OBJECTIVE: To evaluate the efficacy and adverse effects of methylprednisolone in patients with IgA nephropathy at high risk of kidney function decline.

DESIGN, SETTING, AND PARTICIPANTS: An international, multicenter, double-blind, randomized clinical trial that enrolled 503 participants with IgA nephropathy, proteinuria greater than or equal to 1 g per day, and estimated glomerular filtration rate (eGFR) of 20 to 120 mL/min/1.73 m2 after at least 3 months of optimized background care from 67 centers in Australia, Canada, China, India, and Malaysia between May 2012 and November 2019, with follow-up until June 2021.

INTERVENTIONS: Participants were randomized in a 1:1 ratio to receive oral methylprednisolone (initially 0.6-0.8 mg/kg/d, maximum 48 mg/d, weaning by 8 mg/d/mo; n = 136) or placebo (n = 126). After 262 participants were randomized, an excess of serious infections was identified, leading to dose reduction (0.4 mg/kg/d, maximum 32 mg/d, weaning by 4 mg/d/mo) and addition of antibiotic prophylaxis for pneumocystis pneumonia for subsequent participants (121 in the oral methylprednisolone group and 120 in the placebo group).

MAIN OUTCOMES AND MEASURES: The primary end point was a composite of 40% decline in eGFR, kidney failure (dialysis, transplant), or death due to kidney disease. There were 11 secondary outcomes, including kidney failure.

METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.

RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.

OBJECTIVE: To examine treatment patterns, goal attainment, and factors influencing treatment among patients in 6 Asian countries who were taking statins.

METHODS: A retrospective cohort study was conducted in China, Korea, Malaysia, Singapore, Taiwan, and Thailand, where 437 physicians (41% cardiologists) recruited adults with hypercholesterolemia newly initiated on statin monotherapy.

RESULTS: Of 2622 patients meeting inclusion and exclusion criteria, approximately 66% had coronary heart disease (CHD)/diabetes mellitus, 24% had no CHD but > or =2 risk factors, and 10% had no CHD and <2 risk factors. Most patients ( approximately 90%) received statins at medium or lower equipotency doses. Across all cardiovascular risk categories, 48% of patients attained ATP III targets for low-density lipoprotein cholesterol (LDL-C), including 38% of those with CHD/diabetes (goal: <100 mg/dL), 62% of those without CHD but with > or =2 risk factors (goal: <130 mg/dL), and 81% of those without CHD and <2 risk factors (goal: <160 mg/dL). Most patients who achieved goals did so within the first 3 months. Increasing age (odds ratio (OR)=1.015 per 1-year increment; 95% confidence interval (CI)=1.005-1.206; p=0.0038) and initial statin potency (OR=2.253; 95% CI=1.364-3.722; p=0.0015) were directly associated with goal attainment, whereas increased cardiovascular risk (OR=0.085; 95% CI=0.053-0.134; p<0.0001 for CHD/diabetes mellitus at baseline compared with <2 risk factors,) and baseline LDL-C (OR=0.990; 95% CI=0.987-0.993); p<0.0001 per 1-mg/dL increment) were inversely associated with LDL-C goal achievement. Limitations of this study include potential differences in treatment settings and cardiovascular risk factors between different countries and centers. In addition, the effects on cholesterol goal achievement of concomitant changes in lifestyle were not assessed.