Thomas J Reese, Henry J Domenico, Antonio Hernandez, Daniel W Byrne, Ryan P Moore, Jessica B Williams, Brian J Douthit, Elise Russo, Allison B McCoy, Catherine H Ivory, Bryan D Steitz, Adam Wright
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引用次数: 0
摘要
背景:利用电子健康记录数据开发了许多压力损伤预测模型,但医院获得性压力损伤(HAPIs)却在不断增加,这表明在常规护理中实施这些模型是一项严峻的挑战:为了帮助弥合开发与实施之间的差距,我们试图创建一个可行、广泛适用、动态、可操作并经过严格验证的模型,然后将其性能与常规护理(即布莱登量表)进行比较:我们提取了 197,991 例成人住院患者的电子健康记录数据,其中包含 51 个候选特征。在进行风险预测和特征选择时,我们使用了逻辑回归和最小绝对收缩与选择算子(LASSO)方法。为了将该模型与常规护理进行比较,我们使用了接收者操作曲线下面积(AUC)、布赖尔评分、斜率、截距和综合校准指数。该模型使用时间错开的队列进行了验证:2018 年 1 月至 2022 年 7 月期间,共识别出 5458 例 HAPI。我们确定有 22 个特征是建立一个简约且高度准确的模型所必需的。前 5 个特征包括气管切开、水肿、中心管、首次白蛋白测量和年龄。我们的模型比布莱登量表具有更高的区分度(AUC 0.897,95% CI 0.893-0.901 vs AUC 0.798,95% CI 0.791-0.803):我们开发并验证了一个准确的 HAPIs 预测模型,该模型超越了标准护理风险评估,满足了实施的必要条件。未来的工作包括开展一项实用随机试验,以评估我们的模型是否能改善患者的预后。
Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.
Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care.
Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale).
Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort.
Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803).
Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.