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In this study, we validated the DeepECG-HFrEF to identify LVSD in patients with symptomatic HF regardless of EF and evaluated the predictive power of the algorithm for the 5-year all-cause mortality. The DeepECG-HFrEF algorithm showed outstanding performance in discriminating LVSD among patients with HF. DeepECG-HFrEF (+) was associated with a worse 5-year survival, even when compared to using the actual EF value. To our knowledge, this is the first study to validate the performance of a deep learning-based AI algorithm for LVSD detection and to show risk predictability in symptomatic patients with HF.

LVSD is identified in 4050% of patients with HF16. Although survival rates of patients with HF have recently improved in developed countries, patients with HF still show an eight-fold higher mortality than an age-matched population17,18. Not only does HF increase the risk of mortality, but the associated economic burden cannot be overlooked. The economic burden of HF was estimated to be $108 billion per annum globally in 2012, with 60% direct costs to the healthcare system and 40% indirect costs to society through morbidity and others19. Such burden is even higher in Asian countries compared to the United States, with a large proportion of the HF-related healthcare costs directly associated to hospitalization20. The impact of this burden is accentuated among elderly patients, with almost three-quarters of the total resources assigned to HF being solely devoted to the older population21. The increase in the proportion of elderly individuals in the general population, social ageing phenomenon, is consistent throughout the world, with the elderly population projected to double to almost 1.6 billion globally, from 2025 to 205022. Considering the economic burden of HF in the elderly population, there is a need to improve early diagnosis and treatment of LVSD to slow or even prevent its progression to HF.

A summary of currently developed AI algorithms for the detection of LVSD and the validation of these algorithms is provided in Supplementary Table S5. The definition of LVSD and the primary endpoint differed among studies, with an EF cut-off of 35% to 40% having been used. The study population used for validation also differed between the studies, from using patients at a community general hospital to patients in cardiac intensive care unit and patients with COVID-199,12,13. As a result of these differences in the clinical population used, the proportion of patients within the validation population varied between 2 and 20%7,11. Our study is the first to validate the algorithm to detect LVSD solely using patients with HF. Our results showed the strength of the DeepECG-HFrEF algorithm to discriminate LVSD even when the prevalence of HF is high.

Despite recent advances in HF pharmacotherapy, the mortality and rehospitalization rates of patients with HF are still high. Therefore, the identification of high-risk patients who would benefit the most from comprehensive HF treatment is urgently required23. A few studies suggested the promising role of AI support for the early diagnosis of low EF15. Regarding AI for the detection of LVSD, only one study, by Attia et al., reported on the power of an AI algorithm to predict future LVSD development7. Our study is the first to show an association between long-term survival and LVSD of patients with HF based on an AI algorithm. Our results show that the AI algorithm can identify abnormalities in ECG before overt LVSD is observed on echocardiography.

The AI algorithms are known for being a black box with exact mechanism unexplainable. However, there are some ECG characteristics in the DeepECG-HFrEF (+) group which might have contributed to the prognostic performance of the algorithm. The DeepECG-HFrEF (+) group had significantly increased corrected QT intervals and increased proportions of LBBB and IVCD. A study by Lee et al. showed that LBBB and IVCD were associated with an increased risk of all-cause mortality and rehospitalization due to HF aggravation24. Regarding the QTc interval, a study by Park et al. showed a J-curve association between the corrected QT interval and mortality among patients with acute HF, with a nadir of 440450ms in men and 470480ms in women25. Thus, such an association might be one of the factors used by the DeepECG-HFrEF algorithm to differentiate between the two groups. Nevertheless, as our study did not specifically differentiate the corrected QT interval according to sex, the application of results by Park et al. should be done with caution25. Thus, we can carefully interpret that the features shown in the DeepECG-HFrEF (+) group, such as LBBB and IVCD, might be factors that the algorithm is searching for group classification.

There is no clear explanation for the increased false-positive and false-negative rates among patients with an EF near 40%. One plausible explanation might be that the clustering near an EF of 40% may be a heterogeneous group. A previous study by Rastogi et al. showed heterogeneity in the underlying demographics of HFmrEF to be associated with changes in EF over time26. Among the HFmrEF groups, improvement in EF tends to be associated with coronary artery disease, while a worsening of EF is more likely to coexist with hypertension and diastolic dysfunction26. Patients with acute coronary syndrome are more likely to have dynamic changes in their ECGs and EF over a short period of time27,28. As ischemia was the leading cause of acute HF among patients in the KorAHF Registry, such dynamic changes might have contributed to heterogeneity, resulting in a discrepancy between actual EF and DeepECG-HFrEF algorithm results29.

The limitations of our study need to be acknowledged in the interpretation of results. First, owing to the retrospective design used, causation between identified factors of LVSD among patients with HF could not be inferred. Further validation of the algorithm using a prospective study design is needed. Second, generalization of our results is limited, and should be cautiously interpreted, as the study population was drawn from a single hospital site in Korea. Further studies on a wider range of race and ethnicity are necessary, as done per the study conducted by the Mayo Clinic using an artificial intelligence-augmented electrocardiogram (AI-ECG) in the United States and Uganda9,14. Third, although most of the ECGs were matched to echocardiography within 24h, some were performed within 30days. Although these time gaps might influence the performance of our model, the meanstandard deviation of time gaps for true positive, false positive, false negative, and true negative are 22.0 (65.6), 30.6 (86.4), 31.3 (107.3), and 33.6 (90.2), respectively, which was not statistically significant (p=0.192). Also, the performance of the algorithm although the 30-day maximum has generally been accepted in previous studies10,12. It is important to note that the ECG matched to echocardiography within 24h comprised 82.1% of the data used in this study. Fourth, HF medication compliance was not considered. As angiotensin-converting enzyme inhibitors and beta-blockers are known to have a favorable prognosis for the treatment of LVSD, data on such medication adherence would have affected survival. Fifth, our study focused on the association between ECG and echocardiography and included multiple ECG and echocardiographic data from one person. This may have had a slight influence on the survival analysis. A sequential study using a single ECG and echocardiography from individual patients would be useful to confirm our results. Lastly, our study used visually estimated EF values documented by the examiners because EF measurement by Simpsons biplane or other calculated methods were inadequate either by poor echocardiographic window or severely unbalanced myocardial contraction (61 out of 1291 cases).

Read more here:
Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction | Scientific Reports - Nature.com