cmrbdt.calc: The main comorbidity calculator function
In gforge/comorbidities.icd10: Calculates Comorbidity Indices Based on ICD-9/10
Description
Usage
Arguments
Details
See Also
Examples
Description
This function is the main comorbidity calculator. You
should try to use this function whenever you want to get a
full comorbidity count. By providing the individual
functions you can easily switch code identifier, weights,
and code pre-processing.
Usage
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cmrbdt.calc(ds, id_column, icd_columns, icd_ver_column, incl_acute_codes,
icd_code_preprocess_fn, cmrbdt.finder_fn, cmrbdt.finder_hierarchy_fn,
cmrbdt.weight_fn, country_code = "US", ...)
Arguments
ds
The
data.frame/matrix/vector that is to
be analyzed for matching icd-codes.
id_column
The id of the ds parameter. If
included in the ds then provide only column names,
otherwise this should be in the format of a
data.frame/matrix/vector matching
the size of the ds input. You can have multiple
columns as ID-parameters.
icd_columns
If the ds contains more than
just the ICD-code columns then you need to specify the
ICD-columns, either by name or numbers.
icd_ver_column
The ICD-version number if you don't
want auto-detect. It should be a column in the ds
that signals the version, alternatively a vector
of the same length as the ds. As auto-detect may
fail try to specify this if you can. For those that you
are uncertain you can simple set the value to
FALSE and the software will attempt to autodetect
those specific instances.
incl_acute_codes
Certain codes may indicate a
non-chronic disease such as heart infarction, stroke or
similar. Under some circumstances these should be
ignored, e.g. when studying predictors for hip
arthroplasty re-operations codes during the admission for
the surgery should not include myocardial infarction as
this is most likely a postoperative condition not
available for scoring prior to the surgery. Set to
TRUE if you want to include acute codes.
icd_code_preprocess_fn
Sometimes the codes need to
be pre-processed prior to feeding them into the
algorithm. For instance the ICD-columns may be crammed
into one single column where each code is separated by a
' '. When this is the case the pre-processing allows a
split prior to calling the cmrbdt.finder_fn, e.g.
splitting 'M161 E110' could need a function as
function(code){unlist(strsplit(code, " "),
use.name=FALSE)} - note the unlist, your function
should return a vector and not a list. You can find the
package pre-processing functions within the preproc.*
function group, e.g. preproc.strip.dot or
the more advanced preproc.Swedich.ICD9
cmrbdt.finder_fn
This is one of the cmrbdt.finder
functions that you want to apply. The cmrbdt.finder is at
the heart of the algorithm and does the actual
comorbidity identidication. See below for a list of
available functions.
cmrbdt.finder_hierarchy_fn
This functions applies
any hierarchy needed in order to retain logic, e.g.
complicated diabetes and uncomplicated diabetes should
not co-exist in one and the same patient. You can provide
here any of the hierarchy.*() functions. E.g. if
you are using Elixhausers Quan 2005 version you
provide the function
hierarchy.elixhauser_Quan2005.
cmrbdt.weight_fn
The comorbidity weight function
that you want to apply to the current calculation. E.g.
you can use the weight.Charlsons.org if you
want to apply the traditional Charlson comorbidity score
or you can write your own function.
country_code
The two-letter ISO 3166-1
alpha-2 code indicating the country of interest (the
same as the top-level internet domain name of that
country). As certain countries have adapted
country-specific ICD-coding there may be minor
differences between countries. Currently only Swedish
(SE) and US codes are implemented. The function defaults
to 'US'.
...
Arguments that are passed on to the
ddply function.
Details
The function returns a list with:
score/ct
If you have provided a weight function you will have a
score item, otherwise it is just a simple count of how many
comorbidity groups that have been identified. The name of
the item is either "score" or "ct" in order
to avoid mistakes.
cmrbdt This is a matrix with
comorbidity TRUE/FALSE for each group of the comorbidity by
id as returned by ddply.
cmrbdt.weighted If you have provided a comorbidity
weighting function then this will also be included in the
returned list. This matrix is also by id as returned
by ddply.
