急性慢阻肺加重预测工具（ACCEPT）：一项建模研究。
The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study.
机构信息
Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada.
Division of Respiratory Medicine, Department of Medicine, The UBC Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada.
出版信息
Lancet Respir Med. 2020 Oct;8(10):1013-1021. doi: 10.1016/S2213-2600(19)30397-2. Epub 2020 Mar 13.
BACKGROUND
Accurate prediction of exacerbation risk enables personalised care for patients with chronic obstructive pulmonary disease (COPD). We developed and validated a generalisable model to predict individualised rate and severity of COPD exacerbations.
METHODS
In this risk modelling study, we pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over 1 year. Severe exacerbations were those requiring inpatient care. Predictors were history of exacerbations, age, sex, body-mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE), a multicentre cohort study, was used for external validation.
RESULTS
The development dataset included 2380 patients, 1373 (58%) of whom were men. Mean age was 64·7 years (SD 8·8). Mean exacerbation rate was 1·42 events per year and 0·29 events per year were severe. When validated against all patients with COPD in ECLIPSE (mean exacerbation rate was 1·20 events per year, 0·27 events per year were severe), the area-under-curve (AUC) was 0·81 (95% CI 0·79-0·83) for at least two exacerbations and 0·77 (95% CI 0·74-0·80) for at least one severe exacerbation. Predicted exacerbation and observed exacerbation rates were similar (1·31 events per year for all exacerbations and 0·25 events per year for severe exacerbations vs 1·20 events per year and 0·27 events per year). In ECLIPSE, in patients with previous exacerbation history (mean exacerbation rate was 1·82 events per year, 0·40 events per year were severe), AUC was 0·73 (95% CI 0·70-0·76) for two or more exacerbations and 0·74 (95% CI 0·70-0·78) for at least one severe exacerbation. Calibration was accurate for severe exacerbations (predicted 0·37 events per year vs observed 0·40 events per year) and all exacerbations (predicted 1·80 events per year vs observed 1·82 events per year).
INTERPRETATION
This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.
FUNDING
Canadian Institutes of Health Research.
背景
准确预测加重风险可使慢性阻塞性肺疾病（COPD）患者得到个体化护理。我们开发并验证了一种可预测 COPD 加重的个体化发生率和严重程度的可推广模型。
方法
在这项风险建模研究中，我们汇集了三项 COPD 试验中既往有加重史患者的数据。我们建立了一个混合效应模型来预测 1 年内的加重情况。严重加重是指需要住院治疗的情况。预测因素包括加重史、年龄、性别、体重指数、吸烟状况、家庭氧疗、肺功能、症状负担和当前药物使用情况。使用多中心队列研究“评估 COPD 以确定预测替代终点（ECLIPSE）”进行外部验证。
结果
该开发数据集包含 2380 例患者，其中 1373 例（58%）为男性。平均年龄为 64.7 岁（8.8 岁）。平均加重率为每年 1.42 次，每年 0.29 次为严重加重。在 ECLIPSE 中对所有 COPD 患者进行验证（平均加重率为每年 1.20 次，每年 0.27 次为严重加重）时，曲线下面积（AUC）为至少两次加重的 0.81（95%CI 0.79-0.83），至少一次严重加重的 0.77（95%CI 0.74-0.80）。预测的加重率与观察到的加重率相似（所有加重的年发生率为 1.31 次，严重加重的年发生率为 0.25 次；每年 1.20 次，严重加重的年发生率为 0.27 次）。在 ECLIPSE 中，在有既往加重史的患者中（平均加重率为每年 1.82 次，每年 0.40 次为严重加重），AUC 为两次或更多次加重的 0.73（95%CI 0.70-0.76）和至少一次严重加重的 0.74（95%CI 0.70-0.78）。严重加重的校准准确（预测每年 0.37 次，观察每年 0.40 次）和所有加重的校准准确（预测每年 1.80 次，观察每年 1.82 次）。
结论
该模型可用作个性化 COPD 治疗和预防加重的决策工具。
资助
加拿大卫生研究院。
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