSL_PNMN-C"	"B_KLBSL_QSVR"	"Klebsiella pneumoniae complex"	"Klebsiella quasivariicola"
 "B_KLBSL_PNMN-C"	"B_KLBSL_VRCL"	"Klebsiella pneumoniae complex"	"Klebsiella variicola"
 "B_KLBSL_PNMN-C"	"B_KLBSL_VRCL_TRPC"	"Klebsiella pneumoniae complex"	"Klebsiella variicola tropica"
 "B_KLBSL_PNMN-C"	"B_KLBSL_VRCL_LNSS"	"Klebsiella pneumoniae complex"	"Klebsiella variicola tropicalensis"
@@ -332,22 +330,20 @@
 "F_MYRZY_GLLR-C"	"F_MYRZY_GLLR"	"Meyerozyma guilliermondii complex"	"Meyerozyma guilliermondii japonica"
 "F_MYRZY_GLLR-C"	"F_MYRZY_GLLR"	"Meyerozyma guilliermondii complex"	"Meyerozyma guilliermondii muhira"
 "F_MYRZY_GLLR-C"	"F_MYRZY_GLLR"	"Meyerozyma guilliermondii complex"	"Meyerozyma guilliermondii pseudoguilliermondii"
-"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium avium"
-"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_AVIM"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium avium avium"
-"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_PRTB"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium avium paratuberculosis"
-"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_SLVT"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium avium silvaticum"
-"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium intracellulare"
-"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_CHMR"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium intracellulare chimaera"
-"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_INTR"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium intracellulare intracellulare"
-"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_CHMR"	"Mycobacterium avium-intracellulare complex"	"Mycobacterium intracellulare yongonense"
+"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM"	"Mycobacterium avium complex"	"Mycobacterium avium"
+"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_AVIM"	"Mycobacterium avium complex"	"Mycobacterium avium avium"
+"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_PRTB"	"Mycobacterium avium complex"	"Mycobacterium avium paratuberculosis"
+"B_MYCBC_AVIM-C"	"B_MYCBC_AVIM_SLVT"	"Mycobacterium avium complex"	"Mycobacterium avium silvaticum"
+"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE"	"Mycobacterium avium complex"	"Mycobacterium intracellulare"
+"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_CHMR"	"Mycobacterium avium complex"	"Mycobacterium intracellulare chimaera"
+"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_INTR"	"Mycobacterium avium complex"	"Mycobacterium intracellulare intracellulare"
+"B_MYCBC_AVIM-C"	"B_MYCBC_LLRE_CHMR"	"Mycobacterium avium complex"	"Mycobacterium intracellulare yongonense"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium africanum"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium bovis"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium bovis bovis"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium bovis caprae"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium caprae"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium microti"
-"B_MYCBC_TBRC-C"	"B_MYCBC_MUNG"	"Mycobacterium tuberculosis complex"	"Mycobacterium mungi"
-"B_MYCBC_TBRC-C"	"B_MYCBC_ORYG"	"Mycobacterium tuberculosis complex"	"Mycobacterium orygis"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium pinnipedii"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium tuberculosis"
 "B_MYCBC_TBRC-C"	"B_MYCBC_TBRC"	"Mycobacterium tuberculosis complex"	"Mycobacterium tuberculosis caprae"

data-raw/interpretive_rules.tsv

@@ -451,7 +451,7 @@ EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	AMP	R	AMP, SAM, AMX, AM
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> sort() |> toString()
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -543,8 +543,8 @@ EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
-EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
+EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
+EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -582,19 +582,19 @@ EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -643,7 +643,7 @@ EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	AMP	R	AMX, AMC	R
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -731,8 +731,8 @@ EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
-EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
+EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -770,7 +770,7 @@ EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
@@ -778,11 +778,11 @@ EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necropho
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -831,7 +831,7 @@ EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	AMP	R	AMX, AMC	R
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -920,8 +920,8 @@ EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
-EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
+EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -959,19 +959,19 @@ EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococc
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringensm"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -1135,25 +1135,25 @@ EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridan
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	S	AMX, AMC, SAM, PIP, TZP	S	will be expanded in eucast_rules()
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	I	AMX, AMC, SAM, PIP, TZP	I	will be expanded in eucast_rules()
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	R	AMX, AMC, SAM, PIP, TZP	R	will be expanded in eucast_rules()
-EUCAST	Breakpoints		Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints		Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
-EUCAST	Breakpoints		Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
-EUCAST	Breakpoints		Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	S	CRO	S	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	I	CRO	I	
-EUCAST	Breakpoints		Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	R	CRO	R	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	S	CRO	S	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	I	CRO	I	
+EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	R	CRO	R	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	I	AMX	S	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	R	AMX	R	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	S	CTX	S	
@@ -1414,19 +1414,20 @@ EUCAST	Expert Rules	3.2	Table 4: Intrinsic resistance in gram-positive bacteria
 EUCAST	Expert Rules	3.2	Table 4: Intrinsic resistance in gram-positive bacteria	genus	is	Lactobacillus			VAN, TEC	R	
 EUCAST	Expert Rules	3.2	Table 4: Intrinsic resistance in gram-positive bacteria	fullname	like	^Clostridium (ramosum|innocuum)			VAN	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Campylobacter	genus	is	Campylobacter	ERY	S	CLR, AZM	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Campylobacter	genus_species	is	Campylobacter	ERY	R	CLR, AZM	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Campylobacter	genus	is	Campylobacter	ERY	R	CLR, AZM	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	genus_species	one_of	Escherichia coli, Proteus mirabilis	AMP	R	PIP	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	genus_species	one_of	Escherichia coli, Proteus mirabilis	AMP	S	PIP	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Klebsiella(?! aerogenes)|Raoultella)			PIP	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter freundii|Serratia|Morganella morganii|Hafnia alvei|Providencia)	CXM	S	CXM, cephalosporins_2nd	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Klebsiella(?! aerogenes)|Raoultella)			PIP	R	Regex means that K. aerogenes is not matched.
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CXM	S	CXM, cephalosporins_2nd	R	Also includes the C. freundii complex - these Citrobacter species were not literally mentioned in the document, but the complex was.
 EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	genus	one_of	Arsenophonus, Biostraticola, Brenneria, Buchnera, Budvicia, Buttiauxella, Cedecea, Citrobacter, Cosenzaea, Cronobacter, Dickeya, Edwardsiella, Enterobacillus, Enterobacter, Erwinia, Escherichia, Ewingella, Franconibacter, Gibbsiella, Hafnia, Izhakiella, Klebsiella, Kluyvera, Kosakonia, Leclercia, Lelliottia, Leminorella, Lonsdalea, Mangrovibacter, Mixta, Moellerella, Morganella, Obesumbacterium, Pantoea, Pectobacterium, Phaseolibacter, Photorhabdus, Phytobacter, Plesiomonas, Pluralibacter, Pragia, Proteus, Providencia, Pseudescherichia, Pseudocitrobacter, Rahnella, Raoultella, Rosenbergiella, Rouxiella, Saccharobacter, Samsonia, Serratia, Shigella, Shimwellia, Siccibacter, Sodalis, Tatumella, Thorsellia, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia, Yokenella	CIP	R	fluoroquinolones	R	This is Enterobacterales except Salmonella spp.
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Serratia|Providencia|Morganella morganii)			TGC	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CTX	S	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CRO	S	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CAZ	S	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CTX	I	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CRO	I	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
-EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CAZ	I	CTX, CRO, CAZ		This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales	fullname	like	^(Serratia|Providencia|Proteus|Morganella morganii)			TGC	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei)	CTX	S	CTX, CRO, CAZ, TZP		This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei)	CRO	S	CTX, CRO, CAZ, TZP		This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei)	CAZ	S	CTX, CRO, CAZ, TZP		This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei)	TZP	S	CTX, CRO, CAZ, TZP		This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Serratia|Morganella morganii|Providencia)	CTX	S	CTX, CRO, CAZ		This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Serratia|Morganella morganii|Providencia)	CRO	S	CTX, CRO, CAZ		This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+EUCAST	Expert Rules	3.3	Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	fullname	like	^(Serratia|Morganella morganii|Providencia)	CAZ	S	CTX, CRO, CAZ		This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
 EUCAST	Expert Rules	3.3	Expert Rules on Enterococcus	fullname	like	^Enterococcus (faecalis|faecium)	AMP	R	ureidopenicillins, IPM	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Enterococcus	fullname	like	^Enterococcus (faecalis|faecium)	AMX	R	ureidopenicillins, IPM	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Enterococcus	genus	is	Enterococcus	NOR	S	CIP, LVX	S	
@@ -1441,30 +1442,34 @@ EUCAST	Expert Rules	3.3	Expert Rules on Moraxella catarrhalis	genus_species	is	M
 EUCAST	Expert Rules	3.3	Expert Rules on Moraxella catarrhalis	genus_species	is	Moraxella catarrhalis	NAL	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Salmonella	genus	is	Salmonella			cephalosporins_2nd	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Salmonella	genus	is	Salmonella			aminoglycosides	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Salmonella	genus	is	Salmonella	PEF	S	CIP	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Salmonella	genus	is	Salmonella	PEF	R	CIP	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	is	Staphylococcus aureus	FOX	R	betalactams	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	is	Staphylococcus aureus	FOX	S	betalactams_with_inhibitor	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	one_of	Staphylococcus aureus, Staphylococcus lugdunensis	PEN	R	AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC	R	all penicillins without beta-lactamse inhibitor
+EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	is	Staphylococcus aureus	FOX	R	AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZOP	R	Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")]
+EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	is	Staphylococcus aureus	FOX	S	betalactams_with_inhibitor, carbapenems	S	Must be S to all betactams with recognised anti-staphylococcal activity
+EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus_species	is	Staphylococcus aureus	PEN	R	AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC	R	all penicillins without beta-lactamse inhibitor and excluding isoxazolylpenicillines
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	ERY, CLI	S	macrolides, lincosamides	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	NOR	S	CIP, LVX, MFX, OFX	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	NOR	S	CIP, LVX	I	
+EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	NOR	S	MFX	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	LVX	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	MFX	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO, TGC	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	VAN	S	lipoglycopeptides	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	LNZ	S	TZD	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus A, B, C and G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, cephalosporins, carbapenems	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus A, B, C and G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	LVX, MFX	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus A, B, C and G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	R	LVX, MFX	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA	S	PHN, PEN, aminopenicillins, cephalosporins_except_CAZ, carbapenems	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	S	LVX, MFX	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, cephalosporins, carbapenems	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	S	aminopenicillins, CTX, CRO	S	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	R	aminopenicillins, CTX, CRO	R	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	LVX	I	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	MFX	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	R	LVX, MFX	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA	S	AMC, AMP, AMX, CEC, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IPM, MEM, PEN	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))]); x[x %in% betalactams()] |> toString()
