Development and validation of a predictive model for composite adverse outcomes in primary postpartum haemorrhage in a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe.

BackgroundPrimary postpartum haemorrhage remains an important cause of maternal mortality and morbidity globally. It is difficult to predict. There are very few predictive models on composite adverse outcomes on postpartum haemorrhage that are available in the literature. The aim of this study was to develop and validate multivariable predictive model to assist clinicians in decision-making after a diagnosis of postpartum haemorrhage is made, and to prevent the development of composite adverse outcomes.MethodsThis was a retrospective cross-sectional study that covered the period from 1 July 2016 to 30 November 2019, at Mpilo Central Hospital. The study included participants that had a diagnosis of postpartum haemorrhage within 24 hours of delivery at Mpilo Central Hospital. The independent variables included socio-demographic factors, laboratory tests, clinical outcomes, causes and the management of PPH. The outcome of interest for this research was composite adverse outcome in PPH. Predictor variables that had a p<0.2 from the bivariate correlations analyses were considered for the multivariable stepwise backward logistic regression. Performance of the model was assessed with a calibration slope. Discrimination ability was evaluated using the area under curve of the receiver operating characteristic (AU ROC). Internal validation of the model was assessed using bootstrap method. ResultsThe final predicted probability model for composite adverse maternal outcomes was; logit (logarithm of the odds) (pi) = 0.141 + (2.35 x 10-1 x blood loss) + (-1.18 x 10-1 x platelets) + (0.57 x 10-1 x parity) + (2.27 x 10-1 x ruptured uterus).The model was well calibrated. The discrimination ability of the model was excellent. The AU ROC curve was 0.890 (95% CI 0.830-0.949, p<0.0001). Internal validation was by bootstrapping, and the model was still a good fit for the data with a p<0.0001.ConclusionsA predictive model for composite adverse outcomes in PPH was developed. It had a good discriminatory ability, with an AU ROC of 0.890 (95% CI 0.830-0.949). This predictive model for composite adverse outcomes could help clinicians to be alerted to which women with PPH are most likely to develop composite adverse outcomes thereby preventing maternal deaths.