Prevalence and Characteristics of Patients With Systemic Sclerosis Fulfilling the 2019 EULAR/American College of Rheumatology Classification Criteria for Systemic Lupus Erythematosus.

OBJECTIVE: There is limited literature describing the overlap of systemic sclerosis (SSc) and systemic lupus erythematosus (SLE), and the studies have employed a range of case definitions. Our study used the new EULAR/American College of Rheumatology (ACR) SLE classification criteria to define SSc-SLE cases among our center's SSc cohort. METHODS: This is a single-center, retrospective study of a previously described cohort of patients with SSc. Patient data were re-abstracted to evaluate for fulfillment of the 2019 EULAR/ACR classification criteria for SLE. Demographic, laboratory, clinical features, and mortality were compared among patients with SSc-SLE and patients with SSc alone. RESULTS: Among the 402 patients with SSc that were analyzed, 40 (10%) fulfilled the 2019 EULAR/ACR SLE classification criteria. Neuropsychiatric and renal involvement were rare. An initial SLE diagnosis was purported in 43% of the patients with SSc-SLE and 7% of patients with SSc alone (P < 0.001). Patients with SSc-SLE were more likely to be female, African American, and with limited cutaneous SSc. Anti-U1-RNP antibody positivity prevalence was 30% among patients with SSc-SLE and 6.6% among patients with SSc alone (P < 0.001). Death during follow-up occurred in 12 patients (30%) with SSc-SLE and in 81 patients (22%) with SSc alone, but there was no difference in survival among the groups per log rank test (P = 0.404). CONCLUSION: Ten percent of patients with SSc fulfill the 2019 EULAR/ACR classification criteria for SLE. These patients comprise a distinct demographic, serologic, and clinical phenotype but have similar severe SSc-specific end-organ damage and mortality as patients with SSc alone. Patients with SLE with Raynaud phenomenon should be evaluated for SSc-specific autoantibodies and scleroderma organ involvement.

The Ability of Near-Infrared Spectroscopy to Identify Vulnerable Patients and Plaques: A Systematic Review and Meta-Analysis.

Previous studies have analyzed the efficacy of near-infrared spectroscopy-derived lipid core burden index (LCBI) in quantifying and identifying high-risk plaques and patients at increased risk of future major adverse cardiac outcomes/major adverse cardiovascular and cerebrovascular events. A maxLCBI4mm of 400 or greater seems to be an effective threshold for classifying at-risk plaques. This meta-analysis provides a more precise odds ratio with a narrow standard deviation that can be used to guide future studies.

Effect of High-Intensity Statin Therapy on Atherosclerosis (IBIS-4): Manual Versus Automated Methods of IVUS Analysis.

AIMS: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods. METHODS: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm. RESULTS: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of -0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of -1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of -2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of -3.74 % (p < 0.001). CONCLUSIONS: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard.

Human vs. machine vs. core lab for the assessment of coronary atherosclerosis with lumen and vessel contour segmentation with intravascular ultrasound.

A machine learning (ML) algorithm for automatic segmentation of intravascular ultrasound was previously validated. It has the potential to improve efficiency, accuracy and precision of coronary vessel segmentation compared to manual segmentation by interventional cardiology experts. The aim of this study is to compare the performance of human readers to the machine and against the readings from a Core Laboratory. This is a post-hoc, cross-sectional analysis of the IBIS-4 study. Forty frames were randomly selected and analyzed by 10 readers of varying expertise two separate times, 1 week apart. Their measurements of lumen, vessel, plaque areas, and plaque burden were performed in an offline software. Among humans, the intra-observer variability was not statistically significant. For the total 80 frames, inter-observer variability between human readers, the ML algorithm and Core Laboratory for lumen area, vessel area, plaque area and plaque burden were not statistically different. For lumen area, however, relative differences between the human readers and the Core Lab ranged from 0.26 to 12.61%. For vessel area, they ranged from 1.25 to 9.54%. Efficiency between the ML algorithm and the readers differed notably. Humans spent 47 min on average to complete the analyses, while the ML algorithm took on average less than 1 min. The overall lumen, vessel and plaque means analyzed by humans and the proposed ML algorithm are similar to those of the Core Lab. Machines, however, are more time efficient. It is warranted to consider use of the ML algorithm in clinical practice.