Department of Emergency/Intensive Care Unit, Wuhan Third Hospital, Tongren Hospital of Wuhan University, No. 216 Guanshan Avenue, Hongshan District, Wuhan, Hubei, China.
Cardiac Function Department, Asia Heart Hospital, Wuhan, China.
Sci Rep. 2023 Sep 15;13(1):15309. doi: 10.1038/s41598-023-42601-4.
To develop a C-reactive protein-to-albumin ratio (CAR)-based nomogram for predicting the risk of in-hospital death in sepsis patients. Sepsis patients were selected from the MIMIC-IV database. Independent predictors were determined by multiple Cox analysis and then integrated to predict survival. The performance of the model was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC) analysis, and calibration curve. The risk stratifications analysis and subgroup analysis of the model in overall survival (OS) were assessed by Kaplan-Meier (K-M) curves. A total of 6414 sepsis patients were included. C-index of the CAR-based model was 0.917 [standard error (SE): 0.112] for the training set and 0.935 (SE: 0.010) for the validation set. The ROC curve analysis showed that the area under the curve (AUC) of the nomogram was 0.881 in the training set and 0.801 in the validation set. And the calibration curve showed that the nomogram performs well in both the training and validation sets. K-M curves indicated that patients with high CAR had significantly higher in-hospital mortality than those with low CAR. The CAR-based model has considerably high accuracy for predicting the OS of sepsis patients.
建立基于 C 反应蛋白与白蛋白比值（CAR）的列线图，用于预测脓毒症患者住院期间死亡的风险。从 MIMIC-IV 数据库中选择脓毒症患者。通过多 Cox 分析确定独立预测因素，然后将其整合以预测生存。使用一致性指数（C-index）、接受者操作特征曲线（ROC）分析和校准曲线评估模型的性能。通过 Kaplan-Meier（K-M）曲线评估模型在总生存率（OS）中的风险分层分析和亚组分析。共纳入 6414 例脓毒症患者。基于 CAR 的模型在训练集的 C 指数为 0.917（标准误差（SE）：0.112），在验证集的 C 指数为 0.935（SE：0.010）。ROC 曲线分析显示，该列线图在训练集和验证集的 AUC 分别为 0.881 和 0.801。校准曲线表明，该列线图在训练集和验证集均表现良好。K-M 曲线表明，CAR 较高的患者住院死亡率明显高于 CAR 较低的患者。基于 CAR 的模型对预测脓毒症患者的 OS 具有相当高的准确性。
