OBJECTIVE: To develop and validate a maternal comorbidity index to predict severe maternal morbidity, defined as the occurrence of acute maternal end-organ injury, or mortality.
METHODS: Data were derived from the Medicaid Analytic eXtract for the years 2000-2007. The primary outcome was defined as the occurrence of maternal end-organ injury or death during the delivery hospitalization through 30 days postpartum. The data set was randomly divided into a two-thirds development cohort and a one-third validation cohort. Using the development cohort, a logistic regression model predicting the primary outcome was created using a stepwise selection algorithm that included 24-candidate comorbid conditions and maternal age. Each of the conditions included in the final model was assigned a weight based on its beta coefficient, and these were used to calculate a maternal comorbidity index.
RESULTS: The cohort included 854,823 completed pregnancies, of which 9,901 (1.2%) were complicated by the primary study outcome. The derived score included 20 maternal conditions and maternal age. For each point increase in the score, the odds ratio for the primary outcome was 1.37 (95% confidence interval [CI] 1.35-1.39). The c-statistic for this model was 0.657 (95% CI 0.647-0.666). The derived score performed significantly better than available comorbidity indices in predicting maternal morbidity and mortality.
CONCLUSION: This new maternal comorbidity index provides a simple measure for summarizing the burden of maternal illness for use in the conduct of epidemiologic, health services, and comparative effectiveness research.
LEVEL OF EVIDENCE: II.
JoshuaJGagne3⃣The problem occurs only with truly persistent users -- i.e., those who will continue to be exposed until they die -- and not with long-term exposures as long as a steady-state of starting and stopping is reached. See box plot in 1⃣ for unbiased fixed 180-day exposure estimate.
JoshuaJGagne1⃣Bias is small but apparent when 10% of patients are persistent users (see figure: biased odds ratio = 1.11; true OR = 1.00). Bias is sizable (OR = 1.43) when 30% of patients are persistent users and it grows quickly from there. t.co/r0QXhXLvk0