---
title: "Comorbidities"
output:
 rmarkdown::html_vignette:
   toc: true
   number_sections: false
bibliography: references.bib
vignette: >
 %\VignetteIndexEntry{Comorbidities}
 %\VignetteEngine{knitr::rmarkdown}
 %\VignetteEncoding{UTF-8}
---

```{r, label = "setup", include = FALSE}
# IMPORTANT SYNTAX NOTE:
#
# DO NOT USE the pipeOp `|>`
#
# While convenient, that is a R 4.1.0 feature at a minimum. Notable improvements
# to the pipeOp come in 4.2.0 and 4.2.1.  To keep this package dependent on R >=
# 3.5.0 do not use the pipeOp.

library(kableExtra)
knitr::opts_chunk$set(collapse = TRUE, fig.align = "center")
options(qwraps2_markup = "markdown")
```

```{r, label = 'medicalcoder-namespace'}
library(medicalcoder)
packageVersion("medicalcoder")
```

# Comorbidity Algorithms

There are three comorbidity algorithms, each with several variants,
implemented in the medicalcoder package:

1. Pediatric Complex Chronic Condition System (PCCC)
   a. Version 2.0 [@feudtner2014pediatric]
       i. `pccc_v2.0` is consistent with the older R package pccc (v1.0.6) [@dewitt2025pccc].
       ii. `pccc_v2.1` modifies the set of ICD codes to be more consistent with documentation and other implementations of v2.0.

   b. Version 3.0 [@feinstein2024pediatric]
       i. `pccc_v3.0` is consistent with the SAS software published on the [Children's Hospital Association](https://www.childrenshospitals.org/content/analytics/toolkit/complex-chronic-conditions) website in conjunction with @feinstein2024pediatric.
       ii. `pccc_v3.1` modifies the set of ICD codes to be more consistent with documentation.

2. Charlson
   a. `charlson_deyo1992`: Deyo's original set of codes [@deyo1992adapting;@quan2005]
   b. `charlson_quan2005` and `charlson_quan2011`: Codes and index scoring [@quan2005;@quan2011]
   c. `charlson_cdmf2019`: [@glasheen2019]

3. Elixhauser
   a. Based on codes provided by the Agency for Healthcare Research and Quality (AHRQ) for fiscal years 2022 through 2025 [@ahrq2025]
      i. `elixhauser_ahrq2022`
      ii. `elixhauser_ahrq2023`
      iii. `elixhauser_ahrq2024`
      iv. `elixhauser_ahrq2025`

   b. Codes from Table 2 of @quan2005
      i. `elixhauser_elixhauser1988`: [@elixhauser1998;@quan2005]
      ii. `elixhauser_ahrq_web`: [@quan2005;@ahrq2017]
      iii. `elixhauser_quan2005`: [@quan2005]

**IMPORTANT NOTE:** Elixhauser 1998 and AHRQ Web used diagnostic related
group (DRG) codes as part of the methods.  The medicalcoder
package _does not_ use DRG codes.  This is consistent with the way these
methods were implemented in @quan2005.

A list of the valid methods for the package can be accessed via a non-exported
function.  In general, the methods are listed in the form of
`<algorithm>_<version>`.
```{r label = "valid-methods"}
medicalcoder:::comorbidities_methods()
```

Vignettes for each of the major methods are available.

```{r echo = TRUE, eval = FALSE}
vignette(topic = "pccc",       package = "medicalcoder")
vignette(topic = "charlson",   package = "medicalcoder")
vignette(topic = "elixhauser", package = "medicalcoder")
```

The focus of this vignette is to highlight the general use of the
`comorbidities()` function.

```{r, label = 'test-that-the-args-have-not-changed', include = FALSE}
# IF THIS FAILS YOU NEED TO MAKE SURE THE DOCUMENTATION IN THIS VIGNETTE IS
# UPTO DATE
# dput(capture.output(args(comorbidities)))
stopifnot(
  capture.output(args(comorbidities))  ==
    c("function (data, icd.codes, method, id.vars = NULL, icdv.var = NULL, ",
      "    icdv = NULL, dx.var = NULL, dx = NULL, poa.var = NULL, poa = NULL, ",
      "    age.var = NULL, primarydx.var = NULL, primarydx = NULL, flag.method = c(\"current\", ",
      "        \"cumulative\"), full.codes = TRUE, compact.codes = TRUE, ",
      "    subconditions = FALSE) ", "NULL")
)
```

Details of the function arguments call are in the manual.
```{r label = "read-the-manual", eval = FALSE}
help(topic = "comorbidities", package = "medicalcoder")
```

```{r label = "comorbidities-args"}
args(comorbidities)
```

We highlight a general concept for the arguments.  Note that several arguments
are in pairs, e.g., `dx.var` and `dx` (used for denoting if codes are
diagnostic or procedural), or `poa.var` and `poa` (used for denoting if a codes
are present on admission).  The `.var` version is the name of a variable within
the `data.frame` passed into the `data` argument.  The version without `.var` is
a default value to be applied to the entirety of `data`. We will see some
examples where this is useful.