See Also
cmrbdt.finder.numeric.ahrq:
Numeric funciton for identifying AHRQ codes. Works only
with ICD-9 codes.
cmrbdt.finder.numeric.elixhauser_Elixhauser1998:
Numeric function for identifying the original Elixhauser
codes from 1998, note that newer versions code
versions are available. Works only with ICD-9 codes.
cmrbdt.finder.numeric.charlson_Deyo1992:
Numeric function for identifying Deyo's original
translation of the Elixhauser comorbidity groups. Works
only with ICD-9 codes.
cmrbdt.finder.regex.charlson_Sundarajan2004:
A function based on regular expressions for identifying
Sundarajan's codeset for Charlsons index, note that
the Quan article that they wrote one year later is an
update to the current code set.
cmrbdt.finder.regex.charlson_Quan2005:
A function based on regular expressions for identifying
Quan's codeset for Charlsons index. This is currently
(written 2014-05-07) the most up-to-date version of the
Charlson code set unless the Royal College of Surgeons
attempt at changing the Charlson counts.
cmrbdt.finder.regex.charlson_Armitage2010:
A function based on regular expressions for identifying
an adaptation and simplification of the Charlsons index.
Note that this is no longer the Charlsons but an
adaptation with only 14 comorbidity groups.
cmrbdt.finder.regex.elixhauser_Quan2005:
A function based on regular expressions for identifying
Quan's codeset for Charlsons index. This is currently
(written 2014-05-07) the most up-to-date version of the
Elixhauser code set unless the AHRQ is included although
the AHRQ has never been updated to ICD-10.
Examples
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# Completely made up datasets
prim_data <-
data.frame(Patient_ID = c("A",
"B",
"C",
"D",
"MISSING"), # Has on purpose no match - should theoretically no occur
# Transition from ICD-9 to ICD 10 was during 1997 in Sweden
Surgery_date = as.Date(c("1999-01-25",
"2004-02-25",
"1996-07-04",
"1997-12-04",
"2014-05-06")),
Surgery_type = c("Hip", # Add some non-relevant data
"Foot",
"Hand",
"Hip",
"Hand"))
admission_data <-
data.frame(Patient_ID =
c("A", "A", # 2 A admission
"B", # 1 B admissions
"C", "C", "C", # 3 C admissions
"D", "D", "D", "D" # 4 D admissions
),
admission_date =
as.Date(c("1999-01-24", "1998-05-29", # A
"2004-02-24", # B
"1996-07-01", "1995-02-01", "1992-10-04", # C
"1997-12-03", # D
"1998-03-01", # Admission should not be used as it is after surgery
"1995-10-24", "1995-08-20")),
discharge_date =
as.Date(c("1999-02-01", "1998-05-25",# A
"2004-02-27", # B
"1996-07-08", "1995-02-04", "1992-10-14",# C
"1997-12-06", "1998-03-04", "1995-11-01", "1995-08-24" # D
)),
ICD1 =
c("M161", "S7200", # A's codes
"S8240", # B's codes
"3540", "486", "431", # C's codes - carpal tunnel, pneumonia, intracereb. hem.
"M169", # D's codes - Hip code
"B238", # This admission should be ignored! - HIV
"5400", "4220"), # D's codes - Peritonitis + Acute MI
ICD2 =
c("I212", "I701", # A's codes - current MI, PVD
"N390", # B's codes
NA, "4011", "4011", # C's codes - benign hypertension
"N052", # D's codes - ICD-10 glom.nephritis
"C619", # prostate tum.
"7812", "5569"), # ICD-9 Gait, Ulcerative Colitis
ICD3 =
c("E890X", "E039", # A's codes - hypothyr.
NA, # B's codes
NA, "30301", "30009", # C's codes - Alcohol, anxiety
"E001", # D's codes - ICD-10 - iodine def. - thyroid.
