Development and Validation of a Risk Nomogram Model for Predicting Revascularization After Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome

Risk analysis and risk prediction of in-hospital heart failure in patients with acute myocardial infarction after emergency intervention surgery

BACKGROUND

Percutaneous coronary intervention (PCI) can rapidly open the culprit vessels of acute myocardial infarction (AMI) and save ischemic myocardium, but it is often accompanied by a variety of complications, including heart failure (HF).Please check if the article title is presented correctlyYes, it is presented correctly OBJECTIVE: We aimed to (i) analyze the possible risk factors affecting the occurrence of in-hospital HF after emergency PCI in patients with AMI through clinical data and (ii) establish a personalized risk prediction model for the occurrence of HF after emergency PCI in patients with AMI.Please check if the author names and affiliations are captured correctlyYes, they are captured correctly METHODS: Clinical data of 676 AMI patients who consecutively underwent emergency PCI between January 2020 and October 2023 at the First Affiliated Hospital of Guangxi University of Chinese Medicine were collected. Based on whether in-hospital HF occurred after PCI, the study subjects were divided into the HF group (91 cases) and the non-HF group (585 cases). Independent risk factors were screened using univariate and multivariate logistic regression. A nomogram model of the risk of HF was drawn using R, and the discriminative power was evaluated by calculating the area under the ROC curve and drawing the calibration curve and decision curve.

RESULTS

In this study, the incidence of in-hospital HF events in AMI patients after emergency PCI was 13.46%. The analysis showed that age, troponin levels, D-dimer levels, left ventricular ejection fraction (LVEF), and Gensini score were independent predictors of the occurrence of in-hospital HF in AMI patients after emergency PCI (P < 0.05). The AUC of the nomogram model were 0.87 (95% CI: 0. 82-0.91) and 0.85 (95% CI: 0. 76-0.93) in the training and validation sets, respectively. The Hosmer-Lemeshow goodness-of-fit test in the training set suggested that the difference between predicted and actual risks of the predictive model was not statistically significant (χ2 = 5.8185, P = 0.6676), and this was confirmed by the Hosmer-Lemeshow goodness-of-fit test in the validation set (χ2 = 9.4774, P = 0.3036).

CONCLUSIONS

The predictive model for the risk of in-hospital HF in AMI patients after emergency PCI includes age, troponin levels, D-dimer levels, LVEF, and Gensini score. It has a good differentiation ability and good accuracy, it can be used to intuitively and independently screen high-risk populations, and it has high predictive value for the occurrence of HF after PCI in AMI patients, so it can be used to assist clinicians in early screening, in identifying patients at high risk of postoperative HF, and in the implementation of targeted intervention therapy.Please check if "Strengths and limitations of this study" was captured and presented correctlyYes, it was captured and presented correctly.

Nomogram to Predict Outcomes After Staged Revascularization in ST-Segment Elevation Myocardial Infarction and Multivessel Coronary Artery Disease

Objective

Approximately 50% of ST-segment elevation myocardial infarction (STEMI) patients have multivessel coronary artery disease (MVD). The management strategy for these patients remains controversial. This study aimed to develop predictive models and nomogram of outcomes in STEMI patients with MVD for better identification and classification.

Methods

The least absolute shrinkage and selection operator (LASSO) method was used to select the features most significantly associated with the outcomes. A Cox regression model was built using the selected variables. One nomogram was computed from each model, and individual risk scores were obtained by applying the nomograms to the cohort. After regrouping patients based on nomogram risk scores into low- and high-risk groups, we used the Kaplan-Meier method to perform survival analysis.

Results

The C-index of the major adverse cardiovascular event (MACE)-free survival model was 0·68 (95% CI 0·62-0·74) and 0·65 [0·62-0·68]) at internal validation, and that of the overall survival model was 0·75 (95% CI 0·66-0·84) and (0·73 [0·65-0·81]). The predictions of both models correlated with the observed outcomes. Low-risk patients had significantly lower probabilities of 1-year or 3-year MACEs (4% versus 11%, P= 0.003; 7% versus 15%, P=0.01, respectively) and 1-year or 3-year all-cause death (1% versus 3%, P=0.048; 2% versus 7%, respectively, P=0.001) than high-risk patients.

Conclusion

Our nomograms can be used to predict STEMI and MVD outcomes in a simple and practical way for patients who undergo primary PCI for culprit vessels and staged PCI for non-culprit vessels.

