Boer Lonneke M, van der Heijden Maarten, van Kuijk Nathalie Me, Lucas Peter Jf, Vercoulen Jan H, Assendelft Willem Jj, Bischoff Erik W, Schermer Tjard R
Department of Primary and Community Care, Radboud university medical center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands,
Department of Computing Sciences, Radboud University, Nijmegen, the Netherlands.
Int J Chron Obstruct Pulmon Dis. 2018 Oct 10;13:3255-3267. doi: 10.2147/COPD.S167272. eCollection 2018.
To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO), forced expiratory volume in one second (FEV), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS.
We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO, FEV, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations.
Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0-99.3), specificity of 65.6% (95% CI 63.5-67.6), and positive and negative predictive value of 13.4% (95% CI 11.2-15.9) and 99.8% (95% CI 99.3-99.9), respectively, for ACCESS' advice to contact a health care professional in case of an exacerbation.
In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation.
为了支持慢性阻塞性肺疾病（COPD）患者自我管理症状恶化情况，我们开发了适应性计算机化COPD急性加重自我管理支持系统（ACCESS），这是一款创新的软件应用程序，可在无医护人员干预的情况下提供自动化治疗建议。急性加重检测基于12个与症状相关的是非问题以及外周毛细血管血氧饱和度（SpO）、一秒用力呼气量（FEV）和体温的测量。自动化治疗建议基于由临床专家小组意见和贝叶斯网络建模构建的决策模型。本文描述了ACCESS的有效性。
我们对一项为期3个月的前瞻性观察性研究的数据进行了二次分析，在该研究中，COPD患者每天在日记卡上记录呼吸道症状，并测量SpO、FEV和体温。我们根据基于症状和事件的急性加重情况，检验了ACCESS最重要的治疗建议（即联系医护人员）的有效性。
54名患者完成了2928张日记卡。在所有病例中，71.7%的病例提供了一条或多条不同的ACCESS建议。我们识别出115例基于症状的急性加重情况。交叉表显示，对于ACCESS在急性加重时建议联系医护人员的建议，其敏感性为97.4%（95%CI 92.0 - 99.3），特异性为65.6%（95%CI 63.5 - 67.6），阳性预测值和阴性预测值分别为13.4%（95%CI 11.2 - 15.9）和99.8%（95%CI 99.3 - 99.9）。
在许多情况下（71.7%），ACCESS给出了至少一条自我管理建议以减轻症状负担，这表明ACCESS为日常症状变化和急性加重情况均提供了自我管理支持。高敏感性表明，如果发生急性加重，ACCESS会建议患者联系医护人员。高阴性预测值使我们得出结论，当ACCESS未提供联系医护人员的建议时，急性加重的风险非常低。因此，ACCESS可安全地用于COPD患者，以在急性加重时支持自我管理。