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	S	LVX	I	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	S	MFX	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	ERY, CLI	S, S	macrolides, lincosamides	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	R	LVX, MFX	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	LVX	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	MFX	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	TCY	S	DOX, MNO	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	TCY	R	DOX, MNO	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	VAN	S	lipoglycopeptides	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Viridans Group Streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	S	aminopenicillins, CTX, CRO	S	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	order	is	Enterobacterales			PEN, glycopeptides_except_lipo, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	fullname	like	^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)			aminopenicillins, TIC	R	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	fullname	like	^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX	R	

data-raw/intrinsicR.md5

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-990cbdfa55a1c2340aecfa67e8ac84d6
+ec377180475ec3cd61ffaed401643e9a

data-raw/microorganisms.codes.md5

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-6ef98bb1bcd27052fde453bb12c0b285
+5fba98b9dd8845adc9f83d52b28f8254

data-raw/microorganisms.groups.md5

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-dfdbbebfe1a542270d63b94c12889860
+43f0086ac00f84bbda973c6c5e332c49

data-raw/microorganisms.md5

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-6dc4dded108052760bfb626df03435e2
+7613ad032fa1078b6c2ad46d7ae8236f

data-raw/wisca_test/parameters_amr.R

@@ -0,0 +1,622 @@
+# Copyright (c) [2022] [Larisse Bolton]
+
+wisca_params <- function(x, antibiotic_in, pathogen_in, analysis, exclude, isolate_first, susceptible_I, infection_in,infection_full){
+  # Isolate the type of syndrome you would like to investigate
+  if (infection_in != ""){
+  x <- x %>%
+    filter(infection_type == infection_in)
+  } else {
+    x <- x
+  }
+  
+  # Define antibiotic regimens under investigation from "antimicrobials" dataset
+  antibiotic_df <- function(abo_in){
+    mono_ab <- str_to_title(abo_in[!str_detect(abo_in,fixed("+"))]) #identify single antimicrobials
+    
+    mono_antibiotic_x_set <- data.frame()
+    for (j in 1:length(mono_ab)){ #generate dataframe for single antimicrobials with abbreviations and fullnames
+      mono_x_name <- ab_name(mono_ab[j], only_first = TRUE)
+      mono_x_ab <- as.ab(mono_x_name)
+      mono_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% mono_x_ab)[,c("ab","name")]
+      mono_antibiotic_x_set <- rbind.data.frame(mono_antibiotic_x_set,mono_antibiotic, row.names = NULL)
+    }
+    names(mono_antibiotic_x_set) <- c("ab1","namerx")
+    mono_antibiotic_x_set$ab1 <- as.character(mono_antibiotic_x_set$ab1)
+    
+    
+    comb_ab <- str_to_title(abo_in[str_detect(abo_in,fixed("+"))]) #identify combination regimens
+    if(length(comb_ab) > 0){
+      
+      comb_antibiotic_x_total <- data.frame()    
+      for (jj in 1:length(comb_ab)){ #generate dataframe for combination antimicrobials with abbreviations and fullnames
+        comb_ab_sep <- unlist(str_split(comb_ab[jj], fixed("+")))
+        comb_antibiotic_x_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
+        for (ii in 1:length(comb_ab_sep)){
+          comb_x_name <- ab_name(comb_ab_sep[ii], only_first = TRUE)
+          comb_x_ab <- as.ab(comb_x_name)
+          comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% comb_x_ab)[,c("ab","name")]
+          comb_antibiotic_x_pre[ii,] <- comb_antibiotic
+          names(comb_antibiotic_x_pre) <- names(comb_antibiotic)
+        }
+        comb_antibiotic_x <- pivot_wider(comb_antibiotic_x_pre, names_from = name, values_from = ab)
+        comb_antibiotic_x_set <- comb_antibiotic_x %>%
+          mutate(namerx = str_flatten(str_c(names(comb_antibiotic_x), collapse = "+")))
+        new_ab <- sapply(X = as.character(1:(ncol(comb_antibiotic_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
+        names(comb_antibiotic_x_set)[1:(ncol(comb_antibiotic_x_set)-1)] <- new_ab
+        names(comb_antibiotic_x_set)[ncol(comb_antibiotic_x_set)]<- "namerx"
+        comb_antibiotic_x_total <-rbind.data.frame(comb_antibiotic_x_total, comb_antibiotic_x_set, row.names = NULL)
+      }
+      
+      
+      
+      antibiotic_x_set <- full_join(x= mono_antibiotic_x_set,y = comb_antibiotic_x_total) #generate full antibiotic dataset for analysis
+      antibiotic_x_set <- select(antibiotic_x_set, starts_with("ab"),starts_with("namerx"))
+      
+    } else {
+      antibiotic_x_set <- mono_antibiotic_x_set
+    }
+    return(antibiotic_x_set)
+  }
+  
+  antibiotic_rx <- antibiotic_df(abo_in = antibiotic_in) #full dataframe with antibiotic regimens
+  
+  
+  #if the dataset names are in different format to built-in set
+  if ((length(names(x)[sapply(names(x), function(x1){any(str_detect(x1,"[A-Z]$") & !(x1 %in% antimicrobials$ab) & !(x1 %in% antimicrobials$name))})])>0)){
+    names(x)[which(str_detect(names(x),"[a-z]+") & !str_detect(names(x),"fullname") & !str_detect(names(x),"mo"))] <- str_to_upper(names(x)[which(str_detect(names(x),"[a-z]+") & !str_detect(names(x),"fullname") & !str_detect(names(x),"mo"))])
+    names(x)[which(str_detect(names(x),"^ORG") & str_detect(names(x),"NAME$"))] <- "fullname"
+    names(x)[which(str_detect(names(x),"^ORG") & str_detect(names(x),"CODE$"))] <- "organism"
+    names(x)[which(str_detect(names(x),"^DATE") & str_detect(names(x),"CULTURE$") | str_detect(names(x),"^DATE") & str_detect(names(x),"SPECIMEN"))] <- "date"
+    names(x)[which(str_detect(names(x),"^EPISODE") | str_detect(names(x),"^PAT"))] <- "patient"
+    names(x)[which(str_detect(names(x),"^WARD"))] <- "ward"
+    names(x)[which(str_detect(names(x),"^HOSP"))] <- "hospital"
+    names(x)[which(str_detect(names(x),"DATE") & (str_detect(names(x),"REGISTRATION")))] <- "date"
+
+  }
+  
+  #remove any duplicated variables
+  if (any(duplicated(names(x)))){
+    x <- x[,-max(which(names(x) == names(x)[which(duplicated(names(x)))]))]
+  } else {
+    x <- x
+  }
+  
+  x <- arrange(x,"date") #arrange in increasing date
+  
+  #if your dataset does not have an episode identifier
+  if (length(names(x)[sapply(names(x), function(x1){any(str_detect(x1,"patient"))})]) == 0){
+    x$patient <- seq(1,nrow(x),1)
+  }
+  
+  
+  
+  #find demographic data within dataset
+  micro_df_ind <- which(sapply(x,function(y) any(str_detect(y,"^S$"))|any(str_detect(y,"^R$"))|any(str_detect(y,"^SENSITIVE$"))| any(str_detect(y,"^RESISTANT$"))) ==  TRUE)
+  micro_df <-  names(x)[micro_df_ind]#column names for susceptibility data
