A Machine Learning Approach to Management of Heart Failure Populations

Jing, Linyuan; Cerna, Alvaro E. Ulloa; Good, Christopher W.; Sauers, Nathan Kyle; Schneider, Gargi; Hartzel, Dustin N.; Leader, Joseph B.; Kirchner, H. Lester; Hu, Yirui; Riviello, David M.; Stough, Joshua V.; Gazes, Seth; Haggerty, Allyson; Raghunath, Sushravya; Carry, Brendan; Haggerty, Christopher M; Fornwalt, Brandon K.

doi:10.1016/j.jchf.2020.01.012

Cited by 57 publications

(34 citation statements)

References 17 publications

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“…[6][7][8] Machine learning (ML) algorithms on the other hand are being increasingly applied in cardiology to address complex clinical issues, 9,10 and have been successfully used in predicting outcomes and discovering phenotypes in patients with heart failure. 11,12 Recently, the application of ML algorithms to predict outcomes after HT has shown some promising results compared to traditionally derived risk scores. 13,14 However, further work is needed to determine the role of ML in the prediction of outcomes and risk stratification of HT patients.…”

Section: Introductionmentioning

confidence: 99%

State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

Kampaktsis

Tzani

Doulamis

et al. 2021

Clinical Transplantation

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We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). Methods and results:We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 ± 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors.ML models for 3-and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. Conclusion:Machine learning models showed good predictive accuracy of outcomes after heart transplantation.

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Section: Introductionmentioning

confidence: 99%

State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

Kampaktsis

Tzani

Doulamis

et al. 2021

Clinical Transplantation

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“…As containment of healthcare costs has become paramount, increased efficiency must also be achieved with often diminishing resources, and with a strong emphasis on portability and accessibility. The emergence of low cost sensors, ubiquitous computing and the internet of things, as well as artificial intelligence (AI) applied to hospital data hold promise for addressing both individual and population scale diagnostic and treatment gaps [1].…”

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confidence: 99%

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

et al. 2021

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Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography–mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85–0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

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“…3 Although several risk scores have been developed for the prediction of outcomes after HT, their accuracy remains modest. Machine learning (ML) algorithms have shown promising results in predicting outcomes and discovering phenotypes in patients with heart failure 4 and have even been applied in patients undergoing HT. 5 However, further work is needed to determine the role of ML in the prediction of outcomes and risk stratification of HT patients.…”

mentioning

confidence: 99%

State‐of‐the‐art machine learning improves predictive accuracy of 1‐year survival after heart transplantation

Kampaktsis

Moustakidis²,

Tzani

et al. 2021

ESC Heart Failure

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Heart transplantation (HT) remains the treatment of choice for patients with medically refractory end-stage heart failure given its improved long-term outcomes and quality of life. 1 Despite the effectiveness of the treatment, only about 3000 HTs are performed annually in the USA, with a small rise attributed to the opioid epidemic and the expanded use of donors with hepatitis C. 2 Optimization of outcomes and risk stratification is recognized as a critically important issue in HT today. 3 Although several risk scores have been developed for the prediction of outcomes after HT, their accuracy remains modest. Machine learning (ML) algorithms have shown promising results in predicting outcomes and discovering phenotypes in patients with heart failure 4 and have even been applied in patients undergoing HT. 5 However, further work is needed to determine the role of ML in the prediction of outcomes and risk stratification of HT patients. Herein, we present our work on prediction of 1-year survival after HT using state-of-the-art ML algorithms.We queried the United Network for Organ Sharing (UNOS) database for patients that were enlisted and underwent heart transplant between 2010 and 2018. Extensive baseline clinical and laboratory data as well as follow-up survival were collected. We excluded patients with multiple transplants during the index hospitalization, non-adult patients, and patients with >10% missing values or less than 1-year follow-up. The study dataset was then randomly split into training and validation cohorts with a 3:1 ratio. A random oversampling technique was applied to artificially equalize the number of patients that were alive with the number of patients that did not survive at 1-year in the training cohort. This is common practice for many ML algorithms. Next, a feature selection method based on Support Vector Machines (SVM) and Fuzzy Logic, namely, SVM-FuzCoC, 6 was used to (i) identify the most informative variables, (ii) reduce the complexity, and (iii) improve the performance of the ML models. The selected features were then used to train five state-of-the-art supervised ML algorithms: Adaptive boosting (Adaboost), SVM, Decision Trees, K-nearest neighbour, and Logistic Regression. 7 Training of the models was controlled by hyperparameter optimization. After training, the validation cohort was used to internally test the ML algorithms.

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A Machine Learning Approach to Management of Heart Failure Populations

Cited by 57 publications

References 17 publications

State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

State‐of‐the‐art machine learning improves predictive accuracy of 1‐year survival after heart transplantation

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