Risk factors associated with COVID-19 Intensive Care Unit hospitalisation in Guyana: A cross-sectional study
Bibliographic record
Abstract
Objective The purpose of the study was to determine risk factors associated with COVID-19 ICU hospitalisation at Georgetown Public Hospital Corporation (GPHC), Guyana. Methods A retrospective chart-review was conducted on all COVID-19 admissions from March to September 2020. The predictive factors were demographics, comorbidities, signs and symptoms of COVID-19 and laboratory findings on admission. Descriptive frequency analysis was done for all independent variables and the Chi-square test was used to compare differences between groups where suitable. Univariate and multivariate binary logistic regression was used to examine the association between the independent variables and the risk for ICU hospitalisation. Results There were 136 patients with COVID-19 at GPHC during March to September 2020 and after exclusion, 135 patients were used in the study. There were 72 (53.4%) patients who required non-ICU care, while 63 (46.6%) ICU care and average age ± SD (median) was 51 ±16 (n= 49) and 56 ±18 (n= 60), respectively. In the multivariate regression model, the odds of ICU admission for those aged 40-65 was 0.14 (p <.01) compared to those > 65 years. Patients with class 2 and above obesity had higher odds of ICU admission compared to non-obese patients OR 11.09 (p= .006). Patients with 2 and 3 or more comorbidities also had higher odds of ICU admission compared to those with no comorbidities OR 7.83 (p= .03) and 132 (p <.001), respectively. Patients with LDH 228-454 U/L and > 454 U/L on admission had higher odds of ICU admission compared to those with normal LDH OR 19.88 (p= .001) and 23.32 (p= .001), respectively. Patients with albumin < 3.50 mg/dL on admission also had higher odds of ICU admission compared to those with normal albumin OR 5.78 (p= .005). Conclusion Risk factors associated with ICU hospitalisation were advanced age, obesity, multiple comorbidities, elevated LDH and low albumin. Protecting the population at risk for ICU admission and prioritizing them for vaccination is recommended to reduce the risk of running out of ICU capacity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".