Risk Prediction Models for Maternal Mortality: A Systematic Review and Meta-analysis
CCCF ePoster library. Aoyama K. Oct 3, 2017; 198163; 59
Dr. Kazuyoshi Aoyama
Dr. Kazuyoshi Aoyama
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Risk Prediction Models for Maternal Mortality: A Systematic Review and Meta-analysis

Aoyama, Kazuyoshi; D’Souza, Rohan; Pinto, Ruxandra; Ray Joel; Hill, Andrea Hill; Scales, Damon; Lapinsky, Stephen; Fowler, Robert



Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Canada; Department of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mount Sinai Hospital, Toronto, Canada; Department of Critical Care Medicine, Sunnybrook Health Science Center, Toronto, Canada; Department of Obstetrics and Gynecology, St. Michael’s Hospital,Toronto, Canada; Department of Critical Care Medicine, Sunnybrook Health Science Center, Toronto, Canada; Department of Critical Care Medicine, Sunnybrook Health Science Center, Toronto, Canada; Department of Critical Care Medicine, Mount Sinai Hospital and University Health Network, Toronto, Canada; Department of Critical Care Medicine, Sunnybrook Health Science Center, Toronto, Canada


Background:
Pregnancy- and peri-partum-related critical illness leads death for 3-14% of affected women. Identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research.
Objectives:
To systematically review and meta-analyze 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. 
Main results:
Thirty-eight studies that evaluated 12 different mortality prediction models were included. Four models were developed primarily from obstetric populations, and 8 models from general patient populations. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model had the best performance and a low risk of bias for critically ill patients (discrimination: Area Under Receiver Operating Curve (AUC) 0.819 (0.781 - 0.858), calibration: graphic plot [intercept 0.01, slope 1.07]). The Maternal Severity Index had the best performance and a low risk of bias for other hospitalized patients (discrimination: AUC 0.826 [0.802 - 0.851], calibration: Standardized Mortality Ratio 1.02 [0.86 - 1.20] in external validation studies). Overall, prediction models developed from non-obstetric populations, such as the SAPS2 and APACHE2, showed good discrimination (AUC 0.92 (0.90 - 0.95) and 0.88 (0.84 - 0.92), respectively), but were more likely to over- or under-estimate maternal mortality.
Conclusions:
Mortality risk prediction models developed from obstetric patient populations, such as the Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index, have very good discrimination and calibration with a low risk of bias. 

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