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ACCEPT 2·0:重新校准并外部验证慢性阻塞性肺疾病急性加重预测工具(ACCEPT)

ACCEPT 2·0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT).

作者信息

Safari Abdollah, Adibi Amin, Sin Don D, Lee Tae Yoon, Ho Joseph Khoa, Sadatsafavi Mohsen

机构信息

Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.

Department of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran.

出版信息

EClinicalMedicine. 2022 Jul 22;51:101574. doi: 10.1016/j.eclinm.2022.101574. eCollection 2022 Sep.

Abstract

BACKGROUND

The Acute Chronic Obstructive Pulmonary Disease (COPD) Exacerbation Prediction Tool (ACCEPT) was developed for individualised prediction of COPD exacerbations. ACCEPT was well calibrated overall and had a high discriminatory power, but overestimated risk among individuals without recent exacerbations. The objectives of this study were to 1) fine-tune ACCEPT to make better predictions for individuals with a negative exacerbation history, 2) develop more parsimonious models, and 3) externally validate the models in a new dataset.

METHODS

We recalibrated ACCEPT using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE, a three-year observational study, 1,803 patients, 2,117 exacerbations) study by applying non-parametric regression splines to the predicted rates. We developed three reduced versions of ACCEPT by removing symptom score and/or baseline medications as predictors. We examined the discrimination, calibration, and net benefit of ACCEPT 2·0 in the placebo arm of the Towards a Revolution in COPD Health (TORCH, a three-year randomised clinical trial of inhaled therapies in COPD, 1,091 patients, 1,064 exacerbations) study. The primary outcome for prediction was the occurrence of ≥2 moderate or ≥1 severe exacerbation in the next 12 months; the secondary outcomes were prediction of the occurrence of any moderate/severe exacerbation or any severe exacerbation.

FINDINGS

ACCEPT 2·0 had an area-under-the-curve (AUC) of 0·76 for predicting the primary outcome. Exacerbation history alone (current standard of care) had an AUC of 0·68. The model was well calibrated in patients with positive or negative exacerbation histories. Changes in AUC in reduced versions were minimal for the primary outcome as well as for predicting the occurrence of any moderate/severe exacerbations (ΔAUC<0·011), but more substantial for predicting the occurrence of any severe exacerbations (ΔAUC<0·020). All versions of ACCEPT 2·0 provided positive net benefit over the use of exacerbation history alone for some range of thresholds.

INTERPRETATION

ACCEPT 2·0 showed good calibration regardless of exacerbation history, and predicts exacerbation risk better than current standard of care for a range of thresholds. Future studies need to investigate the utility of exacerbation prediction in various subgroups of patients.

FUNDING

This study was funded by a team grant from the Canadian Institutes of Health Research (PHT 178432).

摘要

背景

急性慢性阻塞性肺疾病(COPD)加重预测工具(ACCEPT)旨在对COPD加重进行个体化预测。ACCEPT总体校准良好且具有较高的鉴别力,但高估了近期无加重患者的风险。本研究的目的是:1)对ACCEPT进行微调,以便对加重史为阴性的个体做出更好的预测;2)开发更简洁的模型;3)在新数据集中对模型进行外部验证。

方法

我们使用慢性阻塞性肺疾病纵向评估以识别预测替代终点(ECLIPSE,一项为期三年的观察性研究,1803例患者,2117次加重)研究中的数据,通过对预测率应用非参数回归样条来重新校准ACCEPT。我们通过去除症状评分和/或基线用药作为预测因子,开发了ACCEPT的三个简化版本。我们在慢性阻塞性肺疾病健康革命(TORCH,一项为期三年的COPD吸入疗法随机临床试验,1091例患者,1064次加重)研究的安慰剂组中检验了ACCEPT 2·0的鉴别力、校准和净效益。预测的主要结局是在接下来12个月内发生≥2次中度或≥1次重度加重;次要结局是预测发生任何中度/重度加重或任何重度加重。

结果

ACCEPT 2·0预测主要结局的曲线下面积(AUC)为0·76。仅加重史(当前护理标准)的AUC为0·68。该模型在加重史为阳性或阴性的患者中校准良好。简化版本中,对于主要结局以及预测任何中度/重度加重的发生,AUC变化最小(ΔAUC<0·011),但对于预测任何重度加重的发生,变化更大(ΔAUC<0·020)。在一定阈值范围内,所有版本的ACCEPT 2·0与仅使用加重史相比均提供了正净效益。

解读

无论加重史如何,ACCEPT 2·0均显示出良好的校准,并且在一定阈值范围内比当前护理标准能更好地预测加重风险。未来研究需要调查加重预测在不同患者亚组中的效用。

资金来源

本研究由加拿大卫生研究院的团队资助(PHT 178432)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/803e/9309408/f4e402b8dc48/gr1.jpg

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