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Predicting death and survival without major morbidity for extremely preterm infants using information on hospital
Xincheng Cao1,2, Shujuan Li1,2, Xinyue Gu2
1Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Insights
Accurate prediction models for extremely preterm infants (EPIs) were developed using NICU admission data. These models predict mortality and survival without major morbidity, aiding clinical decisions.
Area of Science:
- Neonatalogy
- Medical Informatics
- Predictive Analytics
Background:
- Accurate prediction of outcomes for extremely preterm infants (EPIs) is crucial for clinical and parental decision-making.
- Early prediction aids in managing care for vulnerable infants born between 24 and 28 weeks' gestation.
Purpose of the Study:
- To develop and validate predictive models for mortality and survival without major morbidity in EPIs.
- To utilize readily available information from neonatal intensive care unit (NICU) admission for prediction.
Main Methods:
- Utilized two large contemporary cohorts of EPIs in China (development and validation).
- Developed logistic regression models to predict mortality and survival without major morbidity.
- Identified predictors including gestational age, birth weight, sex, antenatal steroids, 5-min Apgar score, and invasive ventilation on admission.
Main Results:
- Mortality rate was 17.7% in the development cohort and 9.2% in the validation cohort.
- Survival without major morbidity was 52.5% (development) and 59.1% (validation).
- Mortality model AUC was 0.77 (development) and 0.76 (validation); survival model AUC was 0.72 (development) and 0.70 (validation).
Conclusions:
- Successfully developed and validated two distinct models for predicting EPI outcomes.
- Models use common NICU admission predictors, offering acceptable performance.
- These tools can assist in managing extremely preterm infants.
Background:
Accurate prediction of outcomes for extremely preterm infants (EPIs) during the early stage is important to assist clinicians and parents in making decisions. This study aimed to develop and validate models for predicting mortality and survival without major morbidity for EPIs using information available on neonatal intensive care units (NICUs) admission.
Methods:
Two of the largest contemporary cohorts of EPIs born at 24+0-28+6 weeks' gestation were included in China. Two predictive models were generated separately to predict mortality and survival without major morbidity at discharge. Potential predictors were identified if they had a well-established association with neonatal outcomes in literatures and could be easily obtained on NICU admission, including gestational age, birth weight, sex, inborn, antenatal steroids, 5-min Apgar score, and invasive ventilation on admission. Logistic regression was employed to develop the models. Model performance was assessed via area under the curve (AUC).
Results:
Among 2,438 EPIs in the development cohort, the mortality rate was 17.7% (431/2,438) and the rate of survival without major morbidity was 52.5% (1,281/2,438). Among the 5,045 infants in the validation cohort, 9.2% (463/5,045) died, and 59.1% (2,981/5,045) survived without major morbidity. Gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, and 5-min Apgar score were selected as predictors in the mortality model, yielding the AUC of 0.77 [95% confidence interval (CI): 0.75-0.79]. For the survival without major morbidity model, predictors were gestational age, birth weight, invasive ventilation on NICU admission, sex, and 5-min Apgar score, and the AUC was 0.72 (95% CI: 0.70-0.74). The validation cohort resulted in AUCs of 0.76 (95% CI: 0.73-0.78) and 0.70 (95% CI: 0.68-0.71) for the mortality and survival without major morbidity models, respectively.
Conclusions:
Using commonly available predictors on NICU admission including gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, sex, and 5-min Apgar score, we successfully developed and validated two distinct models with acceptable performance, predicting mortality and survival without major morbidity for EPIs.
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