Clinical expressions, characteristics and treatments of confirmed COVID-19 in nursing home residents: a systematic review
Bibliographic record
Abstract
Abstract Background The coronavirus 2019 (COVID-19) pandemic has led to a high rate of infections, frequent outbreaks, and high mortality rates in nursing homes (NH) worldwide. To protect and improve the treatment and care of the vulnerable NH population, it is pivotal to systematise and synthesise data from cases of COVID-19 among NH residents. In our systematic review, we therefore aimed to describe the clinical expressions, characteristics, and treatments of NH residents confirmed to have COVID-19. Methods We conducted two comprehensive literature searches in several electronic databases: (1) PubMed, (2) CINAHL, (3) AgeLine, (4) Embase, and (5) PsycINFO in April and July 2021. Of the 438 articles screened, 19 were included in our sample, and we used the Newcastle–Ottawa Assessment Scale to assess the quality of the reported studies. A weighted mean ( M weighted ), was calculated to account for the large variation in sample sizes of the studies, and due to heterogeneity among the studies, we report our findings in a narrative synthesis. Results According to the mean weights ( M weighted ), common symptoms and signs in NH residents confirmed to have COVID-19 were fever (53.7%), cough (56.5%), hypoxia (32.3%), and delirium or confusion (31.2%). Common comorbidities were hypertension (78.6%), dementia or cognitive impairment (55.3%), and cardiovascular diseases (52.0%). Six studies presented data concerning medical and pharmacological treatments, such as inhalers, oxygen supplementation, anticoagulation, and parenteral/enteral fluids or nutrition. The treatments were used to improve outcomes, as part of palliative care, or as end-of-life treatment. Transfers to hospital for NH residents with confirmed COVID-19 were reported in six of the included studies, and the rate of hospital transfers ranged from 6.9% to 50% in this population. In the 17 studies reporting mortality, 40.2% of the NH residents died during the studies’ observation periods. Conclusions Our systematic review allowed us to summarise important clinical findings about COVID-19 among NH residents and to identify the population’s risk factors for serious illness and death caused by the disease. However, the treatment and care of NH residents with severe COVID-19 warrant further investigation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".