External validation of clinical scores to predict complications of Clostridium difficile infection
Bibliographic record
Abstract
Abstract Background Clostridium difficile infection (CDI) is the most common cause of nosocomial diarrhea. About one in 5 patients with CDI (median 18%) develop a complication (cCDI), including mortality. Many predictive scores have been published to identify patients at risk of cCDI but none is currently recommended for clinical use and few were validated. We conducted an external validation study of predictive tools for cCDI. Methods Predictive tools were identified through a systematic review. We included those reporting at least an internal validation process. We performed the external validation on a multicenter prospective cohort of 1380 Canadian adults with confirmed CDI. Most cases were elderly (median age 71), had a healthcare facility-associated CDI (90%), and cCDI occurred in 8%. NAP1 strain was found in 52%. The performance of each scoring system was analyzed using individual outcomes. Modifications in predictors were made to match available data in the validation cohort. Results We assessed 3 predictive scores and one predictive model. The performance (95% CI) of higher thresholds are shown in the Table. All scores had a low sensitivity and PPV, and moderate specificity and NPV. The model of Shivashankar 2013 (age, WBC> 15, narcotic use, antacids use, creatinine ratio > 1.5) predicted 25% probability of cCDI. All showed similar AUC (0.63–0.67). Conclusion The predictive tools included in our study showed moderate performance in a validation cohort with a low rate of cCDI and high proportion of NAP1 strains. Further research is needed to develop an accurate predictive tool to guide clinicians in the management of CDI. Disclosures J. Powis, Merck: Grant Investigator, Research grant; GSK: Grant Investigator, Research grant; Roche: Grant Investigator, Research grant; Synthetic Biologicals: Investigator, Research grant
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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 | 0.068 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".