PDF(Pubmed)
Abstract：
BACKGROUND: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.
METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike\'s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model\'s performance.
RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.
CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.
METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike\'s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model\'s performance.
RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.
CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.
摘要：
背景：早期识别顺铂诱导的肾毒性（CIN）高危个体对于避免CIN和改善预后至关重要。在这项研究中,我们基于一般临床数据开发并验证了aCIN预测模型，实验室适应症,肺癌患者化疗前的遗传特征。
方法：我们回顾性纳入了2019年6月至2021年6月使用铂类化疗方案的696例肺癌患者作为使用绝对收缩和选择算子（LASSO）回归构建预测模型的追踪集，交叉验证，和Akaike的信息准则(AIC)来选择重要变量。我们前瞻性选择了2021年7月至2022年12月的283名独立肺癌患者作为测试集，以评估模型的性能。
结果：预测模型显示出良好的判别和校准，AUC分别为0.9217和0.8288，灵敏度分别为79.89%和45.07%，特异性为94.48%和94.81%，分别在训练集和测试集中。临床决策曲线分析表明，当风险阈值在0.1和0.9之间时，该模型具有临床应用价值。以0.5到0.75的召回间隔显示的精度-召回（PR）曲线：随着召回的增加，精度逐渐下降，到0.9。
结论：基于实验室和人口统计学变量的预测模型可以作为识别CIN高危人群的有益补充工具。
方法：我们回顾性纳入了2019年6月至2021年6月使用铂类化疗方案的696例肺癌患者作为使用绝对收缩和选择算子（LASSO）回归构建预测模型的追踪集，交叉验证，和Akaike的信息准则(AIC)来选择重要变量。我们前瞻性选择了2021年7月至2022年12月的283名独立肺癌患者作为测试集，以评估模型的性能。
结果：预测模型显示出良好的判别和校准，AUC分别为0.9217和0.8288，灵敏度分别为79.89%和45.07%，特异性为94.48%和94.81%，分别在训练集和测试集中。临床决策曲线分析表明，当风险阈值在0.1和0.9之间时，该模型具有临床应用价值。以0.5到0.75的召回间隔显示的精度-召回（PR）曲线：随着召回的增加，精度逐渐下降，到0.9。
结论：基于实验室和人口统计学变量的预测模型可以作为识别CIN高危人群的有益补充工具。
