External validation of predictive scores for mortality following Clostridium difficile infection
Bibliographic record
Abstract
Abstract Background The burden of Clostridium difficile infection (CDI) has increased in the last decade, with more adverse outcomes and related mortality. Although many predictive scores were developed, few were validated and their performances were sub-optimal. We conducted an external validation study of predictive scores or models for mortality in CDI. Methods Published predictive tools were identified through a systematic review. We included those reporting at least an internal validation approach. A multicenter prospective cohort of 1380 adults with confirmed CDI enrolled in two Canadian provinces was used for external validation. Most cases were elderly (median age 71), had a healthcare facility-associated CDI (90%), and 52% were infected by NAP1/BI/027 strains. All-cause 30-day death occurred in 12% of patients. The performance of each scoring system was analyzed using individual primary outcomes. Results We identified two scores which performances (95% CI) are shown in the table. Both had low sensitivity and PPV, moderate specificity and NPV, and similar AUC/ROC (0.66 vs. 0.77 in the derivation cohort, and 0.69 vs. 0.75 respectively). One predictive model for 30 days all-cause mortality (Archbald-Pannone 2015, including Charlson score, WBC, BUN, diagnosis in ICU, and delirium*) was associated with only 5% increase in odds of death (crude OR = 1.05 (1.03–1.06)) with an AUC of 0.74 (0.7–0.8). Conclusion The predictive models of CDI mortality evaluated in our study have limitations in their methods and showed moderate performances in a validation cohort consisting of a majority of CDI caused by NAP1 strains. An accurate predictive tool is needed to guide clinicians in the management of CDI to prevent adverse outcomes. 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 | 0.085 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 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".