Chengying Hong,1 Zhenmi Liu,1 Chuanchuan Nan,1 Yinjing Xie,2 Jinquan Xia,3 Yichun Jiang,1 Xiaojun Liu,1 Zhikun Xu,1 Kangping Hui,4 Yihan Xiong,4 Wei Wang,5 Huaisheng Chen1 

1Department of Critical Care Medicine, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China; 2Laboratory Department, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China; 3Department of Clinical Medical Research Center, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China; 4Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China; 5Department of Endocrinology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China

Correspondence: Huaisheng Chen, Department of Critical Care Medicine, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China, Email sunshinic@hotmail.com Wei Wang, Department of Endocrinology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, 518020, People’s Republic of China, Email windy97333@aliyun.com

Objective: The retrospective study established a prognostic nomogram based on red blood cell distribution width-coefficient of variation (RDW-CV) for elderly septic patients.
Methods: We analyzed 1997 critically ill patients admitted between December 2016 and June 2019, and 986 elderly septic patients were included in the study and stratified into survival and non-survival groups. Using machine learning-based feature importance analysis and multivariate logistic regression, we evaluated predictors of mortality in the elderly septic patients, with particular focus on RDW-CV. We constructed a nomogram incorporating RDW-CV to predict clinical outcomes in elderly septic patients and evaluated its performance.
Results: The mortality of 986 elderly sepsis patients was 27.48%. Importance analysis showed that RDW-CV demonstrated superior predictive value for mortality. The RDW-CV (17.22 ± 3.98%) in the non-survival group was significantly higher than that (15.30 ± 2.81%) in the survival group, p < 0.0001. The RDW-CV was used to predict the mortality of patients and the AUC was 0.65 (95% CI: 0.61, 0.69). Multivariate logistic regression showed that mechanical ventilation, drug-resistant bacterial infection, hemofiltration, and RDW-CV independently influenced mortality, a predictive nomogram was developed based on a final model that included RDW-CV and other clinical indicators, the area under the curve (AUC) was found to be 0.755 (95% CI: 0.714, 0.797), decision curve analyses (DCA) revealed superior net benefit of the nomogram across threshold probabilities of 0.30– 1.00 in both derivation and validation cohorts. The calibration curve demonstrates strong agreement between the model’s predicted probabilities and the validation cohort’s predicted probabilities.
Conclusion: Higher RDW-CV was found to have a significant association with mortality prediction, the nomogram based on RDW-CV with other clinical indicators could more accurately predict the clinical outcome of elderly septic patients, validation analysis confirmed the accuracy of the nomogram, the predictive model offered clinical applicability.

Keywords: RDW-CV, elder, sepsis, mortality, predictive model, nomogram