+  demograph_df <- names(x)[which(!names(x) %in% micro_df)] #column names for demographic or organism data
+  
+  #Dataset required to contain both mo and fullname
+  if ("mo" %in% names(x)){
+    # If mo included but not fullname:
+    x <- x %>%
+      mutate(mo = as.mo(mo), keep_synonyms = TRUE)
+    test_isolates_pre <- x %>%
+      mutate(across(.cols = all_of(demograph_df),~ str_to_title(.x))) %>%
+      mutate(mo = toupper(mo)) 
+    if ("fullname" %in% names(x)){
+      test_isolates <- test_isolates_pre[,c(demograph_df)]
+    } else {
+      test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "mo")[,c(demograph_df,"fullname")] #add in fullname
+    }
+    test_isolates <- test_isolates %>% #remove bugs to be excluded and final style conversions
+      filter(!(fullname %in% exclude)) %>%
+      mutate(fullname = str_to_title(fullname))
+    
+    rx_abo <- x %>% #from orginal dataset
+      filter(mo %in% test_isolates$mo)  %>% #select only those bugs that should be included
+      distinct(date, patient, mo, .keep_all = TRUE) #ensure that there are no duplications
+    rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] #extract susceptibility data
+    
+    if (any(str_detect(rx_regs,fixed("+")))){#if combinations have already been accounted for in susceptibility profiling
+      rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df)) # extract susceptibility data
+      rx_regs_comb <- rx_regs[str_detect(rx_regs,fixed("+"))]# find combination regimen variables
+      rx_regs_comb_x_total <- data.frame()
+      
+      for (jj in 1:length(rx_regs_comb)){ # for every combination regimen
+        rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))),"[[:punct:]]") #extract separate antimicrobials included in regimen
+        rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
+        for (ii in 1:length(rx_regs_comb_sep)){ #for every antibiotic in the combination regimen
+          rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE) #extract their antibiotic name
+          rx_regs_comb_ab <- as.ab(rx_regs_comb_name) #convert antibiotic name to abbreviation
+          rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[,c("ab","name")]
+          rx_regs_comb_pre[ii,] <- rx_regs_comb_antibiotic
+          names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
+        }
+        rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
+        rx_regs_comb_x_set <- rx_regs_comb_x %>%
+          mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+"))) #generate new antimicrobials table
+        rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
+        names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set)-1)] <- rx_regs_new_ab
+        names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)]<- "namerx"
+        rx_regs_comb_x_total <-rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
+      }
+      
+      rx_regs_comb_rename <- select(rx_regs_comb_x_total,starts_with("ab")) #extract all antibiotic abbreviations
+      #regenerate variable names for dataset
+      suppressWarnings({
+        for (k in 1:nrow(rx_regs_comb_x_total)){
+          rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k,])
+          rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
+          names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
+          
+        }
+      })
+      names(rx_abo)[!(str_detect(names(rx_abo),fixed("+")))  & !(names(rx_abo) %in% demograph_df)] <- 
+        as.ab(names(rx_abo)[!(str_detect(names(rx_abo),fixed("+")))  & !(names(rx_abo) %in% demograph_df)])
+      names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
+      rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[,c(names(rx_abo),"fullname")]
+      
+    } else {
+      names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
+      names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
+      rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[,c(names(rx_abo),"fullname")]
+      
+    } 
+  } else {
+    #If fullname included in dataset:
+    test_isolates_pre <- x %>%
+      mutate(across(.cols = all_of(demograph_df),~ str_to_title(.x))) %>%
+      filter(!(fullname %in% unique(exclude)))
+    print(nrow(test_isolates_pre))
+    test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "fullname")[,c(demograph_df,"mo")]
+    test_isolates$fullname <- str_to_title(test_isolates$fullname) 
+    test_isolates$mo <- toupper(test_isolates$mo)
+    
+    rx_abo <- x %>%
+      mutate(fullname = str_to_title(fullname)) %>%
+      filter(!(fullname %in% unique(exclude)))  %>%
+      distinct(date, patient, fullname, .keep_all = TRUE)
+    rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] #susceptibility data
+    
+    if (any(str_detect(rx_regs,fixed("+")))){#if combinations have already been accounted for
+      rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df))
+      rx_regs_comb <- rx_regs[str_detect(rx_regs,fixed("+"))]
+      rx_regs_comb_x_total <- data.frame()
+      
+      for (jj in 1:length(rx_regs_comb)){
+        rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))),"[[:punct:]]")
+        rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
+        for (ii in 1:length(rx_regs_comb_sep)){
+          rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE)
+          rx_regs_comb_ab <- as.ab(rx_regs_comb_name)
+          rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[,c("ab","name")]
+          rx_regs_comb_pre[ii,] <- rx_regs_comb_antibiotic
+          names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
+        }
+        rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
+        rx_regs_comb_x_set <- rx_regs_comb_x %>%
+          mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+")))
+        rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
+        names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set)-1)] <- rx_regs_new_ab
+        names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)]<- "namerx"
+        rx_regs_comb_x_total <-rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
+      }
+      
+      rx_regs_comb_rename <- select(rx_regs_comb_x_total,starts_with("ab"))
+      
+      suppressWarnings({
+        for (k in 1:nrow(rx_regs_comb_x_total)){
+          rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k,])
+          rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
+          names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
+        }
+      })
+      names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)] <- 
+        as.ab(names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)])
