Risk factors of nosocomial infection after percutaneous coronary intervention: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To summarize the risk factors for Nosocomial Infection(NI) in patients after Percutaneous coronary intervention(PCI). METHODS: The databases of PubMed, Web of Science, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chinese Biological Medical Database (CBM), Chinese Wanfang Database, and China Science and Technology Journal Database (VIP) were searched for relevant articles from its inception to November 2024. This meta-analysis is based on RevMan5.4 software. The results' robustness was assessed using sensitivity analysis, and publication bias was assessed using Egger's test and funnel plot. The quality of the included studies was evaluated using the Newcastle-Ottawa scale. RESULTS: Twenty-six studies were included, involving 18,825 patients. The meta-analysis showed that: combined diabetes(Patients with post-PCI infection had diabetes) [OR = 2.17, 95%(1.52,3.10)], mechanical ventilation [OR = 4.39, 95%(2.95,6.53)], indwelling urinary catheter [OR = 4.28, 95%(2.66,6.89)], age ≥ 60 years [OR = 1.96, 95%(1.50,2.57)], invasive procedures [OR = 2.72, 95%(1.99,3.71)], somking [OR = 1.88, 95%(1.18,2.99)], cardiac function classification of III to IV [OR = 3.14, 95%(2.03,4.88)], implantation of ≥ 2 stents [OR = 2.36, 95%(1.13,4.93)], and hospitalization days ≥ 14 [OR = 2.36, 95%(1.01,5.52)] were the predictive factors for the development of NI in post-PCI patients. The most common infecting bacteria were Staphylococcus aureus, Streptococcus species, and Klebsiella pneumoniae. The sites of infection were predominantly respiratory and urinary. CONCLUSIONS: This systematic review discusses the risk factors that lead to NI in post-PCI patients. Healthcare professionals may combine these factors to develop targeted interventions to reduce the incidence of infections and promote patient recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".