Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

Harada, Daisuke; Asanoi, Hidetsugu; Noto, T; Takagawa, Junya

doi:10.3389/fcvm.2020.607760

Cited by 22 publications

(22 citation statements)

References 39 publications

(69 reference statements)

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“…Recently, we clarified that cardiac function assessed by echocardiography and jugular venous pulse (JVP) is associated with cardiac events of HFpEF. 7, 8 The prediction of the development of cardiac events in HFpEF may be improved by learning features closely associated with the events. To create such classifiers, we need to select a specific algorithm from among the ML algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…Some limitations are the same as in our previous report. 8 This was a retrospective study conducted at a single center.…”

Section: Study Limitationsmentioning

confidence: 99%

“…As we required satisfactory imaging of echocardiography and JVP, some patients, such as markedly obese patients with limited windows or fatty neck, may have been underrepresented. 8 Patients with tachycardia may also have been excluded because of the difficulty in separating the E and A waves in the mitral inflow or the 'X' and 'Y' descent of the JVP. 44 Moreover, the criterion of HFpEF is updated once every few years and patients meeting the latest criterion for HFpEF were enrolled retrospectively in this study; therefore, some patients were excluded from the original diac events for HFpEF using only the BNP level, and ML of other features is required for precise prediction.…”

Section: Study Limitationsmentioning

confidence: 99%

“…Using ML, Angraal et al created classifiers predicting the development of death and HF T he clinical outcomes of heart failure with preserved ejection fraction (HFpEF) remain poor, 1,2 and the comorbidities of this syndrome increase the severity of the disease. [3][4][5][6][7][8][9][10] To prevent the deterioration of HFpEF, physicians need to clarify the pathophysiology and exacerbating factors, predict aggravation, and individualize therapeutic strategies for each patient with HFpEF; therefore, precision medicine for HFpEF is recommended. 11 If exacerbation of HFpEF can be predicted in the outpatient clinic, it may be prevented.…”

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confidence: 99%

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Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―

Harada

Asanoi

Noto

et al. 2021

Circ J

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Background:The heterogeneity of B-type natriuretic peptide (BNP) levels among individuals with heart failure and preserved ejection fraction (HFpEF) makes predicting the development of cardiac events difficult. This study aimed at creating high-performance Naive Bayes (NB) classifiers, beyond BNP, to predict the development of cardiac events over a 3-year period in individual outpatients with HFpEF. Methods and Results:We retrospectively enrolled 234 outpatients with HFpEF who were followed up for 3 years. Parameters with a coefficient of association ≥0.1 for cardiac events were applied as features of classifiers. We used the step forward method to find a high-performance model with the maximum area under the receiver operating characteristics curve (AUC). A 10-fold cross-validation method was used to validate the generalization performance of the classifiers. The mean kappa statistics, AUC, sensitivity, specificity, and accuracy were evaluated and compared between classifiers learning multiple factors and only the BNP. Kappa statistics, AUC, and sensitivity were significantly higher for NB classifiers learning 13 features than for those learning only BNP (0.69±0.14 vs. 0.54±0.12 P=0.024, 0.94±0.03 vs. 0.84±0.05 P<0.001, 85±8% vs. 64±20% P=0.006, respectively). The specificity and accuracy were similar. Conclusions:We created high-performance NB classifiers for predicting the development of cardiac events in individual outpatients with HFpEF. Our NB classifiers may be useful for providing precision medicine for these patients.

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Section: Methodsmentioning

confidence: 99%

“…Some limitations are the same as in our previous report. 8 This was a retrospective study conducted at a single center.…”

Section: Study Limitationsmentioning

confidence: 99%

Section: Study Limitationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―

Harada

Asanoi

Noto

et al. 2021

Circ J

Self Cite

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“…Most often for this purpose, unsupervised ML cluster analysis has been used, which groups patients in a potentially novel way based upon input data similarities. Commonly the disease of interest has been HF, with different authors finding two ( 45 , 46 ), three ( 47 – 49 ), four ( 50 ) and even six ( 51 ) phenogroups of HFpEF when applying ML clustering to echocardiographic variables.…”

Section: Machine Learning Application To Diastolic Assessmentmentioning

confidence: 99%

Machine Learning Augmented Echocardiography for Diastolic Function Assessment

Fletcher

Lapidaire

Leeson

2021

Front. Cardiovasc. Med.

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Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.

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Development of interpretable machine learning models to predict in‐hospital prognosis of acute heart failure patients

Tanaka,

Kohjitani,

Yamamoto

et al. 2024

ESC Heart Failure

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AimsIn recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in‐hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF).Methods and resultsBased on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in‐hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n = 4040) was split 4:1 into training (n = 3232) and test datasets (n = 808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9 ± 12.0. The in‐hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in‐hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815–0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680–0.685) and the RF model (0.755, 95% CI: 0.753–0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765–0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686–0.689) and the RF model (0.713, 95% CI: 0.711–0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in‐hospital mortality and WHF were similar among each cluster in both the training and test datasets.ConclusionsThe XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in‐hospital mortality and WHF for patients with AHF.

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Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

Cited by 22 publications

References 39 publications

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―

Machine Learning Augmented Echocardiography for Diastolic Function Assessment

Development of interpretable machine learning models to predict in‐hospital prognosis of acute heart failure patients

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Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

Cited by 22 publications

References 39 publications

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic ― Beyond B-Type Natriuretic Peptide ―

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic ― Beyond B-Type Natriuretic Peptide ―

Machine Learning Augmented Echocardiography for Diastolic Function Assessment

Development of interpretable machine learning models to predict in‐hospital prognosis of acute heart failure patients

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Product

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Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic　― Beyond B-Type Natriuretic Peptide ―