Abstract

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.

中文翻译：

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使用护理评估表型和机器学习方法预测压力性损伤

摘要

客观的

压力性损伤是住院患者常见且严重的并发症。压力性损伤率是一项重要的患者安全指标，也是衡量护理质量的指标。及时准确地预测压力性损伤风险可以显着促进早期预防和治疗，避免不良后果。虽然存在许多压力性损伤风险评估工具，但大多数是在访问大型临床数据集和先​​进的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，使用来自护士输入的直接患者评估数据的表型。

方法

我们利用来自大型综合医疗机构附属的 5 家不同医院的丰富电子健康记录数据（包括护士输入的完整评估记录）来开发基于机器学习的压力损伤预测模型。进行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损伤表型：非医院获得性压力损伤 (N = 4398) 和医院获得性压力损伤 (N = 1767)，代表 2 种不同的临床情景。共提取了 28 个临床特征，并为两种压力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好，在 2 个测试集中分别达到了 0.92 和 0.94 的 AUC。格拉斯哥昏迷量表是护士输入的意识水平测量，是两组最重要的特征。

结论

该模型准确预测压力损伤的发展，如果在外部得到验证，可能有助于广泛的压力损伤预防。