Department of Community Health Sciences, University of Calgary, Calgary, Canada.
Department of Public Health Sciences, Queen's University, Kingston, Canada.
J Matern Fetal Neonatal Med. 2022 Dec;35(24):4674-4681. doi: 10.1080/14767058.2020.1860933. Epub 2020 Dec 20.
Approximately 3.5% of deliveries in Canada result in potentially preventable neonatal readmission, often times due to preventable morbidities. With complexities in hospital discharge planning, health care providers may benefit in identifying infants at risk of readmission for additional monitoring.
To develop and validate models for predicting 7-day neonatal readmission following vaginal or cesarean births.
All liveborn term singleton infants without congenital anomalies in the province of Alberta who were not admitted to the NICU were identified using perinatal and hospitalization databases. A temporal split-sample was used for model development (2012-2014, vaginal = 63,378; cesarean = 21,225) and external validation (2014-2015, vaginal = 21,583, cesarean = 7,477). Multivariable logistic regression models using backward stepwise selection were used to identify predictors of 7-day readmission. We evaluated predictors of maternal age, Apgar score, length-of-stay, birthweight, gestational age, parity, residence, and sex. Hosmer-Lemeshow test and c-statistics were used to estimate calibration and discrimination.
The rate of readmission was 3.3% (95% CI 3.1%, 3.4%) and 2.1% (95% CI 1.9%, 2.3%) following vaginal and cesarean births in the development dataset. Prediction model following vaginal birth, excluding predictors of length-of-stay and birthweight, had sub-optimal performance in development (c-statistics 0.69) and validation data (c-statistics 0.68). Prediction model following cesarean birth, excluding predictors of maternal age, birthweight, and residence, had sub-optimal performance in development (c-statistics 0.62) and validation data (c-statistics 0.64). Readmission was observed in 7.9% (95% CI 7.1%, 8.8%) and 4.9% (95% CI 3.9%, 6.1%) of infants of vaginal and cesarean births, respectively, in the top quintile for the risk of 7-day readmission.
Using routinely collected administrative data, we developed and validated prediction models for neonatal readmission following vaginal and cesarean births. Presently the model is sub-optimal for use in risk assessment and planning at discharge, however, additional information may improve the predictive performance.
在加拿大,约有 3.5%的分娩会导致新生儿潜在可预防的再次入院,这通常是由于可预防的并发症导致的。由于医院出院计划的复杂性,医疗保健提供者可能会受益于识别有再次入院风险的婴儿,以便进行额外的监测。
为阴道分娩或剖宫产分娩后 7 天内新生儿再入院建立和验证预测模型。
使用围产期和住院数据库,在艾伯塔省确定所有无先天异常的足月单胎活产婴儿,这些婴儿未入住新生儿重症监护病房。使用时间分割样本进行模型开发(2012-2014 年,阴道分娩=63378 例;剖宫产=21225 例)和外部验证(2014-2015 年,阴道分娩=21583 例,剖宫产=7477 例)。使用向后逐步选择的多变量逻辑回归模型来识别 7 天内再入院的预测因素。我们评估了母亲年龄、阿普加评分、住院时间、出生体重、胎龄、产次、居住地和性别等因素的预测能力。使用 Hosmer-Lemeshow 检验和 C 统计量来评估校准和区分度。
在开发数据集的阴道分娩和剖宫产分娩中,再入院率分别为 3.3%(95%CI 3.1%,3.4%)和 2.1%(95%CI 1.9%,2.3%)。在阴道分娩的预测模型中,排除住院时间和出生体重的预测因素后,其在开发数据(C 统计量 0.69)和验证数据(C 统计量 0.68)中的性能不佳。在剖宫产分娩的预测模型中,排除母亲年龄、出生体重和居住地的预测因素后,其在开发数据(C 统计量 0.62)和验证数据(C 统计量 0.64)中的性能不佳。在阴道分娩和剖宫产分娩的婴儿中,分别有 7.9%(95%CI 7.1%,8.8%)和 4.9%(95%CI 3.9%,6.1%)的婴儿处于 7 天内再入院风险的最高五分位。
我们使用常规收集的行政数据,为阴道分娩和剖宫产分娩后新生儿再入院建立和验证了预测模型。目前,该模型在出院时进行风险评估和计划方面的表现欠佳,但是增加其他信息可能会提高预测性能。