This is the main function which extracts comorbidities from a set of ICD-9
codes. Some comorbidity schemes have rules, for example, what to do when both
'hypertension' and 'hypertension with complications' are present. These rules
are applied by default; if the exact fields from the original mappings are
needed, use hierarchy = FALSE. For comorbidity counting, Charlson or
VanWalraven scores the default should be used to apply the rules. For more
about computing Hierarchical Condition Codes (HCC), see
comorbid_hcc For more about comorbidities following the
Clinical Classification Software (CCS) rules from AHRQ, see
comorbid_ccs.
comorbid(x, map, ...)icd10_comorbid(x, map, visit_name = NULL, icd_name = NULL,
short_code = NULL, short_map = guess_short(map), return_df = FALSE,
icd10_comorbid_fun = icd10_comorbid_reduce, ...)
icd9_comorbid(x, map, visit_name = NULL, icd_name = NULL,
short_code = guess_short(x, icd_name = icd_name),
short_map = guess_short(map), return_df = FALSE, preclean = TRUE,
visitId = NULL, icd9Field = NULL, ...)
icd9_comorbid_ahrq(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd10_comorbid_ahrq(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd9_comorbid_elix(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd10_comorbid_elix(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd9_comorbid_quan_elix(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd10_comorbid_quan_elix(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd9_comorbid_quan_deyo(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
icd10_comorbid_quan_deyo(x, ..., abbrev_names = TRUE, hierarchy = TRUE)
comorbid_ccs(x, icd_name = get_icd_name(x), ...)
icd9_comorbid_ccs(x, ..., single = TRUE, lvl = NULL,
map = icd::icd9_map_single_ccs, short_map = TRUE)
icd10_comorbid_ccs(x, ..., single = TRUE, lvl = NULL)
comorbid_ahrq(x, ...)
comorbid_elix(x, ...)
comorbid_quan_elix(x, ...)
comorbid_quan_deyo(x, ...)
comorbid_charlson(...)
icd_comorbid(...)
icd_comorbid_ahrq(...)
icd_comorbid_elix(...)
icd_comorbid_hcc(...)
icd_comorbid_quan_deyo(...)
icd_comorbid_quan_elix(...)
data.frame containing a column of patient-visit identifiers
and a column of ICD codes. The data.frame should be in ‘long’
format, like the example vermont_dx data. If it is in ‘wide’
format, it must be converted to ‘long’ using
wide_to_long before calling any comorbidity functions.
list of the comorbidities with each list item containing a vector
of decimal ICD-9 codes. This is in the form of a list, with the names of
the items corresponding to the comorbidities (e.g. ‘HTN’, or
‘diabetes’) and the contents of each list item being a character
vector of short-form (no decimal place, zero left-padded) ICD codes. There
is no default: the user should use the family of functions, e.g.
comorbid_ahrq, since these also name the fields correctly, apply any
hierarchical rules (see hierarchy below)
arguments passed on to other functions
The name of the column in the data frame which contains the
patient or visit identifier. Typically this is the visit identifier, since
patients come leave and enter hospital with different ICD-9 codes. It is a
character vector of length one. If left empty, or NULL, then an
attempt is made to guess which field has the ID for the patient encounter
(not a patient ID, although this can of course be specified directly). The
guesses proceed until a single match is made. Data frames may be wide with
many matching fields, so to avoid false positives, anything but a single
match is rejected. If there are no successful guesses, and visit_id
was not specified, then the first column of the data frame is used.
The name of the column in the data.frame which
contains the ICD codes. This is a character vector of length one. If it is
NULL, icd9 will attempt to guess the column name, looking for
progressively less likely possibilities until it matches a single column.
Failing this, it will take the first column in the data frame. Specifying
the column using this argument avoids the guesswork.
single logical value which determines whether the ICD-9
code provided is in short (TRUE) or decimal (FALSE) form.
Where reasonable, this is guessed from the input data.
Same as short, but applied to map instead of the data
frame of ICD codes, x. All the codes in a mapping should be of the
same type, i.e. short or decimal.
single logical value, if TRUE, return the result as a data frame with the first column being
the visit_id, and the second being the count. If visit_id was a factor or named differently in the
input, this is preserved.
function Internal parameter, default will be fast and accurate. A function which calculates comorbidities for ICD-10 codes, in which the comorbidity map only specifies parent codes, not every possible child.
single logical value, which, if TRUE causes ICD-9
'short' code input to be padded to correct three (or four for E code)
length before applying the comorbidity map. For very large data sets, e.g.
ten million rows, this is much slower than the comorbidity calculation. If
you know that the source ICD-9 codes are already well formed (or have
already run icd9_add_leading_zeroes), then preclean can be
set to FALSE to save time.
Deprecated. Use visit_name instead.