Development and Validation of a Nomogram Model Affecting the ACT Targeting Rate During Radiofrequency Ablation of Atrial Fibrillation in China

CONTEXT

A nomogram model affecting the activated clotting time (ACT) targeting rate during radiofrequency ablation of atrial fibrillation (RFCA) in China.

PURPOSE

The aim of this study is to develop and validate a nomogram model for predicting the activated clotting time targeting rate after the initial bolus heparin dosages during the radiofrequency catheter ablation of atrial fibrillation in China.

METHODS AND RESULTS

A retrospective observational study was conducted on the data of 465 patients with atrial fibrillation who underwent radiofrequency catheter ablation (RFCA) from October 2019 to June 2022. All patients were randomized into a training cohort (70%; n = 325) and a validation cohort (30%; n = 140). Independent risk factors were identified using univariate and multifactorial logistic regression analysis. The predictive nomogram model was established using R software. The nomogram was developed and evaluated based on differentiation, calibration, and clinical efficacy using concordance statistic (C-statistic), calibration plots, and decision curve analysis (DCA), respectively. The nomogram was established using three variables, including sex (OR 1.01, 95% CI 0.29-1.76, P = 0.007), heparin dose (OR 0.04; 95%CI 0.02-0.05, P < 0.001), and the baseline ACT (OR 0.03; 95%CI 0.02-0.04, P < 0.001). The C-statistic of the nomogram was 0.736 (95%CI 0.675-0.732) in the training cohort and 0.700 (95%CI 0.622-0.721) in the validation cohort. The calibration plots showed good agreement between the predictions and observations in the training and validation cohorts. The clinical decision curve also proves that the map is useful in clinical settings.

CONCLUSION

The nomogram model has good discrimination and accuracy, which can screen attainment groups intuitively and individually, and has a certain predictive value for the probability of ACT reaching the target after the adequate dosage of initial heparin in Chinese patients with atrial fibrillation.

A nomogram based on HBeAg, AST, and age to predict non-minimal liver inflammation in CHB patients with ALT <80 U/L

Objective

Precise assessment of liver inflammation in untreated hepatitis B e antigen (HBeAg)-positive patients with chronic hepatitis B virus (HBV) infection can determine when to initiate antiviral therapy. The aim of this study was to develop and validate a nomogram model for the prediction of non-minimal liver inflammation based on liver pathological injuries combined with age and alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis B surface antigen (HBsAg), HBeAg, and HBV DNA quantification.

Methods

We retrospectively included 735 HBeAg-positive chronic hepatitis B (CHB) patients with ALT < 80 U/L as the primary cohort and prospectively enrolled 196 patients as the validation cohort. Multivariate logistic regression analysis identified independent impact factors. A nomogram to predict significant liver inflammation was developed and validated.

Results

Multivariate logistic regression analysis showed that HBeAg, AST, and age were independent risk factors for predicting non-minimal liver inflammation in untreated CHB patients. The final formula for predicting non-minimal liver inflammation was Logit(P) = -1.99 - 0.68 × Log₁₀HBeAg + 0.04 × Age + 0.06 × AST. A nomogram for the prediction of non-minimal liver inflammation was established based on the results from the multivariate analysis. The predicted probability of the model being consistent with the actual probability was validated by the calibration curves, showing the best agreement in both the primary and validation cohorts. The C-index was 0.767 (95%CI = 0.734-0.802) in the primary cohort and 0.749 (95%CI = 0.681-0.817) in the prospective validation cohort.

Conclusions

The nomogram based on HBeAg, AST, and age might help predict non-minimal liver inflammation in HBeAg-positive CHB patients with ALT < 80 U/L, which is practical and easy to use for clinicians.

Development and Validation of a Risk Nomogram Model for Predicting Recurrence in Patients with Atrial Fibrillation After Radiofrequency Catheter Ablation

Purpose

This study aimed to develop and validate a risk nomogram model for predicting the risk of atrial fibrillation recurrence after radiofrequency catheter ablation.

Patients and Methods

A retrospective observational study was conducted using data from 485 patients with atrial fibrillation who underwent the first radiofrequency ablation in our hospital from January 2018 to June 2021. All patients were randomized into training cohort (70%; n=340) and validation cohort (30%; n=145). Univariate and multivariate logistic regression analyses were used to identify independent risk factors. The predictive nomogram model was established by using R software. The nomogram was developed and evaluated based on differentiation, calibration, and clinical efficacy by concordance statistic (C-statistic), calibration plots, and decision curve analysis (DCA), respectively.

