Prediction of Cardiogenic Shock in Acute Myocardial Infarction Patients Using a Nomogram

<b>Background</b>: Cardiogenic shock (CS) complicating acute myocardial infarction (AMI) is associated with a high mortality rate. Early identification of patients at risk for in-hospital CS is crucial for timely intervention. This study aimed to develop a risk prediction model for CS using admission data. <b>Methods</b>: This retrospective case-control study included AMI patients and classified them into case and control groups, based on the development of in-hospital CS. Clinical information at admission was obtained and 1:1 propensity score matching (PSM) was performed based on age, gender, and diagnosis of ST-elevation myocardial infarction. Factors with <i>p</i> < 0.10 at baseline were incorporated to identify the independent risk factors, which were further used to construct a predictive nomogram. <b>Results</b>: After PSM, 374 patients were finally enrolled in both groups. After relaxed least absolute shrinkage and selection operator and multivariate logistic regression, independent risk factors identified for CS in AMI patients included systolic blood pressure [odds ratio (OR): 0.866; 95% confidence interval (CI): 0.844-0.888, <i>p</i> < 0.001], diastolic blood pressure (OR: 1.031; 95% CI: 1.001-1.063, <i>p</i> = 0.046), triglycerides (OR: 0.561; 95% CI: 0.385-0.820, <i>p</i> = 0.003), creatinine (OR: 1.005; 95% CI: 1.000-1.010, <i>p</i> = 0.048), globulin (OR: 0.915; 95% CI: 0.862-0.972, <i>p</i> = 0.004), left ventricular ejection fraction (OR: 0.951; 95% CI: 0.928-0.975, <i>p</i> < 0.001), and coronary angiography (OR: 0.183; 95% CI: 0.058-0574, <i>p</i> = 0.004). The nomogram incorporating these variables demonstrated an area under the curve of 0.937 (95% CI: 0.952-0.967), indicating good discriminatory ability in the calibration curve and decision curve. <b>Conclusions</b>: Seven independent risk factors for CS in AMI patients were identified upon admission. The proposed nomogram might facilitate early risk stratification and guide clinical decision-making to improve outcomes.