<i>Clostridium difficile</i> associated risk of death score (<scp>CARDS</scp>): a novel severity score to predict mortality among hospitalised patients with <i>C. difficile</i> infection
Bibliographic record
Abstract
BACKGROUND: Clostridium difficile infection (CDI) is a public health threat and associated with significant mortality. However, there is a paucity of objectively derived CDI severity scoring systems to predict mortality. AIM: To develop a novel CDI risk score to predict mortality entitled: Clostridium difficile associated risk of death score (CARDS). METHODS: We obtained data from the United States 2011 Nationwide Inpatient Sample (NIS) database. All CDI-associated hospitalisations were identified using discharge codes (ICD-9-CM, 008.45). Multivariate logistic regression was utilised to identify independent predictors of mortality. Clostridium difficile associated risk of death score was calculated by assigning a numeric weight to each parameter based on their odds ratio in the final logistic model. Predictive properties of model discrimination were assessed using the c-statistic and validated in an independent sample using the 2010 NIS database. RESULTS: We identified 77 776 hospitalisations, yielding an estimate of 374 747 cases with an associated diagnosis of CDI in the US, 8% of whom died in the hospital. The eight severity score predictors were identified on multivariate analysis: age, cardiopulmonary disease, malignancy, diabetes, inflammatory bowel disease, acute renal failure, liver disease and ICU admission, with weights ranging from -1 (for diabetes) to 5 (for ICU admission). The overall risk score in the cohort ranged from 0 to 18. Mortality increased significantly as CARDS increased. CDI-associated mortality was 1.2% with a CARDS of 0 compared to 100% with CARDS of 18. The model performed equally well in our validation cohort. CONCLUSION: Clostridium difficile associated risk of death score is a promising simple severity score to predict mortality among those hospitalised with C. difficile infection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".