Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Sookyung Hyun; Susan Moffatt-Bruce; Cheryl Cooper; Brenda Hixon; Pacharmon Kaewprag

doi:10.2196/13785

Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

JMIR Med Inform. 2019 Jul 18;7(3):e13785. doi: 10.2196/13785.

Authors

Sookyung Hyun¹, Susan Moffatt-Bruce^{2

3}, Cheryl Cooper⁴, Brenda Hixon⁵, Pacharmon Kaewprag⁶

Affiliations

¹ College of Nursing, Pusan National University, Yangsan-si, Republic of Korea.
² Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
³ Department of Biomedical Informatics, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
⁴ Central Quality and Education, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
⁵ Health System Nursing Education, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
⁶ Department of Computer Engineering, Ramkhamhaeng University, Bangkok, Thailand.

PMID: 31322127
PMCID: PMC6670273
DOI: 10.2196/13785

Abstract

Background: A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team's decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model.

Objective: The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population.

Methods: We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale.

Results: The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively.

Conclusions: Using an extremely large, electronic health record-derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians' decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.

Keywords: critical care; electronic health records; logistic model; pressure ulcers.

©Sookyung Hyun, Susan Moffatt-Bruce, Cheryl Cooper, Brenda Hixon, Pacharmon Kaewprag. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 18.07.2019.