Clinical Research Directory

OPTimising a Screening Program to Detect Pacemaker-associated Heart Failure Using Artificial Intelligence

Sponsor: University of Leeds

Summary

Pacemakers are an effective treatment for slow heart rates which improve symptoms and save lives. However, for some people pacemakers can cause heart failure (HF) because of the unnatural way in which they stimulate heart beats. In several studies conducted in West Yorkshire we showed that \~1/3 of patients with pacemakers have undiagnosed HF. We also showed that where HF is discovered, treating it with safe and inexpensive medications reduces the chances of being admitted to hospital or dying. However, detecting HF requires an echocardiogram (a heart ultrasound scan) which takes \~45 minutes, requires a skilled technician, and costs £120; or, to put it another way \~£540,000 to assess the \~4,500 patients cared for at our hospital. A new approach is needed. We think that using new technologies can improve our ability to screen for HF in people with pacemakers. We will test two approaches. First, we will assess whether a hand-held echocardiogram can measure heart function using artificial intelligence (AI) as accurately as a standard echocardiogram done by a skilled technician. Second, we will assess whether a finger-prick blood test can detect the presence of abnormal function as accurately as a standard echocardiogram.

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