Extracting datasets from KidSIDES • kidsides

library(tidyverse)
library(gt)
library(DT)
library(kidsides)

download_sqlite_db(force=TRUE) #downloads to cache, if not already there
con <- kidsides::connect_sqlite_db()

KidSIDES is large!

For most users, it is a bit unwieldy using a ~900 MB database. Even some of 17 tables in the database are pretty large!

KidSIDES table	Table size
ade_raw	453.95 MB
ade_nichd	355.16 MB
ade	66.36 MB
ade_nichd_enrichment	65.48 MB
gene_expression	15.63 MB
sider	12.13 MB
event	4.41 MB
ade_null_distribution	2.48 MB
drug_gene	1.12 MB
drug	336.13 kB
grip	147.34 kB
gene	104.05 kB
ryan	58.08 kB
dictionary	32.76 kB
cyp_gene_expression_substrate_risk_information	18.19 kB
atc_raw_map	3.31 kB
ade_null	1.59 kB

A subset of the data (up to 10MB) will be more manageable to work with. This vignette gives a non-exhaustive list of manageable datasets from the KidSIDES database.

Extract smaller datasets by drugs and events

Extracting datasets from KidSIDES requires interacting with standard vocabularies for drugs and events. Drugs are represented by the Anatomical Therapeutic Class vocabulary (Reference from the WHO). Events are encoded in the Medical Dictionary of Regulatory Activities vocabulary (Reference from MedDRA). You can interact with these vocabularies for identifying drugs and events using the PDSportal. This a shiny application for first identifying drugs and events of interest and then viewing their drug safety signals across childhood.

In this document, some example datasets are extracted using a specific drug and event:

drug_ <- 
    tbl(con,"drug") %>% 
    filter(atc_concept_name=="montelukast; oral") %>%  
    collect() %>% 
    pull(atc_concept_name)
drug_id_ <- 
    tbl(con,"drug") %>% 
    filter(atc_concept_name==drug_) %>% 
    collect() %>% 
    pull(atc_concept_id)
event_ <- 
    tbl(con,"event") %>% 
    filter(meddra_concept_name_1=="Suicidal ideation") %>%  
    collect() %>% 
    pull(meddra_concept_name_1)
event_id_ <- 
    tbl(con,"event") %>% 
    filter(meddra_concept_name_1==event_) %>%  
    collect() %>% 
    pull(meddra_concept_id)

drug_
#> [1] "montelukast; oral"
drug_id_
#> [1] 21603356
event_
#> [1] "Suicidal ideation"
event_id_
#> [1] 36919235

Example 1: Drug dataset

Table of all reported drugs with the ATC vocabulary in KidSIDES.

table_ <- "drug"

df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 1088   12
df %>% lobstr::obj_size()
#> 336.13 kB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

gt <- 
    dplyr::tbl(con,"dictionary") %>% 
    dplyr::filter(table==table_) %>% 
    gt()

gt

table	field	description	type
drug	atc_concept_id	The ATC 5th level OMOP concept identifier.	int
drug	atc_concept_name	The ATC 5th level OMOP concept name. In the ade_nichd_enrichment table, this ATC concept is from any level in the hierarchy.	character
drug	atc_concept_code	The ATC 5th level OMOP concept code.	character
drug	ndrugreports	The number of reports of the drug in Pediatric FAERS.	int
drug	atc4_concept_name	The ATC 4th level OMOP concept name.	character
drug	atc4_concept_code	The ATC 4th level OMOP concept code.	character
drug	atc3_concept_name	The ATC 3rd level OMOP concept name.	character
drug	atc3_concept_code	The ATC 3rd level OMOP concept code.	character
drug	atc2_concept_name	The ATC 2nd level OMOP concept name.	character
drug	atc2_concept_code	The ATC 2nd level OMOP concept code.	character
drug	atc1_concept_name	The ATC 1st level OMOP concept name.	character
drug	atc1_concept_code	The ATC 1st level OMOP concept code.	character

Example 2: Event dataset

Table of all co-reported events with the MedDRA vocabulary in KidSIDES.

