Profiles of Independent-Comorbidity Groups in Senior COVID-19 Patients Reveal Low Fatality Associated with Standard Care and Low-Dose Hydroxychloroquine over Antivirals
Bibliographic record
Abstract
Kamaleldin B Said,1,2 Ahmed Alsolami,3 Fayez Saud Alreshidi,4 Anas Fathuddin,5 Fawwaz Alshammari,6 Fauwaz Alrashid,7 Ahmed Aljadani,3 Rana Aboras,4 Fatmah Alreshidi,4 Mohammed H Alghozwi,1 Suliman F Alshammari,1 Nawaf F Alharbi1 On behalf of the Ha’il COM Research Unit Group1Department of Pathology and Microbiology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 2Genomics, Bioinformatics and Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada; 3Department of Internal Medicine, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 4Deparmtent of Family, Community Medicine, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 5Department of Plastic Surgery, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 6Department of Dermatology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 7Department of Surgery, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi ArabiaCorrespondence: Kamaleldin B Said, Department of Pathology and Microbiology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia, Tel +966500771459, Email kbs.mohamed@uoh.edu.saIntroduction: The lack of feasible therapies and comorbidities aggravate the COVID-19 case–fatality rate (CFR). However, reports examining CFR associations with diabetes, concomitant cardiovascular diseases, chronic kidney disease, and chronic liver disease (CLD) are limited. More studies assessing hydroxychloroquine (Hcq) and antivirals are needed.Purpose: To examine associations of COVID-19 CFR in comorbid patient groups each with single comorbidities and after treatment with Hcq, favipiravir, and dexamethasone (Dex), either alone or in combination versus standard care.Methods: Using statistical analysis, we descriptively determined these associations among 750 COVID-19 patient groups during the last quarter of 2021.Results: A diabetes comorbidity (40%, n=299) showed twice the fatality (CFR 14%) of the others (CFR 7%; P=0.001). Hypertension (Htn) was the second-commonest comorbidity (29.5%, n=221), with similar CFR to diabetes (15% and 7% for Htn and non-Htn, respectively), but with higher significance (P=0.0006167). Although only 4% (n=30) heart failure (HF) was reported, the CFR (40%) was much higher than in those without it (8%). A similar rate (4%) for chronic kidney disease was reported, with CFRs of 33% and 9% among those with and without it, respectively (P=0.00048). Ischemic heart disease was 11% (n=74), followed by chronic liver disease (0.4%) and history of smoking (1%); however, these were not significant due to the sample sizes. Treatment indicated standard care and Hcq alone or in combination were superior (CFR of 4% and 0.5%, respectively) compared to favipiravir (25%) or Dex (38.5%) independently or in combination (35.4%). Furthermore, Hcq performed well (CFR 9%) when combined with Dex (9%; P=4.28–26).Conclusion: The dominance of diabetes and other comorbidities with significant association with CFR implied existence of a common virulence mechanism. The superiority of low-dose Hcq and standard care over antivirals warrants further studies.Keywords: COVID-19 medicine treatments, COVID-19 supportive treatment, comorbid COVID-19 elders, COVID-19 survivals and deaths
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".