remotes::install_github("ngiangre/kidsides")This R data package contains observation, summary, and model-level data from pediatric drug safety research developed by Nicholas Giangreco for his PhD dissertation in the Tatonetti lab at Columbia University.
The database is comprised of 17 tables including a table with
descriptions of the fields in each table. The main table,
ade_nichd, contains quantitative data from nearly 500,000
pediatric drug safety signals across 7 child development stages spanning
from birth through late adolescence (21 years of age).
The database was created using the methods and analyses in the references.
This data resource can be used under the CC BY 4.0 license agreement.
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(kidsides)
kidsides::download_sqlite_db()## Attempt failed to check sqlite exists or to download from the URL: https://tlab-kidsides.s3.amazonaws.com/data/effect_peds_19q2_v0.3_20211119.sqlite.gzcon <- kidsides::connect_sqlite_db()
DBI::dbListTables(con)## [1] "ade"
## [2] "ade_nichd"
## [3] "ade_nichd_enrichment"
## [4] "ade_null"
## [5] "ade_null_distribution"
## [6] "ade_raw"
## [7] "atc_raw_map"
## [8] "cyp_gene_expression_substrate_risk_information"
## [9] "dictionary"
## [10] "drug"
## [11] "drug_gene"
## [12] "event"
## [13] "gene"
## [14] "gene_expression"
## [15] "grip"
## [16] "ryan"
## [17] "sider"dplyr::tbl(con,"dictionary") %>%
dplyr::collect()## # A tibble: 152 × 4
## table field description type
## <chr> <chr> <chr> <chr>
## 1 drug atc_concept_id The ATC 5th level OMOP concept identifier. int
## 2 drug atc_concept_name The ATC 5th level OMOP concept name. In the ad… char…
## 3 drug atc_concept_code The ATC 5th level OMOP concept code. char…
## 4 drug ndrugreports The number of reports of the drug in Pediatric… int
## 5 drug atc4_concept_name The ATC 4th level OMOP concept name. char…
## 6 drug atc4_concept_code The ATC 4th level OMOP concept code. char…
## 7 drug atc3_concept_name The ATC 3rd level OMOP concept name. char…
## 8 drug atc3_concept_code The ATC 3rd level OMOP concept code. char…
## 9 drug atc2_concept_name The ATC 2nd level OMOP concept name. char…
## 10 drug atc2_concept_code The ATC 2nd level OMOP concept code. char…
## # … with 142 more rowsdplyr::tbl(con,"ade_nichd") %>%
dplyr::collect()## # A tibble: 3,225,859 × 13
## atc_c…¹ meddr…² ade nichd gam_s…³ norm gam_s…⁴ gam_s…⁵ gam_s…⁶ D E
## <int> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 1588648 3.58e7 1588… term… -0.131 0 2.48 -4.21 3.95 0 20
## 2 1588648 3.58e7 1588… infa… 0.947 0.166 1.98 -2.31 4.21 0 80
## 3 1588648 3.58e7 1588… todd… 2.03 0.332 1.79 -0.923 4.98 0 107
## 4 1588648 3.58e7 1588… earl… 3.11 0.499 1.86 0.0553 6.17 0 294
## 5 1588648 3.58e7 1588… midd… 4.21 0.667 2.10 0.745 7.67 0 1046
## 6 1588648 3.58e7 1588… earl… 5.30 0.834 2.52 1.15 9.44 1 2697
## 7 1588648 3.58e7 1588… late… 6.38 1 3.14 1.21 11.5 0 1729
## 8 1588648 3.63e7 1588… term… -0.310 0 5.61 -9.53 8.91 0 0
## 9 1588648 3.63e7 1588… infa… 2.25 0.166 4.43 -5.05 9.54 0 2
## 10 1588648 3.63e7 1588… todd… 4.81 0.332 3.79 -1.43 11.1 0 6
## # … with 3,225,849 more rows, 2 more variables: DE <int>, ade_name <chr>, and
## # abbreviated variable names ¹atc_concept_id, ²meddra_concept_id, ³gam_score,
## # ⁴gam_score_se, ⁵gam_score_90mse, ⁶gam_score_90psedplyr::tbl(con,"ade") %>%
dplyr::collect()## # A tibble: 460,823 × 9
## ade atc_c…¹ meddr…² clust…³ gt_nu…⁴ gt_nu…⁵ max_s…⁶ clust…⁷ ade_n…⁸
## <chr> <int> <int> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 1588648_3580… 1588648 3.58e7 2 1 0 late_a… Increa… 1
## 2 1588648_3631… 1588648 3.63e7 2 1 1 late_a… Increa… 1
## 3 1588648_3641… 1588648 3.64e7 2 1 0 late_a… Increa… 1
## 4 1588648_3701… 1588648 3.70e7 2 1 0 late_a… Increa… 1
## 5 1588648_3701… 1588648 3.70e7 2 1 1 late_a… Increa… 1
## 6 1588648_3752… 1588648 3.75e7 2 1 0 late_a… Increa… 1
## 7 1588697_3510… 1588697 3.51e7 4 0 0 term_n… Decrea… 1
## 8 1588697_3510… 1588697 3.51e7 2 0 0 late_a… Increa… 1
## 9 1588697_3510… 1588697 3.51e7 2 0 0 late_a… Increa… 3
## 10 1588697_3510… 1588697 3.51e7 4 0 0 term_n… Decrea… 1
## # … with 460,813 more rows, and abbreviated variable names ¹atc_concept_id,
## # ²meddra_concept_id, ³cluster_id, ⁴gt_null_statistic, ⁵gt_null_99,
## # ⁶max_score_nichd, ⁷cluster_name, ⁸ade_nreportsdplyr::tbl(con,"ade_raw") %>%
dplyr::collect()## # A tibble: 2,326,383 × 23
## safetyr…¹ ade atc_c…² meddr…³ nichd sex repor…⁴ recei…⁵ XA XB XC
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 2 10003357 2160… 2.16e7 4.29e7 midd… Male Other … 2014-0… 0 0 0
