This study aims to develop a nomogram prediction model for assessing the cardiogenic composite endpoint, which includes intracardiac thrombosis (ICT) combined with heart failure (HF) in patients with non-compaction cardiomyopathy (NCM) patients. We retrospectively analyzed clinical data from NCM patients (January 2018 to May 2024), who were randomly assigned to training and validation cohorts. Independent predictors were identified using logistic regression, and a nomogram model was developed. The model’s discriminative ability, accuracy, and clinical applicability were subsequently validated. A total of 976 patients were included, of whom 54 had ICT and 191 had HF. Diabetes mellitus (DM), left ventricular end-systolic diameter (LVESD), and ejection fraction (EF) were identified as independent predictors for the composite endpoint. The nomogram demonstrated good performance, with an area under the curve (AUC) of 0.747 (95% CI: 0.707–0.787) in the training group and 0.803 (95% CI: 0.752–0.854) in the validation group. The calibration curve for the training group showed an average absolute error of 0.028, with a Hosmer-Lemeshow test P-value of 0.076. Decision curve analysis and clinical impact curves further indicated that the clinical net benefit was maximized at a threshold probability of 0.05–0.61. This study establishes and validates a nomogram for predicting cardiogenic composite endpoint in NVM patients, demonstrating robust clinical predictive value.

Left atrial structural remodeling is closely linked with the prognosis of patients with hypertrophic cardiomyopathy (HCM). This study aimed to evaluate the clinical characteristics and risk factors associated with left atrial remodeling in HCM and to develop an early prediction model. HCM patients who underwent echocardiography during hospitalized enrolled. Patients with a left atrial diastolic anteroposterior diameter ≥ 40 mm were classified as the remodeling group, while others were assigned to the control group. Logistic regression analysis was employed to identify independent predictors, and a nomogram was constructed for prediction. A total of 1554 patients were enrolled, including 442 patients in the remodeling group. Significant differences in clinical and echocardiographic characteristics were observed between the two groups. Multivariate logistic regression analysis identified the following as independent predictors of left atrial remodeling: prothrombin time (P < 0.001; OR 0.863; 95% CI 0.813–0.915), main pulmonary artery diameter (P < 0.001; OR 0.881; 95% CI 0.852–0.911), left ventricular ejection fraction (P < 0.001; OR 1.057; 95% CI 1.043–1.071), and interventricular septal thickness (P < 0.001; OR 0.937; 95% CI 0.916–0.959). A nomogram prediction model based on these factors demonstrated good discriminatory power, with a receiver operating characteristic curve area of 0.7328 (95% CI 0.7052—0.7603). The model’s calibration showed high accuracy and consistency with actual outcomes, particularly in intermediate probability ranges. Prothrombin time, main pulmonary artery diameter, left ventricular ejection fraction, and interventricular septal thickness were identified as risk factors for left atrial remodeling in HCM patients. The developed nomogram provides a valuable tool for early risk assessment, aiding in the early detection of left atrial remodeling and facilitating optimized treatment strategies to improve patient prognosis.

Background/Objectives: Intracardiac thrombosis (ICT) is a serious complication in acute myocardial infarction (AMI) patients. This study aimed to identify potential risk factors of ICT in AMI patients, providing valuable insights for clinical management. Methods: A case–control study was conducted involving consecutive AMI patients admitted to the First Affiliated Hospital of Xi’an Jiaotong University between January 2019 and December 2022. Binary logistic regression identified independent risk factors of ICT and a nomogram prediction model was constructed and validated for accuracy. Conclusions: A total of 7341 patients with ICT and 74 without ICT were included. Multivariate logistic regression identified male gender, acute anterior wall myocardial infarction (AWMI), ventricular aneurysm, and lower prothrombin activity as independent risk factors of ICT in AMI patients. A nomogram based on these factors demonstrated excellent performance (AUC: 0.910, 95% CI: 0.877–0.943, p < 0.001), with calibration and sensitivity analyses confirming its robustness. This nomogram provides an accurate tool for predicting ICT risk, facilitating personalized management and early intervention in AMI patients.