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All Studies   Meta Analysis    Recent:   

Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing

Jun 2020  
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0 0.5 1 1.5 2+ Case 58% Improvement Relative Risk Case (b) 100% Melatonin for COVID-19  Jehi et al.  Prophylaxis Does melatonin reduce COVID-19 infections? Retrospective 11,672 patients in the USA Fewer cases with melatonin (p=0.000077) c19early.org Jehi et al., Chest, June 2020 Favors melatonin Favors control
Melatonin for COVID-19
10th treatment shown to reduce risk in December 2020
 
*, now known with p = 0.0000002 from 18 studies.
Lower risk for mortality, ventilation, and recovery.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
4,200+ studies for 70+ treatments. c19early.org
Retrospective 11,672 patients tested for COVID-19 with 818 testing positive, showing significantly lower risk with melatonin use.
risk of case, 58.0% lower, RR 0.42, p < 0.001, treatment 16 of 529 (3.0%), control 802 of 11,143 (7.2%), NNT 24, development cohort.
risk of case, 99.7% lower, RR 0.003, p = 0.09, treatment 0 of 18 (0.0%), control 290 of 2,005 (14.5%), NNT 6.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), Florida validation cohort.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Jehi et al., 10 Jun 2020, retrospective, USA, peer-reviewed, 8 authors.
This PaperMelatoninAll
Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing
MD, MHCDS Lara Jehi, MS Xinge Ji, MS Alex Milinovich, MD Serpil Erzurum, MD Brian P Rubin, MD Steve Gordon, MD James B Young, PhD Michael W Kattan
Chest, doi:10.1016/j.chest.2020.05.580
BACKGROUND: Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing. RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID-19? STUDY DESIGN AND METHODS: We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model's cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator. RESULTS: In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic ¼ 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19. INTERPRETATION: Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.
Author contributions: L. J. is the guarantor of submission and participated in literature search, figures, study design, data collection, data interpretation, and writing; X. J. participated in data analysis and figures; A. M. participated in data collection and data analysis; S. E. participated in data interpretation, study design, and writing; B. P. R., S. G., and J. B. Y. participated in data interpretation and writing; and M. W. K. participated in literature search, study design, data interpretation, data analysis, and writing. Financial/nonfinancial disclosures: The authors have reported to CHEST the following: A. M. reports grants from Novo Nordisk, Boehringer Ingelheim, Merck, Novartis, and National Institutes of Health (NIH), outside the submitted work. M. W. K. reports grants from Novo Nordisk, Boehringer Ingelheim, Merck, Novartis, and NIH, consulting for Stratify Genomics and RenatlyxAI, outside the submitted work. None declared (L. J., X. J., S. E., B. P. R., S. G., J. B. Y.). Role of sponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript. Additional information: The e-Appendix and e-Figure can be found in the Supplemental Materials section of the online article.
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