Nomogram for Predicting in-Hospital Severe Complications in Patients with Acute Myocardial Infarction Admitted in Emergency Department

Yaqin Song,1 Kongzhi Yang,2 Yingjie Su,1 Kun Song,1 Ning Ding1 1Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People’s Republic of China; 2Department of Emergency Medicine, Clinical Research Center for Emergency and Critical Care in Hunan Province, Hunan Provincial Institute of Emergency Medicine, Hunan Provincial Key Laboratory of Emergency and Critical Care Metabonomics, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People’s Republic of ChinaCorrespondence: Kun Song; Ning Ding, Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, Hunan, 410004, People’s Republic of China, Tel +86731-8566-7935, Email [email protected]; [email protected]; [email protected]: There is lack of predictive models for the risk of severe complications during hospitalization in patients with acute myocardial infarction (AMI). In this study, we aimed to create a nomogram to forecast the likelihood of in-hospital severe complications in AMI.Methods: From August 2020 to January 2023, 1024 patients with AMI including the modeling group (n=717) and the validation group (n=307) admitted in Changsha Central Hospital’s emergency department. Conduct logistic regression analysis, both univariate and multivariate, on the pertinent patient data from the modeling cohort at admission, identify independent risk factors, create a nomogram to forecast the likelihood of severe complications in patients with AMI, and assess the accuracy of the graph’s predictions in the validation cohort.Results: Age, heart rate, mean arterial pressure, diabetes, hypertension, triglycerides and white blood cells were seven independent risk factors for serious complications in AMI patients. Based on these seven variables, the nomogram model was constructed. The nomogram has high predictive accuracy (AUC=0.793 for the modeling group and AUC=0.732 for the validation group). The calibration curve demonstrates strong consistency between the anticipated and observed values of the nomogram in the modeling and validation cohorts. Moreover, the DCA curve results show that the model has a wide threshold range (0.01– 0.73) and has good practicality in clinical practice.Conclusion: This study developed and validated an intuitive nomogram to assist clinicians in evaluating the probability of severe complications in AMI patients using readily available clinical data and laboratory parameters.Keywords: acute myocardial infarction, severe complications, risk factors, nomogram