The Pediatric Complex Chronic Condition (PCCC) coding schema version 2 was published in 2014 (Feudtner et al. 2014) and updated to version 3 in 2024 (Feinstein et al. 2024). Both versions identify 11 conditions, each with multiple subconditions.
| condition | condition_label | |
|---|---|---|
| 1 | congeni_genetic | Other Congenital or Genetic Defect |
| 2 | cvd | Cardiovascular |
| 3 | gi | Gastrointestinal |
| 4 | hemato_immu | Hematologic or Immunologic |
| 5 | malignancy | Malignancy |
| 6 | metabolic | Metabolic |
| 7 | misc | Miscellaneous, Not Elsewhere Classified |
| 8 | neonatal | Premature & Neonatal |
| 9 | neuromusc | Neurologic or Neuromuscular |
| 10 | renal | Renal Urologic |
| 11 | respiratory | Respiratory |
The PCCC system provides a standardized approach to identifying children with complex chronic conditions using administrative data. This has several important implications:
Without a common framework such as the PCCC, studies of chronic pediatric conditions would be fragmented, limiting their impact on both research and practice.
Versions 2 and 3 differ mainly in how technology dependence is treated. Many ICD codes map to both a primary condition and either technology dependence or transplant.
In both versions, transplant-related codes indicate organ system failure. A patient with such a code is flagged as having both a transplant and the related condition.
Technology dependence, however, diverges between versions. In version 2, the presence of a technology dependence code classifies the patient as having both the associated condition and technology dependence. For example, ICD-10 Z46.81 is both a metabolic and technology dependence code. A patient with this code is classified as having a metabolic condition and technology dependence.
Version 3 refines this rule: technology dependence codes are assessed conditionally, recognizing that many do not reflect chronic conditions.
Example: ICD-10 Z46.81 (Encounter for fitting and adjustment of insulin pump) is a metabolic and technology dependence code. If a patient had this code in their medical records then they would be classified has having a metabolic condition and a tech dependence. Under version 2, this patient would be flagged as having a metabolic condition and technology dependence. Under version 3, the patient would only be flagged with a metabolic condition and technology dependence if at least one non-technology condition is flagged.
Let’s look at the codes that are in the PCCC schema. Calling
get_pccc_codes returns a data.frame.
pccc_codes <- get_pccc_codes()
str(pccc_codes)
## 'data.frame': 7913 obs. of 12 variables:
## $ icdv : int 9 9 9 9 9 9 9 9 9 9 ...
## $ dx : int 0 0 0 0 0 0 0 0 0 0 ...
## $ full_code : chr "00.10" "00.50" "00.51" "00.53" ...
## $ code : chr "0010" "0050" "0051" "0053" ...
## $ condition : chr "malignancy" "cvd" "cvd" "cvd" ...
## $ subcondition : chr "neoplasms" "device_and_technology_use" "device_and_technology_use" "device_and_technology_use" ...
## $ transplant_flag: int 0 0 0 0 0 0 0 1 1 1 ...
## $ tech_dep_flag : int 0 1 1 1 1 1 1 0 0 0 ...
## $ pccc_v3.1 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ pccc_v3.0 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ pccc_v2.1 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ pccc_v2.0 : int 1 1 1 1 1 1 1 1 1 1 ...The columns are:
icdv: integer, ICD versiondx: 1 for diagnostic (ICD-9-CM or ICD-10-CM) codes, 0
for procedure (ICD-9-PCS or ICD-10-PCS) codes.full_code: character, the ICD code retaining any
applicable decimal point.code: character, the compact ICD code; any applicable
decimal point omitted. Examples: ICD-9-CM full code 553.3 is represented
as 5533 as a compact code. ICD-10-CM full code C96.9 is represented as
compact code C969.condition: character, the pccc condition with
syntactically valid names.subcondition: character, the pccc subcondition with
syntactically valid names.transplant_flag: integer, 1L if the ICD code is
associated with a transplant, 0L otherwisetech_dep_flag: integer, 1L if the ICD code is
associated with technology dependence, 0L otherwise.pccc_vX.Y: integer, 1L if the code is part of variant
X.Y, 0L otherwiseExample: Consider a patient with following four diagnostic and two procedure ICD-9 codes:
pat1 <-
data.frame(dx = c(1, 1, 1, 1, 0, 0),
icdv = 9L,
code = c("34590", "78065", "3432", "78065", "9929", "8606"))An inner join between the pccc_codes and
pat1 will yield the conditions this patient has.
merge(x = pccc_codes, y = pat1, all = FALSE, by = c("icdv", "dx", "code"))
## icdv dx code full_code condition subcondition transplant_flag
## 1 9 0 8606 86.06 metabolic device_and_technology_use 0
## 2 9 1 3432 343.2 neuromusc infantile_cerebral_palsy 0
## tech_dep_flag pccc_v3.1 pccc_v3.0 pccc_v2.1 pccc_v2.0
## 1 1 1 1 1 1
## 2 0 1 1 1 1For all PCCC variants, there is one matching dx code, 343.2, for infantile cerebral palsy, matches for a neuromuscular condition. The procedure code 86.06 matches for a technology dependent metabolic condition.
Under version 2.0 of PCCC (variants pccc_v2.0 and
pccc_v2.1), this patient has two conditions, neuromuscular,
metabolic. This patient also has a flag for device and technology
use.
pat1_pccc_v2.0 <-
comorbidities(
data = pat1,
icd.codes = "code",
dx.var = "dx",
icdv = 9,
method = "pccc_v2.0",
flag.method = "current", # default
poa = 1 # default for flag.method = 'current'
)
pat1_pccc_v2.1 <-
comorbidities(
data = pat1,
icd.codes = "code",
dx.var = "dx",
icdv = 9,
method = "pccc_v2.1",
flag.method = "current",
poa = 1
)
all.equal(pat1_pccc_v2.0, pat1_pccc_v2.1, check.attributes = FALSE)
## [1] TRUE
pat1_pccc_v2.0
##
## Comorbidities via pccc_v2.0
##
## congeni_genetic cvd gi hemato_immu malignancy metabolic misc neonatal
## 1 0 0 0 0 0 1 0 0
## neuromusc renal respiratory any_tech_dep any_transplant num_cmrb cmrb_flag
## 1 1 0 0 1 0 2 1Under version 3 of the PCCC, this patient has two conditions: neuromuscular and metabolic. The technology dependence flags are also 1 for this patient, but are not counted in the total number of conditions.
pat1_pccc_v3.0 <-
comorbidities(data = pat1,
icd.codes = "code",
dx.var = "dx",
icdv = 9,
method = "pccc_v3.0",
flag.method = 'current',
poa = 1
)
pat1_pccc_v3.1 <-
comorbidities(data = pat1,
icd.codes = "code",
dx.var = "dx",
icdv = 9,
method = "pccc_v3.1",
flag.method = 'current',
poa = 1
)
all.equal(pat1_pccc_v3.0, pat1_pccc_v3.1, check.attributes = FALSE)
## [1] TRUE
# retain the needed columns, there are four columns for each condition in v3
pat1_pccc_v3.0[, grep("^(cmrb_flag|num_cmrb|neuromus|metabolic|tech_dep_flag)", names(pat1_pccc_v3.0))]
## metabolic_dxpr_only metabolic_tech_only metabolic_dxpr_and_tech
## 1 0 1 0
## metabolic_dxpr_or_tech neuromusc_dxpr_only neuromusc_tech_only
## 1 1 1 0
## neuromusc_dxpr_and_tech neuromusc_dxpr_or_tech num_cmrb cmrb_flag
## 1 0 1 2 1In the output from version 3, we have four 0/1 indicator columns for each of the conditions.
<condition>_dxpr_only: the
<condition> has been flagged due to diagnostic
<condition>_tech_only: the
<condition> has been flagged due to the presence of a
technology dependence code and at least one other condition has
been flagged by dx or pr codes.
<condition>_dxpr_and_tech: the
<condition> has been flagged due to the presence of a
dx or pr code that is not associated with a technology dependence
and another dx or pr code which is associated with technology
dependence.
<condition>_dxpr_or_tech: the
<condition> has been flagged. These columns
answer the question “does <condition> exist for this
patient/encounter.”
