Getting Started

Study Reference

This package was developed as part of the following study:

Exploring and Comparing Existing Algorithms for Flagging Pregnancy-related Adverse Event Reports
Sara Hedfors Vidlin, Valentina Giunchi, Levente K-Pápai, Lovisa Sandberg, Cosimo Zaccaria, Takamasa Sakai, Loris Piccolo, Elena Rocca, Michele Fusaroli, Nhung TH Trinh

PVgravID: Identifying Pregnancy Reports in Pharmacovigilance Databases

Overview

PVgravID is an R package for the identification and comparison of pregnancy-related reports in large pharmacovigilance databases, specifically VigiBase and FAERS. It implements and harmonizes three published rule-based algorithms for flagging pregnancy-related adverse event reports, enabling researchers to systematically retrieve, compare, and analyze such reports across different data sources.

Background

Pregnant individuals are often excluded from clinical trials, making post-marketing surveillance crucial for understanding drug safety in this population. However, the lack of a standardized pregnancy indicator in adverse event reporting complicates the identification of relevant cases. Three rule-based algorithms have been developed to address this, each tailored to a specific database (FAERS, EudraVigilance, VigiBase) and with different scopes and rationales. PVgravID harmonizes and implements these algorithms, allowing direct comparison and application to both VigiBase and FAERS datasets.

Features

• Implements three published algorithms for identifying pregnancy-related reports:
  1. Sakai T, Ohtsu F, Sekiya Y, et al (2016) Methodology for Estimating the Risk of Adverse Drug Reactions in Pregnant Women: Analysis of the Japanese Adverse Drug Event Report Database. Yakugaku Zasshi 136:499–505.
  2. Zaccaria C, Piccolo L, Gordillo-Marañón M, et al (2024) Identification of Pregnancy Adverse Drug Reactions in Pharmacovigilance Reporting Systems: A Novel Algorithm Developed in EudraVigilance. Drug Saf 47:1127–1136.
  3. Sandberg L, Vidlin SH, K-Pápai L, et al (2025) Uncovering Pregnancy Exposures in Pharmacovigilance Case Report Databases: A Comprehensive Evaluation of the VigiBase Pregnancy Algorithm. Drug Saf.
• Harmonized logic for cross-database comparison
• Function to tailor the algorithms to the researcher specific need
• Functions for descriptive analysis and visualization (Venn diagrams, upset plots)
• Interoperable with the DiAna R package for pharmacovigilance data

Installation

PVgravID is not yet on CRAN. To install from source:

# install.packages("pak")
pak::pak("Uppsala-Monitoring-Centre/PVgravID")

Data

This project used both VigiBase data (as available to the Uppsala Monitoring Centre) and FAERS data (as provided in a cleaned form by the DiAna package. To install it follow the instruction at the https://github.com/fusarolimichele/DiAna_package github.

The following script will use the FAERS data, because of it ready availability to the public. The package expects input data in the format used by the DiAna package, with data tables for demographics, drugs, reactions, indications, outcomes, etc.

Usage

We first import the library used and, following DiAna workflow, we define the last quarter of the FAERS we want to work with: “25Q1”. Note that all the quarters previous to it will also be included. We then import all the demographic data up to 25Q1.

