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验证 COPDPredictTM:远程监测和加重预测的独特组合,支持 COPD 加重的预防管理。

Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations.

机构信息

Directorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.

Directorate of Respiratory Medicine, University Hospitals Birmingham NHS Foundation Trust, Heartlands Hospital, Birmingham, UK.

出版信息

Int J Chron Obstruct Pulmon Dis. 2021 Jun 21;16:1887-1899. doi: 10.2147/COPD.S309372. eCollection 2021.

Abstract

BACKGROUND

COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes.

METHODS

In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV using connected spirometers. CRP was measured using finger-prick testing.

RESULTS

Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients: 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7-99.2), specificity of 84.0% (95% CI 82.6-85.3), positive and negative predictive value of 38.4% (95% CI 36.4-40.4) and 99.8% (95% CI 99.5-99.9), respectively.

CONCLUSION

High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD.

摘要

背景

COPDPredict™ 是一款新型数字应用程序,专门用于对即将发生的 COPD(慢性阻塞性肺疾病)加重发出预警,以便及时进行干预。加重预测算法是基于决策树模型构建的,该模型基于患者报告的健康状况、用力呼气量(FEV)和 C 反应蛋白(CRP)水平变化的疾病状态百分比阈值。我们的研究确定了 COPDPredict™ 识别加重并及时通知患者和临床医生的有效性,与临床医生定义的发作相比。

方法

在一项为期 6 个月的前瞻性观察研究中,90 名 COPD 患者和频繁加重的患者使用 COPDPredict™ App 每天进行健康自我评估,使用连接的肺活量计测量 FEV。使用指尖采血试验测量 CRP。

结果

健康自评提交的遵守率达到 98%。有 10 名患者没有经历过加重,且治疗没有改变。在其余 80 名患者中,共确定了 112 例临床医生定义的加重:52 例患者经历了 1 次加重;28 例患者有 2.2±0.4 次发作。62 名患者使用处方急救药物进行自我管理。在 14 名患者中,加重更为严重,但在家庭中及时进行了升级治疗。4 名患者去了急诊室;其中 2 名住院时间不到 72 小时。与 COPDPredict™ 之前的 6 个月相比,住院率降低了 98%(90 比 2,p<0.001)。COPDPredict™ 在临床医生定义的发作前 7、3 天(中位数,IQR)识别 COPD 相关加重,向患者和临床医生发送适当的警报。交叉表显示,灵敏度为 97.9%(95%CI 95.7-99.2),特异性为 84.0%(95%CI 82.6-85.3),阳性预测值为 38.4%(95%CI 36.4-40.4),阴性预测值为 99.8%(95%CI 99.5-99.9)。

结论

高灵敏度表明,如果发生加重,COPDPredict™ 会准确地通知患者和临床医生。高阴性预测值意味着当 COPDPredict™ 没有提示加重时,发生加重的风险较低。因此,COPDPredict™ 为 COPD 患者提供安全、个性化、预防性护理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/359a/8232856/87ecbcd8440a/COPD-16-1887-g0001.jpg

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