A model for predicting 7‐day pressure injury outcomes in paediatric patients: A machine learning approach

Abstract

Aims We sought to explore factors associated with early pressure injury progression and build a model for predicting these outcomes using a machine learning approach. Design A retrospective cohort study. Methods In this study, we recruited paediatric patients, with hospital‐acquired stage I pressure injury or suspected deep tissue injury, who met the inclusion criteria between 1 January 2015–31 October 2018. We divided patients into two groups, namely healing or delayed healing, then followed them up for 7 days. We analysed patient pressure injury characteristics, demographics, treatment, clinical situation, vital signs, and blood test results, then build prediction models using the Random Forest and eXtreme Gradient Boosting approaches. Results The best prediction model, trained and tested using Random Forest with 10 variables, achieved an accuracy, sensitivity, specificity, and area under the curve of 0.82 (SD 0.06), 0.80 (SD 0.08), 0.84 (SD 0.08), and 0.89 (SD 0.06), respectively. The most contributing variables, in order of importance, included serum creatinine, red blood cell, and haematocrit. Conclusion An awareness of specific conditions and areas that could lead to delayed healing pressure injury in paediatric patients is needed. Impact This evidence‐based prediction model, coupled with the aforementioned clinical indicators, is expected to enhance early prediction of outcomes in paediatric patients thereby improve the quality of care and the outcome of children with PIs.

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

“…No studies could be found to adopt intelligent tutoring systems or adaptive systems and personalization. For example, Chun et al 12 investigated factors related to early pressure injury progression and applied a machine learning approach to develop a model to predict these outcomes. In terms of aspects of nursing education, the most adopted roles of AI were profiling and prediction (two out of four studies) and intelligent tutoring systems (two studies), whereas no research could be found to adopt assessment and evaluation or adaptive systems and personalization.…”

“…6,10 In recent years, the application of AI in the nursing field has been recognized by several researchers. 4,12,13 The review studies on the application of AI in the nursing field have mainly focused on nursing pain education, 3 primary care, 4 and AI agents in healthcare. 5 Few studies could be found that reviewed papers on AI in nursing activities.…”