table_ <- "event"

df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 16941    22
df %>% lobstr::obj_size()
#> 4.41 MB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

gt <- 
    dplyr::tbl(con,"dictionary") %>% 
    dplyr::filter(table==table_) %>% 
    gt()

gt

table	field	description	type
event	meddra_concept_name_4	The MedDRA system organ class concept name.	character
event	meddra_concept_id	The MedDRA preferred term OMOP concept identifier.	int
event	neventreports	The number of adverse event reports in Pediatric FAERS.	int
event	meddra_concept_class_id_1	The MedDRA preferred term concept class identifier.	character
event	meddra_concept_class_id_2	The MedDRA higher level concept class identifier.	character
event	meddra_concept_class_id_3	The MedDRA higher level greater term concept class identifier.	character
event	meddra_concept_class_id_4	The MedDRA system organ class concept class identifier.	character
event	meddra_concept_code_1	The MedDRA preferred term concept code identifier.	character
event	meddra_concept_code_2	The MedDRA higher level concept code identifier.	character
event	meddra_concept_code_3	The MedDRA higher level greater term concept code identifier.	character
event	meddra_concept_code_4	The MedDRA system organ class concept code identifier.	character
event	meddra_concept_id_2	The MedDRA higher level concept identifier.	int
event	meddra_concept_id_3	The MedDRA higher level greater term concept identifier.	int
event	meddra_concept_id_4	The MedDRA system organ class concept identifier.	int
event	meddra_concept_name_1	The MedDRA preferred term concept name. Same as 'meddra_concept_name'	character
event	meddra_concept_name_2	The MedDRA higher level concept name.	character
event	meddra_concept_name_3	The MedDRA higher level greater term concept name.	character
event	relationship_id_12	The relationship identifier between columns 1 and 2; should be 'Is a' denoting 1-to-1 mapping.	character
event	relationship_id_23	The relationship identifier between columns 2 and 3; should be 'Is a' denoting 1-to-1 mapping.	character
event	relationship_id_34	The relationship identifier between columns 3 and 4; should be 'Is a' denoting 1-to-1 mapping.	character
event	soc_category	The customized category to represent meddra_concept_name_4 events more broadly as used in the manuscript. Developed in consultation with https://admin.new.meddra.org/sites/default/files/guidance/file/intguide_21_0_english.pdf.	character
event	pediatric_adverse_event	Whether this event concept (meddra concept id) was defined by MedDRA 19th edition vocabulary as a pediatric-specific adverse event. One (1) indicates yes and zero (0) indicates no. The list of events were curated from this site: https://www.meddra.org/paediatric-and-gender-adverse-event-term-lists.	int

Example 3: Drug safety report datasets

Table of report characteristics for drugs reports with events in KidSIDES.

table_ <- "ade_raw"

#dataset size for most frequent drug
dplyr::tbl(con,table_) %>% 
    dplyr::filter(atc_concept_id=="21603929") %>% 
    dplyr::collect() %>% 
    lobstr::obj_size()
#> 9.97 MB

#dataset size for least frequent drug
dplyr::tbl(con,table_) %>% 
    dplyr::filter(atc_concept_id=="21600407") %>% 
    dplyr::collect() %>% 
    lobstr::obj_size()
#> 4.21 kB

#datasets using pre-selected drugs and events
df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::filter(
        atc_concept_id==drug_id_
    ) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 28300    23
df %>% lobstr::obj_size()
#> 5.69 MB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::filter(
        atc_concept_id==drug_id_ &
            meddra_concept_id==event_id_
    ) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 505  23
df %>% lobstr::obj_size()
#> 147.40 kB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

gt <- 
    dplyr::tbl(con,"dictionary") %>% 
    dplyr::filter(table==table_) %>% 
    gt()

