BACKGROUND Chronic kidney disease (CKD) is a significant health burden that increases the risk of adverse events. Currently, there is no validated models to predict risk of mortality among CKD patients experienced adverse drug reactions (ADRs) during hospitalization. This study aimed to develop a mortality risk prediction model among hospitalized CKD patients whom experienced ADRs. METHODS Patients data with CKD stages 3-5 admitted at various wards were included in the model development. The data collected included demographic characteristics, comorbid conditions, laboratory tests and types of medicines taken. Sequential series of logistic regression models using mortality as the dependent variable were developed. Bootstrapping method was used to evaluate the model's internal validation. Variables odd ratio (OR) of the best model were used to calculate the predictive capacity of the risk scores using the area under the curve (AUC). RESULTS The best prediction model included comorbidities heart disease, dyslipidaemia and electrolyte imbalance; psychotic agents; creatinine kinase; number of total medication use; and conservative management (Hosmer and Lemeshow test =0.643). Model performance was relatively modest (R square = 0.399) and AUC which determines the risk score's ability to predict mortality associated with ADRs was 0.789 (95% CI, 0.700-0.878). Creatinine kinase, followed by psychotic agents and electrolyte disorder, was most strongly associated with mortality after ADRs during hospitalization. This model correctly predicts 71.4% of all mortality pertaining to ADRs (sensitivity) and with specificity of 77.3%. CONCLUSION Mortality prediction model among hospitalized stages 3 to 5 CKD patients experienced ADR was developed in this study. This prediction model adds new knowledge to the healthcare system despite its modest performance coupled with its high sensitivity and specificity. This tool is clinically useful and effective in identifying potential CKD patients at high risk of ADR-related mortality during hospitalization using routinely performed clinical data.

中文翻译：

---

制定死亡率评分以评估中度至重度慢性肾病住院患者药物不良反应的风险。


背景技术慢性肾病（CKD）是一种重大的健康负担，会增加不良事件的风险。目前，尚无经过验证的模型可以预测住院期间出现药物不良反应 (ADR) 的 CKD 患者的死亡风险。本研究旨在为经历过 ADR 的住院 CKD 患者建立死亡风险预测模型。方法模型开发中纳入了不同病房收治的 CKD 3-5 期患者数据。收集的数据包括人口特征、合并症、实验室检查和服用的药物类型。开发了使用死亡率作为因变量的逻辑回归序列序列。 Bootstrapping 方法用于评估模型的内部验证。最佳模型的变量奇数比 (OR) 用于使用曲线下面积 (AUC) 计算风险评分的预测能力。结果最佳预测模型包括合并症心脏病、血脂异常和电解质紊乱；精神病药物；肌酐激酶；总用药次数；和保守管理（Hosmer 和 Lemeshow 检验 =0.643）。模型性能相对适中（R 方 = 0.399），决定风险评分预测 ADR 相关死亡率能力的 AUC 为 0.789（95% CI，0.700-0.878）。肌酐激酶，其次是精神病药物和电解质紊乱，与住院期间 ADR 后的死亡率关系最为密切。该模型正确预测了与 ADR 有关的所有死亡率的 71.4%（敏感性），特异性为 77.3%。结论 本研究建立了发生 ADR 的住院 3 至 5 期 CKD 患者的死亡率预测模型。 尽管该预测模型的性能有限，但具有高灵敏度和特异性，但它为医疗保健系统增加了新知识。该工具在临床上有用且有效，可使用常规执行的临床数据识别住院期间 ADR 相关死亡高风险的潜在 CKD 患者。