Evolution and Impact of Thrombocytopenia in Septic Shock: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To characterize the prevalence, incidence, and temporal evolution of thrombocytopenia (platelets < 100 × 10/L) in septic shock and to investigate the independent association of thrombocytopenia on clinical outcomes. DESIGN: Retrospective, propensity-matched, cohort study. SETTING: Two academic ICUs in Winnipeg, Canada. PATIENTS: Nine-hundred eighty adult patients diagnosed with septic shock between 2007 and 2012. INTERVENTIONS: Propensity-matched cohort analysis and Cox proportional hazard model evaluating thrombocytopenia over time. MEASUREMENTS AND MAIN RESULTS: Of 980 adults, 165 patients (16.8%) had thrombocytopenia at ICU admission (prevalent), whereas 271 (27.7%) developed thrombocytopenia during ICU admission (incident). Among patients with incident thrombocytopenia, the median time from ICU admission to thrombocytopenia was 2 days (interquartile range, 1-3 d). Among survivors, the median time from incident thrombocytopenia to platelet recovery was 6 days (interquartile range, 4-8 d). The median time from liberation of vasopressors to recovery of platelets concentration (≥ 100 × 10/L) was 2 days (interquartile range, 0-4 d). In a propensity-matched analysis, thrombocytopenia was associated with increased durations of ICU length of stay (9 vs 6 d; p < 0.01), mechanical ventilation (7 vs 4 d; p < 0.01), and vasopressor use (4 vs 3 d; p < 0.01), as well as increased major bleeding events (41% vs 18%; p < 0.01). In an adjusted Cox proportional hazards model, thrombocytopenia was significantly associated with both increased ICU mortality (hazard ratio, 1.99; 95% CI, 1.51-2.63) and hospital mortality (hazard ratio, 1.93; 95% CI, 1.48-2.51). CONCLUSIONS: Both the prevalence and incidence of thrombocytopenia are high in septic shock. Incident thrombocytopenia occurs early in septic shock, and platelet recovery lags behind clinical recovery. In septic shock, thrombocytopenia is associated with increased length of stay, longer duration of organ support, major bleeding events, and mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".