2238. Evaluation of Adjuvant Interferon-Gamma-Level Assessment to Improve the Performance of Procalcitonin Testing in Hospitalized Bacteremic Patients
Bibliographic record
Abstract
Abstract Background Although procalcitonin-guided antimicrobial stewardship has had proven utility in emergency and ICU settings, it is still not widely adopted outside these areas. One limitation to a more universal uptake has been the unreliable performance in discriminating bacterial infected from uninfected individuals. Viral infections have been noted to suppress procalcitonin (PCT) levels through Interferon-gamma (IFN-G)-mediated inhibition of procalcitonin release from parenchymal cells. Unfortunately, clinical application algorithms do not assess INF-G levels at the time evaluation thus treating providers are unable to distinguish a true-negative test from a false-negative test resulting from INF-G-mediated procalcitonin suppression This undermines the performance of PCT, particularly in patients with bacterial and viral co-infections. We hypothesized that adjuvant interferon gamma testing could improve the performance of PCT. To test this hypothesis we prospectively enrolled bacteremic hospitalized patients along with culture-negative controls and then assessed the performance of PCT with adjuvant IFN-G testing. Methods 69 hospitalized patients with bacteremia and 32 culture-negative controls were enrolled. Demographic and clinical parameters were compared between groups alongside INFG and PCT levels Parametric and non-parametric statistical tests were performed where appropriate. Test performance was evaluated by constructing receiver operator curves (ROCs) for PCT, INF-G, and a combination of PCT+INF-G. Results Of 101 patients enrolled, the mean age was 49.46 ± 13.6 years with 47% being female. The following were comparative statistics between the culture-positive vs. culture-negative group: mean age 52.1 ± 15.7 vs. 46.4 ± 14.2 years, P = 0.56; WBC 11.9 ± 9.5 vs. 9.5±5.1, P = 0.170; ANC 8,466 ± 5,686 vs. 8,189 ± 4,769, P = 0.907; eGFR 73.2 ± 23 vs. 74.5 ± 26.1, P = 0.644; PCT 2.79 ± 5.87 vs. 0.71 ± 1.79, P = 0.03. Of these 57 patients had INF-G and PCT values available and their corresponding ROCs are shown in figure. Conclusion Our interim results indicate adjuvant INF-G testing may not improve the performance of procalcitonin in hospitalized bacteremic patients. The additional samples are being analyzed to confirm these findings. Disclosures All authors: No reported disclosures.
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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 | 0.000 | 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 | 0.000 |
| 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".