Incremental Predictive Value of Post-Baseline Surgery and the Impact of Temporal Coding on Mortality Associations in Infective Endocarditis: A Temporally Validated Cohort Study.
Banu Karaca, Aykan Çelik, Mert İnan, Berke Ege, Mustafa Karaca
{"title":"Incremental Predictive Value of Post-Baseline Surgery and the Impact of Temporal Coding on Mortality Associations in Infective Endocarditis: A Temporally Validated Cohort Study.","authors":"Banu Karaca, Aykan Çelik, Mert İnan, Berke Ege, Mustafa Karaca","doi":"10.3390/diagnostics16193209","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> Ninety-two patients (31.4%) died within 180 days, and 122 (41.6%) underwent surgery within 180 days. In the complete-case cohort (<i>n</i> = 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. <b>Conclusions:</b> 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.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"16 19","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13664497/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/diagnostics16193209","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
背景:感染性心内膜炎(IE)的风险预测可能会受到影响,当在初始评估后进行的治疗被纳入基线预测指标时。手术治疗本质上是时间依赖的,但随后的手术已被纳入当代预后模型的静态预测指标,可能影响预测性能和治疗结果的关联。我们评估了在初始指数-事件评估中获得的信息之外,增加后续手术是否能提高死亡率预测,并检查了手术的时间表征如何影响其与死亡率的关联。方法:对293例IE患者进行回顾性研究。主要终点为180天全因死亡率。模型A纳入了在初始指标-事件评估中获得的七个预先指定的预测因子:年龄、NYHA功能等级、血红蛋白、血尿素氮、左心室射血分数、严重反流和c反应蛋白。模型B还加入了180天内手术的二元指标。采用时间相关AUC、Brier评分、1000次重复自助交叉验证和时间验证来评估预测性能,并以2008-2017年为推导期,2018-2026年为验证期。使用固定基线和时间依赖暴露定义比较手术与死亡率的关联。结果:180 d内死亡92例(31.4%),180 d内手术122例(41.6%)。在全病例队列(n = 278)中,模型A的表观AUC为0.792,bootstrap交叉验证的AUC为0.759。增加以后的手术并没有产生明显的改善(AUC 0.793比0.792;ΔAUC +0.001, 95% CI -0.018至+0.019)或自举交叉验证后(AUC 0.749比0.759;ΔAUC -0.010, 95% CI -0.057至+0.014)。在时间验证中,模型A的auc为0.731,模型b的auc为0.733。平均预测180天死亡率为27.3%,而观察到的Kaplan-Meier风险为34.7% (95% CI 25.5-42.8%);观察到的减去预测的差异为+7.5个百分点(bootstrap 95% CI -2.4至+18.8)。在所有278例完整病例中,固定基线编码的调整风险比为0.71 (95% CI 0.44-1.17),在排除5例当日手术/死亡记录后,调整风险比为0.58 (95% CI 0.34-0.99);在相应的273例患者匹配样本中,时间相关编码的风险比为1.44 (95% CI 0.83-2.49)。探索性阶段特定分析表明,术后早期死亡率较高,但其程度对当日事件处理很敏感。结论:在该队列中,在预先设定的7个预测指标基线模型中加入后续手术的二元指标,对180天死亡率预测没有明显改善。相比之下,根据时间暴露编码,估计的手术死亡率关联发生了实质性变化。预测模型应尊重预期的预测时间,而基线后治疗的观察性分析应明确考虑治疗时间。
DiagnosticsBiochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍:
Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.