The `data` element is expected to be a `data.frame`, or at least something that
inherits the `data.frame` class.  The format is expected to be a 'long' format.
One row per code.  Two example data sets in the package show the general
expected form of the `data`.

```{r label = "example-data"}
head(mdcr)
head(mdcr_longitudinal)
```

```{r}
```


# When are conditions flagged?

Whether or not the code is present on admission (POA) is useful when applying
the comorbidity algorithms and considering if the patient has a comorbidity at
the start of an encounter, or if the condition is a result of the current
hospitalization.

Implementation of Elixhauser comorbidities for 2022 and beyond [@ahrq2025]
explicitly define the use of present on admission flags for specific conditions
(see the `poa_required` flag reported in the data set returned by
`get_elixhauser_poa()`).
```{r}
str(get_elixhauser_poa())
```

For Charlson comorbidities, from @quan2011:

> We defined comorbidities in the previous admissions using major and secondary
> diagnoses, without consideration of diagnosis type. The presence of a comorbid
> condition was assigned to a patient when it was present in index or previous
> admission records. Otherwise, the absence of the condition was assigned to the
> patient.

From @quan2005:

> The decision of whether to include or exclude specific codes or conditions
> from a coding algorithm depends to a large extent on a given study’s
> objectives. The original Charlson index used conditions present in
> hospitalized medical patients (regardless of whether the condition was present
> at baseline or arose after admission) to predict survival over an ensuing
> year. For such a study, a decision to exclude conditions arising after
> admission would not be ideal, as it would result in a loss of prognostic
> information relevant to long-term survival, and an 'under-adjustment' in
> risk-adjusted survival analyses. In contrast, in the context of studying
> in-hospital outcomes of a surgical procedure, researchers would be best
> advised to confine their risk adjustment to variables that are predominantly
> present at baseline. In jurisdictions that have diagnosis type indicators, the
> methodological decision is simply one of deciding, based on study objectives,
> whether to use or not use the indicators. In regions or countries without
> diagnosis type indicators, meanwhile, the data that we present ... can help
> researchers make decisions on a condition-by-condition basis of whether to
> include particular variables, depending on their study objectives.

PCCC does not explicitly note the if POA is required.

medicalcoder has been built to consider POA for all comorbidity algorithms.

End users can use a 0/1 indicator variable in the data set to report
which codes are POA via the function argument `poa.var`.  If all the codes are
to be considered POA or not, the functional argument `poa` can be used to set a
common status without adding a column to the input data set.

Additionally, medicalcoder provides a `flag.method` argument for longitudinal
data sets.

**Example:** Let's assume we have a patient record for six encounters.  We use
ICD-10 diagnostic codes C78.4 and I50.40 which maps to a cancer and heart
failure (cardiovascular disease) comorbidity respectively for PCCC, Charlson,
and Elixhauser. For demonstration, we also flag POA with the second report of
I50.40 intentionally marked as not present on admission.

```{r, label = "example-setup", include = FALSE}
lookup_icd_codes(c("C78.4", "I50.40"))

codes <- c("C78.4", "I50.40")
cols  <- c("icdv", "dx", "code", "full_code", "condition")

subset(
  get_pccc_codes(),
  subset = full_code %in% codes,
  select = c(cols, "pccc_v3.0")
)

subset(
  get_charlson_codes(),
  subset = full_code %in% codes,
  select = c(cols, "charlson_quan2011")
)

subset(
  get_elixhauser_codes(),
  subset = full_code %in% codes & elixhauser_ahrq2025 == 1L,
  select = c(cols, "elixhauser_ahrq2025")
)

record <-
  structure(
    list(
      patid = c("A", "A", "A", "A", "A", "A", "A"),
      encid = c(1L, 2L, 3L, 4L, 5L, 5L, 6L),
      code = c(NA, "C78.4", "I50.40", NA, "C78.4", "I50.40", NA),
      poa = c(NA, 0L, 1L, NA, 1L, 0L, NA)),
    row.names = c(NA, -7L),
    class = "data.frame"
  )
```

