Predicting the risk of 7-day readmission in late preterm infants in California: A population-based cohort study.
Predicting the risk of 7-day readmission in late preterm infants in California: A population-based cohort study.
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DOI：
10.1002/hsr2.994
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发表时间：
2023-01
影响因子：
2
通讯作者：
Jelliffe-Pawlowski, Laura
中科院分区：
文献类型：
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作者：
Amsalu, Ribka;Oltman, Scott P.;Medvedev, Melissa M.;Baer, Rebecca J.;Rogers, Elizabeth E.;Shiboski, Stephen C.;Jelliffe-Pawlowski, Laura
美国儿科学会（American Academy of Pediatrics）将妊娠34至36周出生的晚期早产儿与足月婴儿相比，存在再次住院和严重发病率的风险。虽然有针对特定发病率的预测模型，但对晚期早产儿早期再入院的风险预测研究有限。本研究的目的是推导并验证预测7天再入院的模型。这是一项基于人群的回顾性队列研究，研究对象为2007年1月至2011年12月在加州出生的活产婴儿。由加州生命统计部门保存的出生证明与加州全州卫生规划和发展办公室保存的出院、急诊和门诊手术记录相关联。使用随机森林和逻辑回归来确定母婴变量的重要性，检验相关性，并开发和验证预测模型。对预测模型进行了判别和校正。我们将样本限制为健康的晚期早产儿（n = 122,014），其中4.1%在出生出院后7天内再次入院。24变量随机森林模型的预测能力优于8变量logistic模型，验证数据集的c统计量为0.644(95%置信区间为0.629,0.659)，Brier评分为0.0408。八个重要的预测因素：住院时间、分娩方式、胎次、胎龄、出生体重、种族/民族、出生住院时的光疗以及既往或妊娠期糖尿病被用来驱动个体风险评分。风险分层能够识别出19%的婴儿再次入院的最大风险。我们的7天再入院预测模型在鉴别高危晚期早产儿方面表现中等。未来的研究可能会受益于纳入更多的变量，并将重点放在最小化风险的医院实践上。
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. 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. 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. 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.
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