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. 2018 Nov;27(6):461-468.
doi: 10.4037/ajcc2018525.

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model

Jenny Alderden  1 , Ginette Alyce Pepper  2 , Andrew Wilson  2 , Joanne D Whitney  2 , Stephanie Richardson  2 , Ryan Butcher  2 , Yeonjung Jo  2 , Mollie Rebecca Cummins  2
Affiliations

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model

Jenny Alderden et al. Am J Crit Care. 2018 Nov.

Abstract

Background: Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about which patient would benefit most from a specialty bed are difficult because results of existing tools to determine risk for pressure injury indicate that most critical care patients are at high risk.

Objective: To develop a model for predicting development of pressure injuries among surgical critical care patients.

Methods: Data from electronic health records were divided into training (67%) and testing (33%) data sets, and a model was developed by using a random forest algorithm via the R package "randomforest."

Results: Among a sample of 6376 patients, hospital-acquired pressure injuries of stage 1 or greater (outcome variable 1) developed in 516 patients (8.1%) and injuries of stage 2 or greater (outcome variable 2) developed in 257 (4.0%). Random forest models were developed to predict stage 1 and greater and stage 2 and greater injuries by using the testing set to evaluate classifier performance. The area under the receiver operating characteristic curve for both models was 0.79.

Conclusion: This machine-learning approach differs from other available models because it does not require clinicians to input information into a tool (eg, the Braden Scale). Rather, it uses information readily available in electronic health records. Next steps include testing in an independent sample and then calibration to optimize specificity.

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Figures

Figure 1
Figure 1
Receiver operating characteristic curve for stage 1 pressure injury random forest (PI-1 RF) classifier (test data).
Figure 2
Figure 2
Importance of variables for pressure injuries of stage 1 or greater. Abbreviations: ASA score, American Society of Anesthesiologists physical status classification system; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CAM, Confusion Assessment Method; Spo2, oxygen saturation by pulse oximetry.
Figure 3
Figure 3
Receiver operating characteristic curve for stage 2 pressure injury random forest (PI-2 RF) classifier (test data).
Figure 4
Figure 4
Importance of variables for pressure injuries of stage 2 or greater. Abbreviations: ASA score, American Society of Anesthesiologists physical status classification system; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CAM, Confusion Assessment Method; Spo2, oxygen saturation by pulse oximetry.
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