Expanding electrocardiogram abilities for postoperative mortality prediction with deep learning
Authors: Shenda Hong, Qinghao Zhao
Journal: The Lancet Digital Health (2023)

The remarkable evolution of surgical techniques has broadened the scope of treatable conditions. From interventional procedures to open surgeries, the surgical landscape is increasingly complex. These advances, although offering new hope for patients, also amplify the challenges clinicians face in providing optimal perioperative care, given the diverse nature of surgeries and the varying health statuses of patients. The Article by David Ouyang and colleagues' published in this issue of The Lancet Digital Health1Ouyang D Theurer J Stein NR et al.Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.Lancet Digit Health. 2023; (published online Dec 7.)https://doi.org/10.1016/S2589-7500(23)00220-0Summary Full Text Full Text PDF PubMed Scopus (0) Google Scholar shows that a deep-learning algorithm interpreting preoperative electrocardiograms (ECGs) can improve discrimination of postoperative mortality. The model was trained and internally validated on a cohort of 45 969 preoperative patients at Cedars-Sinai Medical Center (with an area under the receiver operating characteristic curve [AUC] of 0·83) and further externally validated on two separate health-care system cohorts (the Stanford Healthcare cohort [AUC 0·79] and the Columbia University Medical Center cohort [AUC 0·75]), without any additional further fine-tuning or training. Preoperative risk stratification serves as the cornerstone for perioperative management. Early identification of high-risk patients allows health-care professionals to comprehensively assess the risks and benefits of surgery and devise individualised treatment strategies. Although numerous risk assessment tools have been established, they come with evident limitations. One major drawback of traditional tools is their procedure-specific nature, resulting in the need for distinct models to assess different surgical procedures. For instance, general surgeries use the P-POSSUM score, whereas cardiovascular surgeries require the EuroSCORE or STS scoring systems.2Prytherch DR Whiteley MS Higgins B Weaver PC Prout WG Powell SJ POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity.Br J Surg. 1998; 85: 1217-1220Crossref PubMed Scopus (558) Google Scholar, 3Ad N Holmes SD Patel J Pritchard G Shuman DJ Halpin L Comparison of EuroSCORE II, original EuroSCORE, and the Society of Thoracic Surgeons risk score in cardiac surgery patients.Ann Thorac Surg. 2016; 102: 573-579Summary Full Text Full Text PDF PubMed Scopus (157) Google Scholar Traditional scoring systems use numerous variables, such as medical history, biomarkers (eg, creatinine, cardiac myosin, and brain natriuretic peptide), ECGs, and imaging data, with some requiring postoperative details for risk adjustment. The complexity of these models contributes to an increased financial burden on patients and increased workload on clinicians. However, most traditional scoring systems exhibit only mediocre predictive accuracy,4Kivrak S Haller G Scores for preoperative risk evaluation of postoperative mortality.Best Pract Res Clin Anaesthesiol. 2021; 35: 115-134Crossref PubMed Scopus (16) Google Scholar causing challenges in meeting the actual requirements of clinical practice. ECG provides vital cardiac information and is easily accessible in clinics. Doctors diagnose heart conditions by analysing ECG waveforms. Advancements in deep learning and abundant ECG data enable computers to detect subtle changes imperceptible to humans, rejuvenating the practice of ECG. Nowadays, the application of artificial intelligence (AI) in ECG is becoming increasingly widespread.5Siontis KC Noseworthy PA Attia ZI Friedman PA Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.Nat Rev Cardiol. 2021; 18: 465-478Crossref PubMed Scopus (240) Google Scholar It improves early prediction of cardiovascular diseases (eg, arrhythmia6Noseworthy PA Attia ZI Behnken EM et al.Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.Lancet. 2022; 400: 1206-1212Summary Full Text Full Text PDF PubMed Scopus (34) Google Scholar and coronary heart disease) and heart structural issues and non-cardiovascular conditions (eg, electrolyte imbalance and anaemia), alongside predicting demographic factors (eg, age and sex). The advancement in the application of AI greatly improves disease screening and daily health management. Ouyang and colleagues' work further expands the boundaries of ECGs' ability. They built a deep-learning model named PreOpNet to predict post-procedural mortality from preoperative ECG signals.1Ouyang D Theurer J Stein NR et al.Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.Lancet Digit Health. 2023; (published online Dec 7.)https://doi.org/10.1016/S2589-7500(23)00220-0Summary Full Text Full Text PDF PubMed Scopus (0) Google Scholar The PreOpNet model stands out for its simplicity and effectiveness in preoperative risk assessment. Using routine preoperative ECGs, the model achieves high accuracy in predicting postoperative mortality and cardiovascular events, and its accuracy remains robust across cardiac surgeries, non-cardiac surgeries, and interventions in the catheterisation laboratory or endoscopy suite.1Ouyang D Theurer J Stein NR et al.Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.Lancet Digit Health. 2023; (published online Dec 7.)https://doi.org/10.1016/S2589-7500(23)00220-0Summary Full Text Full Text PDF PubMed Scopus (0) Google Scholar With ECG-based automated risk assessment, PreOpNet not only simplifies the preoperative evaluation process but also reduces the need for extensive tests and examinations. Consequently, patients will experience a quicker preoperative assessment, with shorter hospitalisation and lower expense, which might translate into considerable health economic value.7Meara JG Leather AJM Hagander L et al.Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development.Lancet. 2015; 386: 569-624Summary Full Text Full Text PDF PubMed Scopus (2181) Google Scholar Moreover, PreOpNet's lightweight architecture is a pivotal feature that enhances its accessibility and applicability. PreOpNet can stably run on standard computers without the need for specialised or high-performance computing resources. This feature ensures its potential application in underdeveloped regions with scarce access to advanced technological infrastructure, bridging the gap in preoperative risk assessment across different levels of hospitals. However, it is crucial to note that in underdeveloped regions, surgeries mainly consist of low-risk procedures due to the establishment of referral systems.7Meara JG Leather AJM Hagander L et al.Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development.Lancet. 2015; 386: 569-624Summary Full Text Full Text PDF PubMed Scopus (2181) Google Scholar Because of the scarcity of relevant data, PreOpNet might face challenges in accurately assessing low-risk patients. Therefore, it is necessary to conduct targeted fine-tuning and validation for low-risk patients in future studies, which might further enhance its applicability and practicality. The application of AI in ECG analysis will bring many opportunities to the health-care system. First, clinical researchers need to catch up to embrace this technology quickly. Enhancing training and education would ensure that medical professionals thoroughly understand and correctly use this technology. Second, integrating AI ECG technology with hospital information systems or wearable devices8Attia ZI Harmon DM Dugan J et al.Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction.Nat Med. 2022; 28: 2497-2503Crossref PubMed Scopus (21) Google Scholar enables real-time ECG analysis and continuous learning and monitoring.9Sun C Li H Song M Cai D Zhang B Hong S Continuous diagnosis and prognosis by controlling the update process of deep neural networks.Patterns (N Y). 2023; 4100687Google Scholar Third, government agencies also need to establish appropriate standards for evaluating AI medical devices.10Wu E Wu K Daneshjou R Ouyang D Ho DE Zou J How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals.Nat Med. 2021; 27: 582-584Crossref PubMed Scopus (182) Google Scholar These responses would not only help harness the potential advantages of using AI to predict and interpret ECGs but would also assist health-care professionals in recognising its possible limitations. This understanding will facilitate better integration with clinical practices, providing improved medical services for patients. This work was supported by the National Natural Science Foundation of China (number 62102008) and Peking University People's Hospital Scientific Research Development Funds (RDJP2022-39). We declare no competing interests. Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation studyA deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. Full-Text PDF Open Access