A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features.

OBJECTIVE: The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated. METHODS: A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC). RESULTS: In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21-2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42-4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840-0.946), and in the validation set, it was 0.910 (95% CI: 0.823-0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis. CONCLUSION: The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.

A novel natural killer-related signature to effectively predict prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor that poses a significant threat to human health, with 80% of cases being primary HCC. At present, Early diagnosis and predict prognosis of HCC is challenging and the it is characterized by a high degree of invasiveness, both of which negatively impact patient prognosis. Natural killer cells (NK) play an important role in the development, diagnosis and prognosis of malignant tumors. The potential of NK cell-related genes for evaluating the prognosis of patients with hepatocellular carcinoma remains unexplored. This study aims to address this gap by investigating the association between NK cell-related genes and the prognosis of HCC patients, with the goal of developing a reliable model that can provide novel insights into evaluating the immunotherapy response and prognosis of these patients. This work has the potential to significantly advance our understanding of the complex interplay between immune cells and tumors, and may ultimately lead to improved clinical outcomes for HCC patients. METHODS: For this study, we employed transcriptome expression data from the hepatocellular carcinoma cancer genome map (TCGA-LIHC) to develop a model consisting of NK cell-related genes. To construct the NK cell-related signature (NKRLSig), we utilized a combination of univariate COX regression, Area Under Curve (AUC) LASSO COX regression, and multivariate COX regression. To validate the model, we conducted external validation using the GSE14520 cohort. RESULTS: We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score. The high-risk group was associated with a poor prognosis, and the risk score had good predictive ability across all clinical subgroups. The risk score and stage were found to be independent prognostic indicators for HCC patients when clinical factors were taken into account. We further created a nomogram incorporating the 5-NKRLSig and clinicopathological characteristics, which revealed that patients in the low-risk group had a better prognosis. Moreover, our analysis of immunotherapy and chemotherapy response indicated that patients in the low-risk group were more responsive to immunotherapy. CONCLUSION: The model that we developed not only sheds light on the regulatory mechanism of NK cell-related genes in HCC, but also has the potential to advance our understanding of immunotherapy for HCC. With its strong predictive capacity, our model may prove useful in evaluating the prognosis of patients and guiding clinical decision-making for HCC patients.