library(PVgravID)
library(DiAna)
library(dplyr)
FAERS_version <- "25Q1"
import("DEMO")
#>           primaryid    sex age_in_days wt_in_kgs             occr_country
#>               <num> <fctr>       <num>     <num>                   <fctr>
#>        1:  45217461      M        9855        NA                     <NA>
#>        2:  57910401   <NA>          NA        NA                     <NA>
#>        3:  56962401   <NA>          NA        NA                     <NA>
#>        4:  54662121      F       22630        NA                     <NA>
#>        5:  31231172      F       21535        68                     <NA>
#>       ---                                                                
#> 18907378: 251464781      F        6205        59                     <NA>
#> 18907379: 251466471      F       18980        NA                     <NA>
#> 18907380: 251466491      M       29200        85                     <NA>
#> 18907381: 251466861      M       12410        NA                     <NA>
#> 18907382:  70283934   <NA>          NA        NA United States of America
#>           event_dt occp_cod          reporter_country rept_cod init_fda_dt
#>              <int>   <fctr>                    <fctr>   <fctr>       <int>
#>        1: 19861106       MD  United States of America      DIR    19861224
#>        2:       NA     <NA>  United States of America      DIR    19920917
#>        3: 19960123     <NA>  United States of America      DIR    19960126
#>        4: 19960919       CN South Africa, Republic of      EXP    19961022
#>        5: 19970927       MD  United States of America      EXP    19971203
#>       ---                                                                 
#> 18907378: 20250331       PH  United States of America      DIR    20250331
#> 18907379: 20250308       HP  United States of America      DIR    20250331
#> 18907380: 20250326       PH  United States of America      DIR    20250331
#> 18907381: 20250331       PH  United States of America      DIR    20250331
#> 18907382:       NA       PH  United States of America      EXP    20090619
#>             fda_dt premarketing literature RB_duplicates
#>              <int>       <lgcl>     <lgcl>        <lgcl>
#>        1: 19861224        FALSE      FALSE         FALSE
#>        2: 19920917        FALSE      FALSE         FALSE
#>        3: 19960126        FALSE      FALSE         FALSE
#>        4: 19961022        FALSE      FALSE         FALSE
#>        5: 19971212        FALSE      FALSE         FALSE
#>       ---                                               
#> 18907378: 20250331        FALSE      FALSE         FALSE
#> 18907379: 20250331        FALSE      FALSE         FALSE
#> 18907380: 20250331        FALSE      FALSE         FALSE
#> 18907381: 20250331        FALSE      FALSE         FALSE
#> 18907382: 20250331        FALSE      FALSE         FALSE
#>           RB_duplicates_only_susp
#>                            <lgcl>
#>        1:                   FALSE
#>        2:                   FALSE
#>        3:                   FALSE
#>        4:                   FALSE
#>        5:                   FALSE
#>       ---                        
#> 18907378:                   FALSE
#> 18907379:                   FALSE
#> 18907380:                   FALSE
#> 18907381:                   FALSE
#> 18907382:                   FALSE

Retrieve Pregnancy Reports

The main function of the package is generate_pregnancy_flags, which takes a list of report identifiers (in our case all the reports included in Demo) and import them from the data folder included in the same project folder (as in the DiAna directory structure), and assess each report for the inclusion and exclusion criteria of the three different pregnancy algorithms.

matrix_flagged_for_pregnancy <- generate_pregnancy_flags(pids_vct = Demo$primaryid)

kable(head(matrix_flagged_for_pregnancy, 100), format = "html") |>
  kable_styling(full_width = FALSE) |>
  scroll_box(height = "400px")

primaryid	E_age_over_50y	E_agegroup_elderly	E_parent_M	E_reac_paternal	E_reac_normalCombined	E_reac_noADR	E_indi_normalCombined	I_gestation	I_route_transplacental	I_route_intramniotic	I_route_extraamniotic	I_indi_exposure_matACunkA	I_indi_exposure_mater_B	I_indi_exposure_nolact	I_indi_fet_abo_pregn_norm	I_reac_exposure_matACunkA	I_reac_exposure_matB	I_reac_fet_abo_pregn	I_reac_normal	I_reac_cong_below_2	I_reac_cong_CA	I_reac_cong_parent	I_reac_neon_below_8days	I_reac_neon_CA	I_reac_neon_parent	I_parent_F_nolac	I_serious_CA	I_concurrent_pregn	I_reac_neon_cong	I_indi_fet_pregn_norm	E_nofertile_F	E_indi_paternal	E_reac_paternal_Sakai	E_indi_congenital	UMC	EMA	SakaiHPPV	SakaiHS	SakaiM
45217461	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
57910401	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
56962401	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
54662121	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
31231172	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
30307871	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
31273651	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32427281	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
37847331	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32030921	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32138152	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
53633692	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32077533	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32077683	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32223462	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
41860621	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32908311	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33069461	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32545282	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33116181	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33189951	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33249531	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32927213	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33576892	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
33914632	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
34331911	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
57697232	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
34460354	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
66685672	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
35653763	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
35750711	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
35764423	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
36299761	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
39884842	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
61584552	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
37225011	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
37293841	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
37209972	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
80351361	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
37592244	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
32976884	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
36317988	FALSE	NA	NA	FALSE	FALSE	FALSE	TRUE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
38131791	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
38675501	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
66877291	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
39497921	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
39666531	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
39306567	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
40054651	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4204616	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE
4214534	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4223542	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4266082	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4250482	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
39876187	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
40456612	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
40558331	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4280585	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261823	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261824	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261825	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261827	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261863	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261865	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261870	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261871	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261878	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261879	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261986	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261987	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4261996	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262055	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262057	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262058	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262059	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262060	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262061	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262062	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262066	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262067	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262072	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262073	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262074	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262075	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262076	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262077	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262079	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262080	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262084	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262085	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262088	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262117	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262186	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262188	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262194	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262195	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262228	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262231	TRUE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262233	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4262234	FALSE	NA	NA	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	NA	FALSE	FALSE	NA	NA	FALSE	NA	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE

As it can be observed, the outcome of the function is a matrix with: in the first column the primaryid, then 34 columns specifying whether specific exclusion or inclusion criteria are respected, and finally 5 columns specifying whether the report is flagged or not by each of the 5 pregnancy algorithms here implemented (UMC, EMA, and the 3 Sakai with different intended performance). See the function documentation (?check_pregnancy_criteria) for details.

We can then extract the vectors of report id flagged by each algorithm (and count them) in the following way:

UMC_pregnancy_reports <- matrix_flagged_for_pregnancy[UMC == TRUE]$primaryid
length(UMC_pregnancy_reports)
#> [1] 355408
EMA_pregnancy_reports <- matrix_flagged_for_pregnancy[EMA == TRUE]$primaryid
length(EMA_pregnancy_reports)
#> [1] 264368
SakaiHPPV_pregnancy_reports <- matrix_flagged_for_pregnancy[SakaiHPPV == TRUE]$primaryid
length(SakaiHPPV_pregnancy_reports)
#> [1] 268962

Visualize Flowchart of Selection of Reports for a Specific Algorithm

flowchart_generator(pregnancy_flags_table = matrix_flagged_for_pregnancy, algorithm = "UMC")

Visualize Comparison Pregnancy Algorithms

The overlap in the reports flagged by the different algorithms can be visualized as follows using an Euler-Venn diagram:

render_venn(pids_sets = list(
  "UMC" = UMC_pregnancy_reports,
  "EMA" = EMA_pregnancy_reports,
  "SakaiH" = SakaiHPPV_pregnancy_reports
))

Here we can see that most of the reports flagged as pregnancy-related by at least one of the algorithms is flagged by the UMC algorithm. But still there are reports that only the other algorithm flags. Let’s look at the criteria that resulted in some reports being flagged by the EMA algorithms but not the other two. And let’s look at them using an upset plot.

nrow(matrix_flagged_for_pregnancy[EMA == TRUE & UMC == FALSE & SakaiHPPV == FALSE])
#> [1] 1123
u_plot <- render_upset(
  matrix_flagged_for_pregnancy[EMA == TRUE & UMC == FALSE & SakaiHPPV == FALSE],
  min_elements_in_intersection = 1
)
u_plot

We see in this case that most of the reports are here because they respect the inclusion criterium of a term related to foetal events, abortion, or delivery among reactions (shared between the UMC and the EMA algorithm) but also include an exclusion criterium that make them ineligible for the UMC pregnancy flag (i.e., they report exclusively paternal exposure).