Deprecated. Use icd_name instead.
single logical value that defaults to TRUE, in
which case the shorter human-readable names stored in e.g.
ahrqComorbidNamesAbbrev are applied to the data frame column names.
single logical value that defaults to TRUE, in which
case the hierarchy defined for the mapping is applied. E.g. in Elixhauser,
you can't have uncomplicated and complicated diabetes both flagged.
a logical value, if TRUE then use single level CCS,
otherwise use multi level
If multiple level CCS, then level must be selected as a number between one and four.
icd10_comorbid: ICD-10 comorbidities
icd9_comorbid: Get comorbidities from data.frame of ICD-9
codes
icd9_comorbid_ahrq: AHRQ comorbidities for ICD-9 codes
icd10_comorbid_ahrq: AHRQ comorbidities for ICD-10 codes
icd9_comorbid_elix: Elixhauser comorbidities for ICD-9 codes
icd10_comorbid_elix: Elixhauser comorbidities for ICD-10 codes
icd9_comorbid_quan_elix: Quan's Elixhauser comorbidities for ICD-9 codes
icd10_comorbid_quan_elix: Quan's Elixhauser comorbidities for ICD-10 codes
icd9_comorbid_quan_deyo: Quan's Deyo (Charlson) comorbidities for ICD-9 codes
icd10_comorbid_quan_deyo: Quan's Deyo (Charlson) comorbidities for ICD-10 codes
comorbid_ccs: Use AHRQ CCS for comorbidity classification
icd9_comorbid_ccs: Compute AHRQ Clinical Classifications Software (CCS)
scores from ICD-9 codes
icd10_comorbid_ccs: Compute AHRQ Clinical Classifications Software (CCS)
scores from ICD-10 codes
comorbid_ahrq: AHRQ comorbidities, infers whether to use ICD-9 or
ICD-10 codes
comorbid_elix: Elixhauser comorbidities, infers whether to use ICD-9 or
ICD-10 codes
comorbid_quan_elix: Quan's Elixhauser comorbidities, infers whether to use
ICD-9 or ICD-10 codes
comorbid_quan_deyo: Quan's Deyo (Charlson) comorbidities, infers whether to
use ICD-9 or ICD-10 codes
comorbid_charlson: Calculate comorbidities using Charlson categories
according to Quan/Deyo ICD categories. Synonymous with
link{comorbid_quan_deyo} in this release.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
Future versions of icd will drop
the icd_ prefix. For example, charlson should be used in
favor of icd_charlson. To distinguish icd function calls,
consider using the namespace prefix icd:: instead, e.g.,
icd::charlson. Functions which specifically operate on either ICD-9
or ICD-10 codes or their subtypes will retain the prefix. E.g.
icd9_comorbid_ahrq. icd specific classes also retain
the prefix, e.g., icd_wide_data.
The order of visits may change depending on the original sequence, and the underlying algorithm used. Usually this would be the order of the first occurence of each visit/patient identifier.
The threading of the C++ can be controlled using e.g.
option(icd.threads = 4). If it is not set, the number of cores in
the machine is used. 'OpenMP' environment variables also work.
data.frames of patient data may have columns within them
which are of class icd9, icd10 etc., but do not themselves
have a class: therefore, the S3 mechanism for dispatch is not suitable. I
may add a wrapper function which looks inside a data.frame of
comorbidities, and dispatches to the appropriate function, but right now
the user must call the icd9_ or icd10_ prefixed function
directly.
Consider using comorbid_ahrq instead of
comorbid_elix for more recently updated mappings based on the
Elixhauser scheme.
# NOT RUN {
vermont_dx[1:5, 1:10]
vd <- wide_to_long(vermont_dx)
# get first few rows and columns of Charlson comorbidities using Quan's mapping
comorbid_quan_deyo(vd)[1:5, 1:14]
# get summary AHRQ (based on Elixhauser) comorbidities for ICD-10 Uranium data:
summary(comorbid_ahrq(uranium_pathology))
pts <- icd_long_data(visit_name = c("2", "1", "2", "3", "3"),
icd9 = c("39891", "40110", "09322", "41514", "39891"))
comorbid(pts, icd9_map_ahrq, short_code = TRUE) # visit_name is now sorted
pts <- icd_long_data(
visit_name = c("1", "2", "3", "4", "4"),
icd_name = c("20084", "1742", "30410", "41514", "95893"),
date = as.Date(c("2011-01-01", "2011-01-02", "2011-01-03",
"2011-01-04", "2011-01-04")))
pt_hccs <- comorbid_hcc(pts, date_name = "date")
head(pt_hccs)
pts10 <- icd_long_data(
visit_name = c("a", "b", "c", "d", "e"),
icd_name =c("I058", NA, "T82817A", "", "I69369"),
date = as.Date(
c("2011-01-01", "2011-01-02", "2011-01-03", "2011-01-03", "2011-01-03")))
icd10_comorbid(pts10, map = icd10_map_ahrq)
# or if library(icd) hasn't been called first:
icd::icd10_comorbid(pts10, map = icd::icd10_map_ahrq)
# or most simply:
icd::icd10_comorbid_ahrq(pts10)
# specify a simple custom comorbidity map:
my_map <- list("malady" = c("100", "2000"),
"ailment" = c("003", "040"))
two_pts <- data.frame(visit_id = c("v01", "v01", "v02", "v02"),
icd9 = as.icd9(c("040", "000", "100", "000")),
stringsAsFactors = FALSE)
comorbid(two_pts, map = my_map)
# }
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