IntroductionPostpartum haemorrhage (PPH) is the most serious clinical problem of childbirth that contributes significantly to maternal mortality worldwide. This systematic review aims to identify predictors of PPH based on a machine learning (ML) approach.Methods and analysisThis review adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol. The review is scheduled to begin on 10 January 2023 and end on 20 March 2023. The main objective is to identify and summarise the predictive factors associated with PPH and propose an ML-based predictive algorithm. From inception to December 2022, a systematic search of the following electronic databases of peer-reviewed journal articles and online search records will be conducted: Cochrane Central Register, PubMed, EMBASE (via Ovid), Scopus, WOS, IEEE Xplore and the Google Scholar search engine. All studies that meet the following criteria will be considered: (1) they include the general population with a clear definition of the diagnosis of PPH; (2) they include ML models for predicting PPH with a clear description of the ML models; and (3) they demonstrate the performance of the ML models with metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity and specificity. Non-English language papers will be excluded. Data extraction will be performed independently by two investigators. The PROBAST, which includes a total of 20 signallings, will be used as a tool to assess the risk of bias and applicability of each included study.Ethics and disseminationEthical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal.PROSPERO registration numberThe protocol for this review was submitted at PROSPERO with ID number CRD42022354896.
ObjectiveTo assess the obstetric and neonatal outcomes associated with adolescent pregnancy in Iran.MethodsWe retrospectively assessed women who gave birth between January 1st, 2020, and January 1st, 2022. These pregnant women were separated into two groups: (1) women aged 19 and younger; (2) women aged 20–34 years. Main outcome measures include preterm birth, maternal comorbidities, preeclampsia, eclampsia, low birth weight (LBW), intrauterine growth restriction (IUGR), placenta abnormalities, placenta abruption, chorioamnionitis, meconium fluid, fetal distress, methods of delivery, rate of cesarean section (CS), perineal lacerations, postpartum hemorrhage, childbirth trauma, shoulder dystocia, congenital malformation, and unfavorable maternal and neonatal outcome. Logistic regression models were used to determine the influence of teenage pregnancy on adverse pregnancy and childbirth outcomes.ResultsOf 7033 deliveries, 92.4% of women were adults, and 7.6% were adolescents. Adolescents residing in rural districts were more common than adults (42.3% vs. 33.7%). However, access to prenatal facility care was the same as the majority of women had 6-10 prenatal care visits during their pregnancy. There was no difference in the risk of preeclampsia, placenta abruption, placenta previa, fetal distress, preterm labor, shoulder dystocia, perineal lacerations, childbirth trauma, congenital malformation, postpartum hemorrhage, intensive care unit admission, maternal death, and unfavorable neonatal outcome including stillbirth, neonatal intensive care unit admission, neonatal death in adolescent pregnancies compared to adults. Adolescents had a significantly higher risk of LBW (OR: 1.47, 95%CI: 1.01–2.73), IUGR (OR: 1.96, 95%CI: 1.31–2.45), and meconium fluid (OR: 1.74, 95%CI: 1.41–2.32), however, there was no statistically significant difference after adjusting the confounding factors. Compared with adults, adolescents had a significantly lower risk of CS (aRR: 0.67, 95%CI: 0.51–0.77) and a lower risk of gestational diabetes (aRR: 0.78, 95%CI: 0.51–0.95).ConclusionsAlthough we found no serious consequences of adolescent pregnancy, more research is needed to reach a more accurate conclusion about teenage pregnancy.
BackgroundPostpartum 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.MethodThis 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.ResultsThe 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.ConclusionThe findings from ML models used to predict PPH are highly encouraging.
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