【摘要】 目的 分析卵巢癌治疗后血小板减少的影响因素，构建列线图预测模型。方法 选取129例卵巢癌患者，依据治疗后血小板减少情况分为对照组（n=51，治疗后没有出现血小板减少）和研究组（n=78，治疗后出现血小板减少）。卵巢癌治疗后血小板减少的影响因素采用多因素Logistic回归分析，据此构建卵巢癌治疗后血小板减少的列线图预测模型，采用受试者工作特征（ROC）曲线、Hosmer-Lemeshow拟合优度检验、校准曲线、临床决策曲线判断模型的性能。结果 单因素分析结果显示，两组患者年龄、病理类型、治疗前白细胞计数、治疗前血小板计数以及治疗前血红蛋白、总胆红素水平比较，差异均有统计学意义（P﹤0.05）。多因素Logistic回归模型分析结果显示，年龄≤59岁、病理类型为高级别浆液性癌均是卵巢癌治疗后血小板减少的独立危险因素（P﹤0.05）；治疗前血小板计数、血红蛋白水平升高均是卵巢癌治疗后血小板减少的保护因素（P﹤0.05）。据此构建预测卵巢癌治疗后血小板减少发生风险的列线图模型，该模型预测卵巢癌治疗后血小板减少的曲线下面积（AUC）为0.889(95%CI:0.832～0.947)，具有良好的预测价值、拟合度、区分能力以及较高的临床获益。结论 年龄≤59岁、病理类型为高级别浆液性癌均是卵巢癌治疗后血小板减少的独立危险因素，治疗前血小板计数、血红蛋白水平升高均是卵巢癌治疗后血小板减少的保护因素，据此构建的列线图模型具有良好的预测性能。更多还原

【Abstract】 Objective To analyze the influencing factors of thrombocytopenia after ovarian cancer treatment, and to construct a nomogram prediction model. Method A total of 129 ovarian cancer patients were selected and divided into control group(n=51, no thrombocytopenia after treatment) and study group(n=78, thrombocytopenia after treatment)based on their thrombocytopenia status after treatment. The influencing factors of thrombocytopenia after ovarian cancer treatment were analyzed by multivariate Logistic regression. Based on this, a nomogram prediction model for thrombocytopenia after ovarian cancer treatment was constructed. The performance of the model was evaluated by receiver operating characteristic(ROC) curve, Hosmer-Lemeshow goodness of fit test, calibration curve, and clinical decision curve. RReesult Univariate analysis results showed that there were statistically significant differences in age, pathological type, pretreatment white blood cell count, platelet count, hemoglobin levels, and total bilirubin levels between the two groups(P<0.05). Multivariate Logistic regression analysis results showed that age≤59 years and pathological type of high-grade serous carcinoma were independent risk factors for thrombocytopenia after ovarian cancer treatment(P<0.05); the elevated pre-treatment platelet count and hemoglobin levels before treatment were protective factors for thrombocytopenia after ovarian cancer treatment(P<0.05). Based on this, a nomogram prediction model was constructed to predict the risk of thrombocytopenia after ovarian cancer treatment. The area under the curve(AUC) of nomogram prediction model in predicting thrombocytopenia after ovarian cancer treatment was 0.889(95%CI: 0.832-0.947), which had good predictive value, fit, discrimination ability, and high clinical benefits. Conclusion Age≤59 years and pathological type of high-grade serous carcinoma are independent risk factors for thrombocytopenia after ovarian cancer treatment, elevated pre-treatment platelet count and hemoglobin levels are protective factors for thrombocytopenia after ovarian cancer treatment. The nomogram prediction model constructed based on this has good predictive performance.更多还原