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基于血液学/身体参数的中国结直肠癌患者化疗相关不良反应风险预测模型。

Risk prediction models based on hematological/body parameters for chemotherapy-induced adverse effects in Chinese colorectal cancer patients.

机构信息

Department of Pharmacy, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.

School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China.

出版信息

Support Care Cancer. 2021 Dec;29(12):7931-7947. doi: 10.1007/s00520-021-06337-z. Epub 2021 Jul 2.

Abstract

PURPOSE

To determine risk factors and develop novel prediction models for chemotherapy-induced adverse effects (CIAEs) in Chinese colorectal cancer (CRC) patients receiving capecitabine.

METHODS

A total of 233 Chinese CRC patients receiving post-operative chemotherapy with capecitabine were randomly divided into a training set (70%) and a validation set (30%). CIAE-related hematological/body parameters were screened by univariate logistic regression. Based on a set of factors selected from LASSO (least absolute shrinkage and selection operator) logistic regression, stepwise multivariate logistic regression was applied to develop prediction models. Area under the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (HL) test were used to evaluate the discriminatory ability and the goodness of fit of each model.

RESULTS

In total, 35 variables were identified to be associated with CIAEs in univariate analysis. Developed multivariable models had AUCs (area under curve) ranging from 0.625 to 0.888 and 0.428 to 0.760 in the training and validation set, respectively. The grade ≥ 1 anemia multivariable model achieved the best discriminatory ability with AUC of 0.760 (95%CI: 0.609-0.912) and good calibration with HL P value of 0.450. Then, a nomogram was constructed to predict grade ≥ 1 anemia, which included variables of age, pre-operative hemoglobin count, and pre-operative albumin count, with C-indexes of 0.775 and 0.806 in the training and validation set, respectively.

CONCLUSIONS

This study identified valuable hematological/body parameters related to CIAEs. A nomogram based on the multivariable model including three hematological/body predictors can accurately predict grade ≥ 1 anemia, facilitating clinicians to implement personalized medicine early for Chinese CRC patients receiving post-operative chemotherapy for better safety treatment. Trial registration This study was registered as a clinical trial at www.clinicaltrials.gov (NCT03030508).

摘要

目的

确定中国结直肠癌(CRC)患者接受卡培他滨化疗后发生化疗相关不良事件(CIAEs)的危险因素,并建立新的预测模型。

方法

本研究共纳入 233 例接受卡培他滨辅助化疗的中国 CRC 患者,将其随机分为训练集(70%)和验证集(30%)。采用单因素逻辑回归筛选与 CIAE 相关的血液学/体参数。基于 LASSO(最小绝对收缩和选择算子)逻辑回归选择的一组因素,采用逐步多因素逻辑回归建立预测模型。通过受试者工作特征(ROC)曲线下面积(AUC)和 Hosmer-Lemeshow(HL)检验评估各模型的判别能力和拟合优度。

结果

单因素分析共确定了 35 个与 CIAEs 相关的变量。在训练集和验证集中,多变量模型的 AUC 范围分别为 0.625 至 0.888 和 0.428 至 0.760。分级≥1 度贫血的多变量模型具有最佳的判别能力,AUC 为 0.760(95%CI:0.609-0.912),HL P 值为 0.450,校准良好。然后,构建了一个预测分级≥1 度贫血的列线图,该模型包含年龄、术前血红蛋白计数和术前白蛋白计数三个变量,在训练集和验证集中的 C 指数分别为 0.775 和 0.806。

结论

本研究确定了与 CIAEs 相关的有价值的血液学/体参数。基于包含三个血液学/体学预测因子的多变量模型的列线图可准确预测分级≥1 度贫血,有助于临床医生为接受术后化疗的中国 CRC 患者实施个体化治疗,以提高安全性。

临床试验注册

本研究在 www.clinicaltrials.gov 上注册为临床试验(NCT03030508)。

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