Predictive models of individual risk of elective caesarean section complications: a systematic review. (July 2021)

• Record Type: Journal Article
• Authors: Ahmeidat, Annes; Kotts, Wiktoria Julia; Wong, Jeremy; McLernon, David J.; Black, Mairead
• Is Part Of: European journal of obstetrics, gynecology, and reproductive biology. Volume 262(2021)
• Page Start: 248
• Page End: 255
• Publication Date: 2021-07
• Subjects: Caesarean section -- Complication -- Prognostic model -- Predictive model -- Risk -- Women's health; Obstetrics -- Periodicals; Gynecology -- Periodicals; Reproductive health -- Periodicals; Gynecology -- Periodicals; Obstetrics -- Periodicals; Reproduction -- Periodicals; Obstétrique -- Périodiques; Gynécologie -- Périodiques; Reproduction -- Périodiques; Verloskunde; Gynaecologie; Voortplanting (biologie); Gynecology; Obstetrics; Reproduction; Electronic journals; Periodicals; Electronic journals; 618.05
• DOI: 10.1016/j.ejogrb.2021.05.011
• Languages: English
• ISSNs: 0301-2115

Abstract

Highlights: A systematic review of five databases for maternal complications of caesarean section. A reliable tool to predict complications would be of clinical benefit in clinics. Models found: blood transfusion, spinal hypotension and postpartum haemorrhage. More focus on identification of predictors known before surgery is needed to be meaningful. Abstract: Introduction: With increasing caesarean section (c-section) rates, personalized communication of risk has become paramount. A reliable tool to predict complications would support evidence-based discussions around planned mode of birth. This systematic review aimed to identify, synthesize and quality appraise prognostic models of maternal complications of elective c-section. Methods: MEDLINE, Embase, Web of Science, CINAHL and the Cochrane Library were searched on 27 January using terms relating to 'c-section', 'prognostic models' and complications such as 'infection'. Any study developing and/or validating a prognostic model for a maternal complication of elective c-section in the English language after January 1995 was selected for analysis. Data were extracted using a predetermined checklist: source of data; participants; outcome to be predicted; candidate predictors; sample size; missing data; model development; model performance; model evaluation; results; and interpretation. Quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) tool. Results: In total, 7752 studies were identified; of these, 16 full papers were reviewed and three eligible studies were identified, containing three prognostic models derived from hospitals in Japan, South Africa and the UK. The models predicted risk of blood transfusion, spinal hypotension and postpartum haemorrhage. The study authors deemed their studies to be exploratory, exploratory and confirmatory, respectively. From the three studies, a total of 29 unique candidate predictors were identified, with 15 predictors in the final models. Maternal age (n = 3), previous c-section (n = 2), placenta praevia (n = 2) and pre-operative haemoglobin (n = 2) were found to be common predictors amongst the included studies. None of the studies were externally validated and all had a high risk of bias due to the analysis technique used. 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 the resulting models.