The details in the list above might be easier to understand in a
tabular form of possible sets. In the case of no conditions, only the
<condition>_dxpr_or_tech columns are flagged as 0/1
with the <condition>_dxpr_only,
<condition>_tech_only, and
<condition>_dxpr_and_tech columns set to
NA. When at least one condition is flagged, all the columns
will be populated as 0/1.
cmrb_flag |
num_cmrb |
<condition>_dxpr_or_tech |
<condition>_dxpr_only |
<condition>_tech_only | <condition>_dxpr_and_tech | <other condition(s)>_dxpr_or_tech |
||
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 | 0 | 0 | 1 |
| 1 | 1 | 1 | 1 | 0 | 0 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 1 | >1 | 0 | 0 | 0 | 0 | 1 |
| 1 | >1 | 1 | 0 | 1 | 0 | 1 |
| 1 | >1 | 1 | 1 | 0 | 0 | 1 |
| 1 | >1 | 1 | 1 | 1 | 1 | 1 |
Now, consider another patient, pat2, with the same codes as pat1 except for 3432, the code mapping to a neuromuscular condition.
Under version 2 of the PCCC this patient will still have metabolic and technology dependence conditions because of the code 86.06 is in the record, but will not have the neuromuscular condition.
pat2_pccc_v2.1 <-
comorbidities(
data = pat2,
icd.codes = "code",
dx.var = "dx",
icdv = 9,
method = "pccc_v2.1",
flag.method = 'current',
poa = 1
)
Filter(f = function(x) x > 0, pat2_pccc_v2.1)
## metabolic any_tech_dep num_cmrb cmrb_flag
## 1 1 1 1 1Under version 3 of the PCCC, this patient will have no conditions. This is because no condition was identified based on non-technology dependent codes and thus the one technology dependent code is ignored.
The expected input data format for comorbidities is a
“long” format. The only mandatory column is one column of ICD codes.
These codes can be full codes (include the decimal point) or compact
codes (omitting the decimal point). Additionally, column(s) for
identifying patients, encounters, and any other important groups are
encouraged. A column to indicate the ICD version (9 or 10), and another
column for identifying the code as a diagnostic or procedure code are
also encouraged. The example mdcr data set has three
columns, a patient id (patid), the ICD compact codes (code), and a
column to indicate if the ICD code is a diagnostic or procedure code,
(dx: 1 for diagnostic, 0 for procedure).
The mdcr data is provided with columns for
head(mdcr)
## patid icdv code dx
## 1 71412 9 99931 1
## 2 71412 9 75169 1
## 3 71412 9 99591 1
## 4 71412 9 V5865 1
## 5 71412 9 V427 1
## 6 17087 10 V441 1
str(mdcr)
## 'data.frame': 319856 obs. of 4 variables:
## $ patid: int 71412 71412 71412 71412 71412 17087 64424 64424 84361 84361 ...
## $ icdv : int 9 9 9 9 9 10 9 9 9 9 ...
## $ code : chr "99931" "75169" "99591" "V5865" ...
## $ dx : int 1 1 1 1 1 1 1 0 1 1 ...Applying pccc_v2.1 and pccc_v3.1 methods to
mdcr could be as simple as:
mdcr_results_v2.1_01 <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
poa = 1,
flag.method = 'current',
method = "pccc_v2.1")
mdcr_results_v3.1_01 <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
poa = 1,
flag.method = 'current',
method = "pccc_v3.1")and a useful summary of the object returned from
comorbidities is gained by calling summary().
The return is a data.table with columns for the count and
percentages. For pccc_v2.0 and pccc_v2.1 the
condition, label, count, and percentage, are reported. For
pccc_v3.0 and pccc_v3.1 the columns are
extended to provide the counts and percentages for
dxpr_or_tech, dxpr_only,
tech_only, and dxpr_and_tech.
str(summary(mdcr_results_v2.1_01))
## 'data.frame': 24 obs. of 4 variables:
## $ condition: chr "congeni_genetic" "cvd" "gi" "hemato_immu" ...
## $ label : chr "Other Congenital or Genetic Defect" "Cardiovascular" "Gastrointestinal" "Hematologic or Immunologic" ...
## $ count : int 3490 5034 6946 2887 4057 3348 1079 1572 5968 3033 ...
## $ percent : num 9.12 13.16 18.15 7.55 10.6 ...
str(summary(mdcr_results_v3.1_01))
## 'data.frame': 24 obs. of 10 variables:
## $ condition : chr "congeni_genetic" "cvd" "gi" "hemato_immu" ...
## $ label : chr "Other Congenital or Genetic Defect" "Cardiovascular" "Gastrointestinal" "Hematologic or Immunologic" ...
## $ dxpr_or_tech_count : int 3225 5185 5808 2943 4075 3466 774 1529 5917 2933 ...
## $ dxpr_or_tech_percent : num 8.43 13.55 15.18 7.69 10.65 ...
## $ dxpr_only_count : int 3225 4526 1520 2943 4075 3415 126 1529 4605 1991 ...
## $ dxpr_only_percent : num 8.43 11.83 3.97 7.69 10.65 ...
## $ tech_only_count : int 0 318 3897 0 0 40 647 0 359 580 ...
## $ tech_only_percent : num 0 0.831 10.185 0 0 ...
## $ dxpr_and_tech_count : int 0 341 391 0 0 11 1 0 953 362 ...
## $ dxpr_and_tech_percent: num 0 0.891 1.022 0 0 ...The summary tables are data.frames and can be
manipulated by the end user for reporting as they want, see the
following table.
x <-
merge(
summary(mdcr_results_v2.1_01),
summary(mdcr_results_v3.1_01),
all = TRUE,
by = c("condition", "label"),
sort = FALSE
)
x[["condition"]] <- NULL| count | % | count | % | count | % | count | % | count | % | |
|---|---|---|---|---|---|---|---|---|---|---|
| Conditions | ||||||||||
| Other Congenital or Genetic Defect | 3490 | 9.1 | 3225 | 8.4 | 3225 | 8.4 | 0 | 0.0 | 0 | 0.0 |
| Cardiovascular | 5034 | 13.2 | 5185 | 13.6 | 4526 | 11.8 | 318 | 0.8 | 341 | 0.9 |
| Gastrointestinal | 6946 | 18.2 | 5808 | 15.2 | 1520 | 4.0 | 3897 | 10.2 | 391 | 1.0 |
| Hematologic or Immunologic | 2887 | 7.5 | 2943 | 7.7 | 2943 | 7.7 | 0 | 0.0 | 0 | 0.0 |
| Malignancy | 4057 | 10.6 | 4075 | 10.7 | 4075 | 10.7 | 0 | 0.0 | 0 | 0.0 |
| Metabolic | 3348 | 8.8 | 3466 | 9.1 | 3415 | 8.9 | 40 | 0.1 | 11 | 0.0 |
| Miscellaneous, Not Elsewhere Classified | 1079 | 2.8 | 774 | 2.0 | 126 | 0.3 | 647 | 1.7 | 1 | 0.0 |
| Premature & Neonatal | 1572 | 4.1 | 1529 | 4.0 | 1529 | 4.0 | 0 | 0.0 | 0 | 0.0 |
| Neurologic or Neuromuscular | 5968 | 15.6 | 5917 | 15.5 | 4605 | 12.0 | 359 | 0.9 | 953 | 2.5 |
| Renal Urologic | 3033 | 7.9 | 2933 | 7.7 | 1991 | 5.2 | 580 | 1.5 | 362 | 0.9 |
| Respiratory | 3327 | 8.7 | 3342 | 8.7 | 1858 | 4.9 | 823 | 2.2 | 661 | 1.7 |
| Flags | ||||||||||
| Any Technology Dependence | 9032 | 23.6 | 7207 | 18.8 | ||||||
| Any Transplantation | 1584 | 4.1 | 1653 | 4.3 | ||||||
| Total Conditions | ||||||||||
| Any Condition | 22815 | 59.6 | 21127 | 55.2 | ||||||
| >= 2 conditions | 11083 | 29.0 | 10987 | 28.7 | ||||||
| >= 3 conditions | 4709 | 12.3 | 4795 | 12.5 | ||||||
| >= 4 conditions | 1593 | 4.2 | 1691 | 4.4 | ||||||
| >= 5 conditions | 439 | 1.1 | 479 | 1.3 | ||||||
| >= 6 conditions | 90 | 0.2 | 101 | 0.3 | ||||||
| >= 7 conditions | 10 | 0.0 | 15 | 0.0 | ||||||
| >= 8 conditions | 2 | 0.0 | 2 | 0.0 | ||||||
| >= 9 conditions | 0 | 0.0 | 0 | 0.0 | ||||||
| >= 10 conditions | 0 | 0.0 | 0 | 0.0 | ||||||
| >= 11 conditions | 0 | 0.0 | 0 | 0.0 | ||||||
There are additional details we should consider with respect to the
ICD codes. The ICD version, 9 or 10, and if the code is a diagnostic or
a procedure code. For example, code ICD-9 diagnostic code 332.1 has the
same compact code as ICD-9 procedure code 33.21, 3321. In the case of
the mdcr data where we have only compact codes, the need to
distinguish between ICD-9 diagnostic and ICD-9 procedure is critically
important. In the mdcr data the code 3321 does appear as
both diagnostic and procedure.
