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

Xiao, Shengjue; Zhang, Linyun; Wu, Qi; Hu, Yue; Wang, Xiaotong; Pan, Qinyuan; Liu, Ailin; Liu, Qiaozhi; Liu, Jie; Zhu, Hong; Zhou, Yufei; Pan, Defeng

doi:10.2147/cia.s325385

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…Nomograms were first used to evaluate the prognosis of cancer patients ( 29 ). They have been extended to other fields because of their reliability, visual intuition, and convenience ( 30 , 31 ). A nomogram based on multivariate logistic regression analysis allows prediction of the diagnosis, stage, and prognosis of tumor or other diseases ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Bi²,

Chen³

et al. 2023

Front. Immunol.

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ObjectivePrecise 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.MethodsWe 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.ResultsMultivariate 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.ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

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

Zhang

Bi²,

Chen³

et al. 2023

Front. Immunol.

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“…Nomogram was a visual chart established by different lines of high and low level to predict the incidence of clinical events. 32 In this study, we finally included four predictors: “LAD”, “LVEF”, “Type of AF”, and “SIS” to create a nomogram model. The nomogram had good discriminatory ability in the training and validation cohorts.…”

Section: Discussionmentioning

confidence: 99%

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

Zhao¹,

Zhang²,

Ma³

et al. 2022

CIA

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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.

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“…When a prediction nomogram was created, the division of patients into at least the two groups was necessary. 21 , 22 Thus, patients in the present were randomly divided into the training cohort (n=100) and the validation cohort (n=63). Patients in the training cohort were used to develop the nomogram, which enrolled the significant risk variables according to the multiple regression analysis (stepwise selection).…”

Section: Methodsmentioning

confidence: 99%

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

Shi¹,

Cui²,

Pan³

et al. 2021

CIA

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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.

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

Cited by 6 publications

References 37 publications

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

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

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

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

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