Department of Nephrology, Affiliated Hospital of Jining Medical University, Jining, 271000, China.
BMC Nephrol. 2024 Jun 11;25(1):195. doi: 10.1186/s12882-024-03628-5.
Acute kidney injury (AKI) is a common and serious condition, particularly among elderly patients. It is associated with high morbidity and mortality rates, further compounded by the need for continuous renal replacement therapy in severe cases. To improve clinical decision-making and patient management, there is a need for accurate prediction models that can identify patients at a high risk of mortality.
Data were extracted from the Dryad Digital Repository. Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a predictive nomogram for mortality within 28 days after continuous renal replacement therapy in elderly patients with acute kidney injury. The discrimination of the model was evaluated in the validation cohort using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using a calibration curve. The clinical utility of the model was assessed using decision curve analysis (DCA).
A total of 606 participants were enrolled and randomly divided into two groups: a training cohort (n = 424) and a validation cohort (n = 182) in a 7:3 proportion. A risk prediction model was developed to identify independent predictors of 28-day mortality in elderly patients with AKI. The predictors included age, systolic blood pressure, creatinine, albumin, phosphorus, age-adjusted Charlson Comorbidity Index (CCI), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. These predictors were incorporated into a logistic model and presented in a user-friendly nomogram. In the validation cohort, the model demonstrated good predictive performance with an AUC of 0.799. The calibration curve showed that the model was well calibrated. Additionally, DCA revealed significant net benefits of the nomogram for clinical application.
The development of a nomogram for predicting 28-day mortality in elderly patients with AKI receiving continuous renal replacement therapy has the potential to improve prognostic accuracy and assist in clinical decision-making.
急性肾损伤（AKI）是一种常见且严重的病症，尤其是在老年患者中。它与高发病率和死亡率相关，在严重情况下需要持续肾脏替代治疗，情况进一步恶化。为了改善临床决策和患者管理，需要有准确的预测模型来识别高死亡率风险的患者。
从 Dryad 数字资源库中提取数据。使用最小绝对收缩和选择算子（LASSO）逻辑回归分析进行多变量分析，以确定独立的危险因素，并为接受持续肾脏替代治疗的老年 AKI 患者构建 28 天内死亡率预测的列线图。使用接收者操作特征曲线下面积（AUC）在验证队列中评估模型的区分度，并使用校准曲线评估校准度。使用决策曲线分析（DCA）评估模型的临床实用性。
共纳入 606 名参与者，随机分为两组：训练队列（n=424）和验证队列（n=182），比例为 7:3。开发了一种风险预测模型，以确定老年 AKI 患者 28 天死亡率的独立预测因素。预测因素包括年龄、收缩压、肌酐、白蛋白、磷、年龄调整 Charlson 合并症指数（CCI）、急性生理学和慢性健康评估 II（APACHE II）评分和序贯器官衰竭评估（SOFA）评分。这些预测因素被纳入逻辑模型，并以用户友好的列线图形式呈现。在验证队列中，该模型具有良好的预测性能，AUC 为 0.799。校准曲线表明模型具有良好的校准度。此外，DCA 显示列线图在临床应用中有显著的净获益。
为接受持续肾脏替代治疗的老年 AKI 患者预测 28 天死亡率开发的列线图有可能提高预后准确性，并辅助临床决策。
