Bacterial Infection Among Covid-19-Infected Patients Admitted to the Intensive Care Unit at King Abdullah Hospital in Bisha: A Single-Centre Retrospective Observational Study
Bibliographic record
Abstract
Background: The covid-19 pandemic has created significant challenges for healthcare systems worldwide, especially in intensive care units (ICU), which face unprecedented hardship. Despite the primary focus on viral infection, the precise influence of bacterial superinfections on the outcome of severe covid-19 cases, particularly in different hospital settings, remains uncertain. Objective: to investigate the prevalence, characteristics, and outcomes of bacterial superinfections in covid-19 patients hospitalized in the ICU in Saudi Arabia during the second wave of the pandemic. Methods: This study was conducted at King Abdullah Hospital in Bisha, Saudi Arabia, and involved retrospective observational analysis. This study examined 121 adult patients admitted to the ICU due to severe covid-19 between April and July 2021. Information regarding demographics, clinical characteristics, radiological findings, and microbiological data was also collected. This study examined the relationship between superinfections and mortality through rigorous statistical analyses, including chi-square testing and multivariable logistic regression. Results: Most participants in the study were men (57.9%) and Saudi citizens (95.0%), with an average age of 63 ± 17 years. The incidence of superinfections among the patients was 43.8%, significantly higher than that reported in previous studies. Microbiological examinations revealed the presence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) organisms, particularly in respiratory samples. The most common isolates were KLEPNE-XDR (10.7%) and ACIBAU-XDR (8.3%). A statistically significant correlation was observed between superinfection and mortality (p=0.042). Patients with superinfections experienced a significantly higher mortality rate of 55.3% in comparison to those without superinfections, who had a mortality rate of 44.7%. Multivariable logistic regression identified age (aOR 1.040, 95% CI: 1.012-1.068, p=0.004) and non-Saudi nationality (aOR 12.320, 95% CI: 1.242-122.177, p=0.032) as significant predictors of mortality. Interestingly, a high percentage of the patients (89.3 %) were treated with carbapenems. Conclusion: Our research revealed a notable prevalence of bacterial superinfections, including highly resistant strains, among severely ill covid-19 patients in the ICU. The significant link between superinfections and mortality underscores the pressing need for enhanced diagnostic tools, targeted antimicrobial therapies, and improved stewardship protocols in the ICU setting. The findings of this study have important implications for clinical care and public health policy in the ongoing battle against covid-19 and its consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".