What if we wanted to include only specific criteria? We can specify the inclusion and exclusion criteria in the following way, and run the function check_criteria. In this example we add two inclusion criteria to the UMC algorithm concerning intraamniotic and extraamniotic exposure.

exclusions_UMC <- c("E_age_over_50y", "E_agegroup_elderly", "E_parent_M", "E_reac_paternal")
inclusions_UMC <- c(
  "I_gestation", "I_serious_CA", "I_reac_cong_below_2", "I_reac_normal", "I_reac_fet_abo_pregn",
  "I_reac_exposure_matACunkA", "I_indi_fet_abo_pregn_norm", "I_indi_exposure_matACunkA", "I_route_transplacental",
  "I_reac_neon_below_8days", "I_concurrent_pregn", "I_parent_F_nolac"
)
inclusions_tailored <- c(inclusions_UMC, "I_route_intramniotic", "I_route_extraamniotic")
matrix_flagged_for_pregnancy[
  , "UMC_tailored" := check_criteria(.SD, exclusions_UMC, inclusions_tailored),
  .SDcols = c(exclusions_UMC, inclusions_tailored)
]
additional_flags <- matrix_flagged_for_pregnancy[UMC == FALSE & UMC_tailored == TRUE]
nrow(additional_flags)
#> [1] 62

We see that we identify this way 62 additional reports, which have the following characteristics:

render_upset(additional_flags |> select(-UMC_tailored), min_elements_in_intersection = 1)

We see this way that only 19 reports are included because of extra-amniotic exposure, and all the others are included because of intraamniotic exposure.

Additional Uses

Descriptive Analysis

We can wonder what are the caracteristics of pregnancy reports, using the descriptive analysis from DiAna.

import("THER")
#>           primaryid   drug_seq start_dt dur_in_days   end_dt time_to_onset
#>               <num>      <int>    <num>       <num>    <num>         <num>
#>        1:   4204616 1004278786 20030812          NA       NA             4
#>        2:   4204616 1004279849 20030813          NA       NA             3
#>        3:   4261823 1004486217 20030317          43 20030428            80
#>        4:   4261823 1004486218 20030317          43 20030428            80
#>        5:   4261824 1004486219 20031111           1 20031111             1
#>       ---                                                                 
#> 24590322: 987703857         32 20150122          NA       NA          -750
#> 24590323: 987703857         33   201504          NA       NA            NA
#> 24590324: 987703857         34     2017         731     2019            NA
#> 24590325: 987703857         35     2015          NA       NA            NA
#> 24590326: 987703857         61   202103          NA       NA            NA
#>           event_dt
#>              <int>
#>        1: 20030815
#>        2: 20030815
#>        3: 20030604
#>        4: 20030604
#>        5: 20031111
#>       ---         
#> 24590322: 20130101
#> 24590323: 20130101
#> 24590324: 20130101
#> 24590325: 20130101
#> 24590326: 20130101
descr_table <- descriptive(matrix_flagged_for_pregnancy[UMC == TRUE]$primaryid)
(descr_table)
#> # A tibble: 278 × 3
#>    `**Characteristic**` N_cases `%_cases`
#>    <chr>                <chr>   <chr>    
#>  1 N                    NA      ""       
#>  2 sex                  NA       NA      
#>  3 Female               244,045 "85.00"  
#>  4 Male                 43,068  "15.00"  
#>  5 Unknown              68,295   NA      
#>  6 Submission           NA       NA      
#>  7 Direct               7,839   "2.21"   
#>  8 Expedited            268,729 "75.61"  
#>  9 Periodic             78,840  "22.18"  
#> 10 Reporter             NA       NA      
#> # ℹ 268 more rows

We can also compare the reports flagged by the algorithm as pregnancy-related with the reference group made by a random sample of the reports in the database.

descr_comp_table <- descriptive(matrix_flagged_for_pregnancy[UMC == TRUE]$primaryid,
  RG = sample(Demo$primaryid, 10000)
)
#> Warning in descriptive(matrix_flagged_for_pregnancy[UMC == TRUE]$primaryid, :
#> Variables role_cod and time_to_onset not considered. If you want to include
#> them please provide the drug investigated
(descr_comp_table)
#> # A tibble: 278 × 7
#>    `**Characteristic**` N_cases `%_cases` N_controls `%_controls` `**p-value**`
#>    <chr>                <chr>   <chr>     <chr>      <chr>        <chr>        
#>  1 N                    355408  ""        9812       ""           ""           
#>  2 __sex__              NA       NA       NA          NA          NA           
#>  3 Female               244,045 "85.00"   5,153      "59.83"      NA           
#>  4 Male                 43,068  "15.00"   3,460      "40.17"      NA           
#>  5 Unknown              68,295   NA       1,199       NA          NA           
#>  6 __Submission__       NA       NA       NA          NA          NA           
#>  7 Direct               7,839   "2.21"    531        "5.41"       NA           
#>  8 Expedited            268,729 "75.61"   5,143      "52.42"      NA           
#>  9 Periodic             78,840  "22.18"   4,138      "42.17"      NA           
#> 10 __Reporter__         NA       NA       NA          NA          NA           
#> # ℹ 268 more rows
#> # ℹ 1 more variable: `**q-value**` <chr>

