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CC BY-NC-ND 4.0 · Am J Perinatol 2025; 42(06): 722-731
DOI: 10.1055/a-2418-9955
Original Article

Novel Approach to Identify Severe Maternal Morbidity Clusters: A Latent Class Analysis

Andrea J. Ibarra
1   Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Samia H. Lopa
2   Department of Biostatistics and Computational Biology University of Rochester, Rochester, New York
,
BaDoi N. Phan
3   Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Katherine Himes
4   Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Meryl A. Butters
5   Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Stacy Beck
4   Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Janet M. Catov
4   Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
6   Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
7   Department of Obstetrics, Gynecology and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
› Institutsangaben
Funding This work was supported by grants from the Richard King Mellon Foundation, Steve N. Caritis Magee Obstetrical Maternal and Infant (MOMI) Database, the Commonwealth of Pennsylvania Department of Health/Health Research Formula Fund, and the Robert A. Winn Diversity in Clinical Trials Award Program, the Foundation for Anesthesia Education and Research/Society for Obstetric Anesthesia and Perinatology (Mentored Research Training Grant, ID: 1061081), the Bristol Myers Squibb Foundation, and the University of Pittsburgh Cluster Hire Initiative. The funders/sponsors had no role in the design and conduct of the study; collection, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication (Commonwealth of Pennsylvania Department of Health/Health Research Formula Fund, Foundation for Anesthesia Education and Research, Richard King Mellon Foundation. Robert A. Winn Diversity in Clinical Trials Award Program, Bristol Myers Squibb Foundation, University of Pittsburgh Cluster Hire Initiative).
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Abstract

Objective

Whether clusters exist within severe maternal morbidity (SMM), a set of life-threatening heterogeneous conditions, is not known. Our primary objective was to identify SMM clusters using a data-driven clustering technique, their associated predictors and outcomes.

Study Design

From 2008 to 2017, we used a delivery database supplemented by state data and medical record abstraction from a single institution in Pennsylvania. To identify SMM clusters, we applied latent class modeling that included 23 conditions defined by 21 Centers for Disease Control SMM indicators, intensive care unit (ICU) admission, or prolonged postpartum length of stay. Logistic regression models estimated risk for SMM clusters and associations between clusters and maternal and neonatal outcomes.

Results

Among 97,492 deliveries, 2.7% (N = 2,666) experienced SMM by any of the 23 conditions. Four clusters were identified as archetypes of SMM. Deliveries labeled as Hemorrhage (37.7%, N = 1,004) were characterized by blood transfusions and sickle cell anemia; Critical Care (28.1%, N = 748) by ICU admission and amniotic embolism; Vascular (24.5%, N = 654) by cerebrovascular conditions; and Shock (9.8%, N = 260) by ventilatory support and shock. Hypertensive disorders of pregnancy, depression, and Medicaid insurance were associated with Shock cluster. People in all clusters had a high risk of maternal death within 1 year (odds ratio: 12.0, 95% confidence interval: 6.2–23). Infants born to those in the shock cluster had the highest odds of neonatal death, low Apgar scores, and neonatal ICU admission.

Conclusion

We identified four novel SMM clusters that may help understand the collection of conditions defining SMM, underlying pathways and the importance of comorbidities such as depression and social determinants of health markers that amplify the well-established risk factors for SMM such as hypertensive disorders of pregnancy.

Key Points

  • A total of 2.7% of deliveries experienced SMM events.

  • There are four distinct SMM clusters: Hemorrhage, Critical Care, Vascular, and Shock.

  • Not all SMM clusters bear the same risk for adverse perinatal outcomes.

Keywords

maternal morbidity - clusters - hypertensive disorders of pregnancy - depression

Supplementary Material



Publikationsverlauf

Eingereicht: 26. Oktober 2023

Angenommen: 11. September 2024

Artikel online veröffentlicht:
08. Oktober 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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