Prevalence and Outcomes of COVID −19 Patients with Happy Hypoxia: A Systematic Review
Bibliographic record
Abstract
Background: In Coronavirus disease 2019 (COVID-19), some patients have low oxygen saturation without any dyspnea. This has been termed “happy hypoxia.” No worldwide prevalence survey of this phenomenon has been conducted. This review aimed to summarize information on the prevalence, risk factors, and outcomes of patients with happy hypoxia to improve their management. Methods: We conducted a systematic search of electronic databases for all studies published up to April 30, 2022. We included high-quality studies using the Newcastle-Ottawa Scale (NOS) tool for qualitative assessment of searches. The prevalence of happy hypoxia, as well as the mortality rate of patients with happy hypoxia, were estimated by pooled analysis and heterogeneity by I 2 . Results: Of the 25,086 COVID-19 patients from the 7 studies, the prevalence of happy hypoxia ranged from 4.8 to 65%. The pooled prevalence was 6%. Happy hypoxia was associated with age > 65 years, male sex, body mass index (BMI)> 25 kg/m2, smoking, chronic obstructive pulmonary disease, diabetes mellitus, high respiratory rate, and high d-dimer. Mortality ranged from 01 to 45.4%. The pooled mortality was 2%. In 2 studies, patients with dyspnea were admitted to intensive care more often than those with happy hypoxia. One study reported that the length of stay in intensive care did not differ between patients with dyspnea and those with happy hypoxia at admission. One study reported the need for extracorporeal membrane oxygenation (ECMO) in patients with happy hypoxia. Conclusion: The pooled prevalence and mortality of patients with happy hypoxia were not very high. Happy hypoxia was associated with advanced age and comorbidities. Some patients were admitted to the intensive care unit, although fewer than dyspneic patients. Its early detection and management should improve the prognosis. Keywords: prevalence, outcomes, COVID − 19, happy hypoxia
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".