Determine 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 = interactive(),
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,
...
)

## Arguments

x a data.frame containing isolates. 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 column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive) column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). column name of the test codes. Use col_testcode = NULL to not exclude certain test codes (like test codes for screening). In that case testcodes_exclude will be ignored. column name of the specimen type or group column name of the logicals (TRUE/FALSE) whether a ward or department is an Intensive Care Unit (ICU) column name of the key antibiotics to determine first weighted isolates, see key_antibiotics(). Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use col_keyantibiotics = FALSE to prevent this. 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. character vector with test codes that should be excluded (case-insensitive) logical whether ICU isolates should be excluded (rows with value TRUE in column col_icu) value in column col_specimen to filter on type to determine weighed isolates; can be "keyantibiotics" or "points", see Details logical to determine whether antibiotic interpretations with "I" will be ignored when type = "keyantibiotics", see Details points until the comparison of key antibiotics will lead to inclusion of an isolate when type = "points", see Details print progress logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code "UNKNOWN", which defaults to FALSE. For WHONET users, this means that all records with organism code "con" (contamination) will be excluded at default. Isolates with a microbial ID of NA will always be excluded as first isolate. parameters passed on to the first_isolate() function

## Source

Methodology of this function is strictly 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/.

## Value

A logical vector

## Details

WHY THIS IS SO IMPORTANT
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.

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 one of:

 x %&gt;% filter(first_isolate(., ...))

The function filter_first_weighted_isolate() is essentially equal to:

 x %&gt;%
mutate(keyab = key_antibiotics(.)) %&gt;%
mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) %&gt;%
filter(only_weighted_firsts == TRUE) %&gt;%
select(-only_weighted_firsts, -keyab)

## Key antibiotics

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.

## Stable lifecycle

The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

## Read more on our website!

On our website https://msberends.github.io/AMR you can find a comprehensive 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.

key_antibiotics()

## Examples

# example_isolates is a dataset available in the AMR package.
# See ?example_isolates.

if (FALSE) {
library(dplyr)
# Filter on first isolates:
example_isolates %>%
mutate(first_isolate = first_isolate(.)) %>%
filter(first_isolate == TRUE)

# Now let's see if first isolates matter:
A <- example_isolates %>%
group_by(hospital_id) %>%
summarise(count = n_rsi(GEN),            # gentamicin availability
resistance = resistance(GEN))  # gentamicin resistance

B <- example_isolates %>%
filter_first_weighted_isolate() %>%      # the 1st isolate filter
group_by(hospital_id) %>%
summarise(count = n_rsi(GEN),            # gentamicin availability
resistance = resistance(GEN))  # gentamicin resistance

# Have a look at A and B.
# B is more reliable because every isolate is counted only once.
# Gentamicin resistance in hospital D appears to be 3.7% higher than
# when you (erroneously) would have used all isolates for analysis.

## OTHER EXAMPLES:

# Short-hand versions:
example_isolates %>%
filter_first_isolate()

example_isolates %>%
filter_first_weighted_isolate()

# 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")
}