Platelet Count Trajectory and Mortality in Septic Shock: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: In Canada, septic shock accounts for approximately 30,000 hospitalizations annually and is associated with a mortality rate of 30%. Thrombocytopenia in septic shock is associated with a poor prognosis including increased length of stay, longer duration of organ support, increased major bleeding events and mortality. The trajectory of the platelet count over time in patients with septic shock has not been well-studied. We hypothesized that the platelet count trajectory in septic shock can identify distinct clinical groups and is an independent predictor of 30-day mortality. Objectives: 1) To identify groups of patients with distinct platelet count trajectories; 2) To evaluate patient and illness factors associated with platelet count trajectories; and 3) To estimate the association of platelet count trajectory with mortality patients with septic shock. Methods: We performed a retrospective cohort study of adult patients admitted with septic shock to an intensive care unit (ICU) in Winnipeg, Canada between 2006-2014. We used group-based trajectory analysis to analyze the trend of platelet count over the first seven days of ICU admission to group patients with similar platelet trajectories. Group-based trajectory analysis is a statistical method that analyzes the pattern of a variable over time and allows distinct groups with similar trajectories to arise from the data. We utilized both the Bayesian Information Criterion (BIC) and clinical validity characteristics to choose the most suitable trajectory model. We developed a multinomial logistic regression model to associate patient characteristics with platelet count trajectories. We created a multivariable Cox proportional hazard model adjusted for age, sex, Acute Physiology and Chronic Health Evaluation (APACHE) II score, comorbidities, site or source of infection, and time to first appropriate antimicrobial to examine the association between platelet count trajectory and 30-day mortality. Results: Our study cohort included 913 patients with septic shock. The favoured trajectory model identified six distinct trajectories (Figure 1) using the platelet count over the first 7 days of ICU admission. We found that the number of organ failures on day 1was independently associated with platelet count trajectory, while other characteristics were not. The 30-day mortality of the entire cohort was 26.2% and ranged from 16.4% in group 1 (rising platelet count) to 44.4% in group 6 (high platelet count throughout). In the multivariable Cox proportional hazard model, compared with group 2 (thrombocytopenia), group 4 (high normal platelet count) was independently associated with a reduced risk of death at 30 days (Hazard Ratio (HR) 0.33, p = 0.002). The trajectory group with thrombocytosis (group 6) was associated with an increased risk of death at 30 days (HR 3.24, p=0.48) however the small number in this group limits the generalizability of this finding. Conclusion: We identified 6 distinct and clinically relevant platelet count trajectories in critically ill patients with septic shock. Platelet count trajectory was associated with the number of organ failures on day 1. Our study confirms that thrombocytopenia is associated with a worse prognosis as other trajectories with higher platelet count were associated with a lower risk of death. While it is well recognized that thrombocytopenia is associated with adverse outcomes in patients with septic shock, it is not known whether other patterns of the platelet trajectory such as thrombocytosis are similarly clinically important. Further studies are needed to fully characterize the impact platelet count trajectory on outcomes in patients with septic shock. The interplay between platelet count trajectory and other parameters (such as the white blood cell count trajectory, or INR trajectory) may have a more predictive role in evaluating prognosis in sepsis. Disclosures No relevant conflicts of interest to declare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".