<i>Clostridioides difficile</i> Infection in COVID-19 Hospitalized Patients: A Nationwide Analysis
Bibliographic record
Abstract
Background: Clostridioides difficile infection (CDI) is a significant healthcare-associated infection with implications for patient morbidity, mortality, and healthcare costs. However, the connection between CDI and coronavirus disease 2019 (COVID-19) infection and its influence on patient outcomes remain uncertain. This study aimed to examine the association between CDI and COVID-19, specifically investigating whether CDI worsens outcomes in patients with COVID-19. By utilizing the extensive National Inpatient Sample (NIS) database and analyzing pertinent factors, this research endeavored to enhance our understanding of CDI within the context of COVID-19. Methods: The NIS database was searched for adult patients hospitalized with a primary diagnosis of COVID-19 infection in 2020. Patients with a secondary diagnosis of CDI were identified and separated into two groups based on CDI status. Baseline characteristics, Charlson Comorbidity Index (CCI), and outcomes were compared between the two groups using Chi-square and t -tests. Multivariate logistic and linear regressions were performed for the identification of independent predictors of CDI and mortality. Results: A total of 1,045,125 COVID-19 hospitalizations were included, of which 4,920 had a secondary diagnosis of CDI. Patients with CDI and COVID-19 were older (mean age 69.9 vs. 64.2 years; P < 0.001), more likely to be female (54.1% vs. 47.1%; P < 0.001) and white (60% vs. 52.4%; P < 0.001). The CDI and COVID-19 group had a longer length of stay (14.1 vs. 7.42 days; P < 0.001), higher total hospital costs ($42,336 vs. $18,974; P < 0.001), and higher inpatient mortality (21.6% vs. 11%; P < 0.001) compared to the COVID-19 group without CDI. Patients in the CDI and COVID-19 group had a higher CCI score (51.7% with a score of 3 or more vs. 27.7%; P < 0.001), indicating a higher comorbidity burden. Multivariate logistic regression analysis revealed CDI was independently associated with increased mortality (odds ratio (OR) 1.37; P = 0.001) and showed that the female gender and several pre-existing comorbidities were associated with a higher likelihood of CDI. Conclusion: CDI is independently associated with increased mortality in patients admitted with COVID-19 infection. Female gender and several pre-existing comorbidities are independent predictors of CDI in COVID-19 patients. Gastroenterol Res. 2023;16(4):234-239 doi: https://doi.org/10.14740/gr1639
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".