Eng_Overview

Development Format

Install Version

Please install the latest Pharm package.

install.packages("devtools")
devtools::install_github("DHLab-TSENG/pharm")
library(pharm)
library(dplyr)

Sample Data

A sample data rda file sample_data_subset is included in Pharm package:

The way to generate the data table sample_data is to de-identify the source data which is from the US real medication records. Then, resort the order of patient Id in the original data, and time-shift the date of each administration. Next, replace the NDC in part of the administration record which has the same drug but different NDC, which means that the NDC of the drug is different in the NDC history. As a result of the data, a total of 2,747,617 dosing records and 39,989 Id numbers from different patients were generated.

The sample data sample_data_subset used in the function demonstration of this package is a subset of sample_data which is SELECT FROM MemberId = 1 AND MemberId = 42. For the field information in the data set:

MemberId patient Id
DispenseDate patient`s drug dispensed date, and the time interval is from 2025-12-06 to 2038-01-25
NationalDrugCode drug NDC, total of 17,326 different drug codes
Quantity quantity of patient`s drug dispensed
DaysSupply days of patient`s drug dispensed

knitr::kable(sample_data_subset)

MemberId	DispenseDate	NationalDrugCode	Quantity	DaysSupply
1	2031-12-01	00186504031	30	30
1	2032-01-31	00186504031	30	30
1	2032-03-25	00186504031	30	30
42	2032-01-05	00186504031	30	30
42	2032-03-07	00186504031	30	30
42	2032-04-30	00186504031	30	30
42	2032-03-08	00006504031	90	90
42	2032-04-25	00006504031	90	90

I. Get Drug Ingredient Via Indication

1. Get RxCui Via Maytreat

The source data is from the U.S. Veterans Administration’s MED-RT database to establish this function. User can get the RxCui of all drug ingredients for the treatment by input the indication.

Exmaple: User input the indication esophagitis, the RxCui of all drug ingredients for the treatment can be obtained and stored in the new data table Esophagitis_List. For the field information in the data set:

RxCui drug RxCui
Name drug name
MinCooncept.Id indication Id
MinConcept.Name indication name

Esophagitis_List <- getRxCuiViaMayTreat("esophagitis")
knitr::kable(
  head(Esophagitis_List)
)

RxCui	Name	MinConcept.Id	MinConcept.Name
1792108	Omeprazole Sodium	D004941	Esophagitis
236486	Omeprazole magnesium	D004941	Esophagitis
7646	Omeprazole	D004941	Esophagitis
10156	Sucralfate	D004941	Esophagitis
1487143	sucralfate malate	D004941	Esophagitis
17128	lansoprazole	D004941	Esophagitis

2. Get SBD SCD RxCui Via RxCui

After obtaining the data table of all drug ingredients for the treatment Esophagitis_List, user can get all the pharmaceutical brands and clinical drugs which has contained these drug ingredients by input the data table Esophagitis_List.

Exmaple: User input the data table Esophagitis_List, the converted SBD or SCD RxCui can be obtained and stored in the new data table Esophagitis_SBD_SCD_List. For the field information in the data set:

RxCui drug RxCui
Name drug name
MinCooncept.Id indication Id
MinConcept.Name indication name
SBD.rxcui drug SBD RxCui

Esophagitis_SBD_SCD_List <- getSBDRxCuiViaRxCui(df = Esophagitis_List[3,],
                                                    RxCuiColName = RxCui,
                                                    cores = 2)
knitr::kable(
  head(Esophagitis_SBD_SCD_List)
)

RxCui	Name	MinConcept.Id	MinConcept.Name	SBD.rxcui
7646	Omeprazole	D004941	Esophagitis	2262026
7646	Omeprazole	D004941	Esophagitis	2003656
7646	Omeprazole	D004941	Esophagitis	198051
7646	Omeprazole	D004941	Esophagitis	199119
7646	Omeprazole	D004941	Esophagitis	389171
7646	Omeprazole	D004941	Esophagitis	389172

II. Drug Code Integration

Pharm package provides the following drug code conversion:

U.S. National Drug Code(NDC)
NLM RxNorm(RxCui)
Semantic Branded Drug(SBD) or Semantic Clinical Drug(SCD)
WHO Anatomical Therapeutic Chemical Classification System(ATC)
Taiwan Health Insurance drug code(NHINo)
Hospital Code(HC)
Chang Gung Medical Hospital drug code(RCFNo)

1. NDC and RxCui conversion

1.1. Get NDC via SBD RxCui

After obtaining the data table of all drug ingredient SBD or SCD RxCui Esophagitis_SBD_SCD_List, user can get NDC which has contained these drug ingredients by input the data table Esophagitis_SBD_SCD_List. However, a drug will have different packaging and NDC because it comes from different pharmaceutical brands. There will be different NDC for the same drug type but different packaging. Hence, the output results will be one-to-many. A drug RxCui will matche to multiple NDC of pharmaceutical brands, and no corresponding NDC will beNA. This function provides user to get NDC via SBD or SCD RxCui.

