Risk factors of stroke complicated with hospital-acquired pneumonia: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired pneumonia (HAP) is a common type of nosocomial infection and a common complication experienced by stroke patients during hospitalization. HAP can aggravate patients' primary disease condition and lead to death. Clinically, a variety of factors may affect the occurrence of HAP in patients. In this study, we conducted a meta-analysis of the literature to investigate the risk factors of stroke with HAP for clinical reference. METHODS: The PubMed, Medline, Embase and Cochrane Library databases were selected as the sources for the literature search. English-language publications were included. The articles related to stroke with HAP were published from January 2000 to January 2021. The articles were screened and their quality was evaluated using the Newcastle-Ottawa Scale. A meta-analysis was performed of the factors affecting the incidence of HAP using Revman 5.4 software. RESULTS: Ultimately, 7 articles with a total of 1,172 patients were included in the meta-analysis. Of the 1,172 patients, 352 (30.03%) had an HAP infection. The results of the meta-analysis showed that patient age [mean difference (MD) =4.91, 95% confidence interval (CI): 3.90 to 5.93; P<0.00001], National Institutes of Health Stroke Scale (NIHSS) score (MD =3.84, 95% CI: 3.01 to 4.67; P<0.00001), and patient malnutrition [odds ratio (OR) =1.85, 95% CI: 1.13 to 3.04; P=0.02] were risk factors for the development of HAP, while gender, stroke history, smoking history, and comorbidities (diabetes, hypertension, coronary heart disease, and hyperlipidemia) were not risk factors for the development of HAP. DISCUSSION: A total of 7 articles were included in this meta-analysis examining the influencing factors of HAP in stroke patients. The results showed that age, NIHSS score, and malnutrition were risk factors of HAP in stroke patients, while gender, stroke history, smoking history, and complications (diabetes, hypertension, coronary heart disease, and hyperlipidemia) were not influencing factors of HAP.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".