Development, evaluation and validation of machine learning algorithms to detect atypical and asymptomatic presentations of Covid-19 in hospital practice.

Autor:	Baktash V; Department of Medicine, Wexham Park Hospital, Frimley Health NHS Foundation Trust, Wexham Street, Slough, Berkshire, SL2 4HL, UK., Hosack T; Department of Medicine, Stoke Mandeville Hospital, Mandeville Rd, Aylesbury, Buckinghamshire, HP21 8AL, UK., Rule R; Department of Medicine, Stoke Mandeville Hospital, Mandeville Rd, Aylesbury, Buckinghamshire, HP21 8AL, UK., Patel N; Department of Medicine, Wexham Park Hospital, Frimley Health NHS Foundation Trust, Wexham Street, Slough, Berkshire, SL2 4HL, UK., Kho J; Department of Medicine, Wexham Park Hospital, Frimley Health NHS Foundation Trust, Wexham Street, Slough, Berkshire, SL2 4HL, UK., Sekhar R; Department of Medicine, Stoke Mandeville Hospital, Mandeville Rd, Aylesbury, Buckinghamshire, HP21 8AL, UK., Mandal AKJ; Department of Medicine, Wexham Park Hospital, Frimley Health NHS Foundation Trust, Wexham Street, Slough, Berkshire, SL2 4HL, UK., Missouris CG; Department of Medicine, Wexham Park Hospital, Frimley Health NHS Foundation Trust, Wexham Street, Slough, Berkshire, SL2 4HL, UK.; Department of Clinical Cardiology, University of Nicosia Medical School, 93 Agiou Nikolaou Street, Engomi 2408 Nicosia, Cyprus.
Jazyk:	angličtina
Zdroj:	QJM : monthly journal of the Association of Physicians [QJM] 2021 Nov 05; Vol. 114 (7), pp. 496-501.
DOI:	10.1093/qjmed/hcab172
Abstrakt:	Background: Diagnostic methods for Covid-19 have improved, both in speed and availability. Because of atypical and asymptomatic carriage of the virus and nosocomial spread within institutions, timely diagnosis remains a challenge. Machine learning models trained on blood test results have shown promise in identifying cases of Covid-19. Aims: To train and validate a machine learning model capable of differentiating Covid-19 positive from negative patients using routine blood tests and assess the model's accuracy against atypical and asymptomatic presentations. Design and Methods: We conducted a retrospective analysis of medical admissions to our institution during March and April 2020. Participants were categorized into Covid-19 positive or negative groups based on clinical, radiological features or nasopharyngeal swab. A machine learning model was trained on laboratory parameters and validated for accuracy, sensitivity and specificity and externally validated at an unconnected establishment. Results: An Ensemble Bagged Tree model was trained on data collected from 405 patients (212 Covid-19 positive) producing an accuracy of 81.79% (95% confidence interval (CI) 77.53-85.55%), the sensitivity of 85.85% (CI 80.42-90.24%) and specificity of 76.65% (CI 69.49-82.84%). Accuracy was preserved for atypical and asymptomatic subgroups. Using an external data set for 226 patients (141 Covid-19 positive) accuracy of 76.82% (CI 70.87-82.08%), sensitivity of 78.38% (CI 70.87-84.72%) and specificity of 74.12% (CI 63.48-83.01%) was achieved. Conclusion: A machine learning model using routine laboratory parameters can detect atypical and asymptomatic presentations of Covid-19 and might be an adjunct to existing screening measures. (© The Author(s) 2021. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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