Screening for Asymptomatic Osteonecrosis of the Hip in Systemic Lupus Erythematous: A Systematic Review and Meta-Analysis of MRI-Based Prevalence.

Objective. This paper aims to estimate asymptomatic hip osteonecrosis prevalence in SLE patients using MRI examination and to determine the prevalence among higher risk subpopulations. Materials and Methods. PubMed, Embase, Cochrane, and SCOPUS were searched from inception to May 9th, 2023. Studies on patients who were clinically diagnosed with systemic lupus erythematosus without reported symptoms attributable to hip osteonecrosis were included. Two independent reviewers extracted data and assessed the risk of bias. Data collected from each study include the study year, the number of hips screened, the number of hips with osteonecrosis, demographics, laboratory data, medications, follow-up time, radiological protocols, and MRI-based osteonecrosis detection and grading criteria. Results. Eleven eligible studies including 503 participants (15-35 years old; 74-100% female) with SLE were identified. Significant risk of bias was determined in one study. The overall prevalence of osteonecrosis of the hip was found to be 14% (184/1006 hip joints, 95% confidence interval: 7-22%, number needed to scan: 7.1). SLE patients who received corticosteroid treatment had a higher prevalence of asymptomatic hip osteonecrosis (18%) compared to non-corticosteroid users (0%, p-value < 0.01). Additionally, meta-regression results revealed that daily corticosteroid dose was associated with increased prevalence of asymptomatic osteonecrosis (0.5%/milligram, p-value < 0.01). Conclusions. The high prevalence of asymptomatic hip osteonecrosis in SLE patients raises concerns about the timeliness of interventions. The limitations of this study include a relatively low number of identified studies; and one study lacked full-text availability.

Statin use and longitudinal changes in quantitative MRI-based biomarkers of thigh muscle quality: data from Osteoarthritis Initiative.

OBJECTIVE: To assess whether changes in MRI-based measures of thigh muscle quality associated with statin use in participants with and without/at-risk of knee osteoarthritis. METHODS: This retrospective cohort study used data from the Osteoarthritis Initiative study. Statin users and non-users were matched for relevant covariates using 1:1 propensity-score matching. Participants were further stratified according to baseline radiographic knee osteoarthritis status. We used a validated deep-learning method for thigh muscle MRI segmentation and calculation of muscle quality biomarkers at baseline, 2nd, and 4th visits. Mean difference and 95% confidence intervals (CI) in longitudinal 4-year measurements of muscle quality biomarkers, including cross-sectional area, intramuscular adipose tissue, contractile percent, and knee extensors and flexors maximum and specific contractile force (force/muscle area) were the outcomes of interest. RESULTS: After matching, 3772 thighs of 1910 participants were included (1886 thighs of statin-users: 1886 of non-users; age: 62 ± 9 years (average ± standard deviation), range: 45-79; female/male: 1). During 4 years, statin use was associated with a slight decrease in muscle quality, indicated by decreased knee extension maximum (mean-difference, 95% CI: - 1.85 N/year, - 3.23 to - 0.47) and specific contractile force (- 0.04 N/cm2/year, - 0.07 to - 0.01), decreased thigh muscle contractile percent (- 0.03%/year, - 0.06 to - 0.01), and increased thigh intramuscular adipose tissue (3.06 mm2/year, 0.53 to 5.59). Stratified analyses showed decreased muscle quality only in participants without/at-risk of knee osteoarthritis but not those with established knee osteoarthritis. CONCLUSIONS: Statin use is associated with a slight decrease in MRI-based measures of thigh muscle quality over 4 years. However, considering statins' substantial cardiovascular benefits, these slight muscle changes may be relatively less important in overall patient care.

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis.

BACKGROUND: Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT. OBJECTIVES: To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status. MATERIALS AND METHODS: This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status. RESULTS: This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia. CONCLUSION: Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD. CLINICAL RELEVANCE STATEMENT: Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients. KEY POINTS: •Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.