+      names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
+      rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[,c(names(rx_abo),"mo")]
+      
+    } else {
+      names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
+      names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
+      rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[,c(names(rx_abo),"mo")]
+      
+    } 
+  }
+  
+  
+  rx_abo_upd$fullname <- str_to_title(rx_abo_upd$fullname)
+  
+  
+  #generate new dataset with demographic information and only antimicrobials under investigation
+  rx_1 <- select(antibiotic_rx,starts_with("ab")) #all antimicrobials
+  rx_df <- test_isolates
+  var_date <- names(rx_df)[str_detect(names(rx_df),"date")]
+  rx_df[[var_date]] <- ymd(rx_df[[var_date]])
+  var_patient <- names(rx_df)[str_detect(names(rx_df),"patient")]
+  rx_df[[var_patient]] <- toupper(rx_df[[var_patient]])
+  
+  names_vec <- vector()
+  suppressWarnings({
+    for (k in 1:nrow(rx_1)){
+      rx_2 <- unlist(rx_1[k,][!is.na(str_extract(rx_1[k,],"[A-Z]+"))])
+      if ((length(rx_2) > 1) & !(str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))){ # if more than one antibiotic in regimen and susceptibility of the combination not considered
+        rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",rx_2)], by = c("date","patient","mo")) #then bind separate antimicrobials in regimen
+        #if prior individual antibiotic was added, suffix will be generated
+        for (jj in 1:length(rx_2)){
+          if (any(str_detect(names(rx_df),paste0(rx_2[jj],".y")))){
+            rx_df[[paste0(rx_2[jj],".y")]] <- str_trim(rx_df[[paste0(rx_2[jj],".y")]])
+          } else {
+            rx_df[[rx_2[jj]]] <- str_trim(rx_df[[rx_2[jj]]])
+          }
+        }
+        names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) #rename both variables as the combination
+        names(rx_df)[names(rx_df) %in% paste0(rx_2,".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
+        names(rx_df)[names(rx_df) %in% paste0(rx_2,".x")] <- rx_2
+        names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
+      } else if ((length(rx_2) > 1) & (str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))){#if more than one antibiotic in regimen and susceptibility of the combination considered
+        rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",str_flatten(str_c(rx_2, collapse = "+")))], by = c("date","patient","mo"))
+        rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]] <- str_trim(rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]])
+        names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
+      } else {
+        rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",rx_2)], by = c("date","patient","mo")) #then bind separate antimicrobials in regimen
+        rx_df[[rx_2]] <- str_trim(rx_df[[rx_2]])
+        names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) #rename both variables as the combination
+        names(rx_df)[names(rx_df) %in% paste0(rx_2,".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
+        names(rx_df)[names(rx_df) %in% paste0(rx_2,".x")] <- rx_2
+        names_vec[k] <- rx_2
+      }
+    }
+  })
+  
+  if (any(is.na(rx_df$mo))){
+    missing1 <- which(is.na(rx_df$mo))
+    if (rx_df[missing1,"fullname"] != "NA"){
+      rx_df[missing1,"mo"] <- as.mo(rx_df[missing1,]$fullname, keep_synonyms = TRUE)
+    } else {
+      rx_df <- rx_df[-missing1,]
+    }
+  }
+  
+  
+  #Generate adapted first isolate dataset on antimicrobial level 
+  if (isolate_first == "yes"){ #if dataset has already been deduplicated for first isolates
+    test_isolates_first <- rx_df
+  } else {
+    test_isolates_first <- rx_df[first_isolate(x = rx_df, 
+                                               method = "e", 
+                                               episode_days = 14, 
+                                               col_date = var_date,
+                                               col_mo = "mo") == TRUE,] #Only include isolates for microorganisms < 14 days apart
+  }
+  
+  
+  
+  test_isolates_first_upd <<- test_isolates_first %>%
+    pivot_longer(cols = names(test_isolates_first)[which(sapply(test_isolates_first,
+                                                                function(y) any(str_detect(y,"^S$"))|any(str_detect(y,"^R$"))|any(str_detect(y,"^SENSITIVE$"))| any(str_detect(y,"^RESISTANT$"))))],
+                 names_to = "keyantimicrobials", values_to = "antibiogram") %>% #combine all regimens and antibiograms into single variables
+    mutate(antibiogram = ifelse(antibiogram == "I"|antibiogram == "INTERMEDIATE",susceptible_I,antibiogram)) %>% #simplify susceptibility outputs - 90-60 rule
+    mutate(antibiogram = ifelse(antibiogram == "SENSITIVE","S", antibiogram)) %>%
+    mutate(antibiogram = ifelse(antibiogram == "RESISTANT","R",antibiogram)) %>%
+    mutate(antibiogram = factor(antibiogram),
+           keyantimicrobials = as.factor(keyantimicrobials))
+  
+  
+    #recode susceptibility of regimes if not already in the dataset: if any S, then all S; if any NA and not S, then combination NA; if all R, then combination R.
+  print("-------------------")
+  print(test_isolates_first_upd)
+  print(names(test_isolates_first_upd))
+  print(duplicated(names(test_isolates_first_upd)))
+  print(any(duplicated(names(test_isolates_first_upd))))
+  print("-------------------")
+  
+  if (any(duplicated(names(test_isolates_first_upd)))){
+    test_isolates_temp3 <- test_isolates_upd %>%
+      mutate(antibiogram = as.character(antibiogram)) %>%
+      mutate(antibiogram = replace_na(antibiogram,"U")) %>%
+      mutate(combiantibiogram = "A")
+    test_isolates_temp4 <- test_isolates_temp3 %>%
+      group_by(date,patient,mo,keyantimicrobials) %>%
+      mutate(combiantibiogram = ifelse(any(str_detect(antibiogram,"S")), "S",combiantibiogram)) %>%
+      mutate(combiantibiogram = ifelse(any(str_detect(antibiogram,"U")) & !any(str_detect(antibiogram,"S")), "U",combiantibiogram)) %>%
+      mutate(combiantibiogram = ifelse(!any(str_detect(antibiogram,"U")) & !any(str_detect(antibiogram,"S")), "R", combiantibiogram)) %>%
+      distinct(date,patient,mo,.keep_all = TRUE) %>%
+      ungroup() %>%
+      select(-antibiogram) %>%
+      rename("antibiogram" = "combiantibiogram") %>%
+      mutate(antibiogram = ifelse(antibiogram == "U",NA_character_, antibiogram)) %>%
+      mutate(antibiogram = as.factor(antibiogram))
+    
+    test_isolates_temp5 <- test_isolates_upd %>% #isolate the single regimens
+      group_by(date,patient,mo) %>%
+      filter(!str_detect(keyantimicrobials,fixed("+"))) %>%
