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
Introduction: 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 A Letter to the Editor has been published for this article . A Response to Letter by Dr Gayatri has been published for this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".