{"id":"7f4b90ac6ac6","type":"article","url":"https://hartvaat.nl/2026/01/16/seismocardiografie-spoort-ernstige-aortastenose-op-met-een-eenvoudige-sensor/","title":"Seismocardiografie spoort ernstige aortastenose op met een eenvoudige sensor","title_en":"Severe aortic stenosis detection using seismocardiography","category":"algemeen","category_label":"Algemeen","professions":["cardioloog","huisarts"],"tags":["aortastenose"],"journal":"Open Heart","doi":"http://openheart.bmj.com/cgi/content/short/13/1/e003563?rss=1","source_url":"https://doi.org/http://openheart.bmj.com/cgi/content/short/13/1/e003563?rss=1","authors":["Pykäri","J.","Elnaggar","I.","Kaisti","M.","Airola","A.","Koivisto","T.","Vasankari","T.","Savontaus","M."],"significance":5,"published":"2026-01-16","source_date":"2026-01-16","image":"","kennis":["https://hartvaat.nl/kennis/kleplijden/aortastenose/","https://hartvaat.nl/kennis/kleplijden/tavi-transcatheter-aortaklepimplantatie/"],"congress":"","summary_en":"Patients with severe aortic stenosis (AS) are at high mortality risk, but timely recognition is difficult and valve replacement lags. This study developed and validated a non-invasive algorithm detecting severe AS with seismocardiography (SCG): a sensor with a single-lead ECG and a three-dimensional accelerometer measuring the vibrations of cardiac activity. The model was trained and validated in 115 subjects and then tested in an independent, blinded cohort of 99 (50 AS patients, 49 age- and sex-matched controls). The algorithm correctly classified 89 of 99, with sensitivity 92%, specificity 88% and AUC 0.96. This SCG-based technique could thus be a low-cost, accessible tool to detect severe aortic stenosis.","created":"2026-07-03T10:32:28Z","updated":"2026-07-03T13:31:25Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Patiënten met ernstige aortaklepstenose (AS) hebben een hoog sterfterisico, maar tijdige herkenning is lastig en de klepvervanging blijft achter. Deze studie ontwikkelde en valideerde een niet-invasief algoritme dat ernstige AS detecteert met seismocardiografie (SCG): een sensor met een eenkanaals-ecg en een driedimensionale accelerometer die de trillingen van de hartactie meet. Het model werd getraind en gevalideerd in 115 personen en daarna getest in een onafhankelijk, geblindeerd cohort van 99 personen (50 AS-patiënten, 49 op leeftijd en geslacht gematchte controles). Het algoritme classificeerde 89 van de 99 personen correct, met een sensitiviteit van 92%, een specificiteit van 88% en een AUC van 0,96. Deze SCG-gebaseerde techniek zou dus een laagdrempelig, goedkoop hulpmiddel kunnen zijn om ernstige aortastenose op te sporen.","abstract_original":"Background\n

Patients with severe aortic stenosis (AS) are at high risk of mortality, regardless of symptom status. Despite this, aortic valve replacement rates remain low for patients with severe AS due to challenges in identifying clinically significant AS in time. This has prompted the need to develop and investigate novel diagnostic modalities. The objective of this study was to develop and validate novel, non-invasive diagnostic algorithm leveraging seismocardiography (SCG) data to detect severe AS.

\n\nMethod\n

A device capable of collecting a single-lead ECG and a three-dimensional SCG signal using a microelectromechanical-based accelerometer was used to collect sensor data. Phase 1 data were collected for training and validation of an algorithm for AS detection. Phase 2 data were collected as a blinded independent test set with age-matched and sex-matched patients as controls.

\n\nResults\n

In phase 1 of the study, 115 subjects (n=56 AS patients and n=59 controls; mean age 73.8±10.4 years) were collected for training and validation of an algorithm for AS detection. Once model development was complete, the frozen model was then evaluated in a fully independent, single blinded phase 2 cohort of 99 subjects (n=50 AS patients and n=49 controls; mean age 76.8±6.4 years) for final analysis. The algorithm accurately classified 89 out of 99 patients, with four true AS cases misclassified as controls and six true control cases misclassified as AS. The sensitivity, specificity and area under the curve of the model were 92% (95% CI 84.5% to 99.5%), 87.8% (95% CI 78.6% to 96.9%), and 96% (95% CI 91.9% to 99.9%), respectively.

\n\nConclusions\n

This SCG-based algorithm to detect severe AS demonstrated high sensitivity and specificity when tested in a blinded, age-matched and sex-matched cohort. These findings suggest that this technology may hold potential as a low-cost diagnostic tool for the detection of AS.

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