Shixuan Peng,1,2,* Qisheng Chen,3,* Weiqi Ke,4 Yongjun Wu5,6 

1Department of Oncology, The First People’s Hospital of Xiangtan City, Xiangtan, Hunan, 411101, People’s Republic of China; 2Department of Oncology, Graduate Collaborative Training Base of The First People’s Hospital of Xiangtan City, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China; 3Department of Anesthesiology, The First People’s Hospital of Chenzhou, The Chenzhou Affiliated Hospital, Hengyang Medical School, University of South China, Chenzhou, Hunan, 423000, People’s Republic of China; 4Department of Anesthesiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, People’s Republic of China; 5Department of Pathology, Xiangtan Center Hospital, Xiangtan City, Hunan Province, 411100, People’s Republic of China; 6Department of Pathology, The Affiliated Xiangtan Center Hospital of Hunan University, Xiangtan City, Hunan Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yongjun Wu, Department of Pathology, Xiangtan Center Hospital, Xiangtan City, Hunan Province, 411100, People’s Republic of China, Email happywuyj@163.com Weiqi Ke, Department of Anesthesiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, People’s Republic of China, Email wqke2@stu.edu.cn

Background: Despite significant advancements in early reperfusion therapy and pharmacological treatment, which have reduced mortality rates after myocardial infarction in recent decades, the in-hospital mortality rate remains high due to factors such as rapid disease progression, comorbid conditions, and potential complications. We aimed to develop and validate a predictive model for in-hospital mortality in myocardial infarction patients.
Methods: LASSO regression analysis, univariate analysis, and multivariate logistic analysis were used to construct the nomogram in the training set, followed by model comparison, internal validation, and sensitivity analysis.
Results: The analysis comprised 4688 patients in total. The population of patients was randomly assigned to the training set (n = 3512) and validation set (n = 1176). According to the results of LASSO regression analysis and other results, our nomogram contained a total of 10 independent variables related to patient death, including age, respiratory rate, blood glucose, lactate, PTT, BUN, cerebrovascular disease, chronic lung disease, mild liver disease, and metastatic solid cancer. Moreover, the web calculator and nomogram performed exceptionally well at predicting in-hospital death in myocardial infarction patients. The AUC for the training and validation sets’ respective prediction models was 0.869 (95% CI: 0.849– 0.889) and 0.846 (95% CI: 0.807– 0.875) (p< 0.01). Compared to the Sequential Organ Failure Assessment (SOFA), the nomogram showed greater discrimination in the training and validation sets, and the calibration plots demonstrated an adequate fit for the nomogram in predicting the risk of in-hospital mortality in both groups. The decision curve analysis (DCA) of the nomogram demonstrated a higher net benefit in the training and validation sets and in terms of clinical usefulness than the SOFA.
Conclusion: We developed a useful nomogram model and developed a nomogram-based web calculator to predict in-hospital mortality in myocardial infarction patients, which will support doctors in patient counseling and logical diagnosis and therapy.

Keywords: myocardial infarction, nomogram, predictive models, web calculator, in-hospital mortality