pccc_codes[pccc_codes$code == "3321", ]
## icdv dx full_code code condition subcondition
## 59 9 0 33.21 3321 respiratory device_and_technology_use
## 1460 9 1 332.1 3321 neuromusc movement_diseases
## transplant_flag tech_dep_flag pccc_v3.1 pccc_v3.0 pccc_v2.1 pccc_v2.0
## 59 0 1 1 1 1 1
## 1460 0 0 1 1 1 1
table(mdcr[mdcr$code == "3321", "dx"])
##
## 0 1
## 77 1To account for the diagnostic or procedure status of the codes
specify a value for the dx.var argument.
mdcr_results_v2.1_02 <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
dx.var = "dx",
flag.method = 'current',
poa = 1,
method = "pccc_v2.1"
)
mdcr_results_v3.1_02 <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
dx.var = "dx",
flag.method = 'current',
poa = 1,
method = "pccc_v3.1"
)Specificity is increased by using the diagnostic/procedure flag.
Using pccc_v2.1 there are 278 false positive flags when the
diagnostic/procedure flag is omitted from the comorbidities
call. Using pccc_v3.1 there are 256 false positive flags
when the diagnostic/procedure flag is omitted from the
comorbidities call.
# verify that the cmrb_flag and number of conditions is the same or less after
# accounting for the diagnostic/procedure flag in the comorbidities call
stopifnot(all(mdcr_results_v2.1_02$cmrb_flag <= mdcr_results_v2.1_01$cmrb_flag))
stopifnot(all(mdcr_results_v2.1_02$num_cmrb <= mdcr_results_v2.1_01$num_cmrb))
sum(mdcr_results_v2.1_02$cmrb_flag != mdcr_results_v2.1_01$cmrb_flag)
## [1] 278
sum(mdcr_results_v2.1_02$num_cmrb != mdcr_results_v2.1_01$num_cmrb)
## [1] 989
stopifnot(all(mdcr_results_v3.1_02$cmrb_flag <= mdcr_results_v3.1_01$cmrb_flag))
stopifnot(all(mdcr_results_v3.1_02$num_cmrb <= mdcr_results_v3.1_01$num_cmrb))
sum(mdcr_results_v3.1_02$cmrb_flag != mdcr_results_v3.1_01$cmrb_flag)
## [1] 256
sum(mdcr_results_v3.1_02$num_cmrb != mdcr_results_v3.1_01$num_cmrb)
## [1] 923Let’s explore the record for patient 87420.
subset(mdcr, patid == "87420")
## patid icdv code dx
## 4073 87420 9 78321 1
## 4074 87420 9 5641 1
subset(get_pccc_codes(), code %in% c("78321", "5641"))
## icdv dx full_code code condition subcondition transplant_flag tech_dep_flag
## 192 9 0 56.41 5641 renal other 0 0
## pccc_v3.1 pccc_v3.0 pccc_v2.1 pccc_v2.0
## 192 1 1 1 1subset(mdcr_results_v2.1_01, patid == "87420", select = c("cmrb_flag", "renal"))
## cmrb_flag renal
## 32849 1 1
subset(mdcr_results_v2.1_02, patid == "87420", select = c("cmrb_flag", "renal"))
## cmrb_flag renal
## 32849 0 0
subset(mdcr_results_v3.1_01, patid == "87420", select = c("cmrb_flag", "renal_dxpr_or_tech"))
## cmrb_flag renal_dxpr_or_tech
## 32849 1 1
subset(mdcr_results_v3.1_02, patid == "87420", select = c("cmrb_flag", "renal_dxpr_or_tech"))
## cmrb_flag renal_dxpr_or_tech
## 32849 0 0
subset(get_icd_codes(with.descriptions = TRUE), full_code %in% c("56.41", "564.1"))
## icdv dx full_code code src known_start known_end assignable_start
## 12530 9 1 564.1 5641 cms 1997 2015 1997
## 12531 9 1 564.1 5641 cms 1997 2015 1997
## 12532 9 0 56.41 5641 cms 1997 2015 1997
## assignable_end desc desc_start desc_end
## 12530 2015 Irritable colon 1997 1999
## 12531 2015 Irritable bowel syndrome 2001 2015
## 12532 2015 Partial ureterectomy 1997 2015In the above, the compact code “5641” matches procedure code 56.41
for a renal condition. In mdcr_results_v2.1_01 and
mdcr_results_v3.1_01 where no distinction was made between
diagnostic and procedure codes this patient was flagged as having a
renal condition. However, when reviewing the patient record, the compact
code “5641” is listed as a diagnostic criteria and the full code 564.1
is for Irritable bowel syndrome. This is an example of where
discriminating between diagnostic and procedure codes is critically
important when looking for complex chronic conditions.
If we explicitly look at an inner join between this patient’s data and the pccc lookup table we see that the code 5641 matches the procedure code in the pccc lookup table. By not accounting for diagnostic and procedure codes, the overlaps between the two coding structures can lead to false positives.
merge(x = subset(mdcr, patid == "87420"),
y = pccc_codes,
by.x = c("code"),
by.y = c("code"),
suffixes = c(".mdcr", ".pccc_codes")
)
## code patid icdv.mdcr dx.mdcr icdv.pccc_codes dx.pccc_codes full_code
## 1 5641 87420 9 1 9 0 56.41
## condition subcondition transplant_flag tech_dep_flag pccc_v3.1 pccc_v3.0
## 1 renal other 0 0 1 1
## pccc_v2.1 pccc_v2.0
## 1 1 1Using full codes can prevent false positives too. Here are several
different ways that comorbidities() could be called
resulting in different outcomes.
Note: this is a good example of how medicalcoder can
handle full and compact codes within a single record.
DF <- data.frame(id = c("full dx", "full pr", "compact dx", "compact pr"),
code = c("564.1", "56.41", "5641", "5641"),
dx = c(1, 0, 1, 0))
# ideal: using the dx/pr status and matching on full and compact codes.
comorbidities(
data = DF,
id.vars = "id",
dx.var = "dx",
icd.codes = "code",
poa = 1,
method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
## id cmrb_flag renal_dxpr_or_tech
## 1 compact dx 0 0
## 2 compact pr 1 1
## 3 full dx 0 0
## 4 full pr 1 1
# false positive for the compact dx
comorbidities(
data = DF,
id.vars = "id",
icd.codes = "code",
poa = 1,
method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
## id cmrb_flag renal_dxpr_or_tech
## 1 compact dx 1 1
## 2 compact pr 1 1
## 3 full dx 0 0
## 4 full pr 1 1
# false negative for compact pr
comorbidities(
data = DF,
id.vars = "id",
icd.codes = "code",
poa = 1,
full.code = TRUE,
compact.codes = FALSE,
method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
## id cmrb_flag renal_dxpr_or_tech
## 1 compact dx 0 0
## 2 compact pr 0 0
## 3 full dx 0 0
## 4 full pr 1 1
# false positive for compact dx
comorbidities(
data = DF,
id.vars = "id",
icd.codes = "code",
poa = 1,
full.code = FALSE,
compact.codes = TRUE,
method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
## id cmrb_flag renal_dxpr_or_tech
## 1 compact dx 1 1
## 2 compact pr 1 1
## 3 full dx 0 0
## 4 full pr 0 0
# false negatives for compact and full pr
comorbidities(
data = DF,
id.vars = "id",
icd.codes = "code",
dx.var = "dx",
poa = 1,
full.code = FALSE,
compact.codes = TRUE,
method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
## id cmrb_flag renal_dxpr_or_tech
## 1 compact dx 0 0
## 2 compact pr 1 1
## 3 full dx 0 0
## 4 full pr 0 0Another consideration is the version of ICD, 9 or 10.
The record for patid 95471 is a great example of the problem that a compact code can cause. “E030” matches ICD-9 dx compact and full code E030 (no decimal point), and matches the ICD-10 dx compact code for full code E03.0 with only the ICD-10 version being related to a chronic complex condition.
