Association between Morality in Covid-19 Patients and Underlying Co-Morbidities in Patients above 40 Years of Age: A Rapid Review
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».