医学
比例危险模型
一致性
外科肿瘤学
多元分析
肝内胆管癌
危险系数
生存分析
多元统计
切除缘
外科
放射科
内科学
肿瘤科
切除术
统计
置信区间
数学
作者
Gaya Spolverato,Giulia Capelli,Giulia Lorenzoni,Dario Gregori,Jin He,Irinel Popescu,Hugo Marques,Luca Aldrighetti,Shishir K. Maithel,Carlo Pulitano,Todd M. Bauer,Feng Shen,George A. Poultsides,Oliver Soubrane,Guillaume Martel,Bas Groot Koerkamp,Endo Itaru,Yi Lv,Timothy M. Pawlik
标识
DOI:10.1245/s10434-022-12156-1
摘要
BackgroundThe current study aimed to develop a dynamic prognostic model for patients undergoing curative-intent resection for intrahepatic cholangiocarcinoma (ICC) using landmark analysis.MethodsPatients who underwent curative-intent surgery for ICC from 1999 to 2017 were selected from a multi-institutional international database. A landmark analysis to undertake dynamic overall survival (OS) prediction was performed. A multivariate Cox proportional hazard model was applied to measure the interaction of selected variables with time. The performance of the model was internally cross-validated via bootstrap resampling procedure. Discrimination was evaluated using the Harrell’s Concordance Index. Accuracy was evaluated with calibration plots.ResultsVariables retained in the multivariable Cox regression OS model included age, tumor size, margin status, morphologic type, histologic grade, T and N category, and tumor recurrence. The effect of several variables on OS changed over time. Results were provided as a survival plot and the predicted probability of OS at the desired time in the future. For example, a 65-year-old patient with an intraductal, T1, grade 3 or 4 ICC measuring 3 cm who underwent an R0 resection had a calculated estimated 3-year OS of 76%. The OS estimate increased if the patient had already survived 1 year (79%). The discrimination ability of the final model was very good (C-index: 0.80).ConclusionThe long-term outcome for patients undergoing curative-intent surgery for ICC should be adjusted based on follow-up time and intervening events. The model in this study showed excellent discriminative ability and performed well in the validation process.
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