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An ARCAD Database Nomogram for Prognostic Prediction of Treatment Refractory Metastatic Colorectal Cancer.

Journal of Clinical Oncology(2024)

Cited 0|Views5
Abstract
107 Background: The "No Placebo Initiative" demonstrated that the survivals of patients (pts) receiving placebo in a salvage-line setting, exhibited no significant difference across trials (CORRECT, RECOURSE, CONCUR and TERRA). Therefore, we believe that a nomogram can be generated using the ARCAD database to predict survival of pts who completed standard therapy. Methods: Of 40,889 Individual patient data (IPD) from 59 studies, 723 pts received placebo from 4 randomized studies were pooled. The potential prognostic factors considered include BMI and Age. Imputed datasets were constructed by a multiple imputation method since the original dataset contains some missing values. Overall survival (OS) was defined as the time from randomization to death from any cause. Multivariate Cox proportional hazards models for OS were estimated based on the variables showing clinically and statistically significant by univariate analysis. Prediction performance of the final model was evaluated using the concordance index for survival data and calibration plots. Results: From univariate and multivariate analysis, ECOG performance status, Royal Marsden Hospital score (calculated by albumin level, LDH level, and number of metastatic site), primary site location, liver meta, peritoneal meta were selected as variables. We successfully developed a highly accurate nomogram to predict post-standard treatment prognosis for pts with mCRC. Concordance index for OS was 0.66. Conclusions: Although further validation in other cohorts is needed, this nomogram could be very useful to estimate the survival for clinical practice and enrolling in clinical trials. [Table: see text]
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要点】:本文通过分析ARCAD数据库,开发了一种诺模图用于预测完成标准治疗后转移性结直肠癌患者的生存期,提高了预后预测的准确性。

方法】:采用多变量Cox比例风险模型,基于723名接受安慰剂的患者的个体数据,筛选出影响预后的重要因素,构建诺模图。

实验】:使用来自59项研究的40,889名患者的个体数据,通过多重插补方法处理缺失值,最终开发出的诺模图在总体生存期预测上的符合指数为0.66,表现出了较高的预测精度。