Developing and validating multivariable prediction models for predicting the risk of 7-day neonatal readmission following vaginal and cesarean birth using administrative databases

Abstract

Background

Approximately 3.5% of deliveries in Canada result in potentially preventable neonatal readmission, often times due to preventable morbidities. With complexities in hospital discharge planning, health care providers may benefit in identifying infants at risk of readmission for additional monitoring.

Objectives

To develop and validate models for predicting 7-day neonatal readmission following vaginal or cesarean births.

Methods

All liveborn term singleton infants without congenital anomalies in the province of Alberta who were not admitted to the NICU were identified using perinatal and hospitalization databases. A temporal split-sample was used for model development (2012–2014, vaginal n = 63,378; cesarean n = 21,225) and external validation (2014–2015, vaginal n = 21,583, cesarean n = 7,477). Multivariable logistic regression models using backward stepwise selection were used to identify predictors of 7-day readmission. We evaluated predictors of maternal age, Apgar score, length-of-stay, birthweight, gestational age, parity, residence, and sex. Hosmer-Lemeshow test and c-statistics were used to estimate calibration and discrimination.

Results

The rate of readmission was 3.3% (95% CI 3.1%, 3.4%) and 2.1% (95% CI 1.9%, 2.3%) following vaginal and cesarean births in the development dataset. Prediction model following vaginal birth, excluding predictors of length-of-stay and birthweight, had sub-optimal performance in development (c-statistics 0.69) and validation data (c-statistics 0.68). Prediction model following cesarean birth, excluding predictors of maternal age, birthweight, and residence, had sub-optimal performance in development (c-statistics 0.62) and validation data (c-statistics 0.64). Readmission was observed in 7.9% (95% CI 7.1%, 8.8%) and 4.9% (95% CI 3.9%, 6.1%) of infants of vaginal and cesarean births, respectively, in the top quintile for the risk of 7-day readmission.

Conclusion

Using routinely collected administrative data, we developed and validated prediction models for neonatal readmission following vaginal and cesarean births. Presently the model is sub-optimal for use in risk assessment and planning at discharge, however, additional information may improve the predictive performance.

Keywords:

• Infants
• neonatal readmission
• hospital readmission
• prediction model
• administrative databases

Acknowledgements

Sangmin Lee is supported by the Alberta Innovates Graduate Studentship and Achievers in Medical Science Doctoral Scholarship.

Dr. Dylan O’Sullivan was supported by an Ontario Graduate Scholarship and a Chen-Aronson Fellowship in Causes of Cancer.

Dr. Darren Brenner is supported by the Armstrong Investigatorship in Molecular Epidemiology from the Cumming School of Medicine.

Dr. Amy Metcalfe is supported by a New Investigator Award from the Canadian Institutes of Health Research

Disclosure statement

Sangmin Lee declares no conflict of interest.

Dr. Dylan O’Sullivan declares no conflict of interest.

Dr. Darren Brenner declare no conflict of interest.

Dr. Amy Metcalfe declares no conflict of interest.