Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study
Abstract Background and aims The 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, t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Health Science Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/hsr2.994 |
| _version_ | 1797737774298693632 |
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| author | Ribka Amsalu Scott P. Oltman Melissa M. Medvedev Rebecca J. Baer Elizabeth E. Rogers Stephen C. Shiboski Laura Jelliffe‐Pawlowski |
| author_facet | Ribka Amsalu Scott P. Oltman Melissa M. Medvedev Rebecca J. Baer Elizabeth E. Rogers Stephen C. Shiboski Laura Jelliffe‐Pawlowski |
| author_sort | Ribka Amsalu |
| collection | DOAJ |
| description | Abstract Background and aims The 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. Methods This 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. Results We 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. Conclusions Our 7‐day readmission predictive model had moderate performance in differentiating at risk late preterm infants. Future studies might benefit from inclusion of more variables and focus on hospital practices that minimize risk. |
| first_indexed | 2024-03-12T13:33:13Z |
| format | Article |
| id | doaj.art-49c4d937998e46c8b90be78e372af9b5 |
| institution | Directory Open Access Journal |
| issn | 2398-8835 |
| language | English |
| last_indexed | 2024-03-12T13:33:13Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj.art-49c4d937998e46c8b90be78e372af9b52023-08-24T06:32:46ZengWileyHealth Science Reports2398-88352023-01-0161n/an/a10.1002/hsr2.994Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort studyRibka Amsalu0Scott P. Oltman1Melissa M. Medvedev2Rebecca J. Baer3Elizabeth E. Rogers4Stephen C. Shiboski5Laura Jelliffe‐Pawlowski6California Preterm Birth Initiative University of California San Francisco San Francisco California USACalifornia Preterm Birth Initiative University of California San Francisco San Francisco California USADepartment of Pediatrics University of California San Francisco San Francisco California USACalifornia Preterm Birth Initiative University of California San Francisco San Francisco California USADepartment of Pediatrics University of California San Francisco San Francisco California USADepartment of Epidemiology & Biostatistics University of California San Francisco San Francisco California USACalifornia Preterm Birth Initiative University of California San Francisco San Francisco California USAAbstract Background and aims The 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. Methods This 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. Results We 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. Conclusions Our 7‐day readmission predictive model had moderate performance in differentiating at risk late preterm infants. Future studies might benefit from inclusion of more variables and focus on hospital practices that minimize risk.https://doi.org/10.1002/hsr2.994predictionpretermrehospitalizationrisk stratification |
| spellingShingle | Ribka Amsalu Scott P. Oltman Melissa M. Medvedev Rebecca J. Baer Elizabeth E. Rogers Stephen C. Shiboski Laura Jelliffe‐Pawlowski Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study Health Science Reports prediction preterm rehospitalization risk stratification |
| title | Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study |
| title_full | Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study |
| title_fullStr | Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study |
| title_full_unstemmed | Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study |
| title_short | Predicting the risk of 7‐day readmission in late preterm infants in California: A population‐based cohort study |
| title_sort | predicting the risk of 7 day readmission in late preterm infants in california a population based cohort study |
| topic | prediction preterm rehospitalization risk stratification |
| url | https://doi.org/10.1002/hsr2.994 |
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