A Therapeutic Machine-Learned Triage Methodology for Moderate to Severe Asthmatics
29 Pages Posted: 5 Feb 2019More...
Background: Current at-home asthma management protocols are crowded with paper guidelines and exploratory health apps that lack rigor and validation at the level of the individual patient. No clear medical consensus has emerged regarding the efficacy of such approaches. We developed a novel digital therapeutic application that uses machine learning predictions for real-time detection of exacerbations and on-demand decision support while obviating the need for burdensome daily symptom entry.
Methods: Physician opinion on a statistically and clinically comprehensive set of simulated patient cases was used to train a set of prediction algorithms. The accuracy of the models was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithms were subsequently deployed in a mobile application and evaluated in a 6 month, pre-post, observational trial of 25 patients with persistent asthma. Outcome data was collected using scored assessments of asthma control (ACT), quality of life (EQ5D5L) and anxiety (AIR).
Findings: Algorithm accuracy and safety indicators surpassed all individual pulmonologists in both identifying exacerbations and identifying the consensus triage. The algorithm was also the top performer in sensitivity, specificity, and PPV when predicting a patient's need for emergency care. The observational trial yielded statistically and clinically significant improvement in mean difference scores in asthma control, 4.8 [95\% CI 2.1 - 7.5] (p = 0.004); quality of life, 15.7 [8.0 - 23.3] (p = 0.001); and anxiety, -3.1 [(-5.2) - (-1)] (p = 0.001).
Interpretation: A mobile application equipped with a highly accurate machine-learning triage algorithm presents a promising and viable support tool with strong early indications of therapeutic value to patients. A randomized control trial is required to prove causality.
Funding: Revon Systems Inc, eThera Technologies, NSF Award No. (FAIN):1820049.
Declaration of Interest: We would like to disclose that Anthony N. Gerber is a consultant for eThera Inc and holds stock options. This does not alter our adherence to the Lancet Respiratory Medicine policies on sharing data and materials.
Ethical Approval: The study was approved by Quorum Review, an independent Ethics Review Board.
Keywords: Asthma, Machine Learning, Mobile Application, Decision Support, Triage, Exacerbation Detection, Asthma Control, Quality of Life
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