Exmaple: User input the data table Esophagitis_SBD_SCD_List, the converted NDC can be obtained and stored in the new data table Esophagitis_NDC_List. For the field information in the data set:

RxCui drug RxCui
Name drug name
MinCooncept.Id indication Id
MinConcept.Name indication name
SBD.rxcui drug SBD RxCui
NDC drug NDC

Esophagitis_NDC_List <- getNDCViaSBDRxCui(df = Esophagitis_SBD_SCD_List[1,],
                                           SBDRxCuiColName = SBD.rxcui,
                                           cores = 2)
knitr::kable(
  head(Esophagitis_NDC_List)
)

RxCui	Name	MinConcept.Id	MinConcept.Name	SBD.rxcui	NDC
7646	Omeprazole	D004941	Esophagitis	2262026	NA

1.2. Get RxCui via NDC

After obtaining the data table of sample data sample_data_subset, user can get RxCui which has contained these drug ingredients by input the data table sample_data_subset.

Exmaple: User input the data table sample_data_subset, the converted RxCui can be obtained and stored in the new data table sample_data_subset_rxcui. For the field information in the data set:

MemberId patient Id
DispenseDate patient`s drug dispensed date, and the time interval is from 2025-12-06 to 2038-01-25
NationalDrugCode drug NDC, total of 17,326 different drug codes
Quantity quantity of patient`s drug dispensed
DaysSupply days of patient`s drug dispensed
RxCui drug RxCui
ndcStatus drug NDC status

sample_data_subset_rxcui <- getRxCuiViaNDC(df = sample_data_subset,
                                            NdcColName = NationalDrugCode)
knitr::kable(head(sample_data_subset_rxcui))

MemberId	DispenseDate	NationalDrugCode	Quantity	DaysSupply	RxCui	ndcStatus
1	2031-12-01	00186504031	30	30	606731	ACTIVE
1	2032-01-31	00186504031	30	30	606731	ACTIVE
1	2032-03-25	00186504031	30	30	606731	ACTIVE
42	2032-01-05	00186504031	30	30	606731	ACTIVE
42	2032-03-07	00186504031	30	30	606731	ACTIVE
42	2032-04-30	00186504031	30	30	606731	ACTIVE

2. RxCui and ATC conversion

2.1. Get ATC via RxCui

WHO Collaborating Center for Drug Statistics Methodology, ATC classification index with DDDs, 2019. Oslo, Norway 2018. After obtaining the data table sample_data_subset_rxcui, user can get ATC which has contained these drug ingredients by input the data table sample_data_subset_rxcui. However, the same drug components in ATC might depend on different ATC medication route. Hence, this function also refer to the research of Bodenreider et al. and adds the parameter MatchRoute for user to consider. If user wants to consider and compare the ATC medication route, input TRUE in the parameter MatchRoute, otherwise input FALSE.

Exmaple: User input the data table sample_data_subset_rxcui and compare the drug route MatchRoute = False, the converted ATC can be obtained and stored in the new data table sample_data_subset_atc. For the field information in the data set:

MemberId patient Id
DispenseDate patient`s drug dispensed date, and the time interval is from 2025-12-06 to 2038-01-25
NationalDrugCode drug NDC, total of 17,326 different drug codes
Quantity quantity of patient`s drug dispensed
DaysSupply days of patient`s drug dispensed
RxCui drug RxCui
ATC drug ATC

sample_data_subset_atc <- getATCViaRxCui(df = sample_data_subset_rxcui,
                                          RxCuiColName = RxCui,
                                          cores = 2,
                                          MatchRoute = FALSE)
knitr::kable(
  head(sample_data_subset_atc)
)

2.2. Get RxCui via ATC

User can get RxCui which has contained these drug ingredients by input the data table sample_ATC.

