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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
› Author Affiliations 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



Publication History

Received: 26 October 2023

Accepted: 11 September 2024

Article published online:
08 October 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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  • References

  • 1 Callaghan WM, Mackay AP, Berg CJ. Identification of severe maternal morbidity during delivery hospitalizations, United States, 1991-2003. Am J Obstet Gynecol 2008; 199 (02) 133.e1-133.e8
    Search in Google Scholar
  • 2 Callaghan WM, Creanga AA, Kuklina EV. Severe maternal morbidity among delivery and postpartum hospitalizations in the United States. Obstet Gynecol 2012; 120 (05) 1029-1036
    Search in Google Scholar
  • 3 Kilpatrick SK, Ecker JL. American College of Obstetricians and Gynecologists and the Society for Maternal–Fetal Medicine. Severe maternal morbidity: screening and review. Am J Obstet Gynecol 2016; 215 (03) B17-B22
    Search in Google Scholar
  • 4 Ray JG, Park AL, Dzakpasu S. et al. Prevalence of severe maternal morbidity and factors associated with maternal mortality in Ontario, Canada. JAMA Netw Open 2018; 1 (07) e184571-e184571
    Search in Google Scholar
  • 5 Soussi S, Sharma D, Jüni P. et al; FROG-ICU, CCCTBG trans-trial group study for InFACT—the International Forum for Acute Care Trialists. Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort. Crit Care 2022; 26 (01) 114
    Search in Google Scholar
  • 6 Depner M, Fuchs O, Genuneit J. et al; PASTURE Study Group. Clinical and epidemiologic phenotypes of childhood asthma. Am J Respir Crit Care Med 2014; 189 (02) 129-138
    Search in Google Scholar
  • 7 Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA. NHLBI ARDS Network. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med 2014; 2 (08) 611-620
    Search in Google Scholar
  • 8 National Institute of Health Office of Research on Women's Health. Maternal morbidity and mortality. What do we know? How are we addressing it?. 2020 . Accessed July 6, 2022 at: www.nih.gov/women/maternalhealth
    Search in Google Scholar
  • 9 Main EK, Abreo A, McNulty J. et al. Measuring severe maternal morbidity: validation of potential measures. Am J Obstet Gynecol 2016; 214 (05) 643.e1-643.e10
    Search in Google Scholar
  • 10 Himes KP, Bodnar LM. Validation of criteria to identify severe maternal morbidity. Paediatr Perinat Epidemiol 2020; 34 (04) 408-415
    Search in Google Scholar
  • 11 Alexander GR, Kogan M, Bader D, Carlo W, Allen M, Mor J. US birth weight/gestational age-specific neonatal mortality: 1995-1997 rates for Whites, Hispanics, and Blacks. Pediatrics 2003; 111 (01) e61-e66
    Search in Google Scholar
  • 12 Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Modeling 2007; 14 (04) 535-569
    Search in Google Scholar
  • 13 Weller BE, Bowen NK, Faubert SJ. Latent class analysis: a guide to best practice. J Black Psychol 2020; 46 (04) 287-311
    Search in Google Scholar
  • 14 Zhang J, Meikle S, Trumble A. Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertens Pregnancy 2003; 22 (02) 203-212
    Search in Google Scholar
  • 15 Grigoriadis S, VonderPorten EH, Mamisashvili L. et al. The impact of maternal depression during pregnancy on perinatal outcomes: a systematic review and meta-analysis. J Clin Psychiatry 2013; 74 (04) e321-e341
    Search in Google Scholar
  • 16 Metzger BE, Lowe LP, Dyer AR. et al; HAPO Study Cooperative Research Group. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 2008; 358 (19) 1991-2002
    Search in Google Scholar
  • 17 Wilson A, Norden N. The R Project for Statistical Computing. 2015 . Accessed December 8, 2021 at: https://www.r-project.org/
    Search in Google Scholar
  • 18 Craici I, Wagner S, Garovic VD. Preeclampsia and future cardiovascular risk: formal risk factor or failed stress test?. Ther Adv Cardiovasc Dis 2008; 2 (04) 249-259
    Search in Google Scholar
  • 19 Ali SM, Khalil RA. Genetic, immune and vasoactive factors in the vascular dysfunction associated with hypertension in pregnancy. Expert Opin Ther Targets 2015; 19 (11) 1495-1515
    Search in Google Scholar
  • 20 Shah DA, Khalil RA. Bioactive factors in uteroplacental and systemic circulation link placental ischemia to generalized vascular dysfunction in hypertensive pregnancy and preeclampsia. Biochem Pharmacol 2015; 95 (04) 211-226
    Search in Google Scholar
  • 21 McKee K, Admon LK, Winkelman TNA. et al. Perinatal mood and anxiety disorders, serious mental illness, and delivery-related health outcomes, United States, 2006-2015. BMC Womens Health 2020; 20 (01) 150
    Search in Google Scholar
  • 22 Farr SL, Hayes DK, Bitsko RH, Bansil P, Dietz PM. Depression, diabetes, and chronic disease risk factors among US women of reproductive age. Prev Chronic Dis 2011; 8 (06) A119
    Search in Google Scholar
  • 23 Rossi RM, Hall E, Dufendach K, DeFranco EA. Predictive model of factors associated with maternal intensive care unit admission. Obstet Gynecol 2019; 134 (02) 216-224
    Search in Google Scholar
  • 24 James AH. The prevention and management of thrombosis in obstetrics and gynecology. Clin Obstet Gynecol 2018; 61 (02) 203-205
    Search in Google Scholar
  • 25 Creanga AA, Bateman BT, Kuklina EV, Callaghan WM. Racial and ethnic disparities in severe maternal morbidity: a multistate analysis, 2008-2010. Am J Obstet Gynecol 2014; 210 (05) 435.e1-435.e8
    Search in Google Scholar
  • 26 Gyamfi-Bannerman C, Srinivas SK, Wright JD. et al. Postpartum hemorrhage outcomes and race. Am J Obstet Gynecol 2018; 219 (02) 185.e1-185.e10
    Search in Google Scholar
  • 27 Crear-Perry J, Correa-de-Araujo R, Lewis Johnson T, McLemore MR, Neilson E, Wallace M. Social and structural determinants of health inequities in maternal health. J Womens Health (Larchmt) 2021; 30 (02) 230-235
    Search in Google Scholar
  • 28 Gray KE, Wallace ER, Nelson KR, Reed SD, Schiff MA. Population-based study of risk factors for severe maternal morbidity. Paediatr Perinat Epidemiol 2012; 26 (06) 506-514
    Search in Google Scholar
  • 29 Hoyert DL. Maternal mortality rates in the United States, 2020. Natl Cent Health Stat 2021; (03) 1
    Search in Google Scholar