Determine First Isolates

Determine first isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindler et al. in 2007 (doi:10.1086/511864 ). To determine patient episodes not necessarily based on microorganisms, use is_new_episode() that also supports grouping with the dplyr package.

Usage

first_isolate(
  x = NULL,
  col_date = NULL,
  col_patient_id = NULL,
  col_mo = NULL,
  col_testcode = NULL,
  col_specimen = NULL,
  col_icu = NULL,
  col_keyantimicrobials = NULL,
  episode_days = 365,
  testcodes_exclude = NULL,
  icu_exclude = FALSE,
  specimen_group = NULL,
  type = "points",
  method = c("phenotype-based", "episode-based", "patient-based", "isolate-based"),
  ignore_I = TRUE,
  points_threshold = 2,
  info = interactive(),
  include_unknown = FALSE,
  include_untested_rsi = TRUE,
  ...
)

filter_first_isolate(
  x = NULL,
  col_date = NULL,
  col_patient_id = NULL,
  col_mo = NULL,
  episode_days = 365,
  method = c("phenotype-based", "episode-based", "patient-based", "isolate-based"),
  ...
)

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/.
Hindler JF and Stelling J (2007). Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864

Arguments

x: a data.frame containing isolates. Can be left blank for automatic determination, see Examples.
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 as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
col_testcode: column name of the test codes. Use col_testcode = NULL to not exclude certain test codes (such as test codes for screening). In that case testcodes_exclude will be ignored.
col_specimen: column name of the specimen type or group
col_icu: column name of the logicals (TRUE/FALSE) whether a ward or department is an Intensive Care Unit (ICU)
col_keyantimicrobials: (only useful when method = "phenotype-based") column name of the key antimicrobials to determine first isolates, see key_antimicrobials(). Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' or 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE to prevent this. Can also be the output of key_antimicrobials().
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: a character vector with test codes that should be excluded (case-insensitive)
icu_exclude: a logical to indicate whether ICU isolates should be excluded (rows with value TRUE in the column set with col_icu)
specimen_group: value in the column set with col_specimen to filter on
type: type to determine weighed isolates; can be "keyantimicrobials" or "points", see Details
method: the method to apply, either "phenotype-based", "episode-based", "patient-based" or "isolate-based" (can be abbreviated), see Details. The default is "phenotype-based" if antimicrobial test results are present in the data, and "episode-based" otherwise.
ignore_I: logical to indicate whether antibiotic interpretations with "I" will be ignored when type = "keyantimicrobials", see Details
points_threshold: minimum number of points to require before differences in the antibiogram will lead to inclusion of an isolate when type = "points", see Details
info: a logical to indicate info should be printed, defaults to TRUE only in interactive mode
include_unknown: a logical to indicate 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.
include_untested_rsi: a logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use include_untested_rsi = FALSE to always return FALSE for such rows. This checks the data set for columns of class <rsi> and consequently requires transforming columns with antibiotic results using as.rsi() first.
...: arguments passed on to first_isolate() when using filter_first_isolate(), otherwise arguments passed on to key_antimicrobials() (such as universal, gram_negative, gram_positive)

Value

A logical vector

Details

To conduct epidemiological analyses on antimicrobial resistance data, only so-called first isolates should be included to prevent overestimation and underestimation of antimicrobial resistance. Different methods can be used to do so, see below.

These functions are context-aware. This means that the x argument can be left blank if used inside a data.frame call, see Examples.

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.

Different methods

According to Hindler et al. (2007, doi:10.1086/511864 ), there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies).

All mentioned methods are covered in the first_isolate() function:

Method	Function to apply
Isolate-based	`first_isolate(x, method = "isolate-based")`
(= all isolates)


Patient-based	`first_isolate(x, method = "patient-based")`
(= first isolate per patient)


Episode-based	`first_isolate(x, method = "episode-based")`, or:
(= first isolate per episode)
- 7-Day interval from initial isolate	- `first_isolate(x, method = "e", episode_days = 7)`
- 30-Day interval from initial isolate	- `first_isolate(x, method = "e", episode_days = 30)`


Phenotype-based	`first_isolate(x, method = "phenotype-based")`, or:
(= first isolate per phenotype)
- Major difference in any antimicrobial result	- `first_isolate(x, type = "points")`
- Any difference in key antimicrobial results	- `first_isolate(x, type = "keyantimicrobials")`

Isolate-based

This method does not require any selection, as all isolates should be included. It does, however, respect all arguments set in the first_isolate() function. For example, the default setting for include_unknown (FALSE) will omit selection of rows without a microbial ID.

