Risk prediction models for maternal mortality: A systematic review and meta-analysis

Pregnancy-related critical illness leads to death for 3-14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.

Search strategy: MEDLINE, EMBASE and Scopus, from inception to May 2017. Selection criteria: Trials or observational studies evaluating risk prediction models for maternal mortality. Data collection and analysis: Two reviewers independently assessed studies for eligibility and methodological quality, and extracted data on prediction performance.

Thirty-eight studies that evaluated 12 different mortality prediction models were included. Mortality varied across the studies, with an average rate 10.4%, ranging from 0 to 41.7%. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index had the best performance, were developed and validated from studies of obstetric population with a low risk of bias. The CIPHER applies to critically ill obstetric patients (discrimination: area under the receiver operating characteristic curve (AUC) 0.823 (0.811-0.835), calibration: graphic plot [intercept-0.09, slope 0.92]). The Maternal Severity Index applies to hospitalized obstetric patients (discrimination: AUC 0.826 [0.802-0.851], calibration: standardized mortality ratio 1.02 [0.86-1.20]).

Despite the high heterogeneity of the study populations and the limited number of studies validating the finally eligible prediction models, the CIPHER and the Maternal Severity Index are recommended for use among critically ill and hospitalized pregnant and postpartum women for risk adjustment in clinical research and quality improvement studies. Neither index has sufficient discrimination to be applicable for clinical decision making at the individual patient level.