Background: Gaining a better understanding of the probability, timing and prediction of rehospitalisation amongst\npreterm babies could help improve outcomes. There is limited research addressing these topics amongst extremely\nand very preterm babies. In this context, unplanned rehospitalisations constitute an important, potentially\nmodifiable adverse event. We aimed to establish the probability, time-distribution and predictability of unplanned\nrehospitalisation within 30 days of discharge in a population of French preterm babies.\nMethods: This study used data from EPIPAGE 2, a population-based prospective study of French preterm babies.\nOnly those babies discharged home alive and whose parents responded to the one-year survey were eligible for\ninclusion in our study. For Kaplan-Meier analysis, the outcome was unplanned rehospitalisation censored at 30 days.\nFor predictive modelling, the outcome was binary, recording unplanned rehospitalisation within 30 days of\ndischarge. Predictors included routine clinical variables selected based on expert opinion.\nResults: Of 3841 eligible babies, 350 (9.1, 95% CI 8.2â??10.1) experienced an unplanned rehospitalisation within 30 days.\nThe probability of rehospitalisation progressed at a consistent rate over the 30 days. There were significant differences in\nrehospitalisation probability by gestational age. The cross-validated performance of a ten predictor model demonstrated\nlow discrimination and calibration. The area under the receiver operating characteristic curve was 0.62 (95% CI 0.59â??0.65).\nConclusions: Unplanned rehospitalisation within 30 days of discharge was infrequent and the probability of\nrehospitalisation progressed at a consistent rate. Lower gestational age increased the probability of rehospitalisation.\nPredictive models comprised of clinically important variables had limited predictive ability.
Loading....