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

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.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.Among 97,492 deliveries, 2.7% (<i>N</i> = 2,666) experienced SMM by any of the 23 conditions. Four clusters were identified as archetypes of SMM. Deliveries labeled as Hemorrhage (37.7%, <i>N</i> = 1,004) were characterized by blood transfusions and sickle cell anemia; Critical Care (28.1%, <i>N</i> = 748) by ICU admission and amniotic embolism; Vascular (24.5%, <i>N</i> = 654) by cerebrovascular conditions; and Shock (9.8%, <i>N</i> = 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.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. · 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..

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Journal
American Journal of Perinatology
Published
2024-10-08
DOI
https://doi.org/10.1055/a-2418-9955
Primary Topic
Maternal and fetal healthcare
Type
article
Field-Weighted Citation Impact
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article

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

Katherine P. Himes, Samia Lopa, Meryl A. Butters, Stacy Beck et al.
American Journal of Perinatology
Maternal and fetal healthcare
article

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

Katherine P. Himes, Samia Lopa, Meryl A. Butters, Stacy Beck, Janet M. Catov, BaDoi N. Phan, Andrea J. Ibarra
article en

Abstract

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.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.Among 97,492 deliveries, 2.7% (<i>N</i> = 2,666) experienced SMM by any of the 23 conditions. Four clusters were identified as archetypes of SMM. Deliveries labeled as Hemorrhage (37.7%, <i>N</i> = 1,004) were characterized by blood transfusions and sickle cell anemia; Critical Care (28.1%, <i>N</i> = 748) by ICU admission and amniotic embolism; Vascular (24.5%, <i>N</i> = 654) by cerebrovascular conditions; and Shock (9.8%, <i>N</i> = 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.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. · 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..

American Journal of PerinatologyVol. 42(06)
University of Pittsburgh (US), Magee-Womens Research Institute (US), University of Rochester (US)
Andrew W. Mellon Foundation, Bristol-Myers Squibb, Pennsylvania Department of Health, University of Pittsburgh
Good health and well-being
Openalex Percentile: Top 73%
Maternal and fetal healthcare
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