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Am J Obstet Gynecol. 2019 Apr;220(4):297-307. doi: 10.1016/j.ajog.2019.01.208. Epub 2019 Jan 22.

Automated early detection of obstetric complications: theoretic and methodologic considerations.

Author information

1
Division of Research, Systems Research Initiative, Kaiser Permanente Northern California, Oakland, CA. Electronic address: Gabriel.Escobar@kp.org.
2
Department of Obstetrics and Gynecology, Kaiser Permanente Medical Center, Oakland, CA.
3
Division of Research, Perinatal Research Unit, Kaiser Permanente Northern California, Oakland, CA.
4
Division of Research, Systems Research Initiative, Kaiser Permanente Northern California, Oakland, CA.
5
Department of Obstetrics and Gynecology, Kaiser Permanente Medical Center, San Francisco, CA.
6
Division of Research, Systems Research Initiative, Kaiser Permanente Northern California, Oakland, CA; Decision Support, Kaiser Foundation Hospitals, Inc, Oakland, CA.

Abstract

Compared with adults who are admitted to general medical-surgical wards, women who are admitted to labor and delivery services are at much lower risk of experiencing unexpected critical illness. Nonetheless, critical illness and other complications that put either the mother or fetus at risk do occur. One potential approach to prevention is to use automated early warning systems, such as those used for nonpregnant adults. Predictive models that use data extracted in real time from electronic records constitute the cornerstone of such systems. This article addresses several issues that are involved in the development of such predictive models: specification of temporal characteristics, choice of denominator, selection of outcomes for model calibration, potential uses of existing adult severity of illness scores, approaches to data processing, statistical considerations, validation, and options for instantiation. These have not been addressed explicitly in the obstetrics literature, which has focused on the use of manually assigned scores. In addition, this article provides some results from work in progress to develop 2 obstetric predictive models with the use of data from 262,071 women who were admitted to a labor and delivery service at 15 Kaiser Permanente Northern California hospitals between 2010 and 2017.

KEYWORDS:

early warning system; electronic medical record; obstetrics; predictive model; severity of illness

PMID:
30682365
DOI:
10.1016/j.ajog.2019.01.208

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