دورية أكاديمية
أعرض في EDS

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

التفاصيل البيبلوغرافية
العنوان: Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.
المؤلفون: Chmiel FP; School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom., Burns DK; School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom., Pickering JB; School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom., Blythin A; my mHealth Limited, Bournemouth, United Kingdom., Wilkinson TM; my mHealth Limited, Bournemouth, United Kingdom.; National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton, Southampton, United Kingdom.; Faculty of Medicine, University of Southampton, Southampton, United Kingdom., Boniface MJ; School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.
المصدر: JMIR medical informatics [JMIR Med Inform] 2022 Mar 21; Vol. 10 (3), pp. e26499. Date of Electronic Publication: 2022 Mar 21.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101645109 Publication Model: Electronic Cited Medium: Print ISSN: 2291-9694 (Print) NLM ISO Abbreviation: JMIR Med Inform Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Toronto : JMIR Publications, [2013]-
مستخلص: Background: Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.
Objective: The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future.
Methods: This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model's predictive ability was evaluated based on self-reports from an independent set of patients.
Results: Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds.
Conclusions: Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.
(©Francis P Chmiel, Dan K Burns, John Brian Pickering, Alison Blythin, Thomas MA Wilkinson, Michael J Boniface. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.03.2022.)
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فهرسة مساهمة: Keywords: COPD; chronic disease; digital applications; digital health; exacerbation events; health care applications; mHealth; machine learning; mobile health; myCOPD; remote monitoring
تواريخ الأحداث: Date Created: 20220321 Latest Revision: 20220408
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC8981014
DOI: 10.2196/26499
PMID: 35311685
قاعدة البيانات: MEDLINE
الوصف
تدمد:2291-9694
DOI:10.2196/26499