Inputs to the comorbidities() call for the ICD version
will impact the output. When calling comorbidities() with a
variable to indicate the ICD version NA values will not be
joined against and the codes are ignored resulting in no condition being
flagged. If we know that we only want to compare against ICD-9 or ICD-10
values then using the icdv argument can simplify the call
and in this case, no condition for ICD-9 and a condition is flagged for
ICD-10.
subset(mdcr, patid == "95471")
## patid icdv code dx
## 125330 95471 10 E030 1
# no flag becuse icdv = 9 which treats all input codes as ICD-9
comorbidities(
data = subset(mdcr, patid == "95471"),
icd.codes = "code",
id.vars = 'patid',
dx.var = "dx",
icdv = 9L,
poa = 1,
method = "pccc_v3.1"
)[, c('patid', 'cmrb_flag')]
## patid cmrb_flag
## 1 95471 0
# flag because icdv = 10 - same as using `icdv.var = "icdv"`
comorbidities(
data = subset(mdcr, patid == "95471"),
icd.codes = "code",
id.vars = 'patid',
dx.var = "dx",
icdv = 10L,
poa = 1,
method = "pccc_v3.1"
)[, c('patid', 'cmrb_flag')]
## patid cmrb_flag
## 1 95471 1
comorbidities(
data = subset(mdcr, patid == "95471"),
icd.codes = "code",
id.vars = 'patid',
dx.var = "dx",
icdv.var = "icdv",
poa = 1,
method = "pccc_v3.0"
)[, c('patid', 'cmrb_flag')]
## patid cmrb_flag
## 1 95471 1Lastly, it should be noted that a lot of the ambiguity resulting from
compact codes can be avoided when full codes are available.
medicalcoder can handle both forms. In the example below we
again use the “E030” and assess it against all full and compact codes
(default), against only full codes, and lastly against only compact
codes. Note in this example that we are not specifying the ICD version
nor the diagnostic/procedure status of the code.
lookup_icd_codes("E030")
## input_code match_type icdv dx full_code code src known_start known_end
## 1 E030 full_code 9 1 E030 E030 cms 2010 2015
## 2 E030 compact_code 10 1 E03.0 E030 who 2008 2019
## 3 E030 compact_code 10 1 E03.0 E030 cdc 2001 2025
## 4 E030 compact_code 10 1 E03.0 E030 cms 2014 2026
## assignable_start assignable_end
## 1 2010 2015
## 2 2008 2019
## 3 2001 2025
## 4 2014 2026
data <- data.frame(id = c("Ambiguous compact code", "Full ICD-9 code", "Full ICD-10 code"),
code = c("E030", "E030", "E03.0"))
data
## id code
## 1 Ambiguous compact code E030
## 2 Full ICD-9 code E030
## 3 Full ICD-10 code E03.0
args <-
list(
data = data,
id.vars = "id",
icd.codes = "code",
poa = 1,
method = "pccc_v3.1"
)
default <-
do.call(comorbidities, c(args, list(full.codes = TRUE, compact.codes = TRUE )))
full_only <-
do.call(comorbidities, c(args, list(full.codes = TRUE, compact.codes = FALSE)))
compact_only <-
do.call(comorbidities, c(args, list(full.codes = FALSE, compact.codes = TRUE )))
default[, c("id", "cmrb_flag")]
## id cmrb_flag
## 1 Ambiguous compact code 1
## 2 Full ICD-10 code 1
## 3 Full ICD-9 code 1
full_only[, c("id", "cmrb_flag")]
## id cmrb_flag
## 1 Ambiguous compact code 0
## 2 Full ICD-10 code 1
## 3 Full ICD-9 code 0
compact_only[, c("id", "cmrb_flag")]
## id cmrb_flag
## 1 Ambiguous compact code 1
## 2 Full ICD-10 code 0
## 3 Full ICD-9 code 1With no information about the “E030” being ICD-9 or ICD-10, full or compact, (can only be a diagnostic code in either ICD-9 or ICD-10) we get different flags. The default, the most liberal approach flags this example patient as having a condition in all cases. When only considering the code to be a full code, then only the ICD-10 version matches. When only considering the compact codes the flag is true for the ambiguous version and the ICD-9 full version since ICD-9 E030 is a full code with the same compact form.
The medicalcoder package includes the example data set,
mdcr_longitudinal, with ICD-9 and ICD-10 codes for 3
synthetic patients with multiple encounters. Each row has a date
(encounter) for when the ICD code was reported.
head(mdcr_longitudinal)
## patid date icdv code
## 1 9663901 2016-03-18 10 Z77.22
## 2 9663901 2016-03-24 10 IMO0002
## 3 9663901 2016-03-24 10 V87.7XXA
## 4 9663901 2016-03-25 10 J95.851
## 5 9663901 2016-03-30 10 IMO0002
## 6 9663901 2016-03-30 10 Z93.0Let’s look at the pccc_v2.1 flags for each patient,
using all the information from all the encounters. This can easily by
done by specifying id.vars = c("patid") such that the
comorbidities method considers call codes as occurring on
one encounter.
longitudinal_v2_patid <-
comorbidities(data = mdcr_longitudinal,
icd.codes = "code",
id.vars = c("patid"),
icdv.var = "icdv",
method = "pccc_v2.1",
flag.method = "current",
poa = 1
)
kableExtra::kbl(longitudinal_v2_patid)| patid | congeni_genetic | cvd | gi | hemato_immu | malignancy | metabolic | misc | neonatal | neuromusc | renal | respiratory | any_tech_dep | any_transplant | num_cmrb | cmrb_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 231597 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 4 | 1 |
| 650838 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 5 | 1 |
| 9663901 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
We can look at the conditions flagged at each encounter by specifying
the id.vars = c("patid", "date").
longitudinal_v2_patid_date <-
comorbidities(data = mdcr_longitudinal,
icd.codes = "code",
id.vars = c("patid", "date"),
icdv.var = "icdv",
method = "pccc_v2.1",
flag.method = "current",
poa = 1)
kableExtra::kbl(
subset(longitudinal_v2_patid_date, patid == "9663901"),
row.names = FALSE
)| patid | date | congeni_genetic | cvd | gi | hemato_immu | malignancy | metabolic | misc | neonatal | neuromusc | renal | respiratory | any_tech_dep | any_transplant | num_cmrb | cmrb_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9663901 | 2016-03-18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-30 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2016-05-19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-07-09 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2017-01-31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2017-02-16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2018-03-29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |
Looking at patid 9663901 at an encounter level we see that the conditions occur at different moments in time and the condition the patient has change overtime. Because these are chronic conditions, once the condition is observed, it should be considered to exist in perpetuity.
For pccc_v2.1 a simple carry-forward method can be
applied to the data set to mark the presence of a condition at the time
of reporting and thereafter.
longitudinal_v2_patid_date_cumulative_poa0 <-
comorbidities(
data = mdcr_longitudinal,
icd.codes = "code",
id.vars = c("patid", "date"),
icdv.var = "icdv",
method = "pccc_v2.1",
flag.method = "cumulative",
poa = 0
)
kableExtra::kbl(
subset(longitudinal_v2_patid_date_cumulative_poa0, patid == "9663901"),
row.names = FALSE
)| patid | date | congeni_genetic | cvd | gi | hemato_immu | malignancy | metabolic | misc | neonatal | neuromusc | renal | respiratory | any_tech_dep | any_transplant | num_cmrb | cmrb_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9663901 | 2016-03-18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-05-19 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2016-07-09 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2017-01-31 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
| 9663901 | 2017-02-16 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
| 9663901 | 2018-03-29 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
longitudinal_v2_patid_date_cumulative_poa1 <-
comorbidities(
data = mdcr_longitudinal,
icd.codes = "code",
id.vars = c("patid", "date"),
icdv.var = "icdv",
method = "pccc_v2.1",
flag.method = "cumulative",
poa = 1
)
kableExtra::kbl(
subset(longitudinal_v2_patid_date_cumulative_poa1, patid == "9663901"),
row.names = FALSE
)| patid | date | congeni_genetic | cvd | gi | hemato_immu | malignancy | metabolic | misc | neonatal | neuromusc | renal | respiratory | any_tech_dep | any_transplant | num_cmrb | cmrb_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9663901 | 2016-03-18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9663901 | 2016-03-30 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2016-05-19 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 |
| 9663901 | 2016-07-09 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
| 9663901 | 2017-01-31 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
| 9663901 | 2017-02-16 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
| 9663901 | 2018-03-29 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 1 |
For pccc_v3.0 and pccc_v3.1 a simple
carry-forward method would not be easy to use as information about
technology dependent codes is omitted when non-technology dependent
codes do not exist.
