مورد إلكتروني
Predicting the risk of 7-day readmission in late preterm infants in California: A population-based cohort study.
| العنوان: | Predicting the risk of 7-day readmission in late preterm infants in California: A population-based cohort study. |
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| المؤلفون: | Amsalu, Ribka |
| المصدر: | Health science reports; vol 6, iss 1, e994; 2398-8835 |
| بيانات النشر: | eScholarship, University of California 2023-01-01 |
| تفاصيل مُضافة: | Amsalu, Ribka Oltman, Scott P Medvedev, Melissa M Baer, Rebecca J Rogers, Elizabeth E Shiboski, Stephen C Jelliffe-Pawlowski, Laura |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Background and aimsThe American Academy of Pediatrics describes late preterm infants, born at 34 to 36 completed weeks' gestation, as at-risk for rehospitalization and severe morbidity as compared to term infants. While there are prediction models that focus on specific morbidities, there is limited research on risk prediction for early readmission in late preterm infants. The aim of this study is to derive and validate a model to predict 7-day readmission.MethodsThis is a population-based retrospective cohort study of liveborn infants in California between January 2007 to December 2011. Birth certificates, maintained by California Vital Statistics, were linked to a hospital discharge, emergency department, and ambulatory surgery records maintained by the California Office of Statewide Health Planning and Development. Random forest and logistic regression were used to identify maternal and infant variables of importance, test for association, and develop and validate a predictive model. The predictive model was evaluated for discrimination and calibration.ResultsWe restricted the sample to healthy late preterm infants (n = 122,014), of which 4.1% were readmitted to hospital within 7-day after birth discharge. The random forest model with 24 variables had better predictive ability than the 8 variable logistic model with c-statistic of 0.644 (95% confidence interval 0.629, 0.659) in the validation data set and Brier score of 0.0408. The eight predictors of importance length of stay, delivery method, parity, gestational age, birthweight, race/ethnicity, phototherapy at birth hospitalization, and pre-existing or gestational diabetes were used to drive individual risk scores. The risk stratification had the ability to identify an estimated 19% of infants at greatest risk of readmission.ConclusionsOur 7-day readmission predictive model had moderate performance in differentiating at risk late preterm infants. Future studies might benefit from inclusion of more va |
| مصطلحات الفهرس: | prediction, preterm, rehospitalization, risk stratification, Pediatric, Prevention, Patient Safety, Perinatal Period - Conditions Originating in Perinatal Period, Preterm, Low Birth Weight and Health of the Newborn, Clinical Research, Infant Mortality, Reproductive health and childbirth, article |
| URL: | |
| الإتاحة: | Open access content. Open access content public |
| ملاحظة: | application/pdf Health science reports vol 6, iss 1, e994 2398-8835 |
| أرقام أخرى: | CDLER oai:escholarship.org:ark:/13030/qt8pj4163s qt8pj4163s https://escholarship.org/uc/item/8pj4163s https://escholarship.org/ 1391583831 |
| المصدر المساهم: | UC MASS DIGITIZATION From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1391583831 |
| قاعدة البيانات: | OAIster |
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