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Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study
Robert Anthony Reed1, Andrei Scott Morgan2,3,4, Jennifer Zeitlin1
1Université de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Insights
Unplanned rehospitalisation within 30 days for preterm babies was infrequent (9.1%). Lower gestational age increased readmission risk, but clinical models had limited predictive ability for these vulnerable infants.
Area of Science:
- Neonatal Medicine
- Pediatric Healthcare Outcomes
- Public Health Research
Background:
- Understanding rehospitalisation in preterm infants is crucial for improving outcomes.
- Limited research exists on the probability, timing, and prediction of rehospitalisation in extremely and very preterm infants.
- Unplanned rehospitalisations are significant, potentially modifiable adverse events in this population.
Purpose of the Study:
- To determine the probability and time-distribution of unplanned rehospitalisation within 30 days of discharge in French preterm infants.
- To assess the predictability of unplanned rehospitalisation using clinical variables.
- To provide insights for improving care and reducing readmissions for preterm babies.
Main Methods:
- Utilised data from the EPIPAGE 2 prospective, population-based study of French preterm infants.
- Included infants discharged alive whose parents completed the one-year survey.
- Employed Kaplan-Meier analysis for time-to-rehospitalisation and logistic regression for predictive modelling.
Main Results:
- 9.1% of eligible preterm infants experienced unplanned rehospitalisation within 30 days.
- The probability of rehospitalisation remained consistent throughout the 30-day period.
- Lower gestational age was associated with a higher probability of rehospitalisation, but predictive models showed limited accuracy (AUC 0.62).
Conclusions:
- Unplanned rehospitalisation within 30 days is infrequent but predictable by gestational age.
- Current predictive models using routine clinical variables have limited ability to identify high-risk preterm infants for readmission.
- Further research is needed to develop more accurate predictive tools for preterm infant rehospitalisation.
Background:
Gaining a better understanding of the probability, timing and prediction of rehospitalisation amongst preterm babies could help improve outcomes. There is limited research addressing these topics amongst extremely and very preterm babies. In this context, unplanned rehospitalisations constitute an important, potentially modifiable adverse event. We aimed to establish the probability, time-distribution and predictability of unplanned rehospitalisation within 30 days of discharge in a population of French preterm babies.
Methods:
This study used data from EPIPAGE 2, a population-based prospective study of French preterm babies. Only those babies discharged home alive and whose parents responded to the one-year survey were eligible for inclusion in our study. For Kaplan-Meier analysis, the outcome was unplanned rehospitalisation censored at 30 days. For predictive modelling, the outcome was binary, recording unplanned rehospitalisation within 30 days of discharge. Predictors included routine clinical variables selected based on expert opinion.
Results:
Of 3841 eligible babies, 350 (9.1, 95% CI 8.2-10.1) experienced an unplanned rehospitalisation within 30 days. The probability of rehospitalisation progressed at a consistent rate over the 30 days. There were significant differences in rehospitalisation probability by gestational age. The cross-validated performance of a ten predictor model demonstrated low discrimination and calibration. The area under the receiver operating characteristic curve was 0.62 (95% CI 0.59-0.65).
Conclusions:
Unplanned rehospitalisation within 30 days of discharge was infrequent and the probability of rehospitalisation progressed at a consistent rate. Lower gestational age increased the probability of rehospitalisation. Predictive models comprised of clinically important variables had limited predictive ability.
