Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource management

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art_LEVIN_Correlating_drug_prescriptions_with_prognosis_in_severe_COVID19_2022.PDF (1.11 MB)
Citações na Scopus
1
Tipo de produção
article
Data de publicação
2022
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
BMC
Autores
Citação
BMC MEDICAL INFORMATICS AND DECISION MAKING, v.22, n.1, article ID 246, 9p, 2022
Projetos de Pesquisa
Unidades Organizacionais
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Resumo
Background Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. Methods The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. Results We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. Conclusions In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.
Palavras-chave
Severe COVID-19, Treatment, Hospitalization, Prognosis
URI
https://observatorio.fm.usp.br/handle/OPI/49230
Referências
  1. [Anonymous], COLL DAT SCI
  2. Buitinck L., 2013, ECML PKDD WORKSH LAN, P108
  3. Datta SD, 2020, JAMA-J AM MED ASSOC, V324, P2251, DOI 10.1001/jama.2020.22717
  4. Denning DW, 1997, J ANTIMICROB CHEMOTH, V40, P401, DOI 10.1093/jac/40.3.401
  5. Fernandez R, 2017, 2017 INTERNATIONAL CONFERENCE ON COMPUTER AND APPLICATIONS (ICCA), P360, DOI 10.1109/COMAPP.2017.8079755
  6. Fonti VF, 2017, PAPER BUSINESS ANAL
  7. Goligher EC, 2021, NEW ENGL J MED, V385, P777, DOI 10.1056/NEJMoa2103417
  8. Grau S, 2021, ANTIBIOTICS-BASEL, V10, DOI 10.3390/antibiotics10020132
  9. Guisado-Gil AB, 2020, ANTIBIOTICS-BASEL, V9, DOI 10.3390/antibiotics9110816
  10. HILF M, 1989, AM J MED, V87, P540, DOI 10.1016/S0002-9343(89)80611-4
  11. Horby P, 2021, NEW ENGL J MED, V384, P693, DOI [10.1056/NEJMoa2021436, 10.1056/NEJMoa2022926]
  12. Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
  13. Joshi S, 2021, INT J INFECT DIS, V102, P501, DOI 10.1016/j.ijid.2020.10.069
  14. Langford BJ, 2020, CLIN MICROBIOL INFEC, V26, P1622, DOI 10.1016/j.cmi.2020.07.016
  15. Lawler PR, 2021, NEW ENGL J MED, V385, P790, DOI 10.1056/NEJMoa2105911
  16. MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9
  17. Perondi B, 2020, BRAZ J INFECT DIS, V24, P570, DOI 10.1016/j.bjid.2020.09.005
  18. Piazza G, 2020, JAMA-J AM MED ASSOC, V324, P2548, DOI 10.1001/jama.2020.23422
  19. Schooley RT, 1995, AIDS, V9, pS15
  20. Silva GA, 2021, MORTALIDADE COVID 19, DOI [10.1590/scielopreprints.1874, DOI 10.1590/SCIELOPREPRINTS.1874]
  21. Tang JJ, 2022, CLIN INFECT DIS, V74, P327, DOI 10.1093/cid/ciab317
  22. Whitlock Mark E, 2019, Innov Clin Neurosci, V16, P12
  23. WHO, WHO COR COVID 19 DAS
  24. Wu F, 2010, P INT C MULTIMEDIA M, DOI [10.1145/1873951.1874129, DOI 10.1145/1873951.1874129]

Coleções
Artigos e Materiais de Revistas Científicas - FM/MIP
Artigos e Materiais de Revistas Científicas - COVID-19
Artigos e Materiais de Revistas Científicas - HC/ICESP
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - HC/InCor
Artigos e Materiais de Revistas Científicas - LIM/46
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