Mortality Prognostic Hematological Parameters in COVID-19 Patients
Bibliographic record
Abstract
Background: Coronavirus disease (COVID-19) pandemic has led to a global pandemic with cases ranging from asymptomatic infection to severe acute respiratory distress syndrome (ARDS). Early in the pandemic, COVID-19 was observed to affect multiple hematological parameters including leukocytes, lymphocytes, and platelets. We conducted this study to assess possible correlation between certain parameters such as neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and severity of COVID-19 infection. Methods: This is a single-center retrospective analysis of the data of adults (aged above 18 years) hospitalized at our facility from March to August 2020. All patients had the following criteria: oxygen saturation below 94% without oxygen supplementation on presentation and positive COVID-19 real-time reverse-transcriptase-polymerase chain reaction (RT-PCR). Results: The study population was 276 patients, and 52.2% were males. Multiple comorbidities were documented. Hypertension, diabetes, and asthma were the most common. Overall mortality was 21.3%. Leukocytosis along with lymphopenia were associated with significantly increased risk for intensive care unit (ICU) admission; however only leukocytosis was associated with increased risk of mechanical ventilation and death. PLR and NLR were significantly associated with disease severity in terms of rates of ICU admission, mechanical ventilation, and death. Although lymphopenia was noted more frequently in patients with severe COVID-19 infection, the association between lymphopenia and in-hospital mortality was not statistically significant in our study. The parameters can be used to predict severity, guide patient triage and early intervention. Conclusions: We conclude NLR and PLR can be used as simple prognostic factors to predict severity of COVID-19 patients and guide possible close monitoring and earlier intervention. Clin Infect Immun. 2023;8(1):13-23 doi: https://doi.org/10.14740/cii159
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".