External validation of prediction models for vaginal delivery after the trial of labour among women with previous one caesarean section – A cohort study

•All three models (Grobman 2007, Grobman 2021 and Zhang model 2020) were observed to have a fair discriminative capacity.•Poor agreement between observed and predicted probability of successful TOLAC limits its usefulness in the study setting.•A population-specific model, including intrapartum factors, may aid in predicting the outcome among women undergoing TOLAC. To externally validate three predictive models (the Grobman model (2007), the Zhang model (2020), and the Grobman model (2021)) for identifying women with increased chances of a successful trial of labour after caesarean section (TOLAC). This retrospective observational cohort study was conducted in a tertiary teaching hospital from 2018 to 2021. Individual probabilities were calculated for women with previous one caesarean section who underwent TOLAC at term, using the predicted probabilities from the logistic regression models. The primary outcome of this study was vaginal delivery following attempted TOLAC. The predictive ability of the models was assessed using the area under the receiver operative characteristics curves (AUC) and a calibration graph. Of 1515 eligible women who underwent TOLAC, we found an overall rate of successful TOLAC of 60.3 %. No significant difference was noticed in adverse scar outcome and neonatal morbidity while comparing successful and failed TOLAC. The discriminative ability of Grobman-2007 and Grobman-2021 and the Zhang model were fair to poor with the AUC of 0.54(95 % CI 0.51–0.57), 0.62(95 % CI 0.59–0.65) and 0.66(95 % CI 0.63–0.69) respectively. The agreement between the observed rates of TOLAC success and the predicted probabilities for all three models was poor. The performance of all three models predicting success after TOLAC was poor in the study population. A population-specific model may be needed, with the addition of factors influencing the labour, such as the methods of induction, which may aid in predicting the outcome.