We will call `comorbidities()` for the three methods using static POA flags and
dynamic POA flags, and both flag methods.  Results are shown in the following
table.

```{r, include = FALSE}
data.table::setDT(record)
args <-
  list(data = record,
       icd.codes = "code",
       id.vars = c("patid", "encid"),
       icdv = 10L,
       dx = 1
  )
args_current_poa0    <- c(args, poa = 0L,        flag.method = "current")
args_current_poa1    <- c(args, poa = 1L,        flag.method = "current")
args_current_poav    <- c(args, poa.var = "poa", flag.method = "current")
args_cumulative_poa0 <- c(args, poa = 0L,        flag.method = "cumulative")
args_cumulative_poa1 <- c(args, poa = 1L,        flag.method = "cumulative")
args_cumulative_poav <- c(args, poa.var = "poa", flag.method = "cumulative")

left_cols <-
  cbind(encid = 1L:6L,
        ICD = c("", "C78.4", paste0("I50.40", footnote_marker_symbol(1L)),
                "", paste0("C78.4", footnote_marker_symbol(1L), "; I50.40"), ""))

rtn <-
  rbind(
    do.call(cbind,
            list(
              left_cols,
              do.call(comorbidities, c(args_current_poa0,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_current_poa0, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_current_poa0, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)],
              do.call(comorbidities, c(args_current_poa1,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_current_poa1, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_current_poa1, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)],
              do.call(comorbidities, c(args_current_poav,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_current_poav, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_current_poav, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)]
    ))
  ,
    do.call(cbind,
            list(
              left_cols,
              do.call(comorbidities, c(args_cumulative_poa0,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_cumulative_poa0, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_cumulative_poa0, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)],
              do.call(comorbidities, c(args_cumulative_poa1,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_cumulative_poa1, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_cumulative_poa1, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)],
              do.call(comorbidities, c(args_cumulative_poav,                 method = "pccc_v3.0"))[,           .(CVD = cvd_dxpr_or_tech, CANCER = malignancy_dxpr_or_tech)],
              do.call(comorbidities, c(args_cumulative_poav, primarydx = 0L, method = "charlson_quan2011"))[,   .(CVD = chf,              CANCER = mst)],
              do.call(comorbidities, c(args_cumulative_poav, primarydx = 0L, method = "elixhauser_ahrq2025"))[, .(CVD = HF,               CANCER = CANCER_METS)]
    ))
  )
```

```{r, echo = FALSE, results = "asis"}
tab <-
  kbl(
    rtn,
    row.names = FALSE,
    escape = FALSE,
    align = rep("c", nrow(rtn)),
    caption = "Indicators for when a comorbidity is flagged based on the algorithm, present on admission (poa), and flag.method. The two ICD codes, C78.4 and I50.40, map to cancer and cardiovascular disease respectively."
  )
tab <-
  footnote(
    tab,
    symbol = c("Present on Admission"),
    general = "C78.4 does not need to be POA to count for Elixhauser. I50.40 does need to be POA to count for Elixhauser."
  )

tab <- kable_styling(tab, bootstrap_options = c("striped", "bordered"), full_width = FALSE, font_size = 8)
#tab <- scroll_box(tab, height = "600px")
tab <- pack_rows(tab, "flag.method = 'current'", 1L, 6L)
tab <- pack_rows(tab, "flag.method = 'cumulative'", 7L, 12L)
tab <- add_header_above(tab, c(" " = 2L, rep(c("PCCC" = 2L, "Charlson" = 2L, "Elixhauser" = 2L), 3L)))
tab <- add_header_above(tab, c(" " = 2L, c("POA = 0" = 6L, "POA = 1" = 6L, "poa.var = 'poa'" = 6L)))
tab
```


# Mapping ICD Codes to Comorbidities

End users can quickly assess the lookup table for all the ICD codes associated
with a comorbidity algorithm using the `get_<comorbidity>_codes` functions.
Each `data.frame` has columns for the ICD version, diagnostic or procedure flag,
the compact code, and the full code.  A column for the condition and other
method specific flags are provided.  Lastly, there are indicator columns for the
variant of each method

```{r, label = "get-codes"}
str(get_pccc_codes())
str(get_charlson_codes())
str(get_elixhauser_codes())
```

End users should be aware that just because an ICD code exists in a data
set does not mean that the patient has the condition.  For Elixhauser, the
presence on admission is important to consider.  For PCCC version 3.0 and 3.1,
tech dependencies on their own are insufficient to flag a condition (see
`vignette(topic = "pccc", package = "medicalcoder")`).

For the `charlson_cdmf2019` method [@glasheen2019], the AIDS categories are
defined by the presence of HIV and an opportunistic infection.  In the
following example, if only considering ICD codes which flag 'aids',
there would be several thousand cases of AIDS, but only six cases
of HIV.

<!-- NOTE: the following example is also in tests/test-charlson_cdmf2019.R -->
```{r}
cdmf_eg <-
  merge(x = mdcr,
        y = subset(get_charlson_codes(),
                   condition %in% c("aids", "hiv") &
                   charlson_cdmf2019 == 1),
        by = c("icdv", "dx", "code"))
data.table::setDT(cdmf_eg)

cdmf_eg <-
  data.table::dcast(data = cdmf_eg,
                    patid ~ condition,
                    value.var = "charlson_cdmf2019",
                    fun.aggregate = function(x) {as.integer(sum(x) > 0)})

cdmf_eg[, .N, keyby = .(hiv, aids)]
```

When calling `comorbidities()` we get the expected result.

```{r}
cmdf_mdcr <-
  comorbidities(data = mdcr,
                icd.codes = "code",
                id.vars = "patid",
                icdv.var = "icdv",
                dx.var = "dx",
                method = "charlson_cdmf2019",
                flag.method = "current",
                primarydx = 0L,
                poa = 1L)
data.table::setDT(cmdf_mdcr)

cmdf_mdcr[, .N, keyby = .(hiv, aids)]
```

# References

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