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# Determine First Isolates

Source: `R/first_isolate.R`
`first_isolate.Rd`

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_sir = 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"),
      ...
    )

## 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) - the default is the first column with a date class.

col_patient_id

  Column name of the unique IDs of the patients - the default is the first column that starts with 'patient' or 'patid' (case insensitive).

col_mo

  Column name of the names or codes of the microorganisms (see `as.mo()`) - the default is 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). This can also be a logical vector with the same length as rows in `x`.

col_keyantimicrobials

  (only useful when `method = "phenotype-based"`) column name of the key antimicrobials to determine first isolates, see `key_antimicrobials()`. The default is 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 - the default is `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_sir

  A logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use `include_untested_sir = FALSE` to always return `FALSE` for such rows. This checks the data set for columns of class `sir` and consequently requires transforming columns with antibiotic results using `as.sir()` 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

The methodology implemented in these functions is strictly based on the recommendations outlined in CLSI Guideline M39 and the research overview by Hindler et al. (2007, doi:10.1086/511864 ).

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 previously-mentioned sources, 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

Minimum variables required: Microorganism identifier

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

Minimum variables required: Microorganism identifier, Patient identifier

This method includes every genus-species combination per patient once. This method makes sure that no duplicate isolates are selected from the same patient. This method is preferred to e.g. identify the first MRSA finding of each patient to determine the incidence. Conversely, in a large longitudinal data set, this could mean that isolates are excluded that were found years after the initial isolate.

Episode-based

Minimum variables required: Microorganism identifier, Patient identifier, Date

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 e.g., 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data or ICU cases, 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

Minimum variables required: Microorganism identifier, Patient identifier, Date, Antimicrobial test results

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:

1. Using `type = "points"` and argument `points_threshold` (default)

   This method weighs all antimicrobial drugs 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.

2. Using `type = "keyantimicrobials"` and argument `ignore_I`

   This method only weighs specific antimicrobial drugs, 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.

## References

Methodology of these functions is strictly based on:

- M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, 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

## See also

`key_antimicrobials()`

## Examples

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

    example_isolates[first_isolate(info = TRUE), ]
    #> ℹ Determining first isolates using an episode length of 365 days
    #> ℹ Using column date as input for `col_date`.
    #> ℹ Using column patient as input for `col_patient_id`.
    #> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
    #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column mo)
    #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
    #> microbial ID was available)
    #> # A tibble: 1,387 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA
    #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R
    #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA
    #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA
    #> # ℹ 1,377 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
    # \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: 441 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R
    #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA
    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R
    #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R
    #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R
    #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG   R     NA    NA    R
    #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI   R     NA    NA    NA
    #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI   R     NA    NA    NA
    #> # ℹ 431 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …

    if (require("dplyr")) {
      # filter on first isolates using dplyr:
      example_isolates %>%
        filter(first_isolate(info = TRUE))
    }
    #> ℹ Determining first isolates using an episode length of 365 days
    #> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
    #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column mo)
    #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
    #> microbial ID was available)
    #> # A tibble: 1,387 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA
    #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R
    #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA
    #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA
    #> # ℹ 1,377 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
    if (require("dplyr")) {
      # short-hand version:
      example_isolates %>%
        filter_first_isolate(info = FALSE)
    }
    #> # A tibble: 1,387 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA
    #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R
    #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA
    #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA
    #> # ℹ 1,377 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
    if (require("dplyr")) {
      # flag the first isolates per group:
      example_isolates %>%
        group_by(ward) %>%
        mutate(first = first_isolate(info = TRUE)) %>%
        select(ward, date, patient, mo, first)
    }
    #> ℹ Determining first isolates using an episode length of 365 days
    #> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
    #> Group: ward = "Clinical"
    #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column mo)
    #> => Found 865 'phenotype-based' first isolates (70.1% of total where a microbial
    #> ID was available)
    #> Group: ward = "ICU"
    #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column mo)
    #> => Found 452 'phenotype-based' first isolates (70.0% of total where a microbial
    #> ID was available)
    #> Group: ward = "Outpatient"
    #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column mo)
    #> => Found 99 'phenotype-based' first isolates (82.5% of total where a microbial
    #> ID was available)
    #> # A tibble: 2,000 × 5
    #> # Groups:   ward [3]
    #>    ward     date       patient mo           first
    #>    <chr>    <date>     <chr>   <mo>         <lgl>
    #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE
    #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE
    #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE
    #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE
    #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE
    #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE
    #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE
    #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE
    #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR TRUE
    #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE
    #> # ℹ 1,990 more rows
    # }