UNASSIGNED: Atopic dermatitis (AD), psoriasis, and drug reactions associated with erythroderma are frequently complicated by infections. However, bloodstream infection (BSI) have received less research attention.
UNASSIGNED: This study aimed to investigate the clinical characteristics and risk factors associated with BSI in patients with erythroderma.
UNASSIGNED: A retrospective analysis was conducted on 141 erythroderma cases. Eleven cases were identified as having BSI. Clinical records of both BSI and non-BSI groups were reviewed and compared.
UNASSIGNED: BSI was diagnosed in 7.80% (11/141) of erythroderma cases, with a breakdown of 7.14% in AD, 2.00% in psoriasis, and 17.14% in drug reactions. Notably, all positive skin cultures (7/7) showed bacterial isolates concordant with blood cultures. Univariate logistic regression analysis revealed several significant associations with BSI, including temperature (≤36.0 or ≥38.5 °C; odds ratio (OR) = 28.06; p < 0.001), chilling (OR = 22.10; p < 0.001), kidney disease (OR = 14.64; p < 0.001), etiology of drug reactions (OR = 4.18; p = 0.03), albumin (ALB) (OR = 0.86; p < 0.01), C-reaction protein (CRP) (OR = 1.01; p = 0.02), interleukin 6 (IL-6) (OR = 1.02; p = 0.02), and procalcitonin (PCT) (OR = 1.07; p = 0.03). Receiver operating characteristic (ROC) curves demonstrated significant associations with ALB (p < 0.001; the area under curve (AUC) = 0.80), PCT (p = 0.009; AUC = 0.74), and CRP (p = 0.02; AUC = 0.71).
UNASSIGNED: Increased awareness of BSI risk is essential in erythroderma management. Patients with specific risk factors, such as abnormal body temperature (≤36.0 or ≥38.5 °C), chilling sensations, kidney disease, a history of drug reactions, elevated CRP (≥32 mg/L), elevated PCT (≥1.00 ng/ml), and low albumin (≤31.0 g/L), require close monitoring for BSI development.

UNASSIGNED: To improve β-lactam delabeling outcomes, we need to understand current practice and the evidence base regarding its outcomes, safety, and impact.
UNASSIGNED: We sought to assess the existing published evidence reporting on the effectiveness of penicillin allergy testing and delabeling.
UNASSIGNED: We conducted a systematic review of studies reporting β-lactam delabeling practices and outcomes after testing, including β-lactam use and patient understanding of the delabeling result. Searches of the PubMed, Scopus, and Embase databases; clinical trial registries; and websites of professional organizations were conducted. Data were extracted from the included studies in duplicate, with a third extraction if discrepancies remained.
UNASSIGNED: We included 284 publications (covering 98,316 participants); 173 were prospective studies, with no randomized controlled trials. The overall study quality was low. In all, 95.6% of individuals who underwent provocation testing were delabeled. Factors associated with successful delabeling could not be determined because of significant heterogeneity between studies. Anaphylaxis due to testing occurred in 0.3% of participants (95 of 31,667). Subjects who did not undergo skin testing (6,980 patients in 31 studies) before challenge had higher rates of provocation test positivity (8.8% vs 4.1% [P < .0001]) and anaphylaxis (15.9% vs 2.7% [P < .0001]) than those subjects who underwent skin testing (51,607 patients in 177 studies). Six studies (2.1%) followed patients after testing to assess their adherence to prescribing recommendations. In all, 136 participants (20.6%) were actively avoiding β-lactams despite delabeling.
UNASSIGNED: The available data suggest that penicillin allergy testing is safe and effective in delabeling most individuals, but the evidence base is incomplete and more work is required to assess the role of skin testing and the impact that delabeling is having on prescribing habits.

Recent studies have suggested that genomic diversity may play a key role in different clinical outcomes, and the importance of SNPs is becoming increasingly clear. In this article, we summarize the bioactivity of SNPs that may affect the sensitivity to or possibility of drug reactions that occur among the signaling pathways of regularly used immunosuppressants, such as glucocorticoids, azathioprine, tacrolimus, mycophenolate mofetil, cyclophosphamide and methotrexate. The development of bioinformatics, including machine learning models, has enabled prediction of the proper immunosuppressant dosage with minimal adverse drug reactions for patients after organ transplantation or for those with autoimmune diseases. This article provides a theoretical basis for the personalized use of immunosuppressants in the future.