An expanded obstetric comorbidity scoring system for predicting severe maternal morbidity

Stephanie A. Leonard, Chris J. Kennedy, Suzan L. Carmichael, Deirdre J. Lyell, Elliott K. Main

Obstetrics & Gynecology
September 1, 2020

Overview: We developed and validated an expanded obstetric comorbidity score based on patient discharge data to improve comparisons of severe maternal morbidity rates (i.e. complications during pregnancy or childbirth) across health systems with different comorbidity case mixes. Using Chris Kennedys's causal variable importance method (varimpact), we estimate confounder-adjusted relative risks for each of 27 risk factors. Those parameter estimates were then normalized into risk factor points, ranging from 0 to 59, that were summed to create an additive risk score for each patient. Our new risk score showed substantially improved performance in terms of AUC and precision-recall AUC compared to prior scores. As an example of risk factors, maternal age of 35 or older contributes only 2 points to a patient's risk score, whereas chronic hypertension contributes 10 points and HIV/AIDS contributes 30 points.

Abstract:

OBJECTIVE: To develop and validate an expanded obstetric comorbidity score for predicting severe maternal morbidity that can be applied consistently across contemporary U.S. patient discharge data sets.

METHODS: Discharge data from birth hospitalizations in California during 2016–2017 were used to develop the score. The outcomes were severe maternal morbidity, defined using the Centers for Disease Control and Prevention index, and nontransfusion severe maternal morbidity (excluding cases where transfusion was the only indicator of severe maternal morbidity). We selected 27 potential patient-level risk factors for severe maternal morbidity, identified using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used a targeted causal inference approach integrated with machine learning to rank the risk factors based on adjusted risk ratios (aRRs). We used these results to assign scores to each comorbidity, which sum to a single numeric score. We validated the score in California and national data sets and compared the performance to that of a previously developed obstetric comorbidity index.

RESULTS: Among 919,546 births, the rates of severe maternal morbidity and nontransfusion severe maternal morbidity were 168 and 74 per 10,000 births, respectively. The highest risk comorbidity was placenta accreta spectrum (aRR of 30.5 for severe maternal morbidity and 54.7 for nontransfusion severe maternal morbidity) and the lowest was gestational diabetes mellitus (aRR of 1.06 for severe maternal morbidity and 1.12 for nontransfusion severe maternal morbidity). Normalized scores based on the aRR were developed for each comorbidity, which ranged from 1 to 59 points for severe maternal morbidity and from 1 to 36 points for nontransfusion severe maternal morbidity. The overall performance of the expanded comorbidity scores was good (C-statistics were 0.78 for severe maternal morbidity and 0.84 for nontransfusion severe maternal morbidity in California data and 0.82 and 0.87, respectively, in national data) and improved on prior comorbidity indices developed for obstetric populations. Calibration plots showed good concordance between predicted and actual risks of the outcomes.

CONCLUSION: We developed and validated an expanded obstetric comorbidity score to improve comparisons of severe maternal morbidity rates across patient populations with different comorbidity case mixes.



Featured Fellows

Chris Kennedy

Biostatistics, UC Berkeley
Alumni - BIDS-BBDT Data Science Fellow