Predicting pressure injury using nursing assessment phenotypes and machine learning methods
Predicting pressure injury using nursing assessment phenotypes and machine learning methods
复制标题
使用护理评估表型和机器学习方法预测压力损伤
DOI：
10.1093/jamia/ocaa336
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发表时间：
2021-02-01
影响因子：
6.4
通讯作者：
Dykes, Patricia C.
中科院分区：
文献类型：
--
作者：
Song, Wenyu;Kang, Min-Jeoung;Dykes, Patricia C.
目的：压力性损伤是住院患者常见且严重的并发症。压力性损伤发生率是一项重要的患者安全指标以及护理质量的指示器。及时且准确地预测压力性损伤风险能够显著促进早期预防和治疗，并避免不良后果。虽然存在许多压力性损伤风险评估工具，但大多数是在能够获取大量临床数据集和先进统计方法之前开发的，这限制了它们的准确性。在本文中，我们描述了基于机器学习的预测模型的开发，该模型使用从护士录入的直接患者评估数据中得出的表型。
方法：我们利用了丰富的电子健康记录数据，包括护士录入的完整评估记录，这些数据来自一家大型综合医疗保健机构下属的5家不同医院，以开发基于机器学习的压力性损伤预测模型。进行了五折交叉验证以评估模型性能。
结果：为模型开发定义了两种压力性损伤表型：非医院获得性压力性损伤（N = 4398）和医院获得性压力性损伤（N = 1767），代表了两种不同的临床情况。共提取了28个临床特征，并针对两种压力性损伤表型开发了多个机器学习预测模型。随机森林模型表现最佳，在两个测试集中分别达到了0.92和0.94的曲线下面积（AUC）。格拉斯哥昏迷评分（一种护士录入的意识水平测量指标）是两组中最重要的特征。
结论：该模型能够准确预测压力性损伤的发生，如果在外部得到验证，可能有助于广泛的压力性损伤预防。
Objective: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data.Methods: We utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance.Results: Two pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N =4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups.Conclusions: This model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.