+      ungroup()
+    
+    test_isolates_final <- bind_rows(test_isolates_temp4,test_isolates_temp5)
+    test_isolates_final <- test_isolates_final %>%
+      arrange(patient) 
+    
+  } else {
+    test_isolates_final <- test_isolates_first_upd
+  }     
+  
+  test_isolates_final <<- test_isolates_final
+  
+  test_isolates_final$fullname <- as.factor(test_isolates_final$fullname)
+  test_isolates_final$date <- ymd(test_isolates_final$date)
+  
+  
+  #Calculate pathogen incidence and susceptible by regimen
+  mo_name_total <- test_isolates_final %>% #deduplicate pathogen list
+    filter(!duplicated(mo)) %>% 
+    select(mo,fullname)
+  
+  organism_count_total <- test_isolates_final %>% #count the number of occurrences of pathogens within first isolates set
+    group_by(mo) %>%
+    distinct(date,patient,.keep_all = TRUE) %>%
+    count(mo) %>%
+    ungroup() %>%
+    arrange(desc(n))
+  
+  organism_count_total_upd <- organism_count_total %>%
+    mutate(mo = as.character(mo)) %>%
+    mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"),"B_KLBSL",mo)) %>%
+    mutate(mo = as.factor(mo)) 
+  klebsiella_combine <- organism_count_total_upd %>%
+    filter(mo == "B_KLBSL") %>%
+    summarise(n_kleb = sum(n))
+  organism_count_total_upd <- organism_count_total_upd %>%
+    mutate(n = ifelse(mo == "B_KLBSL", klebsiella_combine$n_kleb, n)) %>%
+    distinct(mo,.keep_all = TRUE)
+  organism_count_total_upd <- rename(organism_count_total_upd,
+                                     "n_inc" = "n")
+  
+  regimen_organism <- slice_max(organism_count_total_upd, order_by = n_inc, n = pathogen_in) #choose only the top n number of occurring pathogens to consider in WISCA
+  total_pathogens <- sum(regimen_organism$n_inc) #total number of occurrences of pathogens
+  print(paste0("Total pathogens = ",sum(organism_count_total_upd$n_inc)))
+  print(paste0("Number of pathogens contributing to WISCA = ", total_pathogens))
+  print(paste0("Percentage of pathogens contributing to WISCA = ",round((total_pathogens/sum(organism_count_total_upd$n_inc))*100,0),"%"))
+  
+  test_isolates_final <- test_isolates_final %>%
+    mutate(mo = as.character(mo)) %>%
+    mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"), "B_KLBSL",mo)) %>% #combine all Klebsiella species together
+    mutate(mo = as.factor(mo)) %>%
+    mutate(fullname = as.character(fullname)) %>%
+    mutate(fullname = ifelse(str_detect(fullname, "Klebsiella"), "Klebsiella sp.", fullname)) %>% #rename all Klebsiella species
+    mutate(fullname = as.factor(fullname))
+  
+  
+  #for each antimicrobial regimen calculate the pathogen incidence and sensitivity
+  params_set_out <- data.frame()
+  
+  if (analysis %in% names(test_isolates_final)){
+    
+    #if there is a facility/ward/category level analysis required
+    for (j in 1:length(unique(test_isolates_final[[analysis]]))){#for every facility/ward/category
+      infection_episodes <- length(unique(test_isolates_final[test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j],]$patient))
+      
+      if (infection_episodes < 100){#must have at least 100 infection episodes for WISCA
+        warning(paste0("Number of infection episodes = ",infection_episodes,". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
+        
+      } else {
+        print(paste0("Number of infection episodes = ",infection_episodes," > 100 minimum required. Sample size adequate for WISCA analysis."))
+      }
+      
+      
+      y_site <- subset(test_isolates_final, test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j])
+      
+      mo_name_sites <- y_site %>% #deduplicate
+        filter(!duplicated(mo)) %>% 
+        select(mo,fullname,all_of(analysis))
+      
+      organism_count_regimen <- y_site %>% #count the number of organisms per regimen
+        filter(mo %in% regimen_organism$mo) %>%
+        group_by(mo) %>%
+        distinct(date, patient, .keep_all = TRUE) %>%
+        count(mo) %>%
+        ungroup() %>%
+        arrange(desc(n))
+      
+      
+      organism_count_df <- left_join(x = organism_count_regimen, y = mo_name_sites, by = "mo")[,c("mo",analysis,"fullname","n")]
+      
+      total_sites_pathogens <- sum(organism_count_df$n)
+      print(paste0("Number of pathogens contributing to WISCA in ",unique(test_isolates_final[[analysis]])[j]," = ", total_sites_pathogens))
+      
+      organism_count_df <- organism_count_df %>%  
+        mutate(prop.n = n/total_sites_pathogens)  #pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
+      #          
+      
+      susceptible_regimen_tested <<- y_site %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested 
+        filter(mo %in% regimen_organism$mo) %>%
+        filter(!is.na(antibiogram)) %>%
+        group_by(mo,keyantimicrobials) %>%
+        distinct(date, patient, .keep_all = TRUE) %>%
+        count(antibiogram) %>%
+        ungroup() %>%
+        arrange(desc(n)) 
+      susceptible_regimen_ntested <<- y_site %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
+        filter(mo %in% regimen_organism$mo) %>%
+        filter(is.na(antibiogram)) %>% #Not tested
+        group_by(mo,keyantimicrobials) %>%
+        distinct(date, patient, .keep_all = TRUE) %>%
+        count(antibiogram) %>%
+        ungroup() %>%
+        arrange(desc(n))  
+      
+      organism_count_df_tested <<-  susceptible_regimen_tested %>%
+        group_by(mo,keyantimicrobials) %>%
+        summarise(tested_n = sum(n), .groups = "keep") %>%
+        ungroup()
+      organism_count_df_ntested <<-  susceptible_regimen_ntested %>%
+        group_by(mo,keyantimicrobials) %>%
+        summarise(ntested_n = sum(n), .groups = "keep") %>%
+        ungroup()
+      
+      organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo","keyantimicrobials")) #combine tested and nontested
+      organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
+        mutate(tested_n = ifelse(is.na(tested_n), 0 ,tested_n),
+               ntested_n = ifelse(is.na(ntested_n), 0, ntested_n))
+      
+      organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[,c("mo","keyantimicrobials","tested_n")], by = "mo")    
+      
+      #minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
+      condition_tested <- organism_count_df_regimen_tested %>%
+        filter(tested_n <30)
+      if (nrow(condition_tested) > 0){
+        warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for following regimens")
+        View(condition_tested[c("mo","n","keyantimicrobials","tested_n")])
+      } else {
+        print("Number of tested isolates per regimen exceed minimum threshold of 30.")