NA,
"5810", "01280"), # ICD-9 Nephrotic syndr. + infection
ICD4 =
c("J189", NA, # A's codes - pneumonia
NA, # B's codes
NA, "55090", NA, # C's codes
"N309", # D's codes
NA,
"42611", "6802")
)
# Merge the data sets and include the one with no admissions
complete <- merge(prim_data, admission_data,
by="Patient_ID", all=TRUE)
# Choose those with valid observations
# just to stress the code we will keep the MISSSING patient
data2analyze <- subset(complete,
Surgery_date >= admission_date |
is.na(admission_date))
# Deduce the ICD-version from the date variable
data2analyze$icd_version <-
ifelse(data2analyze$discharge_date < "1997-01-01",
9,
ifelse(data2analyze$discharge_date >= "1998-01-01",
10,
FALSE))
# Figure out if the admission is the one registered for the surgery
data2analyze$include_acute <-
with(data2analyze,
ifelse(discharge_date >= Surgery_date &
admission_date <= Surgery_date,
FALSE, # Current admission is the admission of the surgery,
# hence we should not include any acute episodes
# as we are interested in pre-existing conditions
TRUE))
out_incl_acute <-
cmrbdt.calc(data2analyze,
id_column="Patient_ID",
icd_columns=grep("^ICD", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org)
out_without_acute <-
cmrbdt.calc(data2analyze,
incl_acute_codes="include_acute",
id_column="Patient_ID",
icd_columns=grep("^ICD", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org)
# The MI was not included for A when acute was taken into account
data.frame(ID = out_incl_acute$cmrbdt$Patient_ID,
With=out_incl_acute$score,
Without=out_without_acute$score)
###################################
# Test an alterantive way to #
# store diagnosis data by having #
# one string separated characters #
# such as " " or "," #
###################################
admission_data_alt <- admission_data
admission_data_alt$Main_ICD <- admission_data_alt$ICD1
admission_data_alt$Additional_ICD <-
apply(admission_data_alt[,c("ICD2", "ICD3", "ICD4")],
1,
function(x) paste(x[!is.na(x)], collapse=" "))
admission_data_alt$ICD1 <- NULL
admission_data_alt$ICD2 <- NULL
admission_data_alt$ICD3 <- NULL
admission_data_alt$ICD4 <- NULL
# Merge the data sets and include the one with no admissions
complete <- merge(prim_data, admission_data_alt,
by="Patient_ID", all=TRUE)
# Choose those with valid observations
# just to stress the code we will keep the MISSSING patient
data2analyze <- subset(complete,
Surgery_date >= admission_date |
is.na(admission_date))
# Deduce the ICD-version from the date variable
data2analyze$icd_version <-
ifelse(data2analyze$discharge_date < "1997-01-01",
9,
ifelse(data2analyze$discharge_date >= "1998-01-01",
10,
FALSE))
# Figure out if the admission is the one registered for the surgery
data2analyze$include_acute <-
with(data2analyze,
ifelse(discharge_date >= Surgery_date &
admission_date <= Surgery_date,
FALSE, # Current admission is the admission of the surgery,
# hence we should not include any acute episodes
# as we are interested in pre-existing conditions
TRUE))
out_without_acute <-
cmrbdt.calc(data2analyze,
incl_acute_codes="include_acute",
id_column="Patient_ID",
icd_columns=grep("ICD$", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org,
# Below is the magic function that splits the merged codes
icd_code_preprocess_fn=function(icd, icd_ver)
unlist(strsplit(icd, " "), use.names = FALSE))
# The MI was not included for A when acute was taken into account
data.frame(ID = out_incl_acute$cmrbdt$Patient_ID,
With=out_incl_acute$score,
Without=out_without_acute$score)
gforge/comorbidities.icd10 documentation built on May 17, 2019, 2:12 a.m.
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Description Usage Arguments Details See Also Examples
Description
This function is the main comorbidity calculator. You should try to use this function whenever you want to get a full comorbidity count. By providing the individual functions you can easily switch code identifier, weights, and code pre-processing.
Usage
1 2 3 | cmrbdt.calc(ds, id_column, icd_columns, icd_ver_column, incl_acute_codes,
icd_code_preprocess_fn, cmrbdt.finder_fn, cmrbdt.finder_hierarchy_fn,
cmrbdt.weight_fn, country_code = "US", ...)
|
Arguments
ds |
The
|
id_column |
The id of the |
icd_columns |
If the |
icd_ver_column |
The ICD-version number if you don't
want auto-detect. It should be a column in the |
incl_acute_codes |
Certain codes may indicate a
non-chronic disease such as heart infarction, stroke or
similar. Under some circumstances these should be
ignored, e.g. when studying predictors for hip
arthroplasty re-operations codes during the admission for
the surgery should not include myocardial infarction as
this is most likely a postoperative condition not
available for scoring prior to the surgery. Set to
|
icd_code_preprocess_fn |
Sometimes the codes need to
be pre-processed prior to feeding them into the
algorithm. For instance the ICD-columns may be crammed
into one single column where each code is separated by a
' '. When this is the case the pre-processing allows a
split prior to calling the |
cmrbdt.finder_fn |
This is one of the cmrbdt.finder functions that you want to apply. The cmrbdt.finder is at the heart of the algorithm and does the actual comorbidity identidication. See below for a list of available functions. |
cmrbdt.finder_hierarchy_fn |
This functions applies
any hierarchy needed in order to retain logic, e.g.
