699. Clinical Outcomes and Management of NAAT-Positive/Toxin-Negative <i>Clostridium difficile</i> Infection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Standalone nucleic acid amplification tests (NAATs) are increasingly used to diagnose Clostridium difficile infections (CDI), although they may be unable to distinguish colonization from disease. A two-stage algorithm pairing NAAT with a toxin immunoassay (Toxin) has been proposed to improve specificity. We sought to evaluate clinical outcomes among patients testing NAAT+/Toxin+ vs NAAT+/Toxin- by conducting a systematic review and meta-analysis. We further compared outcomes between NAAT+/Toxin- patients who were and were not treated. Methods We searched EMBASE and MEDLINE from inception to September 17, 2022 for articles comparing CDI outcomes among symptomatic patients tested by NAAT and Toxin tests. The risk differences (RD) of all-cause mortality and CDI recurrence were computed by random effects meta-analysis between NAAT+/Toxin+ and NAAT+/Toxin- patients, as well as between treated and untreated NAAT+/Toxin- patients. Results Twenty-three observational studies comprising 11749 patients were included. 30-Day all-cause mortality was not significantly different between NAAT+/Toxin+ and NAAT+/Toxin- patients (8.4% vs 6.8%, respectively; RD=0.2%, 95%CI -2.4, 2.9; I2=52.9%). Recurrence at 60 days was significantly higher among NAAT+/Toxin+ (19.8%) vs NAAT+/Toxin- patients (11.0%) (RD=7.7%, 95%CI=4.6, 10.7, I2=37.4%). Among treated compared to untreated NAAT+/Toxin- patients, the all-cause 30-day mortalities were 5.0% and 14.9%, respectively (RD=-9.5%, 95%CI=-15.0, -3.9, I2=0.0%), but 60-day recurrence was not significantly different (11.6% vs 7.0%, respectively; RD=5.3%, 95%CI -1.7, 12.2; I2=47.0%). Conclusion Compared to NAAT+/Toxin- patients, NAAT+/Toxin+ patients had a greater risk of recurrence at 60 days, but not all-cause mortality. Treatment of NAAT+/Toxin- patients was associated with reduced all-cause mortality, but not recurrence. While subject to the limitations of observational studies including the potential for confounding by indication and immortal time bias, these results suggest that treatment of NAAT+/Toxin- patients may be beneficial. A strategy comparing standalone NAAT or staged testing with up front combined NAAT/Toxin testing could be the topic of a randomized controlled trial to determine cost and efficacy. 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".