Simple Models Versus Deep Learning in Detecting Low Ejection Fraction From The Electrocardiogram

Importance: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram waveforms. Despite their impressive accuracy, they are difficult to interpret and deploy broadly in the clinical setting. Objective: To determine whether simpler models based on standard electrocardiogram measurements could detect LVSD with similar accuracy to deep learning models. Design: Using an observational dataset of 40,994 matched 12-lead electrocardiograms (ECGs) and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. We additionally evaluated models in two independent cohorts from different medical centers, vendors, and countries. Setting: The training data was acquired from Stanford University Medical Center. External validation data was acquired from Cedars-Sinai Medical Center and the UK Biobank. Exposures: The performance of models based on ECG waveforms in their detection of LVSD, as defined by ejection fraction below 35%. Main outcomes: The performance of the models as measured by area under the receiver operator characteristic curve (AUC) and other measures of classification accuracy. Results: The Stanford dataset consisted of 40,994 matched ECGs and echocardiograms, the test set having an average age of 62.13 (17.61) and 55.20% Male patients, of which 9.72% had LVSD. We found that a random forest model using 555 discrete, automated measurements achieves an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). Furthermore, a linear model based on 5 measurements achieves high performance (AUC of 0.86 (0.85-0.87)), close to a deep learning model and better than NT-proBNP (0.77 (0.74-0.79)). Finally, we find that simpler models generalize better to other sites, with experiments at two independent, external sites. Conclusion: Our study demonstrates the value of simple electrocardiographic models which perform nearly as well as deep learning models while being much easier to implement and interpret. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement J.W.H. is an NSF Graduate Research Fellow (DGE-1656518). J.Z. is supported by NSF CAREER 1942926 and grants from the Chan-Zuckerberg Initiative. M.V.P. is supported by funding from NIH/NHLBI and Apple Inc. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of Stanford University gave ethical approval for this work (eProtocol 41045). The same IRB waived the requirement for informed consent owing to the retrospective nature of the data and project. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes UK Biobank data is available through application. Data from Stanford and Columbia Irving Medical Centers cannot be shared due to patient privacy constraints.