摘要
目的探讨不同孤立性肺结节(SPN)临床预测模型的运用价值。方法收集我院2012年至2013年间病理资料明确诊断的SPN患者共36例(女16例,男20例,平均年龄57±11.7岁),前瞻性评估获得的临床预测模型良、恶性判别的准确率,并与文献报道的经典临床预测模型进行比较。结果36例SPN患者中恶性17例(47.2%),良性19例(52.8%)。应用先前得到的临床预测模型预测SPN的良、恶性,其灵敏度为94.1%,特异度为68.4%,阳性预测值为72.7%,阴性预测值为92.9%;曲线下面积(AUC)为0.883±0.025,Mayo模型为0.772±0.042(P=0.005),VA模型为0.747±0.039(P=0.003);H-L检验结果提示各个临床模型的拟合均较好(P>0.05)。本临床预测模型与国外同类型模型比较差异有统计学意义,本预测模型准确率更高。结论本临床预测模型对于患者具有更高的临床价值,预测效果优于其他临床预测模型。
Objective To explore the clinical value of different clinical prediction models used in Qingyuan region for patients with solitary pulmonary nodule （SPN）.Methods Continuous medical records in our hospital from 36 patients （20 men,16 women ; average age：57 ± 11.7 years old） with a definite pathologic diagnosis of SPN between 2012 to 2013 were collected to prospectively estimate the accuracy of our previous clinical prediction model on benign and malignant discrimination,and the model was also compared with two classical clinical prediction models reported in the literature.Results 47.2% （17 in 36 patients） of the nodules were malignant,and 52.8% （19 in 36 patients） were benign in this group.The accuracy of our previous model was verified with sensitivity of 94.1%,specificity of 68.4%,positive predictive value of 72.7% and negative predictive value of 92.9%.The area under the cure（AUC） for our model was 0.883 ±0.025,and 0.772 ± 0.042 in Mayo model（P =0.005 compared to our model）,0.747 ± 0.039 in VA model （P =0.003 compare to our model）,but there Was not significant statistical difference between Mayo model and VA model （P 〉 0.05）.The H-L test showed good fitting in all models（P 〉 0.05）.Overall,our model has the best precision indexed by AUC,which were statistically significant differential compared with mayo model and VA model.Conclusions Clinical prediction model that we have previously established for patients of Qingyuan region has a higher clinical value,and it＇s predictive effect is better than the other clinical prediction model.
出处
《中华肺部疾病杂志（电子版）》
CAS
2013年第5期47-49,共3页
Chinese Journal of Lung Diseases(Electronic Edition)
基金
清远市科技计划项目(00092641320613021)
关键词
孤立性肺结节
预测模型
对比分析
Solitary pulmonary nodule
Prediction model
Comparative analysis
