Intensive Care Unit, Deyang People's Hospital, Deyang, 618000, China.
Oncology Department, Deyang People's Hospital, Deyang, 618000, China.
Sci Rep. 2024 Oct 18;14(1):24483. doi: 10.1038/s41598-024-75480-4.
The potential association between red blood cell distribution width (RDW) at admission and prognosis in patients with sepsis-induced cardiomyopathy(SIC) remains uncertain. The purpose of this study was to explore the prognostic value of RDW on mortality in patients with SIC. Data for this retrospective study were obtained from the MIMIC IV2.2 database. We used propensity score matching (PSM) and Cox proportional hazards regression analysis to evaluate the main risk factors associated with mortality in SIC patients. This analysis was utilized to develop a predictive nomogram. To assess the predictive accuracy and clinical usefulness of the model, we employed the concordance index (C-index) and decision curve analysis. To define the high- and low-RDW groups among patients with SIC, we determined the optimal cut-off value by maximizing the Youden index. According to the screening criteria, we identified a cohort of 1051 patients diagnosed with SIC. When comparing the high-RDW group to the low-RDW group, it was found that the high-RDW group exhibited longer Los_ICU(4.5 days vs.3.8 days, respectively, P = 0.009) and higher mortality rates at 28 days (33.8% vs. 7.8%, respectively, P < 0.001). A nomogram model was created using matched patients which included various factors such as Age, RDW, LDH, CKMB, creatinine and the administration of β-blocker. The C-index predicting 28-day survival probability was 0.846. Decision curves analysis demonstrated that the inclusion of RDW in the model provided a greater net benefit compared to excluding RDW. The prognosis of patients with SIC can be predicted by the RDW value. The nomogram model provides a useful tool in identifying and managing SIC patients.
入院时红细胞分布宽度（RDW）与脓毒症性心肌病（SIC）患者预后的潜在相关性尚不确定。本研究旨在探讨 RDW 对 SIC 患者死亡率的预后价值。本回顾性研究的数据来自 MIMIC IV2.2 数据库。我们使用倾向评分匹配（PSM）和 Cox 比例风险回归分析来评估与 SIC 患者死亡率相关的主要危险因素。该分析用于开发预测列线图。为了评估模型的预测准确性和临床实用性，我们采用了一致性指数（C 指数）和决策曲线分析。为了在 SIC 患者中定义高和低 RDW 组，我们通过最大化 Youden 指数来确定最佳截断值。根据筛选标准，我们确定了 1051 名 SIC 患者的队列。在比较高 RDW 组和低 RDW 组时，发现高 RDW 组的 ICU 住院时间（4.5 天比 3.8 天，P = 0.009）和 28 天死亡率（33.8%比 7.8%，P < 0.001）均更长。使用匹配患者创建了一个列线图模型，该模型包括年龄、RDW、LDH、CKMB、肌酐和β受体阻滞剂的使用等多种因素。预测 28 天生存率的 C 指数为 0.846。决策曲线分析表明，与排除 RDW 相比，将 RDW 纳入模型提供了更大的净收益。RDW 值可预测 SIC 患者的预后。列线图模型为识别和管理 SIC 患者提供了有用的工具。
