This meta-analysis examined the efficacy of different dosing regimens containing rituximab (RTX) in treating pemphigus. The analysis included 578 patients with pemphigus from 30 studies. Seventy-six percent of patients achieved complete remission (CR) after 1 cycle of RTX. Mean time to remission was 5.8 months, with a remission duration of 14.5 months and a 40% relapse rate. Eighteen patients (3.3%) developed serious adverse effects. The pooled estimate showed no significant differences in CR and relapse rates between patients treated with high-dose (near or ≥ 2,000 mg/cycle) vs. low-dose (< 1,500 mg/cycle) RTX. In the fully adjusted analysis, high-dose RTX was associated with longer duration of CR compared with low-dose RTX. No superiority of lymphoma protocol over rheumatoid arthritis or high-dose RTX over low-dose RTX was shown in other outcomes. RTX treatment is efficacious and well-tolerated in treating pemphigus. High-dose RTX treatment may lead to longer duration of remission. However, the choice of optimal regimen depends on the overall condition of the individual patient.

Omalizumab has been used to treat patients with atopic dermatitis (AD) with controversial results. 1 Our objective was to perform a systematic review and meta-analysis to investigate the efficacy of omalizumab and the factors associated with better therapeutic results in patients with AD.The search included PubMed, MEDLINE, EmBase, and the Cochrane Library (Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Cochrane Controlled Trials Register, and Health Technology Assessment Databases) from inception to November 30, 2015, using the search terms atopic dermatitis or eczema combined with omalizumab. Only human clinical studies written in English were included in this analysis. This literature search focused on randomized controlled trials (RCTs), comparative studies, and case series in which 3 or more patients with AD received omalizumab treatment. Review articles, single case reports, guidelines, consensus manuscripts, and correspondence were excluded. The primary outcome assessed was the clinical response of omalizumab in patients with AD.Data on the following measures were extracted: study design, sample size, trial duration, treatment regimen, and author's conclusion (Table I). For studies that provided the patients' raw data (detailed information for each patient), data on patients' age, sex, asthma history, pretreatment IgE concentration, pretreatment and posttreatment severity of eczema (using Severity Scoring of Atopic Dermatitis [SCORAD], Eczema Area and Severity Index [EASI], or Investigators' Global Assessment [IGA] scores), clinical response, adverse event, and treatment duration were collected (Table II). The dosing regimens were divided into 2 groups on the basis of total dosage of the month: 600 mg/month or more (eg, 300 mg omalizumab subcutaneously in 2-week intervals or 150 mg in a 1-week interval) or less than 600 mg/month (eg, 150 mg omalizumab subcutaneously in 2-week intervals or 300 mg in 3-week interval). We then performed multivariate logistic regression on the detailed data to evaluate the possible factors associated with an excellent clinical response. Values of P less than .05 were considered statistically significant. Statistical analysis was performed using PASW Statistics 18.0.An excellent clinical response was defined as a SCORAD reduction of more than 50%, or an EASI reduction of more than 75% or total clearance (improvement to IGA 0), or an improvement of 2-degree or more in IGA/modified IGA severity (eg, severe to mild). Satisfying clinical response was a SCORAD reduction between 25% and 50%, or an EASI reduction between 25% and 50%, or a 1-degree improvement in IGA/

IMPORTANCE A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction. OBJECTIVE To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information. DESIGN, SETTING, AND PARTICIPANTS This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018. MAIN OUTCOMES AND MEASURES Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models. RESULTS A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867;-2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872;-2.32% loss], hypertension [AUROC, 0.879;-1.53% loss], and chronic kidney insufficiency [AUROC, 0.879;-1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; −2.67% reduction; acarbose AUROC, 0.870; −2.50 reduction; and systemic antifungal agents AUROC, 0.875; −1.99 reduction). CONCLUSIONS AND RELEVANCE The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.

BackgroundIn the chronic kidney disease (CKD) population, the impact of serum potassium (sK) on renal outcomes has been controversial. Moreover, the reasons for the potential prognostic value of hypokalemia have not been elucidated.Design, Participants & Measurements2500 participants with CKD stage 1–4 in the Integrated CKD care program Kaohsiung for delaying Dialysis (ICKD) prospective observational study were analyzed and followed up for 2.7 years. Generalized additive model was fitted to determine the cutpoints and the U-shape association between sK and end-stage renal disease (ESRD). sK was classified into five groups with the cutpoints of 3.5, 4, 4.5 and 5 mEq/L. Cox proportional hazard regression models predicting the outcomes were used.ResultsThe mean age was 62.4 years, mean sK level was 4.2±0.5 mEq/L and average eGFR was 40.6 ml/min per 1.73 m2. Female vs male, diuretic use vs. non-use, hypertension, higher eGFR, bicarbonate, CRP and hemoglobin levels significantly correlated with hypokalemia. In patients with lower sK, nephrotic range proteinuria, and hypoalbuminemia were more prevalent but the use of RAS (renin-angiotensin system) inhibitors was less frequent. Hypokalemia was significantly associated with ESRD with hazard ratios (HRs) of 1.82 (95% CI, 1.03–3.22) in sK <3.5mEq/L and 1.67 (95% CI,1.19–2.35) in sK = 3.5–4 mEq/L, respectively, compared with sK = 4.5–5 mEq/L. Hyperkalemia defined as sK >5 mEq/L conferred 1.6-fold (95% CI,1.09–2.34) increased risk of ESRD compared with sK = 4.5–5 mEq/L. Hypokalemia was also associated with rapid decline of renal function defined as eGFR slope below 20% of the distribution range.ConclusionIn conclusion, both hypokalemia and hyperkalemia are associated with increased risk of ESRD in CKD population. Hypokalemia is related to increased use of diuretics, decreased use of RAS blockade and malnutrition, all of which may impose additive deleterious effects on renal outcomes.

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BACKGROUND Nail braces are an alternative treatment for ingrown toenails. OBJECTIVE This study aimed to prospectively examine the efficacy of nail braces for treatment of acute inflamed (AI)-type and chronic dystrophic–type ingrown toenails. MATERIALS AND METHODS The authors conducted a prospective study of patients with ingrown toenails treated at Wan Fang Hospital between January 1, 2017, and July 31, 2018. Evaluation using physician global assessment scores and patient satisfaction questionnaires was performed at 1, 3, and 6 months after the start of treatment and during the final visit. Patient demographics, treatment courses, and outcomes were compared between the 2 types of ingrown toenails. RESULTS Chronic dystrophic–type and AI-type ingrown toenails were observed in 25 (61 sides) and 28 patients (35 sides), respectively. Of the affected sides, 80.9%, 94.9%, and 100% achieved an excellent or fair result at 1, 3, and 6 months, respectively. Treatment duration and follow-up period were 179.2 ± 96.8 days and 281.6 ± 120.9, respectively. The recurrence rate was 7.4%. The treatment course and response were different between the 2 types of ingrown toenails. CONCLUSION Ingrown toenails could be effectively treated with nail braces with excellent outcomes, favorable patient satisfaction, and low recurrence rates.

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