Age affects the prognostic impact of diabetes in chronic heart failure

Cunha, Filipe; Pereira, Joana; Ribeiro, Ana Freitas; Amorim, Marta; Silva, Sérgio; Araújo, José Paulo; Leite‐Moreira, Adelino F.; Bettencourt, Paulo; Lourenço, Patrícia

doi:10.1007/s00592-017-1092-9

Cited by 10 publications

(6 citation statements)

References 48 publications

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“…43 Meanwhile, the importance of these factors has also been confirmed in other studies. 32,44,45 However, several highly important factors of adverse outcomes from the present study such as pulmonary disease, albumin, DBIL, QRS, SG and CL were not reported in previous studies to the best of our knowledge. It suggests that these factors should be paid more attention in the future and it also provides a new basis for the future study of the prognosis of HF.…”

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confidence: 58%

Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

Wang¹,

Tian²,

Zheng³

et al. 2021

RMHP

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This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. Patients and Methods: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 cases were discharged after improvement. The study subjects were hospitalized patients diagnosed with HF from a regional cardiovascular hospital and the cardiology department of a medical university hospital in Shanxi Province of China between January 2014 and June 2019. Synthesizing minority oversampling technology combined with edited nearest neighbors (SMOTE+ENN) was used to pre-process unbalanced data. Traditional logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were used to build risk identification models, and each model was repeated 100 times. Model discrimination and calibration were estimated using F1-score, the area under the receiveroperating characteristic curve (AUROC), and Brier score. The best performing of the five models was used to identify the risk of adverse outcomes and evaluate the influencing factors. Results: The SME-XGBoost was the best performing model with means of F1-score (0.3673, 95% confidence interval [CI]: 0.3633-0.3712), AUC (0.8010, CI: 0.7974-0.8046), and Brier score (0.1769, CI: 0.1748-0.1789). Age, N-terminal pronatriuretic peptide, pulmonary disease, etc. were the most significant factors of adverse outcomes in patients with HF. Conclusion:The combination of SMOTE+ENN and advanced machine learning methods effectively improved the discrimination efficacy of adverse outcomes in HF patients, accurately stratified patients at risk of adverse outcomes, and found the top factors of adverse outcomes. These models and factors emphasize the importance of health status data in determining adverse outcomes in patients with HF.

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confidence: 58%

Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

Wang¹,

Tian²,

Zheng³

et al. 2021

RMHP

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“…In this study, age was an independent risk variable for all-cause death and HF in PD patients. Aging is a major risk factor for cardiovascular disease and a prognostic determinant of chronic HF [ 33 , 34 ]. According to the risk factors which derived from the ML model, we suggest reducing the age-related comorbidities index, which may help select patients in PD.…”

Section: Discussionmentioning

confidence: 99%

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients

Xu,

Cao,

Wang

et al. 2024

Renal Failure

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Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. Results Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. Conclusions We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.

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“…Age was the second most important predictor. The expected 5‐year mortality from HF in diabetic people younger than 75 years of age increased dramatically with age, according to the findings of a prospective research involving 283 patients with chronic HF 32 . A person personal sensitivity to HF and risk of developing HF rises with increasing age due to changes in the structure and function of the heart 33 .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

Wang

Hou

et al. 2023

Clinical Cardiology

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BackgroundThe purpose of this study was to develop and validate a machine learning (ML) based prediction model for the risk of heart failure (HF) in patients with prediabetes or diabetes.MethodsWe used 3527 subjects aged 40 years and older with a prior diagnosis of prediabetes or diabetes from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. The search for independent risk variables linked to HF was conducted using univariate and multivariable logistic regression analysis. The 3527 subjects were randomly divided into training set and validation set in a 7:3 ratio. Five ML models were built on the training set using five ML algorithms, including random forest (RF), and then validated on the validation set. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis and Bootstrap resampling method were used to measure the predictive performance of the five ML models.ResultsMultivariate logistic regression analysis showed that age, poverty‐to‐income ratio, myocardial infarction condition, coronary heart disease condition, chest pain condition, and glucose‐lowering medication use were independent predictors of HF. By comparing the performance of the five ML models, the RF model (AUC = 0.978) was the best prediction model.ConclusionsThe risk of HF in middle‐aged and elderly patients with prediabetes or diabetes can be accurately predicted using ML models. The best prediction performance is presented by RF model, which can assist doctors in making clinical decisions.

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Age affects the prognostic impact of diabetes in chronic heart failure

Cited by 10 publications

References 48 publications

Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

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