Diagnostic and prognostic implications of peak leukocyte count in the intensive care unit
Bibliographic record
Abstract
Background: It is well established that leukocytosis is a predictor of infection and inflammation, and that leukopenia is a marker of immunocompromise. However, it is possible that the degree of leukocytosis may provide additional information to clinicians treating critically ill patients. Our aim was to determine if peak white blood cell (WBC) count could help clinicians in diagnosing patients’ conditions and determining their prognoses. Methods: This was a retrospective cohort study of six adult intensive care units (ICUs) at a US academic medical center. Patients admitted to an adult ICU between 2001 and 2012 were analyzed. Our primary aim was to determine which diagnoses were most commonly encountered in patients with different peak WBC counts during their stay. In our secondary analyses, we determined the length of stay and mortality associated with peak WBC count across diagnoses and used multiple logistic regression to determine whether peak WBC count was more predictive of mortality than other diagnostic and demographic variables. Results: There were 45,340 patients in our cohort. There was substantial variation in the disease prevalence and risk of mortality across peak WBC count categories. Interestingly, the rate of C. difficile was substantially higher in patients with extreme leukocytosis (peak WBC ≥40,000; 12% compared to 1-2% in all other groups, p<0.001). In our multivariate regression, extreme leukocytosis was associated with very high mortality rates (adjusted odds ratio (aOR) 10.4, 95% CI: 8.5-12.7, p<0.001). Conclusions: Degree of peak leukocytosis in critically ill patients provides valuable diagnostic and prognostic information. Having an understanding of the conditions associated with each category of peak WBC count can help clinicians in caring for patients in the intensive care unit. In particular, extreme leukocytosis signals a very high risk of mortality and may, in appropriate clinical contexts, indicate the need for more aggressive or urgent intervention.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".