Establishment and Validation of a Non-invasive Diagnostic Nomogram to Identify Heart Failure in Patients With Coronary Heart Disease

Tan, Juntao; He, Yuxin; Li, Zhanbiao; Xu, Xiaomei; Zhang, Qinghua; Xu, Qian; Zhang, Lingqin; Xiang, Shoushu; Tang, Xuewen; Zhao, Wenlong

doi:10.3389/fcvm.2022.875702

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…Based on above 10 biomarkers, we constructed and validated a novel diagnose nomogram to risk prediction of HF patients. Compared with other predicted nomogram (AUC = 0.655~0.720) ( 52 ), the AUC value of our nomogram, which was 0.91 in the training cohort (GSE66360) and 0.84 in the test cohort (GSE59867), indicated that it may have exceptional potential for making an early diagnosis of HF from patient blood samples.…”

Section: Discussionmentioning

confidence: 62%

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Chen

Jiang²,

Huang³

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveEnergy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.MethodsTranscriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.ResultsA total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.ConclusionThis study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

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Section: Discussionmentioning

confidence: 62%

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Chen

Jiang²,

Huang³

et al. 2022

Front. Cardiovasc. Med.

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“…As an intuitive expression of the analysis results of a statistical model, a nomogram is more concise and effective in quantifying risks. Studies have confirmed that the nomogram has a good application effect in predicting the risk of acute kidney injury in patients with acute myocardial infarction after percutaneous coronary intervention ( 48 ) and in identifying the risk of HF in patients with CHD ( 49 ). However, no studies on the development of 1-year prognosis risk in CHD patients combined with AHF have been published.…”

Section: Discussionmentioning

confidence: 96%

Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure

Huang

Yang

Chen

et al. 2022

Front. Cardiovasc. Med.

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BackgroundThe risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF.Materials and methodsA retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness.ResultsOn step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811–0.882] in the training cohort and 0.839 (95% CI: 0.780–0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful.ConclusionThe proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.

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“…This approach makes them intuitive and clearly present, which is helpful for clinician analysis and diagnosis. Compared with the prediction model of Tan et al, our prediction model owned a higher AUC value with less involved factors, indicating a higher discrimination ability [ 41 ]. Compared with the prediction model of Yan et al, our model is applicable to a larger population of patients than the elderly women alone [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization

Chen,

Pan,

et al. 2023

BMC Cardiovasc Disord

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Background Acute myocardial infarction (AMI) with consequent heart failure is one of the leading causes of death in humans. The aim of this study was to develop a prediction model to identify heart failure risk in patients with AMI during hospitalization. Methods The data on hospitalized patients with AMI were retrospectively collected and divided randomly into modeling and validation groups at a ratio of 7:3. In the modeling group, the independent risk factors for heart failure during hospitalization were obtained to establish a logistic prediction model, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical value. Machine learning models with stacking method were also constructed and compared to logistic model. Results A total of 1875 patients with AMI were enrolled in this study, with a heart failure rate of 5.1% during hospitalization. The independent risk factors for heart failure were age, heart rate, systolic blood pressure, troponin T, left ventricular ejection fraction and pro-brain natriuretic peptide levels. The area under the curve (AUC) of the model in modeling group and validation group were 0.829 and 0.846, respectively. The calibration curve showed high prediction accuracy and the DCA curve showed good clinical value. The AUC value of the ensemble model by the stacking method in the validation group were 0.821, comparable to logistic prediction model. Conclusions This model, combining laboratory and clinical factors, has good efficacy in predicting heart failure during hospitalization in AMI patients.

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Establishment and Validation of a Non-invasive Diagnostic Nomogram to Identify Heart Failure in Patients With Coronary Heart Disease

Cited by 6 publications

References 41 publications

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization

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