Key result
Real-time ECG and BP algorithm predicts syncope onset during tilt testing with ~82% sensitivity.
Why the study?
Head-up tilt testing triggers distressing symptoms including syncope, creating a need to predict vasovagal syncope before onset using electrocardiography and blood pressure data.
Does an algorithm using ECG and BP recordings predict vasovagal syncope before its onset during head-up tilt table testing?
Observational (n=137)
Does an algorithm using ECG and BP recordings predict vasovagal syncope before its onset during head-up tilt table testing?
An algorithm using real-time ECG and blood pressure data during head-up tilt table testing can predict vasovagal syncope several minutes before its onset, potentially reducing patient discomfort.
May allow preemptive measures to reduce tilt-test discomfort; hypothesis-generating pending prospective validation.
Vasovagal syncope (VVS) is the commonest cause of short-term loss of consciousness, which negatively impacts quality of life. To gather diagnostic information, medical professions often perform a head-up tilt test (HUTT) during direct observation. During this test, subjects may experience common symptoms such as nausea, pallor, sweating, palpitations, near faint and syncope. The purpose of the study was to develop an algorithm that uses electrocardiography (ECG) and blood pressure (BP) recordings from HUTT to predict VVS before its onset. In this study, the calculated cumulative risk based on the analysis of the three specific sets of features was compared to a pre-established VVS risk threshold. The purpose of this comparison was to determine if the cumulative risk was above or below the threshold and whether an alert should be generated. An alert would only be triggered when the cumulative risk exceeded the threshold. The prediction time was defined as the duration between the first alert and the actual syncope episode. A total of 137 subjects were recruited in this study. Our proposed model accurately predicted syncope onset in 87 out of 120 subjects. The model's sensitivity was 81.6% while its specificity was 66.2%. The precision was determined to be 62.5%, the F1 score was 70.8%. Additionally, the model was able to predict syncope before its onset with a median prediction time of 221.45 seconds (Interquartile range: 180.0 - 294.0 s). In conclusion, while predicting VVS can be challenging due to its complex nature, recognizing, and treating the underlying causes as well as implementing appropriate treatment methods, can significantly improve outcomes for individuals at risk. The proposed algorithm shows promise in reducing discomfort associated with symptom reproduction with HUTT.
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Ferdowsi et al. (2023) conducted an observational in Vasovagal syncope (n=137). Real-time prediction algorithm using ECG and BP recordings was evaluated on Prediction of syncope onset. A real-time prediction algorithm using ECG and BP recordings during head-up tilt testing predicted syncope onset with 81.6% sensitivity, 66.2% specificity, and a median prediction time of 221.45s.
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