人口统计学和疾病特征与慢性阻塞性肺疾病(COPD)恶化的风险有关。使用以前收集的跨国临床试验数据,我们建立了模型,利用基线危险因素来预测患者在下一年接受各种药物治疗的COPD患者中/重度加重的发生率.
来自ETHOS中20,054名患者的恶化数据,KRONOS,TELOS,索福斯,和PINNACLE-1,PINNACLE-2和PINNACLE-4研究被合并。机器学习用于确定中度/重度加重率的预测因子。选择了重要因素进行广义线性建模,进一步由反向变量选择提供信息。保留一个独立的测试集以进行验证。
先前的恶化,嗜酸性粒细胞计数,预计用力呼气量为1%,先前的维护治疗,使用缓解药物,性别,COPD评估测试评分,吸烟状况,和地区是恶化风险的重要预测因素,随着对吸入性皮质类固醇(ICSs)的反应随着嗜酸性粒细胞计数的增加而增加,更多先前的恶化,或额外的先前治疗。训练集和测试集的模型拟合相似。全模型的预测指标比仅基于嗜酸性粒细胞计数的简化模型好约10%,先前的恶化,和ICS使用。
这些预测中度/重度加重率的模型可应用于广泛的COPD患者气道阻塞,嗜酸性粒细胞计数,恶化史,症状,和治疗史。了解与这些因素相关的相对和绝对风险可能有助于临床医生评估个体患者各种治疗决策的益处:风险比。在www上注册的临床试验。clinicaltrials.gov(NCT02465567、NCT02497001、NCT02766608、NCT027660、NCT01854645、NCT01854658、NCT02343458、NCT03262012、NCT02536508和NCT01970878)。
Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual\'s rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD.
Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation.
Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use.
These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients.Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878).