Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use is_new_episode() that also supports grouping with the dplyr package.
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. |
|---|---|
| col_date | column name of the result date (or date that is was received on the lab), defaults to the first column 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 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 |
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
The first_isolate() function is a wrapper around the is_new_episode() function, but more efficient for data sets containing microorganism codes or names.
All isolates with a microbial ID of NA will be excluded as first isolate.
Why this is so important
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_*() shortcuts
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 either:
x[first_isolate(x, ...), ] x %>% filter(first_isolate(x, ...))
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, -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:
Using
type = "keyantibiotics"and parameterignore_IAny 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 thekey_antibiotics()function.Using
type = "points"and parameterpoints_thresholdA 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 to2, 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 and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
Examples
# `example_isolates` is a dataset available in the AMR package. # See ?example_isolates. # basic filtering on first isolates example_isolates[first_isolate(example_isolates), ] # filtering based on isolates ---------------------------------------------- # \donttest{ if (require("dplyr")) { # filter on first isolates: example_isolates %>% mutate(first_isolate = first_isolate(.)) %>% filter(first_isolate == TRUE) # short-hand versions: example_isolates %>% filter_first_isolate() example_isolates %>% filter_first_weighted_isolate() # 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. } # }