Clinical evolution of COVID-19 patients with hypertension and/or diabetes in the intensive care units: a systematic review
Bibliographic record
Abstract
We aimed to evaluate the association of hypertension and diabetes with the severity prognosis of COVID-19 in ICU hospitalization by analyzing significant differences in epidemiological, clinical and laboratory aspects when compared with non-diabetic and non-hypertensive patients. This review was conducted according to PRISMA, registered on PROSPERO, guided for 4 independent researchers, we used 5 different database and Newcastle-Ottawa scale was the bias analysis applied. A total of 26 articles were included in this review. Diabetics patients admitted to the ICU were older than non-diabetics, about 10 years. Males were associated with a higher chance of death compared to females. Patients with newly diagnosed diabetes and poorly controlled HBA1C had a higher risk of death compared to long-term and controlled patients. Diabetic patients presented evidence that this population tends to have more symptoms in the lower respiratory tract than upper respiratory tract. Procalcitonin levels and CRP proved to be determinant for the patient’s evolution. Hypertensive and diabetics patients who died presented higher d-dimer levels, troponin and NT-proBNP on ICU admission in comparison to patients who survived. Creatinine was higher and this was a marker of severity as it was associated with death. Gender plays an important role in mortality, also advanced age. Diabetes is an independent risk factor for mortality of COVID-19 and mortality, but hypertension is not. We conclude that hypertension and diabetes were significantly associated with COVID-19 patient’s admission to the ICU.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".