Procalcitonin and C-Reactive Protein as a Predictor of Organ Dysfunction and Outcome of Sepsis and Septic Shock Patients in Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Sepsis is a potentially life-threatening disorder in ICU. The worst complication is organ dysfunction and mortality. Procalcitonin (PCT) and C-Reactive Protein (CRP) had been proposed as biomarker and predictor for diagnosis, prognosis, and patient deterioration in sepsis and septic shock patients. To know whether PCT and CRP can be used as a predictor of organ dysfunction and outcome in sepsis and septic shock patients in ICU.MATERIALS & METHODS: Data were cohort retrospectively analyzed in 35 sepsis (45.5%) and 42 septic shock patients (54.5%) admitted to ICU Dr. Wahidin Sudirohusodo General Hospital (January 2014 – December 2015). Data were analyzed using Chi-Square test, Pearson Correlation, and Spearman-Rho Correlation test.RESULTS: Total of 77 patients met the inclusion criteria. Cut-off point in predicting organ dysfunction in sepsis and septic shock was significantly higher in PCT (45.7ng/mL) with 76.6% sensitivity and 70.0% specificity, while CRP was 145.75 mg/mL with 70.2% sensitivity and 56.7% specificity. There was a positive correlation of PCT (0.492 [p=0.000]) and CRP (0.336 [p=0.003]) to organ dysfunction reflected on SOFA score using the Pearson Correlation test (p<0.01 statistically significant). Based on Spearman-Rho Correlation test, correlation of PCT (0.191 [p=0.097]) and CRP (0.110 [p=0.340]) to outcome in day-7 was positive but not statistically significant (p≥0.01). While in day-28, there was positive correlation 0.553 (p=0.001) for PCT, 0.460 (p=0.006) for CRP, and statistically significant (p<0.01).CONCLUSIONS: Procalcitonin and CRP can be used as a predictor of organ dysfunction and outcome in sepsis and septic shock patients.
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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 | 0.001 |
| 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) | 0.001 | 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".