Development of Phenotyping Algorithms for Hypertensive Disorders of Pregnancy (HDP) and Their Application in More Than 22,000 Pregnant Women

Overview

Journal Sci Rep

Specialty Science

Date 2024 Mar 16

PMID 38491024

Authors

Satoshi Mizuno

Maiko Wagata

Satoshi Nagaie

Mami Ishikuro

Taku Obara

Gen Tamiya

Shinichi Kuriyama

Hiroshi Tanaka

Nobuo Yaegashi

Masayuki Yamamoto

Junichi Sugawara

Soichi Ogishima

Affiliations

Soon will be listed here.

Abstract

Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

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