Exmaple: User input the data table sample_ATC, the converted RxCui can be obtained and stored in the new data table sample_atc_rxcui. For the field information in the data set:

RxCui drug RxCui
ATC drug ATC

sample_atc_rxcui <- getRxCuiViaATC(df = sample_ATC,
                                    AtcColName = ATC,
                                    cores = 2)
knitr::kable(
  head(sample_atc_rxcui)
  )

ATC	RxCui
G04BX06	8120
D06AX02	2348
C01DA02	4917
G03AB01	NA
A12CC05	142131

3. Taiwan Drug Code conversion

3.1. Get NHINo via HC

User can get NHINo which has contained these drug ingredients by input HC, and no corresponding NHINo will beNA.

knitr::kable(
  head(
    getNHINoViaHC(df, HospitalCodeColName, SourceDf, Source_NhinoColName, Source_HospitalCodeColName)
  )
)

3.2. Get NHINo via RCFNo

User can get NHINo which has contained these drug ingredients by input the data table sample_taiwan_drug, and no corresponding NHINo will beNA.

Exmaple: User input the data table sample_taiwan_drug, the converted NHINo can be obtained. For the field information in the data set:

CGMH_CODE Chang Gung Medical Hospital drug code RCFNo
NHINO1 drug NHINo

knitr::kable(
  head(
    getNHINoViaRCFNo(df = sample_taiwan_drug, 
                      RCFNoColName = CGMH_CODE)
  )
)

CGMH_CODE	NHINO1
P6A780M	BC26139100
P6A022E	NA
P6A741M	NA
P6A674M	NA
PBA024P	N002967209
PBA024P	A010784209

3.3. Get ATC via NHINO

User can get ATC which has contained these drug ingredients by input the data table sample_nhino_code, and no corresponding ATC will beNA.

Exmaple: User input the data table sample_nhino_code, the converted ATC can be obtained. For the field information in the data set:

NHINo drug NHINo
ATC_CODE drug ATC

knitr::kable(
  head(
    getATCViaNHINo(df = sample_nhino_code,
                    NHINoColName = NHINo)
  )
)

NHINo	ATC_CODE
BC26139100	L02BX03
A014118335	D07XB01
BC26359100	L01XE18
BC25224100	C01BD07
AC22049209	R06AA02
AC45972100	R06AX27

III. Calculate Drug Data

1. Generate Drug Era

This can be used to merge pharmacy claims data into drug era with defined window and Exposure days will be cacluated, too. An event of the time interval is according to the prescription’s dispense date plus the prescription’s drug supply days. This function provides user to concatenate continuous prescription medications into a single prescription length. There are two calculation models:

1.If the time interval gap between the patient taking the drug exceeds the persistent window, these two events are regarded as two different drug era. 2.If the time interval gap between the patient taking the drug less than the persistent window, these two events are regarded as same drug era.

Example: As the picture 1,Event 1 and Event 2 are less than the defined gap length, so they are regarded as the same Drug Era. In the other hand, Event 2 and Event 3 are greater than the defined gap, so the gap length is regarded as different Drug Era. For the field information in the data set:

MemberId patient Id
DispenseDate patient`s drug dispensed date, and the time interval is from 2025-12-06 to 2038-01-25
NationalDrugCode drug NDC, total of 17,326 different drug codes
Quantity quantity of patient`s drug dispensed
DaysSupply days of patient`s drug supplied
Drug Era drug era
Drug Era Start Date the begin date to calculate drug era
Drug Era End Date the end date to calculate drug era
Gap the time interval of drug dispensed date
Exposure Days the date of drug era
Supply Days the days of drug era that patient`s drug supplied

knitr::kable(getDrugEra(MemberIDColName = MemberId,
                         sample_data_subset,
                         DrugColName = NationalDrugCode,
                         DispenseDateColName = DispenseDate,
                         DaysSupplyColName = DaysSupply))