complicated diabetes and uncomplicated diabetes should
not co-exist in one and the same patient. You can provide
here any of the |
cmrbdt.weight_fn |
The comorbidity weight function
that you want to apply to the current calculation. E.g.
you can use the |
country_code |
The two-letter |
... |
Arguments that are passed on to the
|
Details
The function returns a list with:
score/ct If you have provided a weight function you will have a score item, otherwise it is just a simple count of how many comorbidity groups that have been identified. The name of the item is either
"score"or"ct"in order to avoid mistakes.cmrbdt This is a matrix with comorbidity TRUE/FALSE for each group of the comorbidity by
idas returned byddply.cmrbdt.weighted If you have provided a comorbidity weighting function then this will also be included in the returned list. This matrix is also by
idas returned byddply.
See Also
cmrbdt.finder.numeric.ahrq: Numeric funciton for identifying AHRQ codes. Works only with ICD-9 codes.cmrbdt.finder.numeric.elixhauser_Elixhauser1998: Numeric function for identifying the original Elixhauser codes from 1998, note that newer versions code versions are available. Works only with ICD-9 codes.cmrbdt.finder.numeric.charlson_Deyo1992: Numeric function for identifying Deyo's original translation of the Elixhauser comorbidity groups. Works only with ICD-9 codes.cmrbdt.finder.regex.charlson_Sundarajan2004: A function based on regular expressions for identifying Sundarajan's codeset for Charlsons index, note that the Quan article that they wrote one year later is an update to the current code set.cmrbdt.finder.regex.charlson_Quan2005: A function based on regular expressions for identifying Quan's codeset for Charlsons index. This is currently (written 2014-05-07) the most up-to-date version of the Charlson code set unless the Royal College of Surgeons attempt at changing the Charlson counts.cmrbdt.finder.regex.charlson_Armitage2010: A function based on regular expressions for identifying an adaptation and simplification of the Charlsons index. Note that this is no longer the Charlsons but an adaptation with only 14 comorbidity groups.cmrbdt.finder.regex.elixhauser_Quan2005: A function based on regular expressions for identifying Quan's codeset for Charlsons index. This is currently (written 2014-05-07) the most up-to-date version of the Elixhauser code set unless the AHRQ is included although the AHRQ has never been updated to ICD-10.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | # Completely made up datasets
prim_data <-
data.frame(Patient_ID = c("A",
"B",
"C",
"D",
"MISSING"), # Has on purpose no match - should theoretically no occur
# Transition from ICD-9 to ICD 10 was during 1997 in Sweden
Surgery_date = as.Date(c("1999-01-25",
"2004-02-25",
"1996-07-04",
"1997-12-04",
"2014-05-06")),
Surgery_type = c("Hip", # Add some non-relevant data
"Foot",
"Hand",
"Hip",
"Hand"))
admission_data <-
data.frame(Patient_ID =
c("A", "A", # 2 A admission
"B", # 1 B admissions
"C", "C", "C", # 3 C admissions
"D", "D", "D", "D" # 4 D admissions
),
admission_date =
as.Date(c("1999-01-24", "1998-05-29", # A
"2004-02-24", # B
"1996-07-01", "1995-02-01", "1992-10-04", # C
"1997-12-03", # D
"1998-03-01", # Admission should not be used as it is after surgery
"1995-10-24", "1995-08-20")),
discharge_date =
as.Date(c("1999-02-01", "1998-05-25",# A
"2004-02-27", # B
"1996-07-08", "1995-02-04", "1992-10-14",# C
"1997-12-06", "1998-03-04", "1995-11-01", "1995-08-24" # D
)),
ICD1 =
c("M161", "S7200", # A's codes
"S8240", # B's codes
"3540", "486", "431", # C's codes - carpal tunnel, pneumonia, intracereb. hem.
"M169", # D's codes - Hip code
"B238", # This admission should be ignored! - HIV
"5400", "4220"), # D's codes - Peritonitis + Acute MI
ICD2 =
c("I212", "I701", # A's codes - current MI, PVD
"N390", # B's codes
NA, "4011", "4011", # C's codes - benign hypertension
"N052", # D's codes - ICD-10 glom.nephritis
"C619", # prostate tum.
