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Development and Application of an Algorithm to Identify the Primary Underlying Condition for Cases of Severe Maternal
Elliott K Main1, Emily K McCormick, Mark W Tomlinson
1Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, and the Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University, Stanford, the California Maternal Quality Care Collaborative, Palo Alto, Critical Juncture LLC, San Francisco, the Department of Quality Management, PIH Health, Los Angeles, the Department of Obstetrics and Gynecology, University of California, Irvine, School of Medicine, Irvine, and the Department of Obstetrics and Gynecology, Hoag Health System, Newport Beach, California; and Women and Children's Services, Providence, and the Oregon Perinatal Collaborative, Portland, Oregon.
Insights
A new algorithm identifies underlying causes of severe maternal morbidity (SMM). Hemorrhage, hypertensive disorders, and infection are leading causes of SMM, unlike cardiovascular disease which causes most maternal deaths.
Area of Science:
- Maternal Health
- Public Health Surveillance
- Health Informatics
Background:
- The Centers for Disease Control and Prevention's (CDC) severe maternal morbidity (SMM) index identifies complications but not root causes.
- Understanding underlying conditions is crucial for effective SMM prevention strategies.
Purpose of the Study:
- Develop a hierarchical algorithm to identify primary underlying conditions for SMM cases using administrative data.
- Calculate the frequencies of these underlying conditions in large datasets.
Main Methods:
- A hierarchical algorithm was created using ICD-10 codes, validated against medical record reviews.
- The algorithm was applied to California and National Inpatient Sample (NIS) hospital discharge data (2016-2020).
- Underlying conditions for SMM were compared with causes of pregnancy-related mortality using CDC data (2017-2019).
Main Results:
- The algorithm demonstrated 94.5% concordance with medical record reviews for identifying primary underlying conditions.
- Hemorrhage, severe hypertensive disorders, and infection were the most frequent underlying conditions for SMM.
- Cardiovascular conditions were a less common underlying cause of SMM but a leading cause of maternal mortality.
Conclusions:
- A validated hierarchical algorithm can identify primary underlying conditions for SMM using administrative codes.
- Hemorrhage, hypertensive disorders, and infection are key drivers of SMM.
- The leading causes of SMM differ from the leading causes of maternal death, highlighting distinct public health priorities.
Objective:
The Centers for Disease Control and Prevention's (CDC) severe maternal morbidity (SMM) index is a list of major complications, but it does not identify underlying causes. Our objectives were to create a hierarchical algorithm to identify a primary underlying condition for each SMM case and calculate the frequencies of underlying conditions that lead to SMM in large administrative datasets.
Methods:
A hierarchical algorithm using International Classification of Diseases, Tenth Revision codes was developed using a combination of medical record reviews and iterative analyses of large datasets over an 8-year period, 2016-2024. To assess validity, the algorithm's assignment of primary underlying conditions for SMM cases was compared with detailed medical record abstraction. The developed algorithm was then applied to 2016-2020 California and National Inpatient Sample (NIS) hospital discharge datasets. A nonhierarchical approach, which allowed the assignment of multiple diagnosis codes to identify comorbidities, was also evaluated. Frequencies of underlying conditions among SMM cases were compared with causes of pregnancy-related mortality by using the CDC's Pregnancy Mortality Surveillance System data (2017-2019). Total SMM and nontransfusion SMM were examined for each analysis.
Results:
In the 604 SMM cases used to assess validity, the primary underlying condition that resulted in SMM based on the algorithm had 94.5% concordance with a detailed medical record review. When applied to California discharge data (2016-2020) (n=43,897) hemorrhage (combined placental and other) was the most frequent primary underlying condition that resulted in SMM (50.5%) and nontransfusion SMM (38.3%). Severe hypertensive disorders and infection were also common-together accounting for 31.2% of SMM and 44.9% of nontransfusion SMM. Other medical conditions accounted for 12.9% of SMM and 19.8% of nontransfusion SMM. Rates of cardiovascular conditions as the primary underlying conditions were 2.4% of SMM and 4.3% of nontransfusion SMM. Results were similar for NIS data (n=63,880). Causes of maternal mortality in the United States (2017-2019) substantially differed from underlying conditions that resulted in SMM: rates for hemorrhage (12.1%), hypertensive disorders (6.3%), and infection (14.3%) were lower, and rates for cardiovascular conditions (26.6%) were higher.
Conclusion:
The hierarchical algorithm provides a method to assign a primary underlying condition to population SMM cases by using administrative codes. Hemorrhage, hypertensive disorders, and infection dominate underlying conditions for SMM, whereas cardiovascular disease, the most common cause of maternal death, is an uncommon SMM underlying condition.
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