Disproportionality Analysis

To showcase this use, we also use the PVdagger package to draw the DAGs, documented in the following website: https://pvverse.github.io/pv_dagger/.

# run only once. pak::pak("PVverse/pv_dagger")
library(PVdagger)
#> Loading required package: DiagrammeR

We should remember that pregnancy often acts as a confounder, modifying the chance of both being exposed to the drug and manifesting the event. This can introduce a distortion in disproportionality analysis.

Two common scenarios can be represented in this way:

• Pregnancy is a positive confounder, increasing the chance of both the drug and event. In this situation, we can see a disproportionality in the lack of a reaction.

• Pregnancy is a negative confounder, for example because the drug is partly controindicated in pregnancy (e.g., because of lack of evidence of safety) and the event is instead more common in pregnancy. In this situation, we can miss a disproportionality in the presence of a reaction.

Here two examples, represented using directed acyclic graphs (DAGs).

create_dag("antiD_ig", "nausea",
  label_inquiry = "",
  confounder_path = list(
    nodes = list("Pregnancy"), signs = list("+", "+"),
    label = "Confounding by Pregnancy (+)"
  )
)

create_dag("nivolumab", "premature_delivery",
  label_inquiry = "",
  confounder_path = list(
    nodes = list("Pregnancy"), signs = list("-", "+"),
    label = "Confounding by Pregnancy (-)"
  )
)

In these situations we need to condition on the variable that is introducing a confounder. In other words, we can restrict to pregnancy reports, and this is where our algorithms can become extremely useful. We can use them to select the background in the disproportionality_analysis function from DiAna as described below.

restrictions <- list(
  "Crude" = Demo$primaryid,
  "Pregnancy" = matrix_flagged_for_pregnancy[UMC == TRUE]$primaryid
)


drug <- "anti-d immunoglobulin"
event <- "nausea"

disproportionality_df <- data.table()
for (n in seq_along(restrictions)) {
  t <- restrictions[n]
  t_name <- names(t)
  t_pids <- unlist(t)
  df <- disproportionality_analysis(
    drug_selected = unlist(drug),
    reac_selected = unlist(event),
    temp_drug = Drug[role_cod %in% c("PS", "SS", "I")],
    restriction = t_pids
  )[, nested := t_name]
  disproportionality_df <- rbindlist(list(disproportionality_df, df), fill = TRUE)
}
disproportionality_df <- disproportionality_df[, expected := (D * E / (D_E + D_nE + nD_E + nD_nE))]

render_forest_table(disproportionality_df)

We see in this case that the positively confounded disproportionality disappears when restricting to pregnancy reports.

drug <- "nivolumab"
event <- list("preterm birth" = list("premature baby", "premature delivery"))

disproportionality_df <- data.table()
for (n in seq_along(restrictions)) {
  t <- restrictions[n]
  t_name <- names(t)
  t_pids <- unlist(t)
  df <- disproportionality_analysis(
    drug_selected = unlist(drug),
    reac_selected = unlist(event),
    temp_drug = Drug[role_cod %in% c("PS", "SS", "I")],
    restriction = t_pids
  )[, nested := t_name]
  disproportionality_df <- rbindlist(list(disproportionality_df, df), fill = TRUE)
}
disproportionality_df <- disproportionality_df[, expected := (D * E / (D_E + D_nE + nD_E + nD_nE))]

render_forest_table(disproportionality_df)

We see in this case that the negatively confounded disproportionality appears when restricting to pregnancy reports.