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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

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
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

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.

COPD, chronic disease, digital applications, digital health, exacerbation events, health care applications, mHealth, machine learning, mobile health, myCOPD, remote monitoring
2291-9694
11
Chmiel, Francis P
2de259aa-a5eb-460c-bfbf-8b44ed02e2bd
Burns, Dan
40b9dc88-a54a-4365-b747-4456d9203146
Pickering, John Brian
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Blythin, Alison
62fddce4-e5e4-4a28-81fc-3aee38e89fdb
Wilkinson, Thomas MA
8c55ebbb-e547-445c-95a1-c8bed02dd652
Boniface, Michael J
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Chmiel, Francis P
2de259aa-a5eb-460c-bfbf-8b44ed02e2bd
Burns, Dan
40b9dc88-a54a-4365-b747-4456d9203146
Pickering, John Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Blythin, Alison
62fddce4-e5e4-4a28-81fc-3aee38e89fdb
Wilkinson, Thomas MA
8c55ebbb-e547-445c-95a1-c8bed02dd652
Boniface, Michael J
f30bfd7d-20ed-451b-b405-34e3e22fdfba

Chmiel, Francis P, Burns, Dan, Pickering, John Brian, Blythin, Alison, Wilkinson, Thomas MA and Boniface, Michael J (2022) 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. JMIR Medical Informatics, 10 (3), 11, [e26499]. (doi:10.2196/26499).

Record type: Article

Abstract

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.

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Accepted/In Press date: 4 December 2021
Published date: 21 March 2022
Keywords: COPD, chronic disease, digital applications, digital health, exacerbation events, health care applications, mHealth, machine learning, mobile health, myCOPD, remote monitoring

Identifiers

Local EPrints ID: 456975
URI: http://eprints.soton.ac.uk/id/eprint/456975
ISSN: 2291-9694
PURE UUID: 1c083083-03bc-44a7-8bf7-a2862e2b5a70
ORCID for Dan Burns: ORCID iD orcid.org/0000-0001-6976-1068
ORCID for John Brian Pickering: ORCID iD orcid.org/0000-0002-6815-2938
ORCID for Michael J Boniface: ORCID iD orcid.org/0000-0002-9281-6095

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Date deposited: 18 May 2022 17:06
Last modified: 22 Jul 2022 01:50

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Contributors

Author: Francis P Chmiel
Author: Dan Burns ORCID iD
Author: Alison Blythin

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