Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning.

Alp Ozturk,Tina Hernandez-Boussard,Martin G. Seneviratne,Husham Sharifi,Eric M. Cramer

doi:10.5334/egems.307

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.

Highlights

Pressure ulcers (PUs) represent a significant public health issue, afflicting intensive care units (ICUs) internationally [1]
Prognostic evaluation of Braden score For the Carevue patients, where 93.5 percent of ICU admissions had Braden score documented within 24h, we evaluated the performance of standard Braden score thresholds for high and severe risk (≤12 and ≤9 respectively) in predicting future pressure ulcers (PUs) development
Classifier with Braden When Braden features were integrated into the feature matrix, the optimal classifier on Carevue data, the weighted logistic regression, showed essentially unchanged performance relative to the classifier without Braden data – precision 0.09, recall 0.68. In this large electronic health record (EHR)-based study, we demonstrate that a weighted logistic regression using 40 EHR-derived features from the first 24h of an ICU admission outperformed the nurse-calculated Braden score in recall and matched its precision

Summary

Introduction

Pressure ulcers (PUs) represent a significant public health issue, afflicting intensive care units (ICUs) internationally [1]. To reduce PU incidence, it is critical to identify at-risk patients and intervene early As many of these therapies are labor-intensive or expensive (e.g. pressure mattresses), allocating resources according to patient risk is an important clinical challenge. Reducing hospital-acquired pressure ulcers (PUs) in intensive care units (ICUs) has emerged as an important quality metric for health systems internationally. Conclusion: We demonstrate that an EHR-based model can outperform the Braden score as a screening tool for PUs. 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

Methods

Results

Discussion

Conclusion