Procalcitonin Kinetics in the First 72 Hours Predicts 30-Day Mortality in Severely Ill Septic Patients Admitted to an Intermediate Care Unit
Bibliographic record
Abstract
BACKGROUND: Severe sepsis and septic shock are leading causes of morbidity and mortality among critically ill patients, thus the identification of prognostic factors is crucial to determine their outcome. In this study, we explored the value of procalcitonin (PCT) variation in predicting 30-day mortality in patients with sepsis admitted to an intermediate care unit. METHODS: This prospective observational study enrolled 789 consecutive patients with severe sepsis and septic shock admitted to a medical intermediate care unit between November 2012 and February 2014. Kinetics of PCT expressed as percentage were defined by the variation between admission and 72 hours, and 24 and 72 hours; they were defined as Δ-PCT0-72h and Δ-PCT24-72h, respectively. RESULTS: The final study group of 144 patients featured a mean age of 73 ± 14 years, with a high prevalence of comorbidities (Charlson index greater than 6 in 39%). Overall, 30-day mortality was 28.5% (41/144 patients). A receiver-operating-characteristic (ROC) analysis identified a decrease of Δ-PCT0-72h less than 15% (area under the curve: 0.75; 95% confidence interval (CI): 0.67 - 0.82) and a decrease of Δ-PCT24-72h less than 20% (area under the curve: 0.83; 95% CI: 0.74 - 0.92) as the most accurate cut-offs in predicting mortality. Decreases of Δ-PCT0-72h less than 15% (HR: 3.9, 95% CI: 1.6 - 9.5; P < 0.0001) and Δ-PCT24-72h less than 20% (HR: 3.1, 95% CI: 1.2 - 7.9; P < 0.001) were independent predictors of 30-day mortality. CONCLUSIONS: Evaluation of PCT kinetics over the first 72 hours is a useful tool for predicting 30-day mortality in patients with severe sepsis and septic shock admitted to an intermediate care unit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".