Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity

We developed a new AI algorithm using initial 12-lead ECGs to identify disease severity and prognosis in patients hospitalized with COVID-19. The algorithm demonstrated reasonable accuracy for internal and external validations. To the best of our knowledge, this is the first study to develop a deep neural network that assesses the severity of COVID-19 based on initial ECGs at admission. Our algorithm can help identify patients who are more likely to develop severe-to-critical illness, thus enabling the effective deployment of medical resources and provision of adequate patient care in the early stages of a large-scale outbreak. Our AI algorithm showed the predictive value of an ECG in identifying COVID-19 severity using a deep learning algorithm. Compared to the previously commonly used physiological scoring systems, the AI-ECG had reliable performance in estimating the severity of COVID-19 in patients. The AI-ECG, combined with the EWS, had a more desirable performance in predicting the severity of COVID-19 (AUC of 0.833 [95% CI: 0.830–0.835], recall of 0.747, F1 score of 0.747, and overall accuracy of 0.745 than that of previous physiological scoring systems.

Efficient initial patient triage using the AI-ECG

A prior AI model (using a single 12-lead ECG) was created to develop a screening test to exclude those with COVID-19 infection from the general population². Our AI algorithm may support physicians’ decision-making regarding patient referral and assist in screening patients at high risk of progressing to severe disease within the limitations of medical resources. Rapid and accurate point-of-care testing using this AI method can improve patient prognosis by focusing on effective critical care treatment in a limited healthcare system. Furthermore, AI-ECG algorithms have the potential to be applied to recently available smartphones and wearable ECGs. Therefore, AI-ECG provides a fast, reliable, efficient, inexpensive, harmless, and easily accessible method for severity screening and predicting the prognosis of COVID-19. Further, in response to the pandemic, most countries have established community treatment centers for COVID-19 patients or advocated for home isolation to manage medical resources efficiently, particularly regarding bed availability. The rapid clinical deterioration typically experienced by COVID-19 patients, often progressing within a few days from disease onset, underscores the importance of timely transfers from these facilities to hospitals equipped to manage severe to critical conditions^19,20,21. The use of relatively simple, non-invasive, and cost-effective examinations, like an ECG, can be advantageous in these circumstances. This study was conducted with the anticipation that this approach would facilitate the efficient allocation of medical resources and consequently improve patient prognoses in upcoming pandemic scenarios similar to COVID-19.

Impact of COVID-19 on ECG

In this study, patients with severe-to-critical illness had a higher heart rate, prolonged PR interval, QRS duration, and corrected QT interval than patients with mild-to-moderate illness. This may be explained by the effect of coronaviruses on both cardiac function and electrophysiology^22,23,24. COVID-19 affects the QT interval independently of factors that may cause QT prolongation; additionally, it is associated with severe cardiac inflammation and renin–angiotensin system activation, known to affect repolarization^{18, 23, 25, 26}. Therefore, acute COVID-19 may subtly and pluralistically affect the ECG results²⁷. Furthermore, cardiac depolarization and repolarization are complex and delicate processes that can be affected by cardiac dysfunction, metabolic and electrolyte imbalances, and medications, which are factors that affect patients with COVID-19. Moreover, QT prolongation is also a marker of systemic illness severity and increased mortality, as well as an independent risk factor for sudden death both in the general population and those in the ICU²².

Previous studies indicate that several ECG changes, such as prolonged PR interval, P wave duration, QT interval, and left ventricular hypertrophy, have been identified in ICU patients who died²⁸. Heart failure and asymptomatic severe left ventricular dysfunction have both been successfully detected by deep neural networks based on the ECG²⁹. Analyzing ECG waveforms of COVID-19 patients across severity classifications, our CAM analysis revealed distinct patterns. In patients with mild-to-moderate illness, the algorithm highlighted the importance of the P wave, the onset of the QRS complex, and T wave. However, the QRS complex and the T wave emerged as critical areas for those with severe-to-critical disease. Although we cannot fully understand and interpret the decision-making approach in deep learning algorithms due to the “black box” limitation, our results from this analysis support the assumption that ECG changes in mild-to-moderate illness are related to atrial electrical abnormalities, early alterations in ventricular depolarization patterns, and ventricular repolarization abnormalities. Conversely, the severe-to-critical disease exhibited more extensive ventricular depolarization and repolarization abnormalities. These observations suggest atrial and ventricular electrical remodeling and their potential impact on the decision-making process in deep learning algorithms³⁰. Thus, such electrocardiographic changes may help with the risk stratification of severity and prognosis in patients with COVID-19.

