Transcatheter Versus Surgical Valve Replacement: A 24-months Propensity-matched Analysis of the SILCARD Registry

Abstract

Background Data concerning the comparison between transcatheter aortic valve implantation and surgical aortic valve replacement in a real-world setting are scarce and in Central and Eastern Europe no such data exist. In this study, we aimed at analyzing retrospectively the characteristics and outcome of patients with aortic stenosis treated either with surgical aortic valve replacement or transcatheter aortic valve implantation between 2006 and 2016 in the Silesian Province, Poland in a representative real-world cohort. Methods In the Silesian Cardiovascular Database we retrospectively identified 5186 patients who received either transcatheter aortic valve implantation or surgical aortic valve replacement in 1 of 3 tertiary cardiovascular centers. Baseline characteristics, including relevant clinical history, and outcomes were compared before and after propensity-score matching of both groups, with 348 pairs of patients constituting the propensity-matched study cohort. The primary end-point was 24-month all-cause mortality. Results Preoperative characteristics of propensity-matched groups were similar. There was no difference between transcatheter aortic valve implantation and surgical aortic valve replacement groups with respect to the death rate at 2 years (19.9% vs. 15.6%; P = .479). In the transcatheter aortic valve implantation group, cardiac resynchronization therapy devices were more frequently implanted after the procedure (3.7% vs. 0.0, P < .001). The groups had similar rates of myocardial infarction, stroke, and re-hospitalization. Hospital stay in the matched groups was shorter after transcatheter aortic valve implantation: 14.1 versus 15.7 days ( P < .001). Conclusions At 24 months, transcatheter aortic valve implantation patients had similar outcomes as surgical aortic valve replacement except for a higher rate of cardiac resynchronization therapy device implantation and shorter hospital stay.

Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases

“…The primary ICD-10 and ICD-9 codes were used for statistical analysis, reporting high prevalences of HF and CADs, as well as declining trend in 1-year mortality among CVD patients [ 57 ]. Similarly, populational trend evaluations were performed for atrial fibrillation [ 58 ], left atrial appendage occlusion procedures [ 59 ], transcatheter aortic valve implantation and surgical aortic valve replacement operations [ 60 ], implantable cardioverter-defibrillators and cardiac resynchronization therapy [ 61 ]. Furthermore, NLP technology allows for the in-depth EHR assessment of social determinants, which are non-medical factors impacting patient health outcomes [ 62 , 63 , 64 ].…”