临床参数、药物使用和免疫生物标志物预测瑞士多中心 COPD 队列研究（TOPDOCS）中的急性 COPD 加重
Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers.
机构信息
University Clinic of Medicine, Cantonal Hospital Baselland, Liestal, Switzerland,
Faculty of Medicine, University of Basel, Basel, Switzerland,
出版信息
Respiration. 2022;101(5):441-454. doi: 10.1159/000520196. Epub 2021 Dec 23.
BACKGROUND AND OBJECTIVE
Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown.
METHOD
We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from "The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland" cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD.
RESULTS
Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD.
CONCLUSIONS
Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.
背景和目的
在无加重期时测量的免疫学生物标志物与临床特征是否可预测慢性阻塞性肺疾病（COPD）急性加重（AECOPD）的频率和严重程度尚不清楚。
方法
我们在“瑞士阻塞性肺病结局队列”队列中单次测量了 271 例稳定期 COPD 患者（67%为男性，平均年龄 63 岁）的免疫学生物标志物和临床特征。178 例患者有 1 年的随访数据。多变量回归分析确定与 AECOPD 频率和严重程度独立相关的变量。采用受试者工作特征分析获得最佳截断值，并测量曲线下面积（AUC），以评估基线数据是否可用于预测未来的 AECOPD。
结果
更多的 COPD 药物（校正发病风险比 [aIRR] 1.17）和血小板计数（aIRR 1.03），以及较低的 FEV1%预计值（aIRR 0.84）和 IgG2（aIRR 0.84）与基线前 1 年的 AECOPD 频率独立相关。经历频繁（>1 次）AECOPD 的最佳截断值为≥3 种 COPD 药物（AUC=0.72）、FEV1≤40%预计值（AUC=0.72）和 IgG2≤2.6 g/L（AUC=0.64）。使用临床和生物标志物参数预测同一患者未来频繁 AECOPD 事件的模型性能为中等（AUC=0.78），但并不优于仅使用临床参数的模型（AUC=0.79）。IFN-λ rs8099917 GG 基因型在发生严重 AECOPD 的患者中更为常见。
结论
在评估前和评估后 1 年，单次评估的临床和生物标志物参数与 AECOPD 事件的频率相关。然而，只有临床参数在识别可能发生频繁 AECOPD 的患者方面具有中等的鉴别能力。
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