Creation of mortality risk calculator using a I-123 mIBG-based machine learning model: differential prediction of arrhythmic death and heart-failure death. (20th July 2021)

• Record Type: Journal Article
• Authors: Nakajima, K; Nakata, T; Doi, T; Tada, H; Saito, S; Maruyama, K
• Is Part Of: European heart journal. Volume 22(2021)Supplement 3
• Publication Date: 2021-07-20
• Subjects: Cardiovascular system -- Imaging -- Periodicals; Heart -- Imaging -- Periodicals; 616.10754
• DOI: 10.1093/ehjci/jeab111.055
• Languages: English
• ISSNs: 2047-2404

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: None. Background: Although I-123 meta-iodobenzylguanidine (mIBG) has been applied to patients with chronic heart failure (CHF), a diagnostic tool for differential prediction of fatal arrhythmic events (ArE) and heart-failure death (HFD) has been pursued. Purpose: The aim of this study was to create a calculator of mortality risk for differentiating mode of cardiac death using a machine learning (ML) method, and to test the accuracy in a new cohort of patients with CHF. Methods: A total of 529 patients with CHF was used as the training database for ML. The ArE group consisted of patients with arrhythmic death, sudden cardiac death and appropriate therapy by implantable cardioverter defibrillator. A heart-to-mediastinum ratio (H/M) standardized to the medium-energy collimator condition was calculated with a planar anterior mIBG scintigram. The best classifier models for predicting HFD and ArE were determined by four-fold cross validation. Input variables included age, sex, New York Heart Association (NYHA) functional class, left ventricular ejection fraction, ischemic etiology, mIBG H/M and washout rate, and b-type natriuretic peptide (BNP) or NT Pro BNP, estimated glomerular filtration rate, hemoglobin, and complications such as diabetes and hypertension. After creating the ML-based model, the constructed classifier functions for ArE, HFD, and survival were exported for subsequent use. A new cohort of patients (n = 312, age 67 ± 13 years, 2015 or later) was used to test the ML-based model. Results: The training database included 141 events (27%) with ArE (7%) and HFD (20%). Receiver-operating characteristic analysis by four-fold validation showed area under the curve value of 0.90 for HFD and 0.73 for ArE. Among various ML methods, the logistic regression method demonstrated the most stable calculation of the probability of ArE followed by random forest and gradient boosted tree methods. Therefore, the logistic-regression method was used for calculating both HFD and ArE probabilities. In the test cohort, patients with a high HFD probability >8% resulted in 6.3-fold higher HFD than those with low probability (≤ 8%). Patients with high ArE probability >8% showed 2.5-fold higher ArE than those with low probability (≤ 8%). Conclusion: The ML-based mortality risk calculator could be used for stratifying patients at high and low risks, which might be useful for estimating appropriate treatment strategy.