Let’s use three ICD-10 diagnostic codes for this example and we will explore all six possible permutations of the codes. We’ll generate a data set with seven encounters and one code appearing on each of encounters 2, 4, and 6.
The codes we’ll use are: * H49.811: metabolic (other metabolic disorders), * J84.111: respiratory (chronic respiratory diseases), and * Z96.41: metabolic (device and technology use).
codes <- c("H49.811", "J84.111", "Z96.41")
subset(get_pccc_codes(), full_code %in% codes)
## icdv dx full_code code condition subcondition
## 6445 10 1 H49.811 H49811 metabolic other_metabolic_disorders
## 6712 10 1 J84.111 J84111 respiratory chronic_respiratory_diseases
## 7905 10 1 Z96.41 Z9641 metabolic device_and_technology_use
## transplant_flag tech_dep_flag pccc_v3.1 pccc_v3.0 pccc_v2.1 pccc_v2.0
## 6445 0 0 1 1 1 1
## 6712 0 0 1 1 0 0
## 7905 0 1 1 1 1 1The constructed data and permutations are:
permutations <-
data.table::data.table(
permutation = rep(1:6, each = 7),
encounter_id = rep(1:7, times = 6),
code =
codes[c(NA, 1, NA, 2, NA, 3, NA,
NA, 1, NA, 3, NA, 2, NA,
NA, 2, NA, 1, NA, 3, NA,
NA, 2, NA, 3, NA, 1, NA,
NA, 3, NA, 1, NA, 2, NA,
NA, 3, NA, 2, NA, 1, NA)]
)
permutations[, plabel := paste(na.omit(code), collapse = ", "), by = .(permutation)]
permutations[, plabel := paste0("Permutation ", permutation, ": ", plabel)]
str(permutations, vec.len = 1)
## Classes 'data.table' and 'data.frame': 42 obs. of 4 variables:
## $ permutation : int 1 1 ...
## $ encounter_id: int 1 2 ...
## $ code : chr NA ...
## $ plabel : chr "Permutation 1: H49.811, J84.111, Z96.41" ...
## - attr(*, ".internal.selfref")=<externalptr>We’ll apply the pccc_v3.1 to this code set with
flag.method = "cumulative" and all codes considered to be
present-on-admission.
rtn <-
comorbidities(
data = permutations,
icd.codes = "code",
id.vars = c("permutation", "plabel", "encounter_id"),
icdv = 10L,
compact.codes = FALSE,
method = "pccc_v3.1",
flag.method = "cumulative",
poa = 1
)Let’s walk through the results for each permutation.
Permutation 1
| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 4 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 5 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
The first code to appear in this permutation is H49.811, metabolic
(other). This is a diagnostic code and will flag the metabolic condition
for encounters 2 through 7 as _dxpr_or_tech. The Z96.41
code, metabolic (tech), appears on encounter 6. Thus, for encounters 2
through 5 metabolic should be flagged as _dxpr_or_tech = 1,
dxpr_only = 1, tech_only = 0, and
dxpr_and_tech = 0. Encounters 6 and 7 then have
dxpr_only = 0 and tech_only = 0 with
dxpr_and_tech = 1. The J84.111 for respiratory is a
non-tech code appearing on encounter 4 and should flag as
dxpr_or_tech = 1, dxpr_only = 1,
tech_only = 0, and dxpr_and_tech = 0 for
encounters 4 through 7.
| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 5 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
As with permutation 1, having the non-tech dependent metabolic code
H49.811 appearing on encounter 2 means that metabolic is flagged for
encounters 2 through 7. What should differ is that
dxpr_only is 1 for encounters 2 and 3, with
dxpr_and_tech flagging for encounters 4 through 7. Lastly,
the non-tech code J84.111 for respiratory condition flagging as
dxpr_or_tech = dxpr_only = 1 for encounters 6 and 7.
| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 4 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 5 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
Permutation three has respiratory flagged for encounters 2 through 7. The non-tech metabolic code on encounter 4 results in the flagging of metabolic for encounters 4 through 7.
Permutation 4| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 4 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 5 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
Permutation 4 is notable as presence of the respiratory condition on encounters 2 through 7 means that when the technology dependent metabolic code appears on encounter 4, a metabolic is flagged for encounters 4 through 7. Compare this with permutations 5 and 6.
Permutation 5| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 5 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
For permutation 5 the first code is a tech dependent metabolic code on encounter 2. Because the only code for flagging a condition is a technology dependent code the PCCC version 3 algorithm results in no condition being flagged for encounters 2 and 3. On encounter 4, when the non-tech metabolic code appears then the metabolic condition is flagged and the past history of the technology dependent code persists.
Permutation 6| encounter_id | dxpr or tech | dxpr only | tech only | dxpr and tech | dxpr or tech | dxpr only | tech only | dxpr and tech | ccc flag | num ccc |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 5 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 2 |
| 6 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 7 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
As with permutation 5, since the only code in the record for encounter 2 and 3 is the technology dependent metabolic code, there is no flagged condition. On encounter 4, when the dxpr code for a respiratory condition is reported then the respiratory condition and the metabolic condition is flagged as technology dependent. Note that technology only conditions are flagged if at least one other condition is flagged.
The documentation for PCCC version 2 (Feudtner
et al. 2014) and version 3 (Feinstein et
al. 2024) include subconditions under each of the major
conditions. However, to our knowledge, no software prior to
medicalcoder implemented flagging for the
subconditions.
The subconditions for each condition are shown in the next table.
