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已发表论文
脓毒症所致心肌损伤风险预测模型的构建与验证
Authors Gou Y , Cong Y, Guo ZZ, Aikepaer A, Jia WT, Liu SB, Chai YG, Li DD, Yang JZ
Received 24 September 2025
Accepted for publication 11 December 2025
Published 16 December 2025 Volume 2025:18 Pages 7579—7590
DOI https://doi.org/10.2147/IJGM.S565555
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Redoy Ranjan
Yi Gou,1 Yun Cong,2 Zhen-Zhen Guo,3 Ailikuti Aikepaer,1 Wen-Ting Jia,1 Si-Bo Liu,1 Ya-Ge Chai,1 Dan-Dan Li,1 Jian-Zhong Yang1
1Emergency Trauma Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Surgery for Hepatic and Biliary Echinococcosi, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 3The First Clinical Medical College, Xinjiang Medical University, Urumqi, People’s Republic of China
Correspondence: Jian-Zhong Yang, Email yjz6542@126.com Dan-Dan Li, Email 543270110@qq.com
Background: Sepsis patients face a high risk of myocardial injury, which increases the risk of death. Therefore, the rapid and accurate assessment of myocardial injury risk is crucial for improving prognosis.
Objective: To construct and validate a risk prediction model for sepsis-induced myocardial injury (SMCI).
Methods: Patients were randomly assigned to a training cohort and an internal validation cohort in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were used to identify independent predictors for the construction of a nomogram. The model’s discrimination, calibration, and clinical applicability were evaluated using area under curve (AUC), Hosmer-Lemeshow tests, decision curve analysis (DCA) and clinical impact curve (CIC). Meanwhile, internal validation was conducted.
Results: The study included 370 patients, with 262 in the training cohort and 108 in the validation cohort. 3 independent risk factors were identified, including Log myoglobin (Myo), Log B-type natriuretic peptide (BNP), and Log interleukin-6 (IL-6) and a nomogram incorporating these factors was constructed. The AUC in the training and validation cohorts was 0.856 and 0.853, respectively. The Hosmer-Lemeshow test indicated good calibration in both cohorts, while DCA and CIC demonstrated strong clinical applicability.
Conclusion: The nomogram based on Log Myo, Log BNP, and Log IL-6 may serve as a practical tool for the early identification of high-risk patients by facilitating the rapid calculation of SMCI risk.
Keywords: myocardial injury, sepsis, prediction model, IL-6
1Emergency Trauma Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Surgery for Hepatic and Biliary Echinococcosi, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 3The First Clinical Medical College, Xinjiang Medical University, Urumqi, People’s Republic of China
Correspondence: Jian-Zhong Yang, Email yjz6542@126.com Dan-Dan Li, Email 543270110@qq.com
Background: Sepsis patients face a high risk of myocardial injury, which increases the risk of death. Therefore, the rapid and accurate assessment of myocardial injury risk is crucial for improving prognosis.
Objective: To construct and validate a risk prediction model for sepsis-induced myocardial injury (SMCI).
Methods: Patients were randomly assigned to a training cohort and an internal validation cohort in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were used to identify independent predictors for the construction of a nomogram. The model’s discrimination, calibration, and clinical applicability were evaluated using area under curve (AUC), Hosmer-Lemeshow tests, decision curve analysis (DCA) and clinical impact curve (CIC). Meanwhile, internal validation was conducted.
Results: The study included 370 patients, with 262 in the training cohort and 108 in the validation cohort. 3 independent risk factors were identified, including Log myoglobin (Myo), Log B-type natriuretic peptide (BNP), and Log interleukin-6 (IL-6) and a nomogram incorporating these factors was constructed. The AUC in the training and validation cohorts was 0.856 and 0.853, respectively. The Hosmer-Lemeshow test indicated good calibration in both cohorts, while DCA and CIC demonstrated strong clinical applicability.
Conclusion: The nomogram based on Log Myo, Log BNP, and Log IL-6 may serve as a practical tool for the early identification of high-risk patients by facilitating the rapid calculation of SMCI risk.
Keywords: myocardial injury, sepsis, prediction model, IL-6