+      }
+      
+      organism_tested_sensitive <- susceptible_regimen_tested %>%
+        filter(antibiogram == "S")
+      
+      if (nrow(susceptible_regimen_tested)> 0){
+        sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo","keyantimicrobials"))
+        sensitivity <- sensitivity %>%
+          rename("S_n" = "n.y") %>%
+          mutate(S_n = ifelse(is.na(S_n),0,S_n)) %>%
+          select(-antibiogram)
+        sensitivity <- sensitivity %>% #calculate proportion sensitive out of those tested and having results
+          mutate(prop.S = ifelse(tested_n != 0, S_n/tested_n, 0))
+        sensitivity$perc.S <- sensitivity$prop.S * 100
+        
+        params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL)  
+        } else {
+        next
+      }
+    }
+    
+    params_set_out$fullname <- droplevels(params_set_out$fullname)
+    params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]] <- as.factor(params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]])
+    
+  } else{
+    
+    infection_episodes <- length(unique(test_isolates_final$patient))
+    if (infection_episodes < 100){#must have at least 100 infection episodes for WISCA
+      warning(paste0("Number of infection episodes = ",infection_episodes,". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
+      
+    } else {
+      print(paste0("Number of infection episodes = ",infection_episodes," > 100 minimum required. Sample size adequate for WISCA analysis."))
+    }
+    
+    mo_name_sites <- test_isolates_final %>% #deduplicate
+      filter(!duplicated(mo)) %>% 
+      select(mo,fullname)
+    
+    organism_count_path <- test_isolates_final %>% #count the number of organisms 
+      filter(mo %in% regimen_organism$mo) %>%
+      group_by(mo) %>%
+      distinct(date, patient, .keep_all = TRUE) %>%
+      count(mo) %>%
+      ungroup() %>%
+      arrange(desc(n)) 
+    
+    organism_count_df <- left_join(x = organism_count_path, y = mo_name_sites, by = "mo")[,c("mo","fullname","n")]
+    
+    total_sites_pathogens <- sum(organism_count_df$n)
+    
+    
+    
+    organism_count_df <- organism_count_df %>%  
+      mutate(prop.n = n/total_pathogens)  #pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
+    
+    
+    susceptible_regimen_tested <<- test_isolates_final %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested 
+      filter(mo %in% regimen_organism$mo) %>%
+      filter(!is.na(antibiogram)) %>% #Tested
+      group_by(mo,keyantimicrobials) %>%
+      distinct(date, patient, .keep_all = TRUE) %>%
+      count(antibiogram) %>%
+      ungroup() %>%
+      arrange(desc(n)) 
+    susceptible_regimen_ntested <<- test_isolates_final %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
+      filter(mo %in% regimen_organism$mo) %>%
+      filter(is.na(antibiogram)) %>% #Not tested
+      group_by(mo,keyantimicrobials) %>%
+      distinct(date, patient, .keep_all = TRUE) %>%
+      count(antibiogram) %>%
+      ungroup() %>%
+      arrange(desc(n)) 
+    
+    organism_count_df_tested <<-  susceptible_regimen_tested %>%
+      group_by(mo,keyantimicrobials) %>%
+      summarise(tested_n = sum(n), .groups = "keep") %>%
+      ungroup()
+    organism_count_df_ntested <<-  susceptible_regimen_ntested %>%
+      group_by(mo,keyantimicrobials) %>%
+      summarise(ntested_n = sum(n), .groups = "keep") %>%
+      ungroup()
+    
+    organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo","keyantimicrobials")) #combine tested and nontested
+    organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
+      mutate(tested_n = ifelse(is.na(tested_n), 0 ,tested_n),
+             ntested_n = ifelse(is.na(ntested_n), 0, ntested_n))
+    
+    organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[,c("mo","keyantimicrobials","tested_n")], by = "mo")   
+    
+    #minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
+    condition_tested <- organism_count_df_regimen_tested %>%
+      filter(tested_n <30)
+    if (nrow(condition_tested) > 0){
+      warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for these regimens.")
+      View(condition_tested[,c("mo","n","keyantimicrobials","tested_n")])
+    } else {
+      print("Number of tested isolates per regimen exceed minimum threshold of 30.")
+    }
+    
+    
+    organism_tested_sensitive <- susceptible_regimen_tested %>%
+      filter(antibiogram == "S")
+    
+    if (nrow(susceptible_regimen_tested)> 0){
+      sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo","keyantimicrobials"))
+      sensitivity <- sensitivity %>%
+        rename("S_n" = "n.y") %>%
+        mutate(S_n = ifelse(is.na(S_n),0,S_n)) %>%
+        select(-antibiogram)
+      sensitivity <- sensitivity %>% #calculate proportion sensitive out of those tested and having results
+        mutate(prop.S = ifelse(tested_n != 0, S_n/tested_n, 0))
+      sensitivity$perc.S <- sensitivity$prop.S * 100
+      
+      params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL) 
+      
+    } else {
+      next
+    }
+    params_set_out$fullname <- droplevels(params_set_out$fullname)
+   
+  }
+
+  
+  # if (infection_in != ""){
+  # params_set_out <- params_set_out %>%
+  #   mutate(infection_type = infection_in) %>%
+  #   mutate(infection_type_full = infection_full) %>%
+  #   rowwise() %>%
+  #   mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
+  #                            str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "), 
+  #                            ab_name(keyantimicrobials))) %>%
+  #   select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
+  #     
+  #   
+  # } else {
+  #   print("here??")
+  #   params_set_out <- params_set_out %>%
+  #     mutate(infection_type_full = infection_full) %>%
+  #     rowwise() %>%
+  #     mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
+  #                              str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "), 
+  #                              ab_name(keyantimicrobials))) %>%
+  #      select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
+  # }
+  
+
+  
+  return(params_set_out)
+  
+  
+}

data-raw/wisca_test/wisca_AMR.R

@@ -0,0 +1,122 @@
+
+# Copyright (c) [2022] [Larisse Bolton (author), Aislinn Cook (contributor)]
+# Adapted from: Bielicki JA, Sharland M, Heath PT, et al. Evaluation of the coverage of 3 antibiotic regimens for
+# neonatal sepsis in the hospital setting across Asian countries. JAMA Netw Open.
