Association between Morality in Covid-19 Patients and Underlying Co-Morbidities in Patients above 40 Years of Age: A Rapid Review
Bibliographic record
Abstract
COVID-19 pandemic has dramatically affected various aspects of people’s lives worldwide. The severity of the disease, the easy spread and the high mortality associated with COVID-19 has turned this pandemic into an important and high priority research topic. Mortality in patients diagnosed with COVID-19 is multifactorial. We have tried to find the association between mortality and specific comorbidities, especially in people above 40 years of age. The findings can potentially help healthcare providers to make appropriate guidelines to triage patients in COVID-19 care centers and aim to reduce mortality. This can also help policy makers to provide supportive measures especially for vulnerable people with the specific comorbidities to reduce the chance of contracting the infection. Objective: Literature suggests that age is one of the crucial factors in increasing the severity and mortality of COVID-19 patients. Hence in our study, our objective is to see the available evidence on different types of comorbidities associated with mortality in COVID-19 patients. Methods: This study was a rapid review aiming to investigate the leading comorbidities toward mortality among COVID-19 patients. We searched PubMed and Google Scholar and selected English language articles that were published between March and July 2020. The studies were selected based on the pre-set inclusion and exclusion criteria. Data of selected articles have been extracted based on the comorbidities of each organ system and the number of patients in each category. Result: Based on our review, apart from increased age, hypertension (66.63%) has been the most commonly seen comorbidity associated with mortality due to COVID-19. Other comorbidities include diabetes (26.34%), cardio-cerebrovascular diseases (39.61%), COPD (14.93%), chronic kidney disease (17.31%) and cancer (20.66%). From the studies with details on gender ratios, male gender (66.66%) and female gender (33.33%) were respectively associated with mortality. It is estimated that male patients are around 2 times more likely to be deceased with COVID 19 in comparison to other genders. Conclusion: More studies regarding the underlying mechanisms related to mortality are required to further decipher the disease correlation. Understanding the association between these specific underlying comorbidities and mortality due to COVID-19 can help healthcare providers triage patients in COVID-19 care centers. It can also be used to assist in making clinical guidelines and policies on social measures, thereby, protecting the vulnerable people with the mentioned comorbidities from community spread and possible infection
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".