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Reviews of "Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers"

Reviewers: A Hudson (UC Berkeley) | 📒📒📒 ◻️◻️ • T Pujol (RAND Corporation) | 📒📒📒 ◻️◻️

Published onJan 26, 2022
Reviews of "Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers"
key-enterThis Pub is a Review of
Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers
Description

AbstractImportancePassive and non-invasive identification of SARS-CoV-2 infection remains a challenge. Widespread use of wearable devices represents an opportunity to leverage physiological metrics and fill this knowledge gap.ObjectiveTo determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.DesignA multicenter observational study enrolling health care workers with remote follow-up.SettingSeven hospitals from the Mount Sinai Health System in New York CityParticipantsEligibility criteria included health care workers who were ≥18 years, employees of one of the participating hospitals, with at least an iPhone series 6, and willing to wear an Apple Watch Series 4 or higher. We excluded participants with underlying autoimmune/inflammatory diseases, and medications known to interfere with autonomic function. We enrolled participants between April 29th, 2020, and March 2nd, 2021, and followed them for a median of 73 days (range, 3-253 days). Participants provided patient-reported outcome measures through a custom smartphone application and wore an Apple Watch, collecting heart rate variability and heart rate data, throughout the follow-up period.ExposureParticipants were exposed to SARS-CoV-2 infection over time due to ongoing community spread.Main Outcome and MeasureThe primary outcome was SARS-CoV-2 infection, defined as ±7 days from a self-reported positive SARS-CoV-2 nasal PCR test.ResultsWe enrolled 407 participants with 49 (12%) having a positive SARS-CoV-2 test during follow-up. We examined five machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable 10-CV performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC)=85% (Confidence Interval 83-88%). The model was calibrated to improve sensitivity over specificity, achieving an average sensitivity of 76% (CI ±∼4%) and specificity of 84% (CI ±∼0.4%). The most important predictors included parameters describing the circadian HRV mean (MESOR) and peak-timing (acrophase), and age.Conclusions and RelevanceWe show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV2 infection. Utilizing physiological metrics from wearable devices may improve screening methods and infection tracking.

To read the original manuscript, click the link above.

Summary of Reviews: This study develops a prediction model for positive COVID-19 diagnosis using data collected from Apple Watches on heart rate variability (HRV) among healthcare workers. Reviewers highlight unclear model justifications and methodology. 

Reviewer 1 (Aaron H…) | 📒📒📒 ◻️◻️

Reviewer 2 (Toyya P…) | 📒📒📒 ◻️◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below. 


Comments
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