MemberId	DispenseDate	NationalDrugCode	Quantity	DaysSupply	DrugEra	DrugEraStartDate	DrugEraEndDate	Gap	ExposureDays	SupplyDays
1	2031-12-01	00186504031	30	30	1	2031-12-01	2031-12-31	0	30 days	30
1	2032-01-31	00186504031	30	30	2	2032-01-31	2032-04-24	0	84 days	60
1	2032-03-25	00186504031	30	30	2	2032-01-31	2032-04-24	24	84 days	60
42	2032-03-08	00006504031	90	90	1	2032-03-08	2032-07-24	0	138 days	180
42	2032-04-25	00006504031	90	90	1	2032-03-08	2032-07-24	-42	138 days	180
42	2032-01-05	00186504031	30	30	1	2032-01-05	2032-02-04	0	30 days	30
42	2032-03-07	00186504031	30	30	2	2032-03-07	2032-05-30	0	84 days	60
42	2032-04-30	00186504031	30	30	2	2032-03-07	2032-05-30	24	84 days	60

2. Define DDDs

2.1. Calculate DDDs in RxCui

User can calculate DDDs in RxCui via the daily amount of drug taken by the patient.

The calculation model:

The dosage of the drug multiplied by the number of the days of drug taken, and then divided by the number of days of drug supplied.

Example: User input the data table sample_data_subset_atc. For the field information in the data set:

MemberId patient Id
DispenseDate patient`s drug dispensed date, and the time interval is from 2025-12-06 to 2038-01-25
NationalDrugCode drug NDC, total of 17,326 different drug codes
Quantity quantity of patient`s drug dispensed
DaysSupply days of patient`s drug supplied
RxCui drug RxCui
ATC drug ATC
DailyDosage drug DDDs
Unit drug unit

sample_data_subset_atc <- calDDDsViaRxCui(df = sample_data_subset_atc, 
                                         RxCuiColName = RxCui, 
                                         QuantityColName = Quantity, 
                                         DaysSupply = DaysSupply, 
                                         cores = 2)
knitr::kable(
  head(sample_data_subset_atc)
)

2.2. Calculate subjects’ accumulated DDDs

User can calculate patient total amount of DDDs for accumulation medication time by input the data table sample_data_subset_atc.

Example: User input the data table sample_data_subset_atc. For the field information in the data set:

MemberId patient Id
DDDs accumulated DDDs

accumulat_DDD <- calDDDsAccumulation(case = sample_data_subset_atc, 
                                   PatientIdColName = MemberId,
                                   DispensingColName = DispenseDate,
                                   AtcCodeColName = ATC,
                                   DailyDosageColName = DailyDosage,
                                   DurationColName = DaysSupply)
knitr::kable(accumulat_DDD)

2.3 Calculate subjects’ accumulated DDDs in custom interval period

User can calculate the total amount of DDDs accumulated by the patient in a custom interval period index day or specific date .

Example: User input the data table sample_data_subset_atc. For the field information in the data set:

MemberId patient Id
Start_day the begin date of patient take medicine
Index_Day the begin date of calculating the DDDs
End_day the end date of patient take medicine
DDDs_Before_15_Days calculate DDDs in custom time interval before index 15 day
DDDs_After_30_Days calculate DDDs in custom time interval after index 30 day

index_DDD <- calDDDsRange(case = sample_data_subset_atc,
                           index_dayColName = Index_Day,
                           expo_range_before = 15,
                           expo_range_after = 30,
                           idColName = MemberId,
                           AtcCodeColName = ATC,
                           DispenseDateColName = DispenseDate,
                           DaysSupplyColName = DaysSupply,
                           DailyDosageColName = DailyDosage)
knitr::kable(index_DDD)

IV. Visulization

The visualization of the data analyzed results can help user quickly understand the content of the data as well as analysis results. In addition, plots can also quickly see the distribution of drug categories in the data, present the results of drug era analysis and the drug history of individual patients.

1. ATC distribution plot

User can get DDDs in ATC level 1 or 2 histogram plot. A parameter level is provided that user can choose which level to draw whether the first-order or second-order distribution of ATC. Then, show the plot of its distribution in bar chart.

Example: User input the data table sample_data_ATC1LevelPlot. For the field information in the data set:

count number
ATC1Level ATC level, the first-order distribution of ATC

# level distribution 1
getATCLevelPlot(df = sample_data_ATC1LevelPlot, 
                  ATCColName = ATC, 
                  level = 1)

# level distribution 2
getATCLevelPlot(df = sample_data_ATC1LevelPlot, 
                  ATCColName = ATC, 
                  level = 2)

2. drug era plot

User can get the visualized result of the drug era calculated by get.DrugEra. User has to consider different drug, drug era, supplied days, and the distribution of the dospensing drug. Then, show the plot that distribution of the patient`s dispensing drug times in bar chart.

