Effects of Comorbidities on Asthma Hospitalization and Mortality Rates: A Systematic Review
Bibliographic record
Abstract
Background: Recent studies have shown that patients diagnosed with asthma who have other chronic comorbidities have severely worse medical outcomes. However, the number of available published studies in this field is lacking. The aim of this study was to determine the effects of comorbidities in asthmatic patients based on hospitalization and mortality rates. Methods: A systematic review was conducted. Data were obtained from the electronic databases PubMed, CINAHL, and Cochrane until June 15, 2018. The primary objective of this study was to determine the effects of comorbidities on asthma hospitalization and mortality. The secondary objective was to analyze the effects of asthma comorbidity with certain chronic diseases, including COPD, obesity, obstructive sleep apnea, mental illness (anxiety and depression), diabetes mellitus, hypertension, myocardial ischemia, rhinitis, and sinusitis on asthma hospitalization and mortality. Results: From potential 687 articles, only 9 were chosen based on our study inclusion criteria. Almost half of these articles were related to asthma/COPD comorbidity. There were no articles found for hypertension, myocardial ischemia, rhinitis, or sinusitis based on our inclusion/exclusion factors. Each of these 9 published articles had shown an increase in rates of hospitalization, length of stay, and/or mortality, due to asthma-related symptoms, compared to asthma-only patients. Conclusion: There was determined to be a large discrepancy between the available research for various types of comorbid conditions presenting with asthma that focus on hospitalization and mortality rates. The current available literature suggests a large impact that these comorbid diseases can have on asthma-related symptoms when present together, severely affecting a patient's quality of life. We propose that further research on the effects of these comorbidities on asthma mortality and hospitalization can yield beneficial results to improve the management of asthmatic patients.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| 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".