Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records.

Autor: Aguado-García A; Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico.; Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico., Arroyo-Valerio A; Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico., Escobedo G; Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico., Bueno-Hernández N; Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico., Olguín-Rodríguez PV; Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.; Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico., Müller MF; Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico.; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico.; Centro Internacional de Ciencias A.C., Cuernavaca 62131, Mexico., Carrillo-Ruiz JD; Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico.; Universidad Anáhuac, Estado de México, 52786, Mexico., Martínez-Mekler G; Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico.; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico.; Centro Internacional de Ciencias A.C., Cuernavaca 62131, Mexico.
Jazyk: angličtina
Zdroj: Biomedical signal processing and control [Biomed Signal Process Control] 2023 Jul; Vol. 84, pp. 104975. Date of Electronic Publication: 2023 Apr 21.
DOI: 10.1016/j.bspc.2023.104975
Abstrakt: We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference ( p  < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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