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Predicting time to hospital discharge for extremely preterm infants
Susan R Hintz1, Carla M Bann, Namasivayam Ambalavanan
1Department of Pediatrics, Division of Neonatology, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA 94304, USA. srhintz@stanford.edu
Insights
Predicting hospital discharge for extremely preterm infants is challenging. Models incorporating later clinical factors and key risk factors improve prediction accuracy compared to those using only perinatal data.
Area of Science:
- Neonatal Medicine
- Clinical Epidemiology
- Health Services Research
Background:
- Decreasing mortality in extremely preterm infants (<27 weeks' gestational age) raises concerns about healthcare resource utilization.
- Accurate prediction of hospital discharge timing is crucial for resource planning, family support, and quality improvement initiatives.
Purpose of the Study:
- To develop, validate, and compare predictive models for hospital discharge time in extremely preterm infants.
- Models were based on time-dependent covariates and the presence of key risk factors.
Main Methods:
- Retrospective analysis of 2254 infants born <27 weeks' gestational age (July 2002-December 2005).
- Modeled time to discharge (postmenstrual age) using linear and logistic regression with time-dependent covariates.
- Evaluated models incorporating perinatal factors only, perinatal + early-neonatal factors, and perinatal + early-neonatal + later factors.
- Assessed simplified models using 5 key risk factors for early/late discharge prediction.
Main Results:
- Prediction of exact postmenstrual age at discharge was poor.
- Models including later clinical characteristics demonstrated improved prediction of early or late discharge (AUC: 0.76-0.83).
- Simplified key-risk-factor models showed comparable predictive performance (AUC: 0.75-0.77) to comprehensive models.
Conclusions:
- Predicting discharge solely on perinatal factors is inadequate.
- Incorporating later-occurring morbidities significantly improves discharge prediction accuracy.
- Clinically applicable strategies using a few key risk factors offer comparable prediction to complex models.
Background:
As extremely preterm infant mortality rates have decreased, concerns regarding resource use have intensified. Accurate models for predicting time to hospital discharge could aid in resource planning, family counseling, and stimulate quality-improvement initiatives.
Objectives:
To develop, validate, and compare several models for predicting the time to hospital discharge for infants <27 weeks' estimated gestational age, on the basis of time-dependent covariates as well as the presence of 5 key risk factors as predictors.
Patients And Methods:
We conducted a retrospective analysis of infants <27 weeks' estimated gestational age who were born between July 2002 and December 2005 and survived to discharge from a Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge) and categorical (early and late discharge) variables. Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal + early-neonatal factors, and perinatal + early-neonatal + later factors). Models for early and late discharge that used the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared by using the coefficient of determination (R(2)) for the linear models and the area under the curve (AUC) of the receiver operating characteristic curve for the logistic models.
Results:
Data from 2254 infants were included. Prediction of postmenstrual age at discharge was poor. However, models that incorporated later clinical characteristics were more accurate in predicting early or late discharge (AUC: 0.76-0.83 [full models] vs 0.56-0.69 [perinatal factor models]). In simplified key-risk-factors models, the predicted probabilities for early and late discharge compared favorably with the observed rates. Furthermore, the AUC (0.75-0.77) was similar to those of the models that included the full factor set.
Conclusions:
Prediction of early or late discharge is poor if only perinatal factors are considered, but it improves substantially with knowledge of later-occurring morbidities. Predictive models that use a few key risk factors are comparable to the full models and may offer a clinically applicable strategy.
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