Highlights
1. A hierarchical algorithm was developed to identify primary underlying conditions for severe maternal morbidity (SMM) using ICD-10 codes, with 94.5% concordance with medical record reviews.
2. Hemorrhage, hypertensive disorders, and infection were the most common underlying conditions for SMM, while cardiovascular conditions were rare but a leading cause of maternal mortality.
3. The study analyzed over 100,000 cases from California and National Inpatient Sample datasets, providing a robust population-level assessment.
Background
Severe maternal morbidity (SMM) encompasses life-threatening complications during pregnancy, delivery, or postpartum, often serving as a near-miss for maternal mortality. The CDC’s SMM index lists major complications but does not identify underlying causes, limiting targeted interventions. This study aimed to develop and validate a hierarchical algorithm to assign primary underlying conditions to SMM cases using administrative data, offering insights into prevention strategies.
Study Design
The research team created a hierarchical algorithm using ICD-10 codes, refined through medical record reviews and iterative analyses of large datasets from 2016 to 2024. Validation compared algorithm assignments with detailed medical record abstractions in 604 SMM cases. The algorithm was then applied to California discharge data (2016–2020; n=43,897) and National Inpatient Sample (NIS) data (n=63,880). A nonhierarchical approach assessing comorbidities was also evaluated. Findings were compared with CDC Pregnancy Mortality Surveillance System data (2017–2019).
Key Findings
The algorithm demonstrated high concordance (94.5%) with medical record reviews. In California data, hemorrhage (placental and other) was the leading primary underlying condition for SMM (50.5%) and nontransfusion SMM (38.3%). Severe hypertensive disorders and infection accounted for 31.2% of SMM and 44.9% of nontransfusion SMM. Cardiovascular conditions were rare (2.4% of SMM, 4.3% of nontransfusion SMM). NIS data corroborated these trends. Notably, maternal mortality causes diverged: hemorrhage (12.1%), hypertensive disorders (6.3%), and infection (14.3%) were less frequent, while cardiovascular conditions (26.6%) dominated.
Expert Commentary
This study bridges a critical gap by attributing SMM to specific underlying conditions, enabling targeted quality improvement. The disparity between SMM and mortality causes underscores the need for distinct prevention strategies—obstetric hemorrhage protocols for SMM versus cardiovascular risk mitigation for mortality. Limitations include reliance on administrative coding accuracy and potential underrepresentation of rare conditions. Future research should integrate clinical data for granular insights.
Conclusion
The hierarchical algorithm provides a scalable tool to identify primary underlying conditions for SMM, revealing hemorrhage, hypertensive disorders, and infection as dominant drivers. These findings highlight the need for tailored interventions distinct from mortality prevention efforts. The study lays groundwork for future research on SMM etiology and prevention.
Funding and ClinicalTrials.gov
This study was supported by institutional funding. No clinical trial registration was required as it utilized retrospective administrative data.
References
1. Main EK, et al. Development and Application of an Algorithm to Identify the Primary Underlying Condition for Cases of Severe Maternal Morbidity. Obstet Gynecol. 2026; PMID: 41990332.
2. Centers for Disease Control and Prevention. Severe Maternal Morbidity in the United States. 2023.
3. American College of Obstetricians and Gynecologists. Practice Bulletin No. 183: Postpartum Hemorrhage. 2017.
