793. Expert Panel Consensus Ranking of Comorbid Conditions Causally Related to <i>Clostridioides difficile</i> Infection
Bibliographic record
Abstract
Abstract Background Numerous studies have identified comorbidities that are associated with Clostridioides difficile infection (CDI), but current CDC and CMS models for risk adjusting hospital CDI rates do not include patient comorbid conditions. Incorporating patient-level data could improve CDI risk adjustment, but comorbidities would need to be easily electronically available for widescale implementation. Ideally, they would also be causally related to CDI — i.e., true risk factors, not confounders — to facilitate more unbiased inter-hospital comparisons. The current study aimed to determine which comorbid conditions are causally related to CDI based upon expert consensus. Methods We used Delphi methodology to administer an iterative, two-round survey with an intervening teleconference, to eight infectious disease experts. Experts evaluated 40 comorbid conditions included in Charlson and Elixhauser comorbidity indices (and thus validated for electronic capture through administrative data), as well as other comorbidities commonly associated with CDI. Experts rated comorbid conditions from 1 (not at all related) to 5 (strongly related), based upon perceived relatedness with CDI. To assign causal relatedness, the following criteria had to be met at the end of round two: 1) majority (> 50%) of experts rating the condition at 3 (somewhat related) or higher; 2) inter-quartile range (IQR) < = 1; and 3) standard deviation (SD) < = 1. Results 8/40 (20%) comorbid conditions were ranked as causally related to CDI, including patient age, three malignancy comorbidities, two transplant-related comorbidities, HIV/AIDS, and inflammatory bowel disease. A further 18/40 (45%) qualified as indeterminately related, and 14/40 (35%) were ranked as not causally related to CDI (Table). Three of the eight causally related factors were not components of Elixhauser or Charlson indices. Table Conclusion We identified comorbid conditions that may be appropriate candidates to consider for inclusion in patient-level risk adjustment models. Some causal factors did not originate from established comorbidity indices. Thus, future work to validate electronic capture of these conditions could further reduce barriers to risk-adjustment implementation. 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 | 0.097 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".