A Nomogram for Predicting the Severity of COPD Based on CT Voxel Morphometrimetry
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摘要:
目的：探讨基于CT体素的定量指标，分析慢性阻塞性肺疾病（COPD）严重程度相关的危险因素，建立个体化预测重度COPD的列线图模型。方法：收集2020年5月至2021年9月纳入延安大学附属医院行双气相扫描及肺功能检查确诊的COPD患者，最终符合条件的COPD患者共计118例；按照COPD（GOLD）严重程度分级（组），将患者分为轻度COPD组（GOLDⅠ级和Ⅱ级）：66例；重度COPD组（GOLD Ⅲ级和Ⅳ级）：52例；比较轻、重度COPD组间双气相配准CT定量指标及临床指标的差异；应用SPSS及R软件进行统计分析，建立列线图模型，分析重度COPD的独立风险因素。结果：双气相配准CT定量指标功能性小气道疾病区百分比（fSAD%），重度COPD组肺气肿区百分比（Emph%）高于轻度COPD组；随着COPD严重程度增加，肺功能指标FVC、FEV1% pred和FEV1/FVC%均下降；重度COPD组吸烟指数高于轻度COPD组，与之对应重度COPD组fSAD%和Emph%高于轻度COPD组；分析发现，吸烟指数及CT定量指标（fSAD%、Emph%及Normal%）是重度COPD的独立风险因子，进一步建立预测重度COPD的风险模型，CT定量指标较吸烟指数在患者死亡风险权重中相对影响较大；通过校准图对列线图预测模型进行内部验证，校准图显示模型校准曲线与标准曲线接近。结论：基于CT体素的定量指标可预测COPD严重程度，通过CT定量指标和吸烟指数建立预测COPD严重程度的列线图模型具有良好的诊断效能。
Abstract:Objective: In order to explore the quantitative indicators based on CT voxel, analyze the risk factors related to the severity of chronic obstructive pulmonary disease (COPD), and establish an individualized nomogram model for predicting severe COPD. Methods: From May 2020 to September 2021, a total of 118 eligible COPD patients were enrolled in the Affiliated Hospital of Yan’an University after dual gas phase scanning and pulmonary function tests. According to the severity classification of COPD (GOLD) (group), the patients were divided into mild COPD group (GOLD I, II): 66 cases; Severe COPD group (GOLD III, IV): 52 cases. The quantitative and clinical indexes of dual-gas phase registration CT between mild and severe COPD groups were compared. SPSS and R software were used for statistical analysis, and a nomogram model was established to analyze the independent risk factors of severe COPD. Results: The percentage of functional small-airway disease area (fSAD%) and the that of emphysema area (Emph%) in the severe COPD group were higher than those in the mild COPD group. With the increase of COPD severity, the pulmonary function indexes FVC, FEV1%pred, and FEV1/FVC% decreased. The smoking index, fSAD%, and Emph% of the severe COPD group were higher than those of the mild COPD group. Through analysis, it was found that the smoking index and CT quantitative indicators (fSAD%, Empire%, and Normal%) are independent risk factors for severe COPD. Furthermore, a risk model for predicting severe COPD was established. CT quantitative indicators have a relatively greater impact on the weight of patient mortality risk than smoking index. The column chart prediction model was internally validated through a calibration chart, which showed that the model calibration curve was close to the standard curve. Conclusion: Quantitative indicators based on CT voxels can predict the severity of COPD. The established column chart model for predicting COPD severity through CT quantitative indicators and smoking index has good diagnostic efficacy.
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Keywords:
- CT /
- COPD /
- PFT /
- nomogram /
- risk factors
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图 1 双气相配准图
注：男59岁，COPD患者，GOLD II级，双气相配准流程图。（a）吸气相CT；（b）呼气相CT图；（c）呼气相与吸气相配准图。 黄色区为小气道病变区，红色区代表肺气肿区，绿色为正常区。
Figure 1. Two-gas phase registration diagram
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图 4 筛选出的变量预测患者重度COPD风险的ROC曲线
Figure 4. ROC curves of the screened variables predicting the risk of severe COPD in patients
表 1 一般情况
Table 1 General situation
变量 组别 统计检验 轻度COPD组 重度COPD组 t/Z P 年龄 64.95±8.54 63.02±9.61 1.155 0.250 BMI 23.39±3.23 21.99±3.14 1.719 0.091 吸烟指数 619.77±629.31 1202.13 ±849.72−4.138 0.000 FVC 2.96±0.90 2.05±0.55 6.574 0.000 FEV1% pred 66.93±17.91 32.4±8.10 13.953 0.000 FEV1/FVC% 60.73±12.47 47.03±15.01 5.411 0.000 Normal(%) 37.71±15.13 28.71±14.24 −3.499 0.000 Empha(%) 17.75±9.83 24.49±12.31 −2.959 0.003 fSAD(%) 30.73±9.96 35.08±6.83 −2.802 0.006 注：Normal为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。 
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表 2 多因素Logistic回归分析结果
Table 2 Results of multivariate Logistic regression analysis
变量 B SE Wals P OR 95% CI 年龄 0.026 0.036 0.514 0.473 1.026 0.957~1.100 BMI −0.086 0.097 0.795 0.373 0.917 0.759~1.109 吸烟指数 0.001 0.001 4.207 0.040 1.001 1.000~1.002 Normal % 0.305 0.139 4.774 0.029 1.356 1.032~1.783 Empha % 0.394 0.170 5.336 0.021 1.482 1.061~2.070 fSAD % 0.226 0.105 4.665 0.031 1.253 1.021~1.538 注：Normal为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。B为各自变量不同分类水平在模型中的系数；SE为标准误差；Wals为检验每个自变量的系数是否显著；P为统计值；OR为优势比；95% CI为95%置信区间。
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表 3 重度COPD组危险因素、Logistic模型的ROC预测价值
Table 3 Risk factors in the severe COPD group and the predictive values of Logistic models for ROC
变量 截断值 AUC 95%CI 敏感度 特异度 P 吸烟指数 0.379 0.722 0.631~0.813 0.712 0.667 0.000 Normal% 0.365 0.688 0.590~0.786 0.788 0.577 0.000 Empha% 0.352 0.659 0.557~0.761 0.519 0.833 0.003 fSAD% 0.343 0.669 0.571~0.766 0.904 0.439 0.002 Logistic regression model 0.461 0.786 0.704~0.867 0.673 0.788 0.000 注：Normal(%)为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。
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