Determinants of Healthcare-Associated Infections in King Abdulaziz Specialized Hospital in Taif, Saudi Arabia
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) represent a significant challenge in hospital settings, contributing to increased morbidity, mortality, and healthcare costs. This study aimed to estimate the prevalence and socio-demographic and clinical determinants of HAIs at the King Abdulaziz Specialized Hospital (KAASH) in Taif, Saudi Arabia. Methodology: A hospital-based cross-sectional study was conducted from March 2023 to January 2024 targeting inpatients aged 18 and above in all units and wards. Data were collected using the National Healthcare Safety Network (NHSN) criteria for definitions of surveillance. A structured questionnaire gathered socio-demographic and clinical data from patients or next of kin if the patient was not fully oriented. Descriptive statistics were performed, and analytical methods used included Pearson chi-square test, Pearson correlation, independent t-test, and one-way analysis of variance. Results: Among 318 participants included in this study, the mean age of participants was 56.44 years, with a slight female predominance (n=164, 51.6%). Hypertension (n=162, 50.9%) and diabetes (n=126, 39.6%) were the most prevalent comorbidities. Pneumonia (n=60, 26.8%) and trauma (n=55, 17.4%) were the leading causes of admission. The two most common HAIs included catheter-associated urinary tract infections (CAUTI) (n=124, 39%) and central line-associated bloodstream infections (CLABSI) (n=74, 23.3%). The primary causative organisms were Klebsiella pneumoniae (n=96, 30.2%) and Acinetobacter baumannii (n=32, 10.1%). The most significant predictors of HAIs were as follows: For CLABSI, risk factors include having three or more comorbidities, fever above 37.8°C, chills or rigors, hypotension, and positive blood culture. For CAUTI, key predictors were urinary tract infection (UTI), positive urine culture, acute pain or swelling of the testes, suprapubic tenderness, visible hematuria, and leukocytosis. Significant predictors of bloodstream infections (BSI) include having a BSI, positive blood culture, chills or rigors, and hypotension. Fever and hypotension increased CLABSI and BSI risk but reduced the CAUTI risk. Conclusion: The study highlights a significant burden of HAIs at the KAASH, with multiple predictors. The findings underscore the need for robust infection control measures and targeted interventions to reduce HAI incidence and improve patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".