Razzaghdoust Abolfazl, Mofid Bahram, Zangeneh Masoumeh
Student Research Committee, Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Oncology, Shohada-e-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Oncol Pharm Pract. 2020 Apr;26(3):587-594. doi: 10.1177/1078155219861423. Epub 2019 Jul 18.
Chemotherapy-induced thrombocytopenia is a serious complication in chemotherapy-treated patients. Identification of patients at risk for chemotherapy-induced thrombocytopenia could have clinical value in personalized management of patients and optimized administration of prophylactic thrombopoietic agents. The aim of this study was to develop a predictive model for chemotherapy-induced thrombocytopenia (platelet count < 100,000/µl) in cancer patients undergoing chemotherapy.
A total of 14 covariates were prospectively assessed as explanatory variables in a cohort of consecutive patients with solid tumors or lymphoma. A multivariable logistic regression model was developed after univariable analysis. A bootstrapping technique was applied for internal validation.
Data from 305 patients during 1732 chemotherapy cycles were considered for analysis. Forty-eight patients (15.73%) developed chemotherapy-induced thrombocytopenia during their treatment course. The multivariable model exhibited three final predictors for chemotherapy-induced thrombocytopenia, including high ferritin (odds ratio, 4.41; bootstrap = 0.001), estimated glomerular filtration rate <60 ml/min/1.73 m (odds ratio, 3.08; bootstrap = 0.005), and body mass index <23 kg/m (odds ratio, 2.23; bootstrap = 0.044). The main characteristics of the model include sensitivity 75%, specificity 65.4%, positive likelihood ratio 2.16, and negative likelihood ratio 0.382. Moreover, the model was well calibrated (Hosmer-Lemeshow = 0.713) and the area under the receiver operating characteristic curve was 0.735 (95% confidence interval, 0.654-0.816; < 0.001).
We developed a predictive model for chemotherapy-induced thrombocytopenia based on readily available and easily assessable clinical and laboratory factors. This study may provide a valuable insight to guide optimized treatment of cancer patients. Further studies with larger sample size are warranted.
化疗引起的血小板减少是化疗患者的一种严重并发症。识别有化疗引起血小板减少风险的患者对于患者的个性化管理和预防性血小板生成药物的优化使用可能具有临床价值。本研究的目的是为接受化疗的癌症患者建立化疗引起血小板减少(血小板计数<100,000/µl)的预测模型。
在一组连续的实体瘤或淋巴瘤患者中,前瞻性评估了总共14个协变量作为解释变量。单变量分析后建立了多变量逻辑回归模型。采用自举技术进行内部验证。
分析了305例患者在1732个化疗周期中的数据。48例患者(15.73%)在治疗过程中出现化疗引起的血小板减少。多变量模型显示了化疗引起血小板减少的三个最终预测因素,包括高铁蛋白(比值比,4.41;自举法=0.001)、估计肾小球滤过率<60 ml/min/1.73 m²(比值比,3.08;自举法=0.005)和体重指数<23 kg/m²(比值比,2.23;自举法=0.044)。该模型的主要特征包括敏感性75%、特异性65.4%、阳性似然比2.16和阴性似然比0.382。此外,该模型校准良好(Hosmer-Lemeshow=0.713),受试者工作特征曲线下面积为0.735(95%置信区间,0.654-0.816;P<0.001)。
我们基于易于获得和易于评估的临床及实验室因素建立了化疗引起血小板减少的预测模型。本研究可能为指导癌症患者的优化治疗提供有价值的见解。有必要进行更大样本量的进一步研究。