ObjectiveTo investigate the independent risk factors for concurrent myocardial injury before intervention in patients with primary hepatic carcinoma （PHC）， to construct and validate a nomogram prediction model for the early identification of such patients， and to provide a basis for early clinical prevention and management. MethodsA retrospective analysis was performed for the clinical data of 1 822 patients with PHC in two medical institutions from January 2019 to June 2025， among whom the 1 518 patients from The Third People’s Hospital of Kunming were used as the training set to construct the nomogram prediction model， and the 304 patients from The Second People’s Hospital of Baoshan were used as the validation set. According to the result of high-sensitivity cardiac troponin T measurement before intervention， the patients with PHC were divided into myocardial injury group and non-myocardial injury group. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis were used to identify the risk factors of concurrent myocardial injury in PHC patients before intervention， and then a nomogram prediction model was constructed based on these findings. The independent data of the 304 patients in the validation set were used for external validation， and the area under the receiver operating characteristic curve （AUC）， the Hosmer-Lemeshow test， the calibration curve， decision curve analysis， and clinical impact curve were used to assess model performance. ResultsIn the training set， there were 346 patients in the myocardial injury group and 1 172 patients in the non-myocardial injury group， and the prevalence rate of concurrent myocardial injury was 22.79%. The mean age was 56.44±9.45 years， and the male patients accounted for 81.49%. The patients with drinking， smoking， chronic hepatitis B， chronic hepatitis C， hypertension， diabetes， Child-Pugh class A liver disease， BCLC stage B liver disease， or abnormal electrocardiographic findings accounted for 49.34%， 57.44%， 61.73%， 32.48%， 22.66%， 17.19%， 50.53%， 33.86%， and 62.12%， respectively. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis showed that age （odds ratio ［OR］=1.051， 95% confidence interval ［CI］： 1.036 — 1.067， P<0.001）， history of diabetes （OR=1.862， 95%CI： 1.346 — 2.574， P<0.001）， electrocardiographic findings （OR=1.446， 95%CI： 1.077 — 1.940， P=0.014）， hemoglobin （HGB） （OR=0.993， 95%CI： 0.988 — 0.998， P=0.003）， albumin （Alb） （OR=0.951， 95%CI： 0.930 — 0.972， P<0.001）， and creatinine （Cr） （OR=1.021， 95%CI： 1.016 — 1.026， P<0.001） were independent influencing factors for myocardial injury in PHC patients. A regression equation was established as Logit（Y）=-3.406+0.050×age+0.621×history of diabetes+0.369×electrocardiogram－0.007×HGB－0.051×Alb+0.021×Cr， and a nomogram prediction model was constructed based on these factors. The Bootstrap internal validation of the model yielded an AUC of 0.743 （95%CI： 0.707 — 0.778）， and the model had an AUC of 0.766 （95%CI： 0.738 — 0.794） in the training set and 0.730 （95%CI： 0.688 — 0.793） in the validation set. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.716 for the training set and 0.408 for the validation set， suggesting that the model had a good degree of fitting. Calibration curves， decision curve analysis， and clinical impact curves in both the training set and the validation set showed that the model had a good clinical application value. ConclusionAge， history of diabetes， electrocardiographic findings， HGB， Alb， and Cr are independent influencing factors for myocardial injury in patients with PHC， and the nomogram prediction model constructed based on these factors has good discriminatory ability， accuracy， and clinical practicability.