| subcondition | subcondition_label |
|---|---|
| congeni_genetic: Other Congenital or Genetic Defect | |
| bone_and_joint_anomalies | Bone And Joint Anomalies |
| chromosomal_anomalies | Chromosomal Anomalies |
| diaphragm_and_abdominal_wall_anomalies | Diaphragm And Abdominal Wall Anomalies |
| other_congenital_anomalies | Other Congenital Anomalies |
| cvd: Cardiovascular | |
| cardiomyopathies | Cardiomyopathies |
| conduction_disorder | Conduction Disorder |
| device_and_technology_use | Device And Technology Use |
| dysrhythmias | Dysrhythmias |
| endocardium_diseases | Endocardium Diseases |
| heart_and_great_vessel_malformations | Heart And Great Vessel Malformations |
| other | Other |
| transplantation | Transplantation |
| gi: Gastrointestinal | |
| chronic_liver_disease_and_cirrhosis | Chronic Liver Disease And Cirrhosis |
| congenital_anomalies | Congenital Anomalies |
| device_and_technology_use | Device And Technology Use |
| inflammatory_bowel_disease | Inflammatory Bowel Disease |
| other | Other |
| transplantation | Transplantation |
| hemato_immu: Hematologic or Immunologic | |
| acquired_immunodeficiency | Acquired Immunodeficiency |
| aplastic_anemias | Aplastic Anemias |
| coagulation_hemorrhagic | Coagulation Hemorrhagic |
| diffuse_diseases_of_connective_tissue | Diffuse Diseases Of Connective Tissue |
| hemophagocytic_syndromes | Hemophagocytic Syndromes |
| hereditary_anemias | Hereditary Anemias |
| hereditary_immunodeficiency | Hereditary Immunodeficiency |
| leukopenia | Leukopenia |
| other | Other |
| polyarteritis_nodosa_and_related_conditions | Polyarteritis Nodosa And Related Conditions |
| sarcoidosis | Sarcoidosis |
| transplantation | Transplantation |
| malignancy: Malignancy | |
| neoplasms | Neoplasms |
| transplantation | Transplantation |
| metabolic: Metabolic | |
| amino_acid_metabolism | Amino Acid Metabolism |
| carbohydrate_metabolism | Carbohydrate Metabolism |
| device_and_technology_use | Device And Technology Use |
| endocrine_disorders | Endocrine Disorders |
| lipid_metabolism | Lipid Metabolism |
| other_metabolic_disorders | Other Metabolic Disorders |
| storage_disorders | Storage Disorders |
| misc: Miscellaneous, Not Elsewhere Classified | |
| device_and_technology_use | Device And Technology Use |
| transplantation | Transplantation |
| neonatal: Premature & Neonatal | |
| birth_asphyxia | Birth Asphyxia |
| cerebral_hemorrhage_at_birth | Cerebral Hemorrhage At Birth |
| extreme_immaturity | Extreme Immaturity |
| fetal_malnutrition | Fetal Malnutrition |
| hypoxic_ischemic_encephalopathy | Hypoxic Ischemic Encephalopathy |
| other | Other |
| respiratory_diseases | Respiratory Diseases |
| spinal_cord_injury_at_birth | Spinal Cord Injury At Birth |
| neuromusc: Neurologic or Neuromuscular | |
| brain_and_spinal_cord_malformations | Brain And Spinal Cord Malformations |
| cns_degeneration_and_diseases | Cns Degeneration And Diseases |
| device_and_technology_use | Device And Technology Use |
| epilepsy | Epilepsy |
| infantile_cerebral_palsy | Infantile Cerebral Palsy |
| intellectual_disabilities | Intellectual Disabilities |
| movement_diseases | Movement Diseases |
| muscular_dystrophies_and_myopathies | Muscular Dystrophies And Myopathies |
| occlusion_of_cerebral_arteries | Occlusion Of Cerebral Arteries |
| other_neurologic_disorders | Other Neurologic Disorders |
| renal: Renal Urologic | |
| chronic_bladder_diseases | Chronic Bladder Diseases |
| chronic_renal_failure | Chronic Renal Failure |
| congenital_anomalies | Congenital Anomalies |
| device_and_technology_use | Device And Technology Use |
| other | Other |
| transplantation | Transplantation |
| respiratory: Respiratory | |
| chronic_respiratory_diseases | Chronic Respiratory Diseases |
| cystic_fibrosis | Cystic Fibrosis |
| device_and_technology_use | Device And Technology Use |
| other | Other |
| respiratory_malformations | Respiratory Malformations |
| transplantation | Transplantation |
To get the subconditions all you need to do is use the
subconditions = TRUE argument in the
comorbidities call. For this example we will apply
pccc_v3.1 with and without comorbidities.
without_subconditions <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
icdv.var = "icdv",
dx.var = "dx",
poa = 1,
method = "pccc_v3.1",
subconditions = FALSE
)
with_subconditions <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
icdv.var = "icdv",
dx.var = "dx",
poa = 1,
method = "pccc_v3.1",
subconditions = TRUE
)The structure of the return object with_subconditions is
a list with two elements. The first element, conditions, is
identical to the results of calling comorbidities() with
subconditions = FALSE.
with_subconditions
##
## Comorbidities and Subconditions via pccc_v3.1
##
## List of 2
## $ conditions :'data.frame': 38262 obs. of 49 variables:
## $ subconditions:List of 11
## ..$ congeni_genetic:'data.frame': 3225 obs. of 5 variables:
## ..$ cvd :'data.frame': 5122 obs. of 9 variables:
## ..$ gi :'data.frame': 5602 obs. of 7 variables:
## ..$ hemato_immu :'data.frame': 2832 obs. of 13 variables:
## ..$ malignancy :'data.frame': 3784 obs. of 3 variables:
## ..$ metabolic :'data.frame': 3109 obs. of 8 variables:
## ..$ misc :'data.frame': 762 obs. of 3 variables:
## ..$ neonatal :'data.frame': 1516 obs. of 9 variables:
## ..$ neuromusc :'data.frame': 5826 obs. of 11 variables:
## ..$ renal :'data.frame': 2768 obs. of 7 variables:
## ..$ respiratory :'data.frame': 3200 obs. of 7 variables:
all.equal(with_subconditions$conditions,
without_subconditions,
check.attributes = FALSE)
## [1] TRUEThe second element of with_subconditions is list of
data.frames, one for each condition, with indicators for
only those with the condition.
A quick and easy way to get a summary of the subconditions is to call
summary().
str(
summary(with_subconditions)
)
