Evidence on whether patients with psoriasis have a higher risk for staphylococcal colonization than healthy controls remains controversial. To synthesize the current literature, we performed a systematic review on the prevalence and relative risk (RR) of Staphylococcus aureus colonization in patients with psoriasis. We modified the QUADAS-2 instrument to assess the reporting quality of individual studies and applied random-effects models in meta-analysis. Overall we identified 21 eligible studies, of which 15 enrolled one or more comparison groups. The pooled prevalence of staphylococcal colonization in patients with psoriasis was 35·3% [95% confidence interval (CI) 25·0-45·6] on lesional skin and 39·2% (95% CI 33·7-44·8) in the nares. Patients with psoriasis were 4·5 times more likely to be colonized by S. aureus than healthy controls were on the skin (RR 5·54, 95% CI 3·21-9·57) and 60% more in the nares (RR 1·60, 95% CI 1·11-2·32). Cutaneous and nasal colonization by meticillin-resistant S. aureus also appeared higher in patients with psoriasis (pooled prevalence 8·6%) than in healthy controls (2·6%), yet the difference was not statistically significant (P = 0·74). In contrast, despite of a similar risk for nasal staphylococcal colonization (RR 0·67, 95% CI 0·38-1·18), patients with psoriasis were less likely to carry S. aureus on lesional skin than atopic patients (RR 0·64, 95% CI 0·40-1·02). In summarizing the current literature, we found that patients with psoriasis were at an increased risk for staphylococcal colonization compared with healthy individuals. Prospective studies on how bacterial loads correlate with disease activity can guide the clinical management of bacterial colonization while preventing the emergence of drug-resistant strains.

are co-first authors.

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.