Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Autor: Mason AE; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA. ashley.mason@ucsf.edu., Hecht FM; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Davis SK; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Natale JL; Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA., Hartogensis W; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Damaso N; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Claypool KT; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Dilchert S; Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, USA., Dasgupta S; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA., Purawat S; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA., Viswanath VK; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA., Klein A; Department of Bioengineering: Bioinformatics, University of California San Diego, San Diego, CA, USA., Chowdhary A; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Fisher SM; Department of Psychology, Drexel University, Pennsylvania, PA, USA., Anglo C; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Puldon KY; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Veasna D; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Prather JG; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Pandya LS; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Fox LM; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA., Busch M; Vitalant Research Institute, University of California San Francisco, San Francisco, CA, USA., Giordano C; Department of Psychology, University of Minnesota - Twin Cities, Minneapolis, MN, USA., Mercado BK; Love School of Business, Elon University, Elon, NC, USA., Song J; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA., Jaimes R; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Baum BS; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Telfer BA; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Philipson CW; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Collins PP; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA., Rao AA; School of Medicine, University of California San Francisco, San Francisco, CA, USA., Wang EJ; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA., Bandi RH; Department of Anesthesiology, Northwestern McGaw Medical Center, Feinberg School of Medicine, Chicago, IL, USA., Choe BJ; Department of Emergency Medicine, University of California Los Angeles Health, Los Angeles, CA, USA., Epel ES; Center for Health and Community, University of California San Francisco, San Francisco, CA, USA., Epstein SK; Department of Emergency Medicine, Beth Israel Deaconess Medical Center Boston, Boston, MA, USA., Krasnoff JB; Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA., Lee MB; Department of Neurosurgery, Santa Clara Valley Medical Center, Stanford University, San Jose, CA, USA., Lee SW; Department of Emergency Medicine, Jamaica Hospital Medical Center, Jamaica, NY, USA., Lopez GM; Department of Emergency Medicine, Boston Medical Center, Boston, MA, USA., Mehta A; Department of Anesthesiology: Pain Management and Perioperative Medicine, University of Miami, Miami, FL, USA., Melville LD; Department of Emergency Medicine, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA., Moon TS; Department of Anesthesiology and Pain Management, University of Texas Southwestern, Dallas, TX, USA., Mujica-Parodi LR; Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA., Noel KM; Stony Brook Medicine, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA., Orosco MA; Department of Anesthesia: Perioperative and Pain Medicine, Kaiser Permanente San Diego, San Diego, CA, USA., Rideout JM; Department of Emergency Medicine, Tufts Medical Center, Boston, MA, USA., Robishaw JD; Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA., Rodriguez RM; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA., Shah KH; Weill Cornell Medical Center, Weill Cornell Medical School, New York, NY, USA., Siegal JH; New York Presbyterian Queens, Weill-Cornell Medical College, Queens, NY, USA., Gupta A; Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA., Altintas I; Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA., Smarr BL; Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.; Department of Bioengineering: Bioinformatics, University of California San Diego, San Diego, CA, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Mar 02; Vol. 12 (1), pp. 3463. Date of Electronic Publication: 2022 Mar 02.
DOI: 10.1038/s41598-022-07314-0
Abstrakt: Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.
(© 2022. The Author(s).)
Databáze: MEDLINE
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