AI-ECG and previous early warning scoring systems predict the severity in patients with COVID-19

EWSs are widely used in clinical practice to help doctors estimate the risk of deterioration, monitor the patient’s evolution, and make clinical decisions to enhance the critical patient’s safety. Many EWS models have been developed, including the NEWS, MEWS, and WPS³¹. These models are based on the effects of COVID-19 on the cardiovascular and pulmonary systems and several extrapulmonary organs³². However, limitations in assessing the vital signs, consciousness, oxygen saturation, and other indirect indicators may be overcome by the AI-based approach based on the ECG.

In a recent study, the AUROCs for the NEWS and MEWS in predicting mortality were shown to be 0.809 (95% CI: 0.727–0.891) and 0.670 (95% CI: 0.573–0.767), respectively³¹. We demonstrated a reasonable accuracy of COVID-19 severity prediction in both internal and external validations. In our study, the developed AI using the initial ECG combined with the EWS for detecting severe-to-critical illness in COVID-19 presented a better performance compared with that of the physiologic scoring systems, MEWS, NEWS, and WPS (AUC of 0.833 [95% CI: 0.830–0.835]). In the early stage of COVID-19, ECG-based AI demonstrated better performance in predicting the progression to severe-to-critical illness than the physiologic scoring systems.

This study had some limitations. First, as this was a retrospective study conducted in a single tertiary hospital in Korea, it is necessary to validate the model with patients in other hospitals and countries. A prospective study is warranted to establish the model’s usefulness as a new, feasible, and noninvasive screening tool. Second, although we used CAM to visualize ECG waveforms for COVID-19 patients across various severity classifications to understand better COVID-19’s impact on ECG, the interpretation of deep learning models and the underlying rationale of AI decision-making remain inherently challenging due to the nature of AI. Third, given the heterogeneity of the patient population, it is possible that the use of drugs that affect the ECG (e.g., antiarrhythmic drugs) may also have affected the network output. Fourth, it remains unclear whether the changes in the ECGs in the presence of a fever or acute respiratory distress associated with the presence of other infectious agents differed from those of COVID-19. Moreover, SARS-CoV-2 is constantly changing. Many notable strains have emerged, including the Alpha, Beta, Delta, and Omicron, and it remains unclear whether COVID-19-related ECG changes differ if the new mutation is more aggressive, highly contagious, vaccine-resistant, can cause more severe illness, or all of the above, compared with the original strain of the virus. Thus, newer variants may require prospective research into what our AI algorithms will accurately predict. Fifth, despite the favorable performance of our deep learning algorithm, overcoming false positives and negatives to identify the optimal treatment and predict the prognosis remains a critical issue. Although it is difficult to fully rely on the AI-ECG, the algorithm could predict disease severity using the initial 12-lead ECG, which is a rapid, simple, and inexpensive point-of-care test. Sixth, utilizing ECGs obtained from local health centers, private clinics, and primary and secondary hospitals might potentially be more closely aligned with the initial onset following a COVID-19 diagnosis. However, almost all patients were rapidly transferred to our hospital’s ED without ECGs, resulting in a minimal time discrepancy from disease onset. Seventh, while our research robustly tested our model compared to established ones and used a separate dataset for validation, the single-center nature coupled with challenges from an imbalanced dataset and limited patients underscores the need for a large-scale study. Finally, recent studies have linked COVID-19 exposure to a higher risk of adverse cardiovascular outcomes, even after recovery from acute illness^{33, 34}. Consequently, further research with long-term follow-up in patients with COVID-19 complicated with cardiovascular involvement is required to better understand the long-term cardiovascular consequences of COVID-19 on the AI-ECG.

In conclusion, AI using the initial 12-lead ECG demonstrated reasonable performance for predicting COVID-19 severity in hospitalized patients. This AI algorithm could significantly improve COVID-19 severity screening, both efficiently and inexpensively, considering the limited availability of medical resources in a recurrent pandemic.