O40 Predictive models of individual risk of elective caesarean section complications: a systematic review

Abstract

Abstract Introduction With increasing caesarean section (c-section) rates, personalised communication of risk has become paramount. A reliable tool to predict complications would support evidence-based discussion around planned mode of birth. Method MEDLINE, Embase, Web of Science, CINAHL, and the Cochrane Library were searched on 27th January with the terms relating to c-section, prognostic models, and complications such as infection. Any study developing and/or validating a prognostic model for maternal complications of c-section in the English language after January 1995 was included. Data extracted encompassed: source of data, participant criteria, outcome to be predicted, candidate predictors, actual predictors, sample size, model development, and model performance. PROBAST (Prediction model Risk Of Bias Assessment Tool) was utilised for risk of bias analysis and applicability concern in the prognostic model studies. Result 7,752 studies were identified, of which 16 were reviewed producing 3 studies where 3 prognostic models were identified which predicted risk of: blood transfusion, spinal hypotension, and postpartum haemorrhage. From the 3 studies, a total of 29 unique candidate predictors were identified and 15 predictors in the final model. Study authors deemed their studies to be exploratory, exploratory, and confirmatory respectively. None were externally validated and all had a high risk of bias due to analysis technique. Conclusion Few models have been developed to predict complications of elective c-section. Existing models predicting blood transfusion, spinal hypotension, and postpartum haemorrhage cannot be recommended for clinical practice. Future research should focus on identifying predictors known before surgery and validating resulting models. Take-home Message Systematic review of prediction models for planned C-section complications found none suitable for practice.