NEWS.md
- Function is_new_episode() to determine patient episodes which are not necessarily based on microorganisms. It also supports grouped variables with e.g. mutate(), filter() and summarise() of the dplyr package:
example_isolates %>% - group_by(patient_id, hospital_id) %>% - filter(is_new_episode(date, episode_days = 60))
Functions mo_is_gram_negative() and mo_is_gram_positive() as wrappers around mo_gramstain(). They always return TRUE or FALSE (except when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria. If you have the dplyr package installed, they can even determine the column with microorganisms themselves when used inside dplyr verbs:
Functions mo_is_gram_negative() and mo_is_gram_positive() as wrappers around mo_gramstain(). They always return TRUE or FALSE (except when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria. They can even determine the column with microorganisms themselves when used inside dplyr verbs:
example_isolates %>% - filter(mo_is_gram_positive()) + filter(mo_is_gram_positive()) #> NOTE: Using column `mo` as input for mo_is_gram_positive()
Function mo_is_intrinsic_resistant() to test for intrinsic resistance, based on EUCAST Intrinsic Resistance and Unusual Phenotypes v3.2 from 2020. As with the new mo_is_gram_*() functions, if you have the dplyr package installed the column with microorganisms will be automatically determined when used inside dplyr verbs:
example_isolates %>% - filter(mo_is_intrinsic_resistant(ab = "Vancomycin")) + filter(mo_is_intrinsic_resistant(ab = "Vancomycin")) #> NOTE: Using column `mo` as input for mo_is_intrinsic_resistant()
All isolates with a microbial ID of NA will be excluded as first isolate.
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode (ref). 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.
+To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode (Hindler et al. 2007). 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.
filter_*() shortcutsThe 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 either:
x[first_isolate(x, ...), ] + x %>% filter(first_isolate(x, ...))diff --git a/docs/reference/ggplot_rsi.html b/docs/reference/ggplot_rsi.html index b48e86574..bfbf3f0db 100644 --- a/docs/reference/ggplot_rsi.html +++ b/docs/reference/ggplot_rsi.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/index.html b/docs/reference/index.html index ef39a782f..3e78d4b5e 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -81,7 +81,7 @@ diff --git a/docs/reference/intrinsic_resistant.html b/docs/reference/intrinsic_resistant.html index 9034d9af5..5a31e6f4d 100644 --- a/docs/reference/intrinsic_resistant.html +++ b/docs/reference/intrinsic_resistant.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/is_new_episode.html b/docs/reference/is_new_episode.html index 5032c1c80..124c90bbf 100644 --- a/docs/reference/is_new_episode.html +++ b/docs/reference/is_new_episode.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/like.html b/docs/reference/like.html index 7c0403b28..ab54088d1 100644 --- a/docs/reference/like.html +++ b/docs/reference/like.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/mo_matching_score.html b/docs/reference/mo_matching_score.html index ec9acd725..28d92e4bc 100644 --- a/docs/reference/mo_matching_score.html +++ b/docs/reference/mo_matching_score.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/mo_property.html b/docs/reference/mo_property.html index 94acf13aa..345f8d745 100644 --- a/docs/reference/mo_property.html +++ b/docs/reference/mo_property.html @@ -82,7 +82,7 @@ diff --git a/docs/survey.html b/docs/survey.html index 344ae1172..4dc402d2f 100644 --- a/docs/survey.html +++ b/docs/survey.html @@ -81,7 +81,7 @@ diff --git a/man/first_isolate.Rd b/man/first_isolate.Rd index fdd267a9e..8f5bdba88 100755 --- a/man/first_isolate.Rd +++ b/man/first_isolate.Rd @@ -98,7 +98,7 @@ The \code{\link[=first_isolate]{first_isolate()}} function is a wrapper around t All isolates with a microbial ID of \code{NA} will be excluded as first isolate. \subsection{Why this is so important}{ -To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https:/pubmed.ncbi.nlm.nih.gov/17304462/}{(ref)}. 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 \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. +To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://pubmed.ncbi.nlm.nih.gov/17304462/}{(Hindler \emph{et al.} 2007)}. 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 \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. } \subsection{\verb{filter_*()} shortcuts}{ @@ -106,6 +106,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to either:\preformatted{ x[first_isolate(x, ...), ] + x \%>\% filter(first_isolate(x, ...)) }