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. 2021 Mar 18;28(4):759-765.
doi: 10.1093/jamia/ocaa336.

Predicting pressure injury using nursing assessment phenotypes and machine learning methods

Wenyu Song  1   2 , Min-Jeoung Kang  1   2 , Linying Zhang  3 , Wonkyung Jung  4 , Jiyoun Song  5 , David W Bates  1   2   6 , Patricia C Dykes  1   2   6
Affiliations

Predicting pressure injury using nursing assessment phenotypes and machine learning methods

Wenyu Song et al. J Am Med Inform Assoc. .

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.

Keywords: artificial intelligence; clinical phenotype; electronic health record; patient safety; quality of care.

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Figures

Figure 1.
Figure 1.
Process of data cleaning and study cohort development.
Figure 2.
Figure 2.
The model performance from Random Forest (ROC curve).
See this image and copyright information in PMC

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