ArticleScientific reports2022
Prediction of inpatient pressure ulcers based on routine healthcare data using machine learning methodology.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.International wound journal · 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
- Study on the construction and verification of intraoperative pressure injury risk prediction model for children undergoing cardiac surgery.BMC pediatrics · 2026Article
- Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models.Journal of clinical medicine · 2026Article
- Early prediction of pressure injury risk in hospitalized patients using supervised machine learning models based on nursing records.Scientific reports · 2026Article
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Observational
- Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.Diagnostic and prognostic research · 2025Review
- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- Multidimensional-Based Prediction of Pressure Ulcers Development and Severity in Hospitalized FrailClinical interventions in aging · 2024Article
- In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables.Journal of clinical medicine · 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
- A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
- Enhancing clinical reliability in pressure injury prediction: A conformal prediction approach with machine learning models.Digital healthArticle
- Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder.International journal of clinical and health psychology : IJCHPArticle
- Identifying Candidate Predictors of Hospital-Acquired Pressure Injuries among Adult, Medical-Surgical Patients Using Clinical Expert Knowledge: A Delphi Study.Journal of wound, ostomy, and continence nursing : official publication of The Wound, Ostomy and Continence Nurses SocietyArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the relevance of pressure ulcers (PU) in inpatient care, the predictive power and role of care-related risk factors (e.g. anesthesia) remain unclear. We investigated the predictability of PU incidence and its association with multiple care variables. We included all somatic cases between 2014 and 2018 with length of stay ≥ 2d in a German university hospital. For regression analyses and prediction we used Bayesian Additive Regression Trees (BART) as nonparametric modeling approach. To assess predictive accuracy, we compared BART, random forest, logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) using area under the curve (AUC), confusion matrices and multiple indicators of predictive performance (e.g. sensitivity, specificity, F1, positive/ negative predictive value) in the full dataset and subgroups. Analysing 149,006 cases revealed high predictive variable importance and associations between incident PU and ventilation, age, anesthesia (≥ 1 h) and number of care-involved wards. Despite high AUCs (range 0.89-0.90), many false negative predictions led to low sensitivity (range 0.04-0.10). Ventilation, age, anesthesia and number of care-involved wards were associated with incident PU. Using anesthesia as a proxy for immobility, an hourly repositioning is indicated. The low sensitivity indicates major challenges for correctly predicting PU based on routine data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.