first_isolate.RdDetermine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
first_isolate(x, col_date = NULL, col_patient_id = NULL, col_mo = NULL, col_testcode = NULL, col_specimen = NULL, col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365, testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2, info = TRUE, include_unknown = FALSE, ...) filter_first_isolate(x, col_date = NULL, col_patient_id = NULL, col_mo = NULL, ...) filter_first_weighted_isolate(x, col_date = NULL, col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL, ...)
| x | a |
|---|---|
| col_date | column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class |
| col_patient_id | column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive) |
| col_mo | column name of the unique IDs of the microorganisms (see |
| col_testcode | column name of the test codes. Use |
| col_specimen | column name of the specimen type or group |
| col_icu | column name of the logicals ( |
| col_keyantibiotics | column name of the key antibiotics to determine first weighted isolates, see |
| episode_days | episode in days after which a genus/species combination will be determined as 'first isolate' again. The default of 365 days is based on the guideline by CLSI, see Source. |
| testcodes_exclude | character vector with test codes that should be excluded (case-insensitive) |
| icu_exclude | logical whether ICU isolates should be excluded (rows with value |
| specimen_group | value in column |
| type | type to determine weighed isolates; can be |
| ignore_I | logical to determine whether antibiotic interpretations with |
| points_threshold | points until the comparison of key antibiotics will lead to inclusion of an isolate when |
| info | print progress |
| include_unknown | logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code |
| ... | parameters passed on to the |
Methodology of this function is based on: M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
Logical vector
WHY THIS IS SO IMPORTANT
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [1]. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.
All isolates with a microbial ID of NA will be excluded as first isolate.
The functions filter_first_isolate and filter_first_weighted_isolate are helper functions to quickly filter on first isolates. The function filter_first_isolate is essentially equal to:
x %>% mutate(only_firsts = first_isolate(x, ...)) %>% filter(only_firsts == TRUE) %>% select(-only_firsts)
The function filter_first_weighted_isolate is essentially equal to:
x %>%
mutate(keyab = key_antibiotics(.)) %>%
mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) %>%
filter(only_weighted_firsts == TRUE) %>%
select(-only_weighted_firsts)
There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results:
1. Using type = "keyantibiotics" and parameter ignore_I
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics function.
2. Using type = "points" and parameter points_threshold
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.
On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
# NOT RUN { # septic_patients is a dataset available in the AMR package. It is true, genuine data. ?septic_patients library(dplyr) # Filter on first isolates: septic_patients %>% mutate(first_isolate = first_isolate(., col_date = "date", col_patient_id = "patient_id", col_mo = "mo")) %>% filter(first_isolate == TRUE) # Which can be shortened to: septic_patients %>% filter_first_isolate() # or for first weighted isolates: septic_patients %>% filter_first_weighted_isolate() # Now let's see if first isolates matter: A <- septic_patients %>% group_by(hospital_id) %>% summarise(count = n_rsi(GEN), # gentamicin availability resistance = portion_IR(GEN)) # gentamicin resistance B <- septic_patients %>% filter_first_weighted_isolate() %>% # the 1st isolate filter group_by(hospital_id) %>% summarise(count = n_rsi(GEN), # gentamicin availability resistance = portion_IR(GEN)) # gentamicin resistance # Have a look at A and B. # B is more reliable because every isolate is only counted once. # Gentamicin resitance in hospital D appears to be 3.1% higher than # when you (erroneously) would have used all isolates for analysis. ## OTHER EXAMPLES: # }# NOT RUN { # set key antibiotics to a new variable x$keyab <- key_antibiotics(x) x$first_isolate <- first_isolate(x) x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab') x$first_blood_isolate <- first_isolate(x, specimen_group = 'Blood') x$first_blood_isolate_weighed <- first_isolate(x, specimen_group = 'Blood', col_keyantibiotics = 'keyab') x$first_urine_isolate <- first_isolate(x, specimen_group = 'Urine') x$first_urine_isolate_weighed <- first_isolate(x, specimen_group = 'Urine', col_keyantibiotics = 'keyab') x$first_resp_isolate <- first_isolate(x, specimen_group = 'Respiratory') x$first_resp_isolate_weighed <- first_isolate(x, specimen_group = 'Respiratory', col_keyantibiotics = 'keyab') # }