gt

table	field	description	type
ade_raw	safetyreportid	The unique identifier for the report.	character
ade_raw	ade	Primary key. This is the unique identifier of an adverse drug event (drug-event). It is a combination of the atc_concept_id and the meddra_concept_id.	character
ade_raw	atc_concept_id	The ATC 5th level OMOP concept identifier.	int
ade_raw	meddra_concept_id	The MedDRA preferred term OMOP concept identifier. In the event table, this would be equivalent in 'meddra_concept_id_1'.	int
ade_raw	nichd	This is the NICHD-defined child development stage. Defined in https://doi.org/10.1542/peds.2012-0055I.	character
ade_raw	sex	The reported sex.	character
ade_raw	reporter_qualification	The type of reporter.	character
ade_raw	receive_date	The date the report was first submitted.	date
ade_raw	XA	GAM covariate name for the ATC 1st level concept name 'ALIMENTARY TRACT AND METABOLISM'	float
ade_raw	XB	GAM covariate name for the ATC 1st level concept name 'BLOOD AND BLOOD FORMING ORGANS'	float
ade_raw	XC	GAM covariate name for the ATC 1st level concept name 'CARDIOVASCULAR SYSTEM'	float
ade_raw	XD	GAM covariate name for the ATC 1st level concept name 'DERMATOLOGICALS'	float
ade_raw	XG	GAM covariate name for the ATC 1st level concept name 'GENITO URINARY SYSTEM AND SEX HORMONES'	float
ade_raw	XH	GAM covariate name for the ATC 1st level concept name 'SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX HORMONES AND INSULINS'	float
ade_raw	XJ	GAM covariate name for the ATC 1st level concept name 'ANTIINFECTIVES FOR SYSTEMIC USE'	float
ade_raw	XL	GAM covariate name for the ATC 1st level concept name 'ANTINEOPLASTIC AND IMMUNOMODULATING AGENTS'	float
ade_raw	XM	GAM covariate name for the ATC 1st level concept name 'MUSCULO-SKELETAL SYSTEM'	float
ade_raw	XN	GAM covariate name for the ATC 1st level concept name 'NERVOUS SYSTEM'	float
ade_raw	XP	GAM covariate name for the ATC 1st level concept name 'ANTIPARASITIC PRODUCTS, INSECTICIDES AND REPELLENTS'	float
ade_raw	XR	GAM covariate name for the ATC 1st level concept name 'RESPIRATORY SYSTEM'	float
ade_raw	XS	GAM covariate name for the ATC 1st level concept name 'SENSORY ORGANS'	float
ade_raw	XV	GAM covariate name for the ATC 1st level concept name 'VARIOUS'	float
ade_raw	polypharmacy	The number of drugs reported.	int

Example 4: Pediatric drug safety signal dataset

Table of drug-event observations including signal chharacteristics in KidSIDES.

table_ <- "ade"

#Significant signals
df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::filter(gt_null_99==1) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 19438     9
df %>% lobstr::obj_size()
#> 2.81 MB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

gt <- 
    dplyr::tbl(con,"dictionary") %>% 
    dplyr::filter(table==table_) %>% 
    gt()

gt

table	field	description	type
ade	ade	Primary key. This is the unique identifier of an adverse drug event (drug-event). It is a combination of the atc_concept_id and the meddra_concept_id.	character
ade	atc_concept_id	The ATC 5th level OMOP concept identifier.	int
ade	meddra_concept_id	The MedDRA preferred term OMOP concept identifier. In the event table, this would be equivalent in 'meddra_concept_id_1'.	int
ade	cluster_id	The identifier for the cluster group assigned to a drug-event by our data-driven clustering approach. See the manuscript's methods for details.	character
ade	gt_null_statistic	The boolean value indicating whether at least one stage's score was greater than nominal significance (the 90 percent confidence interval was above 0).	float
ade	gt_null_99	The boolean value indicating whether at least one stage's score was greater than significance by the null model, as referenced in the paper (the score was greater than the 99th percentile of the null distribution of randomly co-reported drugs and events).	float
ade	max_score_nichd	The child development stage that had the highest risk score for the drug-event.	float
ade	cluster_name	The dynamics name given to the identfier of a cluster group. This is descriptive of the risk trend across stages, from birth through adolescence.	character
ade	ade_nreports	The number of reports of the drug and event co-occurring	character

Example 5: Pediatric drug safety signal time series dataset

Table of drug safety signals across childhood in KidSIDES.

table_ <- "ade_nichd"

df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::filter(ade_name==paste0(drug_," and ",event_)) %>% 
    dplyr::collect()

df %>% dim()
#> [1]  7 13
df %>% lobstr::obj_size()
#> 3.75 kB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))

df <- 
    dplyr::tbl(con,table_) %>% 
    dplyr::filter(atc_concept_id==drug_id_) %>% 
    dplyr::collect()

df %>% dim()
#> [1] 17360    13
df %>% lobstr::obj_size()
#> 1.90 MB
df %>% head(1000) %>% DT::datatable(options = list(pageLengt=5,scrollX = TRUE))