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0 0.5 1 1.5 2+ ICU admission 43% Improvement Relative Risk HCQ for COVID-19  AlShehhi et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 1,797 patients in United Arab Emirates (Mar - Apr 2020) Lower ICU admission with HCQ (p=0.0013) AlShehhi et al., PLOS ONE, January 2024 Favors HCQ Favors control

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

AlShehhi et al., PLOS ONE, doi:10.1371/journal.pone.0291373
Jan 2024  
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HCQ for COVID-19
1st treatment shown to reduce risk in March 2020
*, now known with p < 0.00000000001 from 421 studies, recognized in 42 countries.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
3,800+ studies for 60+ treatments.
Retrospective 1,787 hospitalized COVID-19 patients in the United Arab Emirates, identifying hydroxychloroquine as reducing the risk of ICU admission in a machine learning model. Only unadjusted quantitative results are provided, which also show a benefit.
This study is excluded in the after exclusion results of meta analysis: unadjusted results with no group details.
risk of ICU admission, 42.8% lower, RR 0.57, p = 0.001, treatment 114 of 1,460 (7.8%), control 46 of 337 (13.6%), NNT 17.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
AlShehhi et al., 11 Jan 2024, retrospective, United Arab Emirates, peer-reviewed, 4 authors, study period 1 March, 2020 - 20 April, 2020. Contact:,
This PaperHCQAll
Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates
Aamna Alshehhi, Taleb M Almansoori, Ahmed R Alsuwaidi, Hiba Alblooshi
PLOS ONE, doi:10.1371/journal.pone.0291373
Background The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models. Methods We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index. Results The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.
Supporting information S1 Author Contributions Conceptualization: Aamna AlShehhi, Taleb M. Almansoori, Ahmed R. Alsuwaidi, Hiba Alblooshi. Data curation: Hiba Alblooshi. Formal analysis: Aamna AlShehhi. Investigation: Taleb M. Almansoori, Ahmed R. Alsuwaidi, Hiba Alblooshi. Methodology: Aamna AlShehhi. Project administration: Hiba Alblooshi. Resources: Taleb M. Almansoori, Ahmed R. Alsuwaidi. Validation: Taleb M. Almansoori, Ahmed R. Alsuwaidi. Visualization: Aamna AlShehhi. Writing -original draft: Aamna AlShehhi, Hiba Alblooshi. Writing -review & editing: Taleb M. Almansoori, Ahmed R. Alsuwaidi, Hiba Alblooshi.
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Late treatment
is less effective
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