,
Tell Bennett [ctb] ,
James Feinstein [aut] ,
Seth Russell [aut, cre] | Title: | Pediatric Complex Chronic Conditions |
|---|---|
| Description: | An implementation of the pediatric complex chronic conditions (CCC) classification system using R and C++. |
| Authors: | Peter DeWitt [aut] ,
Tell Bennett [ctb] ,
James Feinstein [aut] ,
Seth Russell [aut, cre] |
| Maintainer: | Seth Russell <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.5.9000 |
| Built: | 2024-11-04 06:20:34 UTC |
| Source: | https://github.com/cud2v/pccc |
Generate CCC and CCC subcategory flags and the number of categories.
ccc(data, id, dx_cols = NULL, pc_cols = NULL, icdv)ccc(data, id, dx_cols = NULL, pc_cols = NULL, icdv)
data |
a |
id |
bare name of the column containing the patient id |
dx_cols, pc_cols
|
column names with the diagnostic codes and procedure
codes respectively. These argument are passed to |
icdv |
ICD version 9 or 10 |
It is recommended that you view the codes defining the CCC via
get_codes and make sure that the ICD codes in your data set are
formatted in the same way. The ICD codes used for CCC are character strings
must be formatted as follows:
*Do not* use decimal points or other separators
ICD 9 codes: Codes less than 10 should be left padded with 2 zeros. Codes less than 100 should be left padded with 1 zero.
See 'vignette("pccc-overview")' for more details.
A data.frame with a column for the subject id and integer (0
or 1) columns for each each of the categories.
See pccc-package for published paper on the topic of identifying
Complex Chronic Conditions
get_codes to view the ICD codes used to define the
CCC. select for more examples and details on how to
identify and select the diagnostic and procedure code columns.
eg_data <- data.frame(id = letters, dx1 = c(NA, NA, sample(get_codes(10)[["hemato_immu", "dx"]], 24)), dx2 = c("A", sample(get_codes(10)[["gi", "dx"]], 25)), dx3 = LETTERS, pc1 = c("B", sample(get_codes(10)[["cvd", "pc"]], 25)), pc2 = LETTERS, other_col = LETTERS) ccc(eg_data, id, dx_cols = dplyr::starts_with("dx"), pc_cols = dplyr::starts_with("pc"), icdv = 10)eg_data <- data.frame(id = letters, dx1 = c(NA, NA, sample(get_codes(10)[["hemato_immu", "dx"]], 24)), dx2 = c("A", sample(get_codes(10)[["gi", "dx"]], 25)), dx3 = LETTERS, pc1 = c("B", sample(get_codes(10)[["cvd", "pc"]], 25)), pc2 = LETTERS, other_col = LETTERS) ccc(eg_data, id, dx_cols = dplyr::starts_with("dx"), pc_cols = dplyr::starts_with("pc"), icdv = 10)
The Center for Disease Control has made publicly available death certificate data. This data set is a subset of the 2006 MCOD file for decedents aged <= 21 showing just the underlying cause of death diagnosis code in ICD-9-CM and ICD-10.
comparabilitycomparability
A data frame with 65037 rows and 3 variables.
Sequentially assigned patient identifier
Underlying Cause of Death ICD 9 CM diagnosis code
Underlying Cause of Death ICD 10 diagnosis code
vignette("pccc-example") for more details about the MCOD
source file.
View the ICD, version 9 or 10, for the Complex Chronic Conditions (CCC) categories.
get_codes(icdv)get_codes(icdv)
icdv |
and integer value specifying ICD version. Accepted values are 9 or 10. |
The CCC categories for diagnostic and procedure codes are:
| category | dx |
dx_fixed |
pc |
pc_fixed |
| neuromuscul | X | X | X | |
| cvd | X | X | X | |
| respiratory | X | X | X | |
| renal | X | X | ||
| gi | X | X | ||
| hemato_immu | X | X | ||
| metabolic | X | X | X | |
| congeni_genetic | X | |||
| malignancy | X | X | ||
| neonatal | X | |||
| tech_dep | X | X | ||
| transplant | X | X | ||
The ICD codes were taken from the SAS macro provided by the reference paper.
A matrix of character vectors. Rows are the categories and columns for diagnostic and procedure codes.
Feudtner C, et al. Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation, BMC Pediatrics, 2014, 14:199, DOI: 10.1186/1471-2431-14-199
# All ICD 9 codes for CCC get_codes(9) # All ICD 10 codes for CCC get_codes(10) # Get all the codes for ICD 9 related to malignancy get_codes(9)["malignancy", ]# All ICD 9 codes for CCC get_codes(9) # All ICD 10 codes for CCC get_codes(10) # Get all the codes for ICD 9 related to malignancy get_codes(9)["malignancy", ]
This dataset was produced from a tool available at https://github.com/magic-lantern/icd_file_generator. ICD codes were taken from CMS. The code source, for both the diagnosis and produced codes can be found at https://www.cms.gov/Medicare/Coding/ICD10/2017-ICD-10-CM-and-GEMs.html
pccc_icd10_datasetpccc_icd10_dataset
A data frame with 1000 rows and 31 variables. There is a patient identifier, ten diagnosis codes, ten procedure codes, and ten "other data" values, specifically:
Sequentially assigned patient identifier
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 diagnosis code
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
a ICD 10 procedure codes
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
This dataset was produced from a tool available at https://github.com/magic-lantern/icd_file_generator. ICD codes were taken from CMS. The ICD 9 diagnosis and procedure codes were generated with 20 missing values. Code source: https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html
pccc_icd9_datasetpccc_icd9_dataset
A data frame with 1000 rows and 31 variables. There is a patient identifier, ten diagnosis codes, ten procedure codes, and ten "other data" values, specifically:
Sequentially assigned patient identifier
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 diagnosis code
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
a ICD 9 procedure codes
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Random data to simulate other data often present in export of patient data with 20% of values missing.
Tool to help access internal variables to use in testthat scripts, or anytime
access via ::: would be needed.
test_helper(var)test_helper(var)
var |
bare name of the internal variable to be accessed. |
Object from internal PCCC name space (if it exists)