## 3 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 4 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 5 10003357 2160… 2.16e7 4.29e7 midd… Male Other … 2014-0… 0 0 0
## 6 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 7 10003388 2160… 2.16e7 3.52e7 late… Fema… Consum… 2014-0… 0 0 1
## 8 10003388 2160… 2.16e7 3.52e7 late… Fema… Consum… 2014-0… 0 0 1
## 9 10003388 2160… 2.16e7 3.58e7 late… Fema… Consum… 2014-0… 0 0 1
## 10 10003401 2160… 2.16e7 3.61e7 earl… Fema… Consum… 2014-0… 0 0 1
## # … with 2,326,373 more rows, 12 more variables: XD <dbl>, XG <dbl>, XH <dbl>,
## # XJ <dbl>, XL <dbl>, XM <dbl>, XN <dbl>, XP <dbl>, XR <dbl>, XS <dbl>,
## # XV <dbl>, polypharmacy <int>, and abbreviated variable names
## # ¹safetyreportid, ²atc_concept_id, ³meddra_concept_id,
## # ⁴reporter_qualification, ⁵receive_datekidsides::disconnect_sqlite_db(con)Adverse drug reactions are a leading cause of morbidity and mortality that costs billions of dollars for the healthcare system. In children, there is increased risk for adverse drug reactions with potentially lasting adverse effects into adulthood. The current pediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. We hypothesize that adverse drug reactions manifest from the interaction between drug exposure and dynamic biological processes during child growth and development.
We hypothesize that by developing statistical methodologies with prior knowledge of dynamic, shared information during development, we can improve the detection of adverse drug events in children. This data package downloads the SQLite database created by applying covariate-adjusted disproportionality generalized additive models (dGAMs) in a systematic way to develop a resource of nearly half a million adverse drug event (ADE) risk estimates across child development stages.
The observation-level data, case reports for drug(s) potentially
linked to adverse event(s), was collected by the Food and Drug
Administration Adverse Event System (FAERS) in the US. This data is
publicly available on the openFDA platform here as downloadable json files. However,
utilizing this data as-is is non-trivial, where the drug event report
data is published in chunks as a nested json structure each quarter per
year since the 1990s. With an API key with extended permissions, I
developed custom python notebooks and scripts available in the
‘openFDA_drug_event-parsing’ github repository (DOI: https://doi.org/10.5281/zenodo.4464544) to extract and
format all drug event reports prior to the third quarter of 2019. This
observation-level data used, called Pediatric FAERS, for downstream
analyses is stored in the table ade_raw.
The drugs and adverse events reported were coded into standard, hierarchical vocabularies. Adverse events were standardized by the Medical Dictionary of Regulatory Activities (MedDRA) vocabulary (details of the hierarcy founds here). Drugs were standardized by the Anatomical Therapeutic Class (ATC) vocabulary (details found here). The reporting of adverse events and drugs can be dependent on the disease context of a report’s subject. This was represented by summarizing the number of drugs of a therapeutic class for each report.
We invented the disproportionality generalized additive model (dGAM) method for detecting adverse drug events from these spontaneous reports. We applied the logistic generalized additive model to all unique drug-event pairs in Pediatric FAERS. The drug-event GAM was used to quantify adverse event risk due to drug exposure versus no exposure across child development stages. Please see the references for the full specification and details on the GAM.
We provide the PDSportal as an accessible web application as well as a plaatform to download our database for the community to explore from identifying safety endpoints in clinical trials to evaluating known and novel developmental pharmacology.
The kidsides R package downloads a sqlite database to
your local machine and connects to the database using the
DBI R package. This is a novel data resource of half a
million pediatric drug safety signals across growth and development
stages. Please see the references for details on data fields and the code
repository for the paper.
Giangreco, Nicholas. Mind the developmental gap: Identifying adverse drug effects across childhood to evaluate biological mechanisms from growth and development. 2022. Columbia University, PhD dissertation.
Giangreco NP, Tatonetti NP. A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development. Med (N Y). 2022 Jun 20:S2666-6340(22)00232-X. doi: 10.1016/j.medj.2022.06.001. Epub ahead of print. PMID: 35752163.