External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.
External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.
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DOI：
10.1371/journal.pone.0288233
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发表时间：
2024
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
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评估从多机构数据库开发的动态临界指数（CI-D）模型的单站点性能，以预测未来的护理。其次，当使用具有相同变量和建模方法的单站点数据重新开发CI-D模型时，评估单个机构中未来护理位置的预测。评估四种CI-D模型预测未来bbb6 - 12小时、> 12-18小时、> 18-24小时和> 24-30小时的护理地点。预后研究比较多机构CI-D模型在单站点电子健康记录数据集中的表现与使用相同变量和建模方法开发的机构特定CI-D模型。该机构未参与多机构数据集。2018年1月1日至2020年2月29日通过急诊科入院的所有儿科住院患者。主要结局是在常规或ICU护理地点的住院治疗。共纳入29,037例儿科住院患者，其中直接入住ICU的5,563例（19.2%），从常规转ICU的869例（3.0%），从ICU转常规护理的5,023例（17.3%）。患者的中位[IQR]年龄为68个月（15-157），47.5%为女性，43.4%为黑人。多机构CI-D模型应用于单站点测试数据集的受试者工作特征曲线下面积（AUROC）为0.493 ~ 0.545，精密度召回曲线下面积（AUPRC）为0.262 ~ 0.299。应用于独立单点测试数据集的单点CI-D模型AUROC为0.906 ~ 0.944，AUPRC范围为0.754 ~ 0.824。从常规护理转到ICU护理的准确率为0.95，敏感性为72.6%-81.0%。从ICU转到常规护理的患者的准确率为58.2%-76.4%，特异性为0.95。由多所院校的数据集开发而拟应用于个别院校的模型，应在本地进行评估，并可在部署前根据具体地点的数据进行重新开发。
To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6–12 hours, >12–18 hours, >18–24 hours, and >24–30 hours in the future. Prognostic study comparing multi-institutional CI-D models’ performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset. All pediatric inpatients admitted from January 1st 2018 –February 29th 2020 through the emergency department. The main outcome was inpatient care in routine or ICU care locations. A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15–157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493–0.545 and area under the precision-recall curve (AUPRC) was 0.262–0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906–0.944 and AUPRC range from 0.754–0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care. Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.
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