Main Article Content

Abstract

Introduction: Urinary incontinence (UI) is a common health problem and is often undiagnosed in hospital patients. UI can cause complications such as urinary tract infections, dermatitis, and decreased quality of life. This study aims to apply a risk prediction model to identify patients at high risk of experiencing UI at Tengku Peukan General Hospital, Southwest Aceh, Indonesia.


Methods: This study used a prospective cohort design. Data was collected from 100 patients hospitalized at Tengku Peukan General Hospital, Southwest Aceh. A risk prediction model was developed using logistic regression. Model performance is measured by AUC-ROC values and accuracy.


Results: The risk prediction model developed had an AUC-ROC value of 0.85 (95% CI: 0.78-0.92) and an accuracy of 82%. The most significant risk factors for UI are age, gender, history of UI, and use of diuretic medications.


Conclusion: This risk prediction model can help nurses and doctors identify patients who are at high risk of experiencing UI at Tengku Peukan General Hospital, Southwest Aceh. Early intervention in high-risk patients can help prevent UI complications and improve the patient's quality of life.

Keywords

Hospital Risk prediction model Southwest Aceh Urinary incontinence

Article Details

How to Cite
Amal, R. J., Suherdy, Delfi Sanutra, Munawmarah, & Jevo Rifan Sandikta. (2024). Analysis of Risk Prediction Models to Identify Patients at High Risk of Urinary Incontinence . Sriwijaya Journal of Internal Medicine, 2(1), 64-69. https://doi.org/10.59345/sjim.v2i1.110