## 'data.frame': 82 obs. of 5 variables:
## $ condition : chr "congeni_genetic" "congeni_genetic" "congeni_genetic" "congeni_genetic" ...
## $ subcondition : chr NA "bone_and_joint_anomalies" "chromosomal_anomalies" "diaphragm_and_abdominal_wall_anomalies" ...
## $ count : num 3225 825 1544 300 754 ...
## $ percent_of_cohort : num 8.429 2.156 4.035 0.784 1.971 ...
## $ percent_of_those_with_condition: num NA 25.6 47.9 9.3 23.4 ...The subconditions are available for all pccc variants. A summary is presented in the following table.
| count | % of cohort | % of those with condition | count | % of cohort | % of those with condition | count | % of cohort | % of those with condition | count | % of cohort | % of those with condition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Other Congenital or Genetic Defect | 3399 | 8.9 | 3490 | 9.1 | 3186 | 8.3 | 3225 | 8.4 | ||||
| Bone And Joint Anomalies | 1239 | 3.2 | 36.5 | 1239 | 3.2 | 35.5 | 825 | 2.2 | 25.9 | 825 | 2.2 | 25.6 |
| Chromosomal Anomalies | 1509 | 3.9 | 44.4 | 1509 | 3.9 | 43.2 | 1544 | 4.0 | 48.5 | 1544 | 4.0 | 47.9 |
| Diaphragm And Abdominal Wall Anomalies | 300 | 0.8 | 8.8 | 300 | 0.8 | 8.6 | 300 | 0.8 | 9.4 | 300 | 0.8 | 9.3 |
| Other Congenital Anomalies | 552 | 1.4 | 16.2 | 671 | 1.8 | 19.2 | 709 | 1.9 | 22.3 | 754 | 2.0 | 23.4 |
| Cardiovascular | 4952 | 12.9 | 4952 | 12.9 | 5055 | 13.2 | 5122 | 13.4 | ||||
| Cardiomyopathies | 240 | 0.6 | 4.8 | 240 | 0.6 | 4.8 | 239 | 0.6 | 4.7 | 240 | 0.6 | 4.7 |
| Conduction Disorder | 653 | 1.7 | 13.2 | 653 | 1.7 | 13.2 | 653 | 1.7 | 12.9 | 653 | 1.7 | 12.7 |
| Device And Technology Use | 438 | 1.1 | 8.8 | 438 | 1.1 | 8.8 | 586 | 1.5 | 11.6 | 587 | 1.5 | 11.5 |
| Dysrhythmias | 1130 | 3.0 | 22.8 | 1130 | 3.0 | 22.8 | 1130 | 3.0 | 22.4 | 1130 | 3.0 | 22.1 |
| Endocardium Diseases | 247 | 0.6 | 5.0 | 247 | 0.6 | 5.0 | 278 | 0.7 | 5.5 | 278 | 0.7 | 5.4 |
| Heart And Great Vessel Malformations | 2298 | 6.0 | 46.4 | 2298 | 6.0 | 46.4 | 2289 | 6.0 | 45.3 | 2320 | 6.1 | 45.3 |
| Other | 1071 | 2.8 | 21.6 | 1071 | 2.8 | 21.6 | 1177 | 3.1 | 23.3 | 1247 | 3.3 | 24.3 |
| Transplantation | 237 | 0.6 | 4.8 | 237 | 0.6 | 4.8 | 246 | 0.6 | 4.9 | 246 | 0.6 | 4.8 |
| Gastrointestinal | 6233 | 16.3 | 6264 | 16.4 | 5584 | 14.6 | 5602 | 14.6 | ||||
| Chronic Liver Disease And Cirrhosis | 285 | 0.7 | 4.6 | 290 | 0.8 | 4.6 | 290 | 0.8 | 5.2 | 290 | 0.8 | 5.2 |
| Congenital Anomalies | 718 | 1.9 | 11.5 | 718 | 1.9 | 11.5 | 698 | 1.8 | 12.5 | 709 | 1.9 | 12.7 |
| Device And Technology Use | 4882 | 12.8 | 78.3 | 4882 | 12.8 | 77.9 | 4210 | 11.0 | 75.4 | 4217 | 11.0 | 75.3 |
| Inflammatory Bowel Disease | 264 | 0.7 | 4.2 | 264 | 0.7 | 4.2 | 264 | 0.7 | 4.7 | 264 | 0.7 | 4.7 |
| Other | 232 | 0.6 | 3.7 | 289 | 0.8 | 4.6 | 289 | 0.8 | 5.2 | 289 | 0.8 | 5.2 |
| Transplantation | 300 | 0.8 | 4.8 | 300 | 0.8 | 4.8 | 312 | 0.8 | 5.6 | 312 | 0.8 | 5.6 |
| Hematologic or Immunologic | 2695 | 7.0 | 2776 | 7.3 | 2766 | 7.2 | 2832 | 7.4 | ||||
| Acquired Immunodeficiency | 11 | 0.0 | 0.4 | 11 | 0.0 | 0.4 | 11 | 0.0 | 0.4 | 11 | 0.0 | 0.4 |
| Aplastic Anemias | 823 | 2.2 | 30.5 | 823 | 2.2 | 29.6 | 776 | 2.0 | 28.1 | 823 | 2.2 | 29.1 |
| Coagulation Hemorrhagic | 98 | 0.3 | 3.6 | 100 | 0.3 | 3.6 | 100 | 0.3 | 3.6 | 100 | 0.3 | 3.5 |
| Diffuse Diseases Of Connective Tissue | 81 | 0.2 | 3.0 | 81 | 0.2 | 2.9 | 125 | 0.3 | 4.5 | 125 | 0.3 | 4.4 |
| Hemophagocytic Syndromes | 59 | 0.2 | 2.2 | 59 | 0.2 | 2.1 | 59 | 0.2 | 2.1 | 59 | 0.2 | 2.1 |
| Hereditary Anemias | 771 | 2.0 | 28.6 | 771 | 2.0 | 27.8 | 771 | 2.0 | 27.9 | 771 | 2.0 | 27.2 |
| Hereditary Immunodeficiency | 813 | 2.1 | 30.2 | 911 | 2.4 | 32.8 | 875 | 2.3 | 31.6 | 909 | 2.4 | 32.1 |
| Leukopenia | 28 | 0.1 | 1.0 | 28 | 0.1 | 1.0 | 28 | 0.1 | 1.0 | 28 | 0.1 | 1.0 |
| Other | 12 | 0.0 | 0.4 | 12 | 0.0 | 0.4 | 12 | 0.0 | 0.4 | 12 | 0.0 | 0.4 |
| Polyarteritis Nodosa And Related Conditions | 40 | 0.1 | 1.5 | 40 | 0.1 | 1.4 | 46 | 0.1 | 1.7 | 46 | 0.1 | 1.6 |
| Sarcoidosis | 2 | 0.0 | 0.1 | 2 | 0.0 | 0.1 | 3 | 0.0 | 0.1 | 3 | 0.0 | 0.1 |
| Transplantation | 142 | 0.4 | 5.3 | 142 | 0.4 | 5.1 | 181 | 0.5 | 6.5 | 181 | 0.5 | 6.4 |
| Malignancy | 3733 | 9.8 | 3766 | 9.8 | 3783 | 9.9 | 3784 | 9.9 | ||||
| Neoplasms | 3525 | 9.2 | 94.4 | 3525 | 9.2 | 93.6 | 3524 | 9.2 | 93.2 | 3525 | 9.2 | 93.2 |
| Transplantation | 358 | 0.9 | 9.6 | 416 | 1.1 | 11.0 | 452 | 1.2 | 11.9 | 452 | 1.2 | 11.9 |
| Metabolic | 2983 | 7.8 | 3009 | 7.9 | 3100 | 8.1 | 3109 | 8.1 | ||||
| Amino Acid Metabolism | 187 | 0.5 | 6.3 | 187 | 0.5 | 6.2 | 194 | 0.5 | 6.3 | 194 | 0.5 | 6.2 |
| Carbohydrate Metabolism | 130 | 0.3 | 4.4 | 130 | 0.3 | 4.3 | 130 | 0.3 | 4.2 | 130 | 0.3 | 4.2 |
| Device And Technology Use | 71 | 0.2 | 2.4 | 71 | 0.2 | 2.4 | 51 | 0.1 | 1.6 | 51 | 0.1 | 1.6 |
| Endocrine Disorders | 748 | 2.0 | 25.1 | 748 | 2.0 | 24.9 | 865 | 2.3 | 27.9 | 865 | 2.3 | 27.8 |
| Lipid Metabolism | 294 | 0.8 | 9.9 | 317 | 0.8 | 10.5 | 321 | 0.8 | 10.4 | 321 | 0.8 | 10.3 |
| Other Metabolic Disorders | 1736 | 4.5 | 58.2 | 1740 | 4.5 | 57.8 | 1620 | 4.2 | 52.3 | 1630 | 4.3 | 52.4 |
| Storage Disorders | 69 | 0.2 | 2.3 | 69 | 0.2 | 2.3 | 73 | 0.2 | 2.4 | 73 | 0.2 | 2.3 |
| Miscellaneous, Not Elsewhere Classified | 822 | 2.1 | 1010 | 2.6 | 759 | 2.0 | 762 | 2.0 | ||||
| Device And Technology Use | 626 | 1.6 | 76.2 | 880 | 2.3 | 87.1 | 638 | 1.7 | 84.1 | 641 | 1.7 | 84.1 |
| Transplantation | 224 | 0.6 | 27.3 | 158 | 0.4 | 15.6 | 121 | 0.3 | 15.9 | 121 | 0.3 | 15.9 |
| Premature & Neonatal | 1559 | 4.1 | 1559 | 4.1 | 1513 | 4.0 | 1516 | 4.0 | ||||
| Birth Asphyxia | 153 | 0.4 | 9.8 | 153 | 0.4 | 9.8 | 11 | 0.0 | 0.7 | 11 | 0.0 | 0.7 |
| Cerebral Hemorrhage At Birth | 35 | 0.1 | 2.2 | 35 | 0.1 | 2.2 | 84 | 0.2 | 5.6 | 84 | 0.2 | 5.5 |
| Extreme Immaturity | 349 | 0.9 | 22.4 | 349 | 0.9 | 22.4 | 349 | 0.9 | 23.1 | 349 | 0.9 | 23.0 |
| Fetal Malnutrition | 45 | 0.1 | 2.9 | 45 | 0.1 | 2.9 | 45 | 0.1 | 3.0 | 45 | 0.1 | 3.0 |
| Hypoxic Ischemic Encephalopathy | 130 | 0.3 | 8.3 | 130 | 0.3 | 8.3 | 127 | 0.3 | 8.4 | 130 | 0.3 | 8.6 |
| Other | 248 | 0.6 | 15.9 | 248 | 0.6 | 15.9 | 247 | 0.6 | 16.3 | 247 | 0.6 | 16.3 |
