背景:多元药物已成为一个主要的和日益严重的公共卫生问题,对健康结果和医疗资源支出有重大影响。在这项研究中,构建了基于中国人群的社区居住老年患者的以列线图为代表的多重用药风险预测模型.方法:在上海进行了一项横断面研究,中国。从上海市卫生政府部门的信息系统数据库中获取了影响复方的变量数据。采用最小绝对收缩选择算子(LASSO)回归分析选择预测变量,采用多因素logistic回归建立预测模型。建立了列线图的视觉工具,用于预测老年人群的多重用药风险。此外,接收器工作特性(ROC)曲线,校正曲线,和决策曲线分析(DCA)来估计模型的性能。结果:本研究共纳入80,012例老年患者。八个变量,包含年龄,住宅区,首选医疗机构,去三级医院的次数,二级医院就诊次数,去社区卫生中心的次数,诊断的数量,和主要的疾病类型,包括在列线图的风险预测模型中。两组列线图的曲线下面积(AUC)为0.782,证明该模型具有良好的判别能力。校准图显示预测模型与验证集吻合良好。DCA结果显示,预测模型中两组的阈值概率达到90%,这意味着该模型具有较好的应用价值。结论:本研究探讨了上海市老年人多用药的危险因素,中国,并应用列线图通过八个变量建立预测模型,这为早期筛查和及时预防多重用药提供了有效的工具。家庭医生或药剂师可以科学地使用该工具来密切观察社区居住的老年患者,减少药物治疗对老年人的不利健康影响。
Background: Polypharmacy has become a major and growing public health issue, with significant implications for health outcomes and expenditure on healthcare resources. In this study, a risk prediction model of polypharmacy represented by a nomogram for community-dwelling elderly patients based on the Chinese population was constructed. Methods: A cross-sectional study was conducted in Shanghai,
China. The variables data affecting polypharmacy were fetched from the information system database of health government departments in Shanghai. The Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the predictor variables, and multivariate logistic regression was used to establish the prediction model. A visual tool of the nomogram was established for predicting the risk of polypharmacy in the elderly population. In addition, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to estimate the performance of the model. Results: A total of 80,012 elderly patients were included in this study. Eight variables, containing age, residential area, preferred medical institutions, number of visits to tertiary hospitals, number of visits to secondary hospitals, number of visits to community health centers, number of diagnoses, and main types of disease, were included in the risk prediction model of nomogram. The area under the curve (AUC) of the nomogram was 0.782 in both sets, demonstrating that the model has a good discriminant ability. The calibration chart shows that the prediction model fits well with the validation set. DCA results displayed that the threshold probabilities of the two sets in the prediction model reached up to 90%, implying that the model had a preferable application value. Conclusion: This study explored the risk factors for polypharmacy among the elderly in Shanghai,
China, and applied the nomogram to establish a predictive model via eight variables, which provided an effective tool for early screening and timely prevention of polypharmacy. Family physicians or pharmacists could scientifically use the tool to closely observe community-dwelling elderly patients, decreasing the adverse health effects caused by medication for the elderly.