JTCVS Open. 2026 Jun 27;32:101819. doi: 10.1016/j.xjon.2026.101819. eCollection 2026 Aug.
ABSTRACT
OBJECTIVE: We evaluated how event definitions, data-collection methods, and follow-up completeness influence reporting of long-term nonfatal complications after mechanical aortic valve replacement in local versus referral patients.
METHODS: We analyzed 558 patients undergoing primary mechanical aortic valve replacement from 2000 to 2023. Patients were stratified by residential distance (<50 miles, local vs >50 miles, referral) to assess completeness of follow-up. Outcomes included major bleeding, neurological thromboembolic events, and survival. Major bleeding was defined by International Society on Thrombosis and Hemostasis criteria or by restricted International Classification of Diseases codes. Events were captured by manual review and by International Classification of Diseases coding. Propensity score-matched local (n = 186) and referral groups (n = 372) were analyzed using Fine-Gray competing risk models for nonfatal outcomes and Kaplan-Meier analysis for survival.
RESULTS: Baseline and perioperative characteristics of propensity score-matched local and referral patients were comparable. Median clinical follow-up was longer in the less than 50-mile group (10.2 vs 4.1 years, P < .001). Local patients had higher crude event rates for major bleeding (40.9% vs 24.2%, P < .001) and neurological events (13.4% vs 4.0%, P < .001). These differences were mitigated but still statistically significant in time-dependent competing risk analysis. Rates varied with ascertainment method: International Classification of Diseases coding amplified differences compared with manual adjudication. Long-term survival did not differ significantly between groups (P = .44).
CONCLUSIONS: Reporting late nonfatal events after aortic valve replacement varies with event definitions, data-collection methods, and follow-up completeness. Although mortality estimates remain robust, bleeding and stroke may be underestimated in referral patients. Competing risk analysis enables more accurate interpretation of late outcomes.
PMID:42604316 | PMC:PMC13477076 | DOI:10.1016/j.xjon.2026.101819