| Respiratory Diseases | 916 | 2.4 | 58.8 | 916 | 2.4 | 58.8 | 950 | 2.5 | 62.8 | 950 | 2.5 | 62.7 |
| Spinal Cord Injury At Birth | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 |
| Neurologic or Neuromuscular | 5580 | 14.6 | 5732 | 15.0 | 5799 | 15.2 | 5826 | 15.2 | ||||
| Brain And Spinal Cord Malformations | 1767 | 4.6 | 31.7 | 1767 | 4.6 | 30.8 | 1767 | 4.6 | 30.5 | 1767 | 4.6 | 30.3 |
| Cns Degeneration And Diseases | 1244 | 3.3 | 22.3 | 1247 | 3.3 | 21.8 | 1417 | 3.7 | 24.4 | 1474 | 3.9 | 25.3 |
| Device And Technology Use | 1384 | 3.6 | 24.8 | 1405 | 3.7 | 24.5 | 1240 | 3.2 | 21.4 | 1262 | 3.3 | 21.7 |
| Epilepsy | 759 | 2.0 | 13.6 | 759 | 2.0 | 13.2 | 833 | 2.2 | 14.4 | 833 | 2.2 | 14.3 |
| Infantile Cerebral Palsy | 1084 | 2.8 | 19.4 | 1322 | 3.5 | 23.1 | 1322 | 3.5 | 22.8 | 1322 | 3.5 | 22.7 |
| Intellectual Disabilities | 161 | 0.4 | 2.9 | 161 | 0.4 | 2.8 | 161 | 0.4 | 2.8 | 161 | 0.4 | 2.8 |
| Movement Diseases | 169 | 0.4 | 3.0 | 169 | 0.4 | 2.9 | 135 | 0.4 | 2.3 | 146 | 0.4 | 2.5 |
| Muscular Dystrophies And Myopathies | 147 | 0.4 | 2.6 | 147 | 0.4 | 2.6 | 147 | 0.4 | 2.5 | 147 | 0.4 | 2.5 |
| Occlusion Of Cerebral Arteries | 48 | 0.1 | 0.9 | 48 | 0.1 | 0.8 | 92 | 0.2 | 1.6 | 92 | 0.2 | 1.6 |
| Other Neurologic Disorders | 631 | 1.6 | 11.3 | 622 | 1.6 | 10.9 | 847 | 2.2 | 14.6 | 848 | 2.2 | 14.6 |
| Renal Urologic | 2807 | 7.3 | 2855 | 7.5 | 2728 | 7.1 | 2768 | 7.2 | ||||
| Chronic Bladder Diseases | 507 | 1.3 | 18.1 | 507 | 1.3 | 17.8 | 519 | 1.4 | 19.0 | 519 | 1.4 | 18.8 |
| Chronic Renal Failure | 627 | 1.6 | 22.3 | 627 | 1.6 | 22.0 | 627 | 1.6 | 23.0 | 627 | 1.6 | 22.7 |
| Congenital Anomalies | 914 | 2.4 | 32.6 | 914 | 2.4 | 32.0 | 915 | 2.4 | 33.5 | 915 | 2.4 | 33.1 |
| Device And Technology Use | 950 | 2.5 | 33.8 | 1000 | 2.6 | 35.0 | 869 | 2.3 | 31.9 | 911 | 2.4 | 32.9 |
| Other | 216 | 0.6 | 7.7 | 217 | 0.6 | 7.6 | 217 | 0.6 | 8.0 | 217 | 0.6 | 7.8 |
| Transplantation | 288 | 0.8 | 10.3 | 288 | 0.8 | 10.1 | 303 | 0.8 | 11.1 | 303 | 0.8 | 10.9 |
| Respiratory | 3040 | 7.9 | 3040 | 7.9 | 3199 | 8.4 | 3200 | 8.4 | ||||
| Chronic Respiratory Diseases | 330 | 0.9 | 10.9 | 330 | 0.9 | 10.9 | 1092 | 2.9 | 34.1 | 1092 | 2.9 | 34.1 |
| Cystic Fibrosis | 343 | 0.9 | 11.3 | 343 | 0.9 | 11.3 | 343 | 0.9 | 10.7 | 343 | 0.9 | 10.7 |
| Device And Technology Use | 1592 | 4.2 | 52.4 | 1592 | 4.2 | 52.4 | 1408 | 3.7 | 44.0 | 1409 | 3.7 | 44.0 |
| Other | 22 | 0.1 | 0.7 | 22 | 0.1 | 0.7 | 22 | 0.1 | 0.7 | 22 | 0.1 | 0.7 |
| Respiratory Malformations | 1094 | 2.9 | 36.0 | 1094 | 2.9 | 36.0 | 1091 | 2.9 | 34.1 | 1091 | 2.9 | 34.1 |
| Transplantation | 36 | 0.1 | 1.2 | 36 | 0.1 | 1.2 | 38 | 0.1 | 1.2 | 38 | 0.1 | 1.2 |
The longitudinal assessment for subconditions work as well. Using the
same permutations data set from above we will look at the
metabolic and respiratory conditions and subconditions.
rslts <-
comorbidities(
data = permutations,
icd.codes = "code",
id.vars = c("permutation", "plabel", "encounter_id"),
icdv = 10L,
compact.codes = FALSE,
method = "pccc_v3.1",
flag.method = "cumulative",
poa = 1,
subconditions = TRUE
)Let’s start by looking at the respiratory results. The only
subcondition that should be, and is, flagged is chronic respiratory
diseases. A reminder: the data.frame for a subcondition
only report rows for when the primary condition was flagged. We see in
the following encounters where the chronic respiratory disease is
flagged is consistent with when the primary respiratory condition is
flagged.
all(rslts$subconditions$respiratory$chronic_respiratory_diseases == 1)
## [1] TRUE
sapply(rslts$subconditions$respiratory[, -(1:3)], max)
## chronic_respiratory_diseases cystic_fibrosis
## 1 0
## device_and_technology_use other
## 0 0
## respiratory_malformations transplantation
## 0 0
# which encounters flag for primary condition respiratory?
cnd <-
rslts$conditions[
respiratory_dxpr_or_tech == 1,
.(cencid = paste(encounter_id, collapse = ", ")),
by = .(plabel)
]
# which encounters flag for the subcondition chronic_respiratory_diseases?
scnd <-
rslts$subconditions$respiratory[
,
.(sencid = paste(encounter_id, collapse = ", ")),
by = .(plabel)
]| Condition | Subcondition | |
|---|---|---|
| Permutation 1: H49.811, J84.111, Z96.41 | 4, 5, 6, 7 | 4, 5, 6, 7 |
| Permutation 2: H49.811, Z96.41, J84.111 | 6, 7 | 6, 7 |
| Permutation 3: J84.111, H49.811, Z96.41 | 2, 3, 4, 5, 6, 7 | 2, 3, 4, 5, 6, 7 |
| Permutation 4: J84.111, Z96.41, H49.811 | 2, 3, 4, 5, 6, 7 | 2, 3, 4, 5, 6, 7 |
| Permutation 5: Z96.41, H49.811, J84.111 | 6, 7 | 6, 7 |
| Permutation 6: Z96.41, J84.111, H49.811 | 4, 5, 6, 7 | 4, 5, 6, 7 |
For the metabolic condition we have two subconditions to look at, 1) device and technology use, and 2) other metabolic disorders.
# which encounters flag for primary condition metabolic?
cnd <-
rslts$conditions[
metabolic_dxpr_or_tech == 1,
.(cencid = paste(encounter_id, collapse = ", ")),
by = .(plabel)
]
# which encounters flag for the subconditions?
scnd <-
data.table::melt(
rslts$subconditions$metabolic,
id.vars = c("plabel", "encounter_id"),
measure.vars = c("device_and_technology_use", "other_metabolic_disorders"),
variable.factor = FALSE,
variable.name = "subcondition"
)
scnd <- scnd[value == 1]
scnd <-
scnd[
,
.(sencid = paste(encounter_id, collapse = ", ")),
by = .(plabel, subcondition)
]
scnd <-
data.table::dcast(
scnd,
plabel ~ subcondition,
value.var = "sencid"
)| Condition | Device and Technology Use | Other Metabolic Disorders | |
|---|---|---|---|
| Permutation 1: H49.811, J84.111, Z96.41 | 2, 3, 4, 5, 6, 7 | 6, 7 | 2, 3, 4, 5, 6, 7 |
| Permutation 2: H49.811, Z96.41, J84.111 | 2, 3, 4, 5, 6, 7 | 4, 5, 6, 7 | 2, 3, 4, 5, 6, 7 |
| Permutation 3: J84.111, H49.811, Z96.41 | 4, 5, 6, 7 | 6, 7 | 4, 5, 6, 7 |
| Permutation 4: J84.111, Z96.41, H49.811 | 4, 5, 6, 7 | 4, 5, 6, 7 | 6, 7 |
| Permutation 5: Z96.41, H49.811, J84.111 | 4, 5, 6, 7 | 4, 5, 6, 7 | 4, 5, 6, 7 |
| Permutation 6: Z96.41, J84.111, H49.811 | 4, 5, 6, 7 | 4, 5, 6, 7 | 6, 7 |