Prognostic Value of Baseline Biochemical Parameters Among Severe COVID-19 Patients Admitted to an Intensive Care Unit of a Tertiary Hospital in South Africa.
Bibliographic record
Abstract
Abstract BackgroundData on biochemical markers and their association with mortality rates observed in patients with severe COVID-19 disease admitted to Intensive Care Units (ICUs) in sub-Saharan Africa are scanty. We performed an evaluation of baseline routine biochemical parameters as prognostic biomarkers in COVID-19 patients admitted to ICU. MethodsDemographic, clinical and laboratory data were collected prospectively on patients with PCR-confirmed COVID-19 admitted to the adult ICU in a tertiary hospital in Cape Town, South Africa, between October 2020 and February 2021. Robust Poisson regression methods and receiver operating characteristic (ROC) curve were used to explore the association of biochemical parameters with severity and mortality. ResultsA total of 82 patients [(median age 53.8 years (IQR: 46.4-59.7)] were enrolled, of whom 27 (33%) were male. The median duration of ICU stay was 10 days (IQR: 5-14); 54/82 (66% CFR) patients died. Baseline lactate dehydrogenase (LDH) (aRR: 1.002, 95%CI: 1.0004-1.004; P = 0.016) and N-terminal pro B-type natriuretic peptide (NTProBNP) (aRR: 1.0004, 95%CI: 1.0001-1.0007; P = 0.014) were both independent risk factors of a poor prognosis with optimal cut-off values of 449.5 U/L (sensitivity: 1; specificity: 0.43) and 551 pg/mL (sensitivity: 0.49; specificity: 0.86), respectively.ConclusionLDH and NTProBNP appear to be promising predictors of COVID-19 poor prognosis in the ICU. Larger sample size studies are required to confirm the validity of this combination of biomarkers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".