Results

The nomogram was established by four variables including left atrial diameter (OR 1.057, 95% CI 1.010–1.107, P=0.018), left ventricular ejection fraction (OR 0.943, 95% CI 0.905–0.982, P=0.005), type of atrial fibrillation (OR 2.164, 95% CI: 1.262–3.714), and systemic inflammation score (OR 1.905, 95% CI 1.408–2.577). The C-statistic of the nomogram was 0.741 (95% CI: 0.689–0.794) in the training cohort and 0.750 (95% CI: 0.670–0.831) in the validation cohort. The calibration plots showed good agreement between the predictions and observations in the training and validation cohorts. Decision curve analysis and clinical impact curves indicated the clinical utility of the predictive nomogram.

Conclusion

The nomogram model has good discrimination and accuracy, which can screen high-risk groups intuitively and individually, and has a certain predictive value for atrial fibrillation recurrence in patients after radiofrequency ablation.

Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention

Objective

To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness.

Methods

Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI) in our cardiology department from September 2018 to June 2021. Of these, patients who underwent PCI in an earlier period formed the training cohort (70%; n = 809) for nomogram development, and those who underwent PCI thereafter formed the validation cohort (30%; n = 347) to confirm the model’s performance. The independent risk factors of CI-AKI were determined by LASSO regression and multivariable logistic regression analysis. By using R software from which nomogram models were subsequently generated. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot, and decision curve analysis (DCA), respectively.

Results

The nomogram consisted of six variables: age >75, left ventricular ejection fraction, diabetes mellitus, fibrinogen-to-albumin ratio, high-sensitive C-reactive protein, and lymphocyte count. The C-index of the nomogram is 0.835 (95% CI: 0.800–0.871) in the training cohort and 0.767 (95% CI: 0.711–0.824) in the validation cohort, respectively. The calibration plots exhibited that the nomogram was in good agreement between prediction and observation in the training and validation cohorts. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility.

Conclusion

The nomogram model established has a good degree of differentiation and accuracy, which is intuitively and individually to screen high-risk groups and has a certain predictive value for the occurrence of CI-AKI in NSTE-ACS patients after PCI.

A Nomogram to Predict Intra-Spinal Canal Cement Leakage Among Elderly Patients with Spine Metastases: An Internal-Validated Model

Purpose

This study aimed to assess the risk variables for predicting intra-spinal canal cement leakage, especially among elderly patients with spine metastases after being treated with percutaneous vertebroplasty (PVP). Furthermore, we proposed and validated a nomogram to stratify risks of intra-spinal canal cement leakage.

Methods

We retrospectively analyzed 163 elderly patients (age ≧65 years) with spine metastases who underwent PVP. Patients were randomly divided into a training cohort (n=100) and a validation cohort (n=63). The multivariate logistic regression analysis was used to screen potential risk variables in the training cohort. Significant risk variables were included in the nomogram, and the nomogram was developed according to the estimates of the each included variable. The predictive effectiveness of the nomogram was validated using discrimination and calibration performance.

Results

The overall prevalence of intra-spinal canal cement leakage was 9.82% (16/163). In the training cohort, female patients (14.71%, 5/34) showed a higher rate of intra-spinal canal cement leakage as compared with male patients (4.55%, 3/66). The nomogram consisted of sex, cortical osteolytic destruction in posterior wall, and load-bearing lines of spine. The nomogram had acceptable discrimination, with the area under the receiver operating characteristic (AUROC) of 0.75 in the training cohort, 0.64 in the validation cohort, and 0.69 in the entire cohort, and also showed favorable calibration based on the goodness-of-fit test. According to the nomogram, three risk groups were developed: the low risk group had an actual probability of 7.03%, the medium risk group was 11.54%, and high risk group was 44.44%. The difference between the three groups was significant (P ˂ 0.01).

Conclusion

Intra-spinal canal cement leakage after PVP is not scarce among elderly patients. We proposed and internally validated a nomogram that is capable of calculating the risk of intra-spinal canal cement leakage among elderly patients with spine metastases. Careful surgical plan should be conducted among patients with a high risk of developing intra-spinal canal cement leakage.