Patient-based

To include every genus-species combination per patient once, set the episode_days to Inf. Although often inappropriate, this method makes sure that no duplicate isolates are selected from the same patient. In a large longitudinal data set, this could mean that isolates are excluded that were found years after the initial isolate.

Episode-based

To include every genus-species combination per patient episode once, set the episode_days to a sensible number of days. Depending on the type of analysis, this could be 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data, long episodes are common for analysing regional and national data.

This is the most common method to correct for duplicate isolates. Patients are categorised into episodes based on their ID and dates (e.g., the date of specimen receipt or laboratory result). While this is a common method, it does not take into account antimicrobial test results. This means that e.g. a methicillin-resistant Staphylococcus aureus (MRSA) isolate cannot be differentiated from a wildtype Staphylococcus aureus isolate.

Phenotype-based

This is a more reliable method, since it also weighs the antibiogram (antimicrobial test results) yielding so-called 'first weighted isolates'. There are two different methods to weigh the antibiogram:

Using type = "points" and argument points_threshold (default)

This method weighs all antimicrobial agents available in the data set. Any difference from I to S or R (or vice versa) counts as 0.5 points, a difference from S to R (or vice versa) counts as 1 point. When the sum of points exceeds points_threshold, which defaults to 2, an isolate will be selected as a first weighted isolate.

All antimicrobials are internally selected using the all_antimicrobials() function. The output of this function does not need to be passed to the first_isolate() function.
Using type = "keyantimicrobials" and argument ignore_I

This method only weighs specific antimicrobial agents, called key antimicrobials. Any difference from S to R (or vice versa) in these key antimicrobials will select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S or R (or vice versa) will lead to this.

Key antimicrobials are internally selected using the key_antimicrobials() function, but can also be added manually as a variable to the data and set in the col_keyantimicrobials argument. Another option is to pass the output of the key_antimicrobials() function directly to the col_keyantimicrobials argument.

The default method is phenotype-based (using type = "points") and episode-based (using episode_days = 365). This makes sure that every genus-species combination is selected per patient once per year, while taking into account all antimicrobial test results. If no antimicrobial test results are available in the data set, only the episode-based method is applied at default.

Examples

# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.

example_isolates[first_isolate(), ]
#> # A tibble: 1,379 × 49
#>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo          
#>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>        
#>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI
#>  2 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR
#>  3 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS
#>  4 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR
#>  5 2002-01-17 D        FALSE   TRUE    FALSE      67 M      495616  B_STPHY_EPDR
#>  6 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI
#>  7 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND
#>  8 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL
#>  9 2002-02-03 B        TRUE    FALSE   FALSE      76 M      481442  B_STPHY_CONS
#> 10 2002-02-05 A        FALSE   TRUE    FALSE      50 M      023456  B_STPHY_HMNS
#> # … with 1,369 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
#> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
#> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
#> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
#> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
#> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
#> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# \donttest{
# get all first Gram-negatives
example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]
#> ℹ Using column 'mo' as input for `mo_is_gram_negative()`
#> # A tibble: 437 × 49
#>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo          
#>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>        
#>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI
#>  2 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI
#>  3 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND
#>  4 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL
#>  5 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS
#>  6 2002-02-27 D        FALSE   TRUE    FALSE      85 F      066895  B_KLBSL_PNMN
#>  7 2002-03-08 C        FALSE   TRUE    FALSE      69 M      4FC193  B_ESCHR_COLI
#>  8 2002-03-16 C        FALSE   TRUE    FALSE      69 M      4FC193  B_PSDMN_AERG
#>  9 2002-04-01 B        TRUE    FALSE   FALSE      46 F      496896  B_ESCHR_COLI
#> 10 2002-04-23 B        TRUE    FALSE   FALSE      69 F      EE2510  B_ESCHR_COLI
#> # … with 427 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
#> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
#> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
#> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
#> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
#> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
#> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

if (require("dplyr")) {
  # filter on first isolates using dplyr:
  example_isolates %>%
    filter(first_isolate())
 
  # short-hand version:
  example_isolates %>%
    filter_first_isolate(info = FALSE)
    
 # flag the first isolates per group:
 example_isolates %>%
   group_by(hospital_id) %>%
   mutate(first = first_isolate()) %>%
   select(hospital_id, date, patient_id, mo, first)
  
  # 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_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.
  A
  B
  
  # B is more reliable because every isolate is counted only once.
  # Gentamicin resistance in hospital D appears to be 4.2% higher than
  # when you (erroneously) would have used all isolates for analysis.
}
#> # A tibble: 4 × 3
#>   hospital_id count resistance
#>   <fct>       <int>      <dbl>
#> 1 A             216      0.204
#> 2 B             407      0.270
#> 3 C             202      0.218
#> 4 D             457      0.293
# }