ArticleBMC medical informatics and decision making2021
Hospital acquired pressure injury prediction in surgical critical care patients.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Inflection Point: Artificial Intelligence, Digital Health, and the Evolution of Pre-Licensure Nursing Informatics Competencies.Computers, informatics, nursing : CIN · 2026Review
- Article
- Prediction and Stage Classification of Pressure Ulcers in Intensive Care Patients by Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review.Nursing reports (Pavia, Italy) · 2025Review
- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.Computers in biology and medicine · 2024Article
- Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.International wound journal · 2023Article
- The paradox of obesity in pressure ulcers of critically ill patients.International wound journal · 2023Article
- Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Review
- An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur.International journal of environmental research and public health · 2023Article
- An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur.International journal of environmental research and public health · 2023Article
- Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.International wound journal · 2022Article
- Analysis of Occurrence Characteristics and Influencing Factors of Out-of-Hospital Induced Stress Injury in Patients with Community-Acquired Pneumonia in Respiratory Intensive Care Unit.Iranian journal of public health · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundHospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5-10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data.
methodsIn this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F
resultsAmong 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F
conclusionsResults from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.
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