ObjectiveThe objective of this study was to determine the diagnostic accuracy in detecting valvular heart disease (VHD) by heart auscultation, performed by medical doctors.Design/methodsA systematic literature search for diagnostic studies comparing heart auscultation to echocardiography or angiography, to evaluate VHD in adults, was performed in MEDLINE (1947–November 2021) and EMBASE (1947–November 2021). Two reviewers screened all references by title and abstract, to select studies to be included. Disagreements were resolved by consensus meetings. Reference lists of included studies were also screened. The results are presented as a narrative synthesis, and risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2.Main outcome measuresSensitivity, specificity and likelihood ratios (LRs).ResultsWe found 23 articles meeting the inclusion criteria. Auscultation was compared with full echocardiography in 15 of the articles; pulsed Doppler was used as reference standard in 2 articles, while aortography and ventriculography was used in 5 articles. One article used point-of-care ultrasound. The articles were published from year 1967 to 2021. Sensitivity of auscultation ranged from 30% to 100%, and specificity ranged from 28% to 100%. LRs ranged from 1.35 to 26. Most of the included studies used cardiologists or internal medicine residents or specialists as auscultators, whereas two used general practitioners and two studied several different auscultators.ConclusionSensitivity, specificity and LRs of auscultation varied considerably across the different studies. There is a sparsity of data from general practice, where auscultation of the heart is usually one of the main methods for detecting VHD. Based on this review, the diagnostic utility of auscultation is unclear and medical doctors should not rely too much on auscultation alone. More research is needed on how auscultation, together with other clinical findings and history, can be used to distinguish patients with VHD.PROSPERO registration numberCRD42018091675.

Objective To investigate interrater and intrarater agreement between physicians and medical students on heart sound classification from audio recordings, and factors predicting agreement with a reference classification. Design Intra- and interrater agreement study. Subjects Seventeen GPs and eight cardiologists from Norway and the Netherlands, eight medical students from Norway. Main outcome measures Proportion of agreement and kappa coefficients for intrarater agreement and agreement with a reference classification. Results The proportion of intrarater agreement on the presence of any murmur was 83% on average, with a median kappa of 0.64 (range k = 0.09–0.86) for all raters, and 0.65, 0.69, and 0.61 for GPs, cardiologist, and medical students, respectively. The proportion of agreement with the reference on any murmur was 81% on average, with a median kappa of 0.67 (range 0.29–0.90) for all raters, and 0.65, 0.69, and 0.51 for GPs, cardiologists, and medical students, respectively. Distinct murmur, more than five years of clinical practice, and cardiology specialty were most strongly associated with the agreement, with ORs of 2.41 (95% CI 1.63–3.58), 2.19 (1.58–3.04), and 2.53 (1.46–4.41), respectively. Conclusion We observed fair but variable agreement with a reference on heart murmurs, and physician experience and specialty, as well as murmur intensity, were the factors most strongly associated with agreement. Key points: Heart auscultation is the main physical examination of the heart, but we lack knowledge of inter- and intrarater agreement on heart sounds. • Physicians identified heart murmurs from heart sound recordings fairly reliably compared with a reference classification, and with fair intrarater agreement. • Both intrarater agreement and agreement with the reference showed considerable variation between doctors • Murmur intensity, more than five years in clinical practice, and cardiology specialty were most strongly linked to agreement with the reference.

Background Although neural networks have shown promise in classifying pathological heart-sounds, 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 heart sound (HS) recordings in an unselected cohort. Methods and results Using annotated HSs and echocardiogram data from 2124 subjects from the Tromso 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.