Comparison of rectal swabs and fecal samples for the detection of <i>Clostridioides difficile</i> infections with a new in-house PCR assay
Bibliographic record
Abstract
ABSTRACT The detection of Clostridioides difficile infections (CDI) relies on testing the stool of patients by toxin antigen detection or PCR methods. Although PCR and antigenic methods have significantly reduced the time to results, delays in stool collection can significantly add to the turnaround time. The use of rectal swabs to detect C. difficile could considerably reduce the time to diagnosis of CDI. We developed a new rapid PCR assay for the detection of C. difficile and evaluated this PCR assay on both stool and rectal swab specimens. We recruited a total of 623 patients suspected of C. difficile infection. Stool samples and rectal swabs were collected from each patient and tested by our PCR assay. Stool samples were also tested by the cell cytotoxicity neutralization assay (CCNA) as a reference. The PCR assay detected C. difficile in 60 stool specimens and 61 rectal swabs for the 64 patients whose stool samples were positive for C. difficile by CCNA. The PCR assay detected an additional 35 and 36 stool and rectal swab specimens positive for C. difficile , respectively, for sensitivity with stools and rectal swabs of 93.8% and 95.3%, specificity of 93.7% and 93.6%, positive predictive values of 63.2% and 62.9%, and negative predictive values of 99.2% and 99.4%. Detection of C. difficile using PCR on stools or rectal swabs yielded reliable and similar results. The use of PCR tests on rectal swabs could reduce turnaround time for CDI detection, thus improving CDI management and control of C. difficile transmission. IMPORTANCE Clostridioides difficile infection (CDI) is the leading cause of healthcare-associated diarrhea, resulting in high morbidity, mortality, and economic burden. In clinical laboratories, CDI testing is currently performed on stool samples collected from patients with diarrhea. However, the diagnosis of CDI can be delayed by the time required to collect stool samples. Barriers to sample collection could be overcome by using a rectal swab instead of a stool sample. Our study showed that CDI can be identified rapidly and reliably by a new PCR assay developed in our laboratory on both stool and rectal swab specimens. The use of PCR tests on rectal swabs could reduce the time for the detection of CDI and improve the management of this infection. It should also provide a useful alternative for infection-control practitioners to better control the spread of C. difficile .
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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) | 0.001 | 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 | 0.000 |
| 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".