A Longitudinal Investigation of Stage 2 Pressure Injury Outcomes With Machine Learning Technique to Identify Relevant Factors.

Abstract

Objective: Pressure injuries (PIs) have become a global issue due to the significant social costs associated with various factors. Although many factors have been shown to have an impact on PIs, what specifically contributes to the worsening of the disease remains unclear. The aim of this study was to analyze variables that are highly correlated with PI aggravation using machine learning.

Methods: This observational study examined 71 Stage 2 PI patients from May 2018 to June 2021. The authors classified patients into 2 groups according to wound progression: (1) group A, aggravated group, and (2) group B, healed group. All 24 factors were analyzed using a Random Forest with hyperensemble approach, one of the machine learning algorithms. Each Random Forest is composed of 50,000 decision trees, and results from 100 Random Forests were hyperensembled. The mean decrease accuracy was calculated to evaluate the importance of the factor, and overlapped partial dependence plots were obtained to interpret the risk factors.

Results: Group A had 14 patients, whereas group B had 57. In an analysis using machine learning, the following factors were found to be highly associated with the aggravation of PIs: serum-albumin, Braden Scale, hemoglobin, wound size, serum-blood urea nitrogen, body mass index, serum-protein, and serum-creatinine. But the following variables were less associated: end-stage renal disease, sex, and myocardial infarction.

Conclusions: The PIs prediction model has broad application as a PI prevention tool. In addition, these findings can aid in the development of strategies to minimize the risk of PI aggravation.