Empirical research
Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning
Authors:
Eric M. Cramer ,
Department of Biomedical Informatics, Stanford University, US
Martin G. Seneviratne ,
Department of Biomedical Informatics, Stanford University, US
Husham Sharifi ,
Department of Biomedical Informatics, Stanford University, US
Alp Ozturk,
Department of Computer Science, Stanford University, US
Tina Hernandez-Boussard
Department of Biomedical Informatics, Stanford University, US
Abstract
Background: Reducing hospital-acquired pressure ulcers (PUs) in intensive care units (ICUs) has emerged as an important quality metric for health systems internationally. Limited work has been done to characterize the profile of PUs in the ICU using observational data from the electronic health record (EHR). Consequently, there are limited EHR-based prognostic tools for determining a patient’s risk of PU development, with most institutions relying on nurse-calculated risk scores such as the Braden score to identify high-risk patients.
Methods and Results: Using EHR data from 50,851 admissions in a tertiary ICU (MIMIC-III), we show that the prevalence of PUs at stage 2 or above is 7.8 percent. For the 1,690 admissions where a PU was recorded on day 2 or beyond, we evaluated the prognostic value of the Braden score measured within the first 24 hours. A high-risk Braden score (<=12) had precision 0.09 and recall 0.50 for the future development of a PU. We trained a range of machine learning algorithms using demographic parameters, diagnosis codes, laboratory values and vitals available from the EHR within the first 24 hours. A weighted linear regression model showed precision 0.09 and recall 0.71 for future PU development. Classifier performance was not improved by integrating Braden score elements into the model.
Conclusion: We demonstrate that an EHR-based model can outperform the Braden score as a screening tool for PUs. This may be a useful tool for automatic risk stratification early in an admission, helping to guide quality protocols in the ICU, including the allocation and timing of prophylactic interventions.
How to Cite:
Cramer EM, Seneviratne MG, Sharifi H, Ozturk A, Hernandez-Boussard T. Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2019;7(1):49. DOI: http://doi.org/10.5334/egems.307