"7812", "5569"), # ICD-9 Gait, Ulcerative Colitis
ICD3 =
c("E890X", "E039", # A's codes - hypothyr.
NA, # B's codes
NA, "30301", "30009", # C's codes - Alcohol, anxiety
"E001", # D's codes - ICD-10 - iodine def. - thyroid.
NA,
"5810", "01280"), # ICD-9 Nephrotic syndr. + infection
ICD4 =
c("J189", NA, # A's codes - pneumonia
NA, # B's codes
NA, "55090", NA, # C's codes
"N309", # D's codes
NA,
"42611", "6802")
)
# Merge the data sets and include the one with no admissions
complete <- merge(prim_data, admission_data,
by="Patient_ID", all=TRUE)
# Choose those with valid observations
# just to stress the code we will keep the MISSSING patient
data2analyze <- subset(complete,
Surgery_date >= admission_date |
is.na(admission_date))
# Deduce the ICD-version from the date variable
data2analyze$icd_version <-
ifelse(data2analyze$discharge_date < "1997-01-01",
9,
ifelse(data2analyze$discharge_date >= "1998-01-01",
10,
FALSE))
# Figure out if the admission is the one registered for the surgery
data2analyze$include_acute <-
with(data2analyze,
ifelse(discharge_date >= Surgery_date &
admission_date <= Surgery_date,
FALSE, # Current admission is the admission of the surgery,
# hence we should not include any acute episodes
# as we are interested in pre-existing conditions
TRUE))
out_incl_acute <-
cmrbdt.calc(data2analyze,
id_column="Patient_ID",
icd_columns=grep("^ICD", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org)
out_without_acute <-
cmrbdt.calc(data2analyze,
incl_acute_codes="include_acute",
id_column="Patient_ID",
icd_columns=grep("^ICD", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org)
# The MI was not included for A when acute was taken into account
data.frame(ID = out_incl_acute$cmrbdt$Patient_ID,
With=out_incl_acute$score,
Without=out_without_acute$score)
###################################
# Test an alterantive way to #
# store diagnosis data by having #
# one string separated characters #
# such as " " or "," #
###################################
admission_data_alt <- admission_data
admission_data_alt$Main_ICD <- admission_data_alt$ICD1
admission_data_alt$Additional_ICD <-
apply(admission_data_alt[,c("ICD2", "ICD3", "ICD4")],
1,
function(x) paste(x[!is.na(x)], collapse=" "))
admission_data_alt$ICD1 <- NULL
admission_data_alt$ICD2 <- NULL
admission_data_alt$ICD3 <- NULL
admission_data_alt$ICD4 <- NULL
# Merge the data sets and include the one with no admissions
complete <- merge(prim_data, admission_data_alt,
by="Patient_ID", all=TRUE)
# Choose those with valid observations
# just to stress the code we will keep the MISSSING patient
data2analyze <- subset(complete,
Surgery_date >= admission_date |
is.na(admission_date))
# Deduce the ICD-version from the date variable
data2analyze$icd_version <-
ifelse(data2analyze$discharge_date < "1997-01-01",
9,
ifelse(data2analyze$discharge_date >= "1998-01-01",
10,
FALSE))
# Figure out if the admission is the one registered for the surgery
data2analyze$include_acute <-
with(data2analyze,
ifelse(discharge_date >= Surgery_date &
admission_date <= Surgery_date,
FALSE, # Current admission is the admission of the surgery,
# hence we should not include any acute episodes
# as we are interested in pre-existing conditions
TRUE))
out_without_acute <-
cmrbdt.calc(data2analyze,
incl_acute_codes="include_acute",
id_column="Patient_ID",
icd_columns=grep("ICD$", colnames(data2analyze)),
icd_ver_column=data2analyze$icd_version,
cmrbdt.finder_fn=cmrbdt.finder.regex.charlson_Quan2005,
cmrbdt.finder_hierarchy_fn=hierarchy.charlson_Quan2005,
cmrbdt.weight_fn=weight.Charlsons.org,
# Below is the magic function that splits the merged codes
icd_code_preprocess_fn=function(icd, icd_ver)
unlist(strsplit(icd, " "), use.names = FALSE))
# The MI was not included for A when acute was taken into account
data.frame(ID = out_incl_acute$cmrbdt$Patient_ID,
With=out_incl_acute$score,
Without=out_without_acute$score)
|
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