Abstract：
Pemetrexed plus platinum chemotherapy is the first-line treatment option for lung adenocarcinoma. However, hematological toxicity is major dose-limiting and even life-threatening. The ability to anticipate hematological toxicity is of great value for identifying potential chemotherapy beneficiaries with minimal toxicity and optimizing treatment. The study aimed to develop and validate a prediction model for hematologic toxicity based on real-world data.
Data from 1754 lung adenocarcinoma patients with pemetrexed plus platinum chemotherapy regimen as first-line therapy were used to establish and calibrate a risk model for hematological toxicity using multivariate and stepwise logistic regression analysis based on real-world data. The predictive performance of the model was tested in a validation cohort of 753 patients. An area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis were used to assess the prediction model.
5 independent factors (platinum, pre-use vitamin B12, cycle of chemotherapy before hematological toxicity, Hb before first chemotherapy, and PLT before first chemotherapy) identified from multivariate and stepwise logistic regression analysis were included in the prediction model. The hematological toxicity prediction model achieved a sensitivity of 0.840 and a specificity of 0.822. The model showed good discrimination in both cohorts (an AUC of 0.904 and 0.902 for the derivation and validation cohort ROC) at the cut-off value of 0.591. The calibration curve showed good agreement between the actual observations and the predicted results.
We developed a prediction model for hematologic toxicity with good discrimination and calibration capability in lung adenocarcinoma patients receiving a pemetrexed plus platinum chemotherapy regimen based on real-world data.
Data from 1754 lung adenocarcinoma patients with pemetrexed plus platinum chemotherapy regimen as first-line therapy were used to establish and calibrate a risk model for hematological toxicity using multivariate and stepwise logistic regression analysis based on real-world data. The predictive performance of the model was tested in a validation cohort of 753 patients. An area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis were used to assess the prediction model.
5 independent factors (platinum, pre-use vitamin B12, cycle of chemotherapy before hematological toxicity, Hb before first chemotherapy, and PLT before first chemotherapy) identified from multivariate and stepwise logistic regression analysis were included in the prediction model. The hematological toxicity prediction model achieved a sensitivity of 0.840 and a specificity of 0.822. The model showed good discrimination in both cohorts (an AUC of 0.904 and 0.902 for the derivation and validation cohort ROC) at the cut-off value of 0.591. The calibration curve showed good agreement between the actual observations and the predicted results.
We developed a prediction model for hematologic toxicity with good discrimination and calibration capability in lung adenocarcinoma patients receiving a pemetrexed plus platinum chemotherapy regimen based on real-world data.
摘要：
背景：培美曲塞联合铂类化疗是肺腺癌的一线治疗选择。然而,血液毒性是主要的剂量限制，甚至危及生命。预测血液学毒性的能力对于识别具有最小毒性的潜在化疗受益者和优化治疗具有重要价值。该研究旨在开发和验证基于真实世界数据的血液学毒性预测模型。
方法：采用培美曲塞联合铂类化疗方案一线治疗的1754例肺腺癌患者的数据，基于真实数据，采用多因素和逐步logistic回归分析，建立并校准血液学毒性风险模型。在753名患者的验证队列中测试了模型的预测性能。受试者工作特征(ROC)曲线的曲线下面积(AUC),校正曲线,和决策曲线分析用于评估预测模型。
结果：5个独立因素(铂金，使用前维生素B12，血液学毒性前化疗周期，首次化疗前Hb,和首次化疗前的PLT）从多变量和逐步逻辑回归分析中确定，并将其纳入预测模型。血液毒性预测模型的灵敏度为0.840，特异性为0.822。在截断值为0.591时，模型在两个队列中显示出良好的区分（推导和验证队列ROC的AUC为0.904和0.902）。校准曲线显示实际观测值与预测结果之间的良好一致性。
结论：我们根据实际数据，开发了一种对接受培美曲塞联合铂类化疗方案的肺腺癌患者的血液学毒性的预测模型，该模型具有良好的辨别和校准能力。
方法：采用培美曲塞联合铂类化疗方案一线治疗的1754例肺腺癌患者的数据，基于真实数据，采用多因素和逐步logistic回归分析，建立并校准血液学毒性风险模型。在753名患者的验证队列中测试了模型的预测性能。受试者工作特征(ROC)曲线的曲线下面积(AUC),校正曲线,和决策曲线分析用于评估预测模型。
结果：5个独立因素(铂金，使用前维生素B12，血液学毒性前化疗周期，首次化疗前Hb,和首次化疗前的PLT）从多变量和逐步逻辑回归分析中确定，并将其纳入预测模型。血液毒性预测模型的灵敏度为0.840，特异性为0.822。在截断值为0.591时，模型在两个队列中显示出良好的区分（推导和验证队列ROC的AUC为0.904和0.902）。校准曲线显示实际观测值与预测结果之间的良好一致性。
结论：我们根据实际数据，开发了一种对接受培美曲塞联合铂类化疗方案的肺腺癌患者的血液学毒性的预测模型，该模型具有良好的辨别和校准能力。
