Evidence map›Paper›PMID 36037998›Full record

ArticleAmerican journal of obstetrics and gynecology2023

A novel approach to joint prediction of preeclampsia and delivery timing using semicompeting risks.

Harrison T Reeder, Sebastien Haneuse, Anna M Modest, Michele R Hacker, Leanna S Sudhof, Stefania I Papatheodorou

Open access · greenAbstract read
In one paragraph

Article in American journal of obstetrics and gynecology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Harrison T ReederDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Sebastien HaneuseDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Anna M ModestDepartment of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA; Department of Obstetrics, Gynecology, and Reproductive Biology, Harvard Medical School, Boston, MA.
Michele R HackerDepartment of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA; Department of Obstetrics, Gynecology, and Reproductive Biology, Harvard Medical School, Boston, MA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Leanna S SudhofDepartment of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA; Department of Obstetrics, Gynecology, and Reproductive Biology, Harvard Medical School, Boston, MA.
Stefania I PapatheodorouDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA. Electronic address: spapathe@hsph.harvard.edu.
Beth Israel Deaconess Medical Center · USHarvard University · US

Funding

Methods for High-Dimensional Statistical Inference and Individualized Risk Prediction under Semi-Competing RisksF31HD102159 · NICHD · HARVARD SCHOOL OF PUBLIC HEALTH · PI REEDER, HARRISON · 2020 to 2021
$74k
NICHD NIH HHS F31 HD102159
6 · The paper itself

Abstract

backgroundPreeclampsia is a pregnancy complication that contributes substantially to perinatal morbidity and mortality worldwide. Existing approaches to modeling and prediction of preeclampsia typically focus either on predicting preeclampsia risk alone, or on the timing of delivery following a diagnosis of preeclampsia. As such, they are misaligned with typical healthcare interactions during which the 2 events are generally considered simultaneously.

objectiveThis study aimed to describe the "semicompeting risks" framework as an innovative approach for jointly modeling the risk and timing of preeclampsia and the timing of delivery simultaneously. Through this approach, one can obtain, at any point during the pregnancy, clinically relevant summaries of an individual's predicted outcome trajectories in 4 risk categories: not developing preeclampsia and not having delivered, not developing preeclampsia but having delivered because of other causes, developing preeclampsia but not having delivered, and developing preeclampsia and having delivered. STUDY

designTo illustrate the semicompeting risks methodology, we presented an example analysis of a pregnancy cohort from the electronic health record of an urban, academic medical center in Boston, Massachusetts (n=9161 pregnancies). We fit an illness-death model with proportional-hazards regression specifications describing 3 hazards for timings of preeclampsia, delivery in the absence of preeclampsia, and delivery following preeclampsia diagnosis.

resultsThe results indicated nuanced relationships between a variety of risk factors and the timings of preeclampsia diagnosis and delivery, including maternal age, race/ethnicity, parity, body mass index, diabetes mellitus, chronic hypertension, cigarette use, and proteinuria at 20 weeks' gestation. Sample predictions for a diverse set of individuals highlighted differences in projected outcome trajectories with regard to preeclampsia risk and timing, and timing of delivery either before or after preeclampsia diagnosis.

conclusionThe semicompeting risks framework enables characterization of the joint risk and timing of preeclampsia and delivery, providing enhanced, meaningful information regarding clinical decision-making throughout the pregnancy.

Indexed as

Pre-EclampsiaPregnancy ComplicationsFemaleGestational AgeHumansInfantMaternal AgeParityPregnancyclinical risk predictiongestational hypertensionmedically-indicated preterm birthpreeclampsiasemicompeting risks

Identifiers

PMID36037998
PMCPMC9968360
OpenAlexW4293217200

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.