Introduction: Transcatheter aortic valve replacement (TAVR) is increasingly in demand for treating severe aortic stenosis in a variety of surgical risk profiles. This means increasing wait times and elevated morbidity and mortality on the waitlist. To address this, we developed the SWIFT TAVR algorithm to prioritize patients based on clinical risk and reduce wait times.
Methods: The SWIFT algorithm, implemented in Microsoft Excel, calculates a clinical risk score from three parameters: left ventricular ejection fraction (LVEF), peak aortic valve gradient, and syncope. Scores categorize patients into four prioritisation profiles: high (9-10 points), intermediate (4-8 points), low (2-3 points), and minimal (0-1 point). The study prospectively applied the SWIFT algorithm to patients in 2022 (SWIFT group) and retrospectively to a 2021 cohort (CONTROL group). Outcomes measured were wait times from consultation to procedure and major adverse cardiac events (MACE) while awaiting TAVR.
Results: A total of 228 patients were included (117 SWIFT, 111 CONTROL). There was no significant difference in baseline characteristics between groups (p > 0.05). Overall wait times were significantly shorter in the SWIFT group (21 vs 28 weeks, p < 0.001), particularly for high-risk patients (12 vs 31 weeks, p < 0.001). MACE rates were similar (9 % vs 10 %, p = 0.722).
Discussion: The SWIFT algorithm significantly reduced wait times, particularly for high-risk patients, without increasing MACE rates. This automated, risk-based prioritisation tool improves equity and efficiency in TAVR waitlist management and is globally applicable. Further randomized studies are warranted to validate these findings.
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