Incremental Predictive Value of Post-Baseline Surgery and the Impact of Temporal Coding on Mortality Associations in...

Background: Risk prediction in infective endocarditis (IE) may be compromised when treatments occurring after the initial evaluation are incorporated as though they were baseline predictors. Surgical treatment is inherently time-dependent, yet subsequent surgery has been incorporated as a static predictor in contemporary prognostic models, potentially affecting both predictive performance and treatment-outcome associations. We evaluated whether adding subsequent surgery improves mortality prediction beyond information obtained during the initial index-episode evaluation and examined how temporal representation of surgery influences its association with mortality. Methods: We retrospectively studied 293 consecutive patients with IE. The primary outcome was 180-day all-cause mortality. Model A incorporated seven prespecified predictors obtained during the initial index-episode evaluation: age, NYHA functional class, hemoglobin, blood urea nitrogen, left ventricular ejection fraction, severe regurgitation, and C-reactive protein. Model B additionally incorporated a binary indicator of surgery within 180 days. Predictive performance was assessed using time-dependent AUC, Brier score, 1000-replicate bootstrap cross-validation, and temporal validation using 2008-2017 as the derivation period and 2018-2026 as the validation period. Surgery-mortality associations were compared using fixed baseline and time-dependent exposure definitions. Results: Ninety-two patients (31.4%) died within 180 days, and 122 (41.6%) underwent surgery within 180 days. In the complete-case cohort (n = 278), Model A had an apparent AUC of 0.792 and a bootstrap cross-validated AUC of 0.759. Adding future surgery produced no detectable improvement in apparent discrimination (AUC 0.793 vs. 0.792; ΔAUC +0.001, 95% CI -0.018 to +0.019) or after bootstrap cross-validation (AUC 0.749 vs. 0.759; ΔAUC -0.010, 95% CI -0.057 to +0.014). In temporal validation, AUCs were 0.731 for Model A and 0.733 for Model B. Mean predicted 180-day mortality was 27.3% versus an observed Kaplan-Meier risk of 34.7% (95% CI 25.5-42.8%); the observed-minus-predicted difference was +7.5 percentage points (bootstrap 95% CI -2.4 to +18.8). Fixed baseline coding yielded an adjusted HR of 0.71 (95% CI 0.44-1.17) in all 278 complete cases and 0.58 (95% CI 0.34-0.99) after excluding five same-day surgery/death records; in the corresponding 273-patient matched sample, time-dependent coding yielded an HR of 1.44 (95% CI 0.83-2.49). Exploratory phase-specific analyses suggested higher mortality during the early postoperative period, but the magnitude was sensitive to same-day event handling. Conclusions: In this cohort, adding a binary indicator of subsequent surgery to the prespecified seven-predictor baseline model produced no detectable improvement in 180-day mortality prediction. In contrast, the estimated surgery-mortality association changed substantially according to temporal exposure coding. Prediction models should respect the intended prediction time, while observational analyses of post-baseline treatments should explicitly account for treatment timing.