Predicting Risk of Postpartum Hemorrhage Using Machine Learning Approach: A Systematic Review Publisher



Ranjbar A¹ ; Rezaei Ghamsari S² ; Boujarzadeh B³ ; Mehrnoush V³ ; Darsareh F³ Show Affiliations
Source: Gynecology and Obstetrics Clinical Medicine Published:2023

Abstract

Background: Postpartum hemorrhage (PPH) could be avoided by identifying high-risk women. The objective of this systematic review is to determine PPH predictors using machine learning (ML) approaches. Method: This strategy included searching for studies from inception through November 2022 through the database included: Cochrane Central Register, PubMed, MEDLINE, EMBASE, ProQuest, Scopus, WOS, IEEE Xplore, and the Google Scholar database. The search methodology employed the PICO framework (population, intervention, control, and outcomes). In this study, “P” represents PPH populations, “I” represents the ML approach as intervention, “C” represents the traditional statistical analysis approach as control, and “O” represents prediction and diagnosis outcomes. The quality assessment of each included study was performed using the PROBAST methodology. Results: The initial search strategy resulted in 2048 citations, which were subsequently refined by removing duplicates and irrelevant studies. Ultimately, four studies were deemed eligible for inclusion in the review. Among these studies, three were classified as having a low risk of bias, while one was considered to have a low to moderate risk of bias. A total of 549 unique variables were identified as candidate predictors from the included studies. Nine distinct models were chosen as ML algorithms from the four studies. Each of the four studies employed different metrics, such as the area under the curve, false positive rate, false negative rate, and sensitivity, to report the accuracy of their models. The ML models exhibited varying accuracies, with the area under the curve (AUC) ranging from 0.706 to 0.979. Several weighted predictors were identified as significant factors in PPH risk prediction. These included pre-pregnancy maternal weight, maternal weight at the time of admission, fetal macrosomia, gestational age, level of hematocrit at the time of admission, shock index, frequency of contractions during labor, white blood cell count, pregnancy-induced hypertension, the weight of the newborn, duration of the second stage of labor, amniotic fluid index, body mass index, and cesarean delivery before labor. These factors were determined to have a notable influence on the prediction of PPH risk. Conclusion: The findings from ML models used to predict PPH are highly encouraging. © 2023 The Authors