+# 2020:3(2):e1921124. doi:10.1001.jamanetworkopen.2019.21124
+
+
+wisca_funct <- function(wisca_in,analysis){
+
+#function used to gerenrate coverage estimate parameters from input data
+priors <- function(wisca_prior_in){
+  #INCIDENCE
+  #prior Dirichlet (Gamma) parameters
+  A <- rep(1, times = length(unique(wisca_prior_in$mo)))
+  Multiplier <- rep(1, times = length(unique(wisca_prior_in$mo)))
+  multinomial_obs <- round((wisca_prior_in$n.x)*Multiplier,0)
+  
+  #posterior Dirichlet (Gamma) parameters
+  gamma_A <- A + multinomial_obs
+  B <- rep(1, times = length(gamma_A))
+  
+  ##SUSCEPTIBILITY 
+  #prior Beta parameters
+  beta_A <- rep(1, times = length(unique(wisca_prior_in$mo)))
+  beta_B <- rep(1, times = length(unique(wisca_prior_in$mo)))
+  
+  #Binomial distribution parameters
+  r <- wisca_prior_in$S_n #number of pathogens that tested sensitive
+  n_obs <- wisca_prior_in$tested_n #number of pathogens tested
+  # n_tested <- round(n_obs*(wisca_calc_input$prop.sens))
+  diff_nr <- n_obs - r #difference between number of pathogens that were tested and those that tested sensitive
+  
+  #posterior beta parameters
+  post_beta_1 <- beta_A + r
+  post_beta_2 <- beta_B + diff_nr
+  
+  priors_df <- bind_cols(gamma_A,B,post_beta_1,post_beta_2)
+  names(priors_df) <- c("gamma_A","B","post_beta_1","post_beta_2")
+  return(priors_df)
+}
+
+#simulations per bug
+#function to generate simulations
+coverage <- function(wisca_sim_in){
+  sim_coverage <- wisca_sim_in %>%
+    mutate(random_incidence =  runif(n = nrow(wisca_sim_in), min = 0, max = 1),
+           random_susceptibility = runif(n = nrow(wisca_sim_in), min = 0, max = 1)) %>%
+    mutate(simulation_bug = qgamma(random_incidence, shape = gamma_A, scale = B),
+           simulation_suscep = qbeta(p = random_susceptibility, 
+                                     shape1 = post_beta_1, shape2 = post_beta_2)) %>%
+    mutate(sim_inc_total = sum(simulation_bug),
+           simulated_incidence = simulation_bug/sim_inc_total) %>%
+    mutate(simulated_bugcover = simulated_incidence *simulation_suscep,
+           coverage = sum(simulated_bugcover))
+  sim_coverage_out <- slice_head(sim_coverage, n = 1)$coverage  
+  return(sim_coverage_out)
+}
+
+##will run this for every regimen
+set.seed(1243)
+cover <- data.frame()
+
+for (gg in 1:length(unique(wisca_in$keyantimicrobials))){
+   simulation_nr <- 1000
+  #generate 1000 coverage estimates and determine mean and CI
+  wisca <- subset(wisca_in, keyantimicrobials == unique(wisca_in$keyantimicrobials)[gg])
+    
+  if (analysis %in% names(wisca)){
+    cover_level <- data.frame()
+    for (ii in 1:length(levels(wisca[[analysis]]))){
+      wisca_prep <- subset(wisca, wisca[[analysis]] == levels(wisca[[analysis]])[ii])
+      params_priors <- priors(wisca_prior_in = wisca_prep)
+      
+    coverage_simulation_total <- replicate(n = simulation_nr,coverage(wisca_sim_in = params_priors))
+    
+    av_coverage <- mean(coverage_simulation_total)
+    ci_coverage_lower <- quantile(coverage_simulation_total, probs = 0.025)
+    ci_coverage_upper <- quantile(coverage_simulation_total, probs = 0.975)
+    
+    combine_out <- cbind.data.frame(unique(wisca_in$keyantimicrobials)[gg],
+                                    levels(wisca[[analysis]])[ii],
+                                    av_coverage,
+                                    ci_coverage_lower,
+                                    ci_coverage_upper,
+                                    row.names = NULL)
+    names(combine_out) <- c("Regimen",str_to_sentence(analysis),"Coverage","Lower_CI","Upper_CI")
+    cover_level <- rbind.data.frame(cover_level,combine_out, row.names = NULL)
+    }
+    cover <- rbind.data.frame(cover,cover_level, row.names = NULL)
+    
+  
+    } else {
+    params_priors <- priors(wisca_prior_in = wisca)
+    
+    coverage_simulation_total <- replicate(n = simulation_nr,coverage(wisca_sim_in = params_priors))
+    
+    av_coverage <- mean(coverage_simulation_total)
+    ci_coverage_lower <- quantile(coverage_simulation_total, probs = 0.025)
+    ci_coverage_upper <- quantile(coverage_simulation_total, probs = 0.975)
+    
+    combine_out <- cbind.data.frame(unique(wisca_in$keyantimicrobials)[gg],
+                                    av_coverage,
+                                    ci_coverage_lower,
+                                    ci_coverage_upper,
+                                    row.names = NULL)
+    names(combine_out) <- c("Regimen","Coverage","Lower_CI","Upper_CI")
+    cover <- rbind.data.frame(cover,combine_out, row.names = NULL)
+    
+    }
+  
+}
+cover <- cover %>%
+  mutate(across(.cols = Coverage:Upper_CI, ~ round(.x*100,1))) %>%
+  rowwise() %>%
+  mutate(Regimen_full = ifelse(str_detect(Regimen, fixed("+")),
+                               str_c(sapply(unlist(unique(str_split(Regimen, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "), 
+                               ab_name(Regimen))) %>%
+  relocate(Regimen_full,.before = Regimen)
+
+return(cover)
+
+}