Example: User input the data table sample_data_DrugEraPlot. For the field information in the data set:

Drug Era number of drug era
Total Supply Days length of drug era The values in the bar graph indicate the drug era in this time interval, and the drug records for each drug era are several times.

getDrugEraPlot(df = sample_data_DrugEraPlot,
                MemberIDColName = MemberId,
                DrugColName = ATC,
                DrugEraColName = DrugEra,
                SupplyDaysColName = SupplyDays)

3. dispensing drug history plot

User can get specific patient`s drug history plot in bar chart and get separately sub-plots of different drug history. In addition, user can choose to classify drugs, this chart is classified by NDC code. The time interval between each drug record is displayed as a negative number if there is overlap.

Example: User input the data table sample_data_ATC1LevelPlot. For the field information in the data set:

Dispensing Time delivery order of the same drug
Date timeline of drug history

getDispensingPlot(df = sample_data_subset, 
                   MemberIDColName = MemberId,
                   DrugColName = NationalDrugCode,
                   DispenseDateColName = Dispensing,
                   Member = 42, 
                   TimeInterval = 20, 
                   Unit = day)

Reference

I. Get Drug Ingredient Via Indication

National Health Insurance Administration(2019), 藥品藥理治療分類ATC碼資料集. available at https://data.gov.tw/dataset/9119

WHO(2019d), ATC/DDD Index 2018. available at https://www.whocc.no/atc_ddd_index/

U.S. National Library of Medicine(2019b), RxNorm API. available at https://mor.nlm.nih.gov/download/rxnav/RxNormAPIs.html#

MAYO CLINIC (2019), Esophagitis Diagnosis & Treatment. available at https://www.mayoclinic.org/diseases-conditions/esophagitis/diagnosis-treatment/drc-20361264

II. Drug Code Integration

National Health Insurance Administration(2019), 全民健康保險藥品編碼原則. https://reurl.cc/R0qkZn

Food and Drug Administration (2019), National Drug Code Directory. available at https://www.fda.gov/drugs/informationondrugs/ucm142438.htm

U.S. National Library of Medicine(2019c), RxNav. available at https://mor.nlm.nih.gov/RxNav/

Bodenreider et al. (2014), Analyzing U.S. Prescription Lists with RxNorm and the ATC/DDD Index. AMIA … Annual Symposium proceedings. AMIA Symposium, 2014: 297–306. https://pubmed.ncbi.nlm.nih.gov/25954332/

III. Calculate Drug Data

WHO(2019c), Guidelines for ATC Classification and DDD Assignment 2018. https://www.whocc.no/atc_ddd_index_and_guidelines/guidelines/

Vollmer et al.(2012), Comparison of Pharmacy-Based Measures of Medication Adherence. BMC Health Services Research, 12(1): https://pubmed.ncbi.nlm.nih.gov/22691240/

Dima et al. (2017), Computation of Adherence to Medication and Visualization of Medication Histories in R with AdhereR: Towards Transparent and Reproducible Use of Electronic Healthcare Data. PLoS ONE, 12(4): 1–14. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174426

R Foundation (2019), R. available at https://www.r-project.org/

Bender et al. (2015), Pragmatic Trial of Health Care Technologies to Improve Adherence to Pediatric Asthma Treatment a Randomized Clinical Trial. JAMA Pediatrics, 169(4): 317–323. https://pubmed.ncbi.nlm.nih.gov/25664620/

Yeh-Yung, Chiu

2021-06-25

Development Format

Install Version

Sample Data

I. Get Drug Ingredient Via Indication

1. Get RxCui Via Maytreat

2. Get SBD SCD RxCui Via RxCui

II. Drug Code Integration

1. NDC and RxCui conversion

1.1. Get NDC via SBD RxCui

1.2. Get RxCui via NDC

2. RxCui and ATC conversion

2.1. Get ATC via RxCui

2.2. Get RxCui via ATC

3. Taiwan Drug Code conversion

3.1. Get NHINo via HC

3.2. Get NHINo via RCFNo

3.3. Get ATC via NHINO

III. Calculate Drug Data

1. Generate Drug Era

2. Define DDDs

2.1. Calculate DDDs in RxCui

2.2. Calculate subjects’ accumulated DDDs

2.3 Calculate subjects’ accumulated DDDs in custom interval period

IV. Visulization

1. ATC distribution plot

2. drug era plot

3. dispensing drug history plot

Reference