Futility Risk Model for Predicting Outcome After Transcatheter Aortic Valve Implantation

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Lantelme, Pierre | Lacour, Thibaud | Bisson, Arnaud | Herbert, Julien | Ivanes, Fabrice | Bourguignon, Thierry | Quilliet, Laurent | Angoulvant, Denis | Harbaoui, Brahim | Babuty, Dominique | Etienne, Christophe Saint | Deharo, Pierre | Bernard, Anne | Fauchier, Laurent

Edité par CCSD ; Elsevier -

International audience. Risk-benefit assessment for transcatheter aortic valve implantation (TAVI) is still a matter of debate. We aimed to identify patients with a bad outcome within 1 year after TAVI, and to develop a Futile TAVI Simple score (FTS). Based on the administrative hospital-discharge database, all consecutive patients treated with percutaneous TAVI in France between 2010 and 2018 were included. A prediction model was derived and validated for 1-year all-cause death after TAVI (considered as futility) by using split-sample validation: 20,443 patients were included in the analysis (mean age 83 +/- 7 years). 7,039 deaths were recorded (yearly incidence rate 15.5%), among which 3,702 (53%) occurred in first year after TAVI procedure. In the derivation cohort (n = 10,221), the final logistic regression model included male sex, history of hospital stay with heart failure, history of pulmonary oedema, atrial fibrillation, previous stroke, vascular disease, renal disease, liver disease, pulmonary disease, anaemia, history of cancer, metastasis, depression and denutrition. The area under the curve (AUC) for the FTS was 0.674 (95%CI 0.660 to 0.687) in the derivation cohort and 0.651 (95%CI 0.637 to 0.665) in the validation cohort (n = 10,222). The Hosmer-Lemeshow test had a p-value of 0.87 suggesting an accurate calibration. The FTS score outperformed EuroSCORE II, Charlson comorbidity index and frailty index for identifying futility. Based on FTS score, 7% of these patients were categorized at high risk with a 1-year mortality at 43%. In conclusion, the FTS score, established from a large nationwide cohort of patients treated with TAVI, may provide a relevant tool for optimizing healthcare decision.

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