Algorithm for Predicting Valvular Heart Disease from Heart Sounds in an Unselected Cohort

Background Although neural networks have shown promise in classifying pathological heart sounds (HS), algorithms have so far either been trained or tested on selected cohorts which can result in selection bias. Herein, the main objective is to explore the ability of neural networks to predict valvular heart disease (VHD) from recordings in an unselected cohort. Methods and results Using annotated HSs and echocardiogram data from 2124 subjects from the Tromsø 7 study, we trained a recurrent neural network to predict murmur grade, which was subsequently used to predict VHD. Presence of aortic stenosis (AS) was detected with sensitivity 90.9%, specificity 94.5%, and area-under-the-curve (AUC) 0.979 (CI:0.963-0.995). At least moderate AS was detected with AUC 0.993 (CI:0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC 0.634 (CI:0.565-703) and 0.549 (CI:0.506-0.593) respectively, which increased to 0.766 and 0.677 when adding clinical variables as predictors. Excluding asymptomatic cases from the positive class increased sensitivity to AR from 54.9% to 85.7%, and sensitivity to MR from 55.6% to 83.3%. Screening jointly for at least moderate regurgitation or presence of stenosis resulted in detection of 54.1% of positive cases, 60.5% of negative cases, 97.7% of AS cases (n=44), and all 12 MS cases. Conclusions Despite the cohort being unselected, the algorithm detected AS with performance exceeding performance achieved in similar studies based on selected cohorts. Detection of AR and MR based on HS audio was unreliable, but sensitivity was considerably higher for symptomatic cases, and inclusion of clinical variables improved prediction significantly. ### Competing Interest Statement Medsensio AS is a company that provides products for automatic detection of abnormal lung sounds. J.R., M.K.J, and H.M. are employed by Medsensio. J.R., M.K.J., and L.A.B. owns shares in Medsensio. P.N.W, H.M., H.S., M.K.J., T.D., J.R., S.A., A.H.D., J.C.A.S., L.A.B has filed a patent for the heart rate estimation algorithm outlined in this paper. ### Funding Statement Collection of data used in this study was funded by Helse Nord RFT, UiT The Arctic University of Norway, and Simen Fougner Hartnanns familiefond. No external funding was received. ### 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: Ethics committee/IRB of "Regionale komiteer for medisinsk og helsefaglig forskningsetikk (REK)" gave ethical approval for this work 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The Tromso Study data is not publicly available, but researchers can apply for access at: * AVPGmean : aortic valve mean pressure gradient AUC : area under the receiver operating curve HS : heart sound VHD : ventricular heart disease GP : general practitioner CV : cross validation AR : aortic regurgitation MR : mitral regurgitation AS : aortic stenosis MS : mitral stenosis