Evidence map›Paper›PMID 35288092›Full record

ArticleAmerican journal of obstetrics and gynecology2022

Latent class analysis of placental histopathology: a novel approach to classifying early and late preterm births.

Alexander J Layden, Marnie Bertolet, W Tony Parks, James M Roberts, Jennifer J Adibi, Janet M Catov

Open access · greenAbstract read
In one paragraph

Article in American journal of obstetrics and gynecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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 2 countries.

Alexander J LaydenDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA. Electronic address: ajl117@pitt.edu.
Marnie BertoletDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA; Clinical and Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA.
W Tony ParksDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
James M RobertsDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA; Clinical and Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA; Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA; Magee-Womens Research Institute, Pittsburgh, PA.
Jennifer J AdibiDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA; Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA.
Janet M CatovDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA; Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA; Magee-Womens Research Institute, Pittsburgh, PA.
University of Pittsburgh · USUniversity of Toronto · CA

Funding

NRSA Training CoreTL1TR001858 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RADOMSKI, THOMAS, RUBIO, DORIS M · 2016 to 2025
$11.1M
NCATS NIH HHS TL1 TR001858
6 · The paper itself

Abstract

backgroundNeonatal morbidity attributable to prematurity predominantly occurs among early preterm births (<32 weeks) rather than late preterm births (32 to <37 weeks). Methods to distinguish early and late preterm births are lacking given the heterogeneity in pathophysiology and risk factors, including maternal obesity. Although preterm births are often characterized by clinical presentation (spontaneous or clinically indicated), classifying deliveries by placental features detected on histopathology reports may help identify subgroups of preterm births with similar etiology and risk factors. Latent class analysis is an empirical approach to characterize preterm births on the basis of observed combinations of placental features.

objectiveTo identify histopathologic markers that can distinguish early (<32 weeks) and late preterm births (32 to <37 weeks) that are also associated with maternal obesity and neonatal outcomes. STUDY

designWomen with a singleton preterm birth at University of Pittsburgh Medical Center Magee-Womens Hospital (Pittsburgh, PA) from 2008 to 2012 and a placental evaluation (89% of preterm births) were stratified into early (n=900, 61% spontaneous) and late preterm births (n=3362, 57% spontaneous). Prepregnancy body mass index was self-reported at first prenatal visit and 16 abstracted placental features were analyzed. Placental subgroups (ie, latent classes) of early and late preterm births were determined separately by latent class analysis of placental features. The optimal number of latent classes was selected by comparing fit statistics. The probability of latent class membership across prepregnancy body mass indexes was estimated in early preterm births and in late preterm births by an extension of multinomial regression called pseudo-class regression, adjusting for race, smoking, education, and parity. The frequencies of severe neonatal morbidity (composite outcome: respiratory distress, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, periventricular leukomalacia, patent ductus arteriosus, and retinopathy of prematurity), small-for-gestational-age, and length of neonatal intensive care unit stay were compared across latent classes by chi-square and Kruskal-Wallis tests.

resultsEarly preterm births were grouped into 4 latent classes based on placental histopathologic features: acute inflammation (38% of cases), maternal vascular malperfusion with inflammation (29%), maternal vascular malperfusion (25%), and fetal vascular thrombosis with hemorrhage (8%). As body mass index increased from 20 to 50kg/m

conclusionObesity may predispose women to early but not late preterm birth through placental vascular impairment. Latent class analysis of placental histopathologic data provides an evidence-based approach to group preterm births with shared underlying etiology and risk factors.

Indexed as

Infant, Newborn, DiseasesInfant, Premature, DiseasesPregnancy in ObesityPremature BirthFemaleFetal Growth RetardationHumansInfant, NewbornInflammationLatent Class AnalysisPlacentaPregnancyadverse birth outcomeschorioamnionitisfetal vascular thrombosismaternal obesitymaternal vascular malperfusionneonatal morbidityprepregnancy body mass indexpseudo-class regressionsmall-for-gestational-age

Identifiers

PMID35288092
PMCPMC9308632
OpenAlexW4221032292

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.