Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Leur, Rutger R van de; Bleijendaal, Hidde; Taha, Karim; Mast, Thomas P.; Gho, Johannes M.I.H.; Linschoten, Marijke; Rees, Bianca van; Henkens, M T H M; Heymans, Stéphane; Sturkenboom, NH; Tio, RA; Offerhaus, J A; Bor, W L; Maarse, Moniek; Haerkens-Arends, H.E.; Kolk, M Z H; Lingen, Anne-Lotte C.J. van der; Selder, J J; Wierda, Eric; Bergen, P F M M van; Winter, Michiel M.; Zwinderman, Aeilko H.; Doevendans, Pieter A.; Harst, Pim van der; Pinto, YM; Asselbergs, Folkert W.; Es, René van; Tjong, FVY

doi:10.1007/s12471-022-01670-2

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Although ECG data were derived from a single vendor, previous studies have shown that ECG-based deep learning results generalize well to other cohorts with different ECG manufacturers. 34 , 35 Despite QRS AREA being calculated manually, performance is identical to that of automated calculation. 36 Although measurement of LVESV is user dependent, excellent intra- and inter-observer reliabilities were previously demonstrated in a subpopulation of this study.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

Wouters

Leur

Vessies

et al. 2022

European Heart Journal

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Aims This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning–based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA. Methods and results A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66–0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58–0.64) and 0.57 (95% CI 0.54–0.60), P < 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai). Conclusion Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.

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Section: Discussionmentioning

confidence: 99%

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

Wouters

Leur

Vessies

et al. 2022

European Heart Journal

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“…In these situations, even the most experienced clinicians may be unable to adjust and respond in a timely manner to the new situation. Thus, ML and AI models were proposed for clinical decision-making, helping detect complex patterns in large datasets [20,92,171,172]. Several studies have shown the capability of ML-based models to predict mortality at the level of individual patients [108,115,119,121], and the aggregate level of cities [119].…”

Section: Mortality Predictionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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Deep Learning—Prediction

Al Gerges,

Vessies,

van de Leur

et al. 2023

Clinical Applications of Artificial Intelligence in Real-World Data

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Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Cited by 9 publications

References 17 publications

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Deep Learning—Prediction

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