Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
PLoS One. 2024 Jun 14;19(6):e0290215. doi: 10.1371/journal.pone.0290215. eCollection 2024.
Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.
每年，全世界有超过 1 亿人受到尿路感染（UTI）的影响。早期发现高危人群有助于预防 UTI 住院，这会给患者和护理人员带来巨大的经济和社会负担。我们提出了两种方法来生成 UTI 住院风险评分模型。我们利用医疗保险和医疗补助服务中心提供的保险索赔数据中的患者样本来开发和验证所提出的方法。我们的数据集包含了患者的广泛特征，如人口统计学、病史和医疗保健利用情况，以及提供者质量指标和基于社区的指标。所提出的方法对基础逻辑回归模型的系数进行缩放和舍入，以创建评分表。我们提出了计算实验来评估这两种模型的预测性能。我们还讨论了这些模型的不同特征，以及它们对可解释性的影响。我们的研究结果强调了风险评分模型作为识别高风险患者的实用工具的有效性，并提供了对 UTI 住院中各种风险因素（如过去 3 个月内 ICU 入院、认知障碍和过去 6 个月内低住院、门诊和携带者费用）的重要性的定量评估。
