Citations

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.

ABSTRACTObjectivesWe aimed to investigate the gender-based differences in idiopathic inflammatory myopathies (IIMs), with a particular focus on patient-reported outcomes, utilizing the data obtained through the international COVID-19 vaccination in autoimmune disease e-survey.MethodsPatient-reported outcomes including fatigue, pain, and physical function were extracted from the COVID-19 vaccination in autoimmune disease database and compared between genders, adjusting for demographics and IIM subgroups by multivariable analysis. Inclusion body myositis (IBM) was analysed separately because of the substantial differences in outcomes.ResultsA total of 1197 complete responses from patients with IIMs as of 31 August 2021 were analysed. Seventy percent were women. Women were younger (58 [48–68] vs. 69 [58–75] years old, median [interquartile range], P &lt; .001) and were more likely to suffer from autoimmune multimorbidity, defined as three or more autoimmune diseases in an individual patient (11.4% vs. 2.8%, P &lt; .001). In non-IBM IIMs, fatigue visual analogue scale scores were higher in women (5 [3–7] vs. 4 [2–6], median [interquartile range], P = .004), whereas no significant gender-based differences were noted in IBM. Multivariable analysis in non-IBM IIMs revealed that women, residence in high-income countries, overlap myositis, and autoimmune multimorbidity were independently associated with increased fatigue.ConclusionsWomen with IIMs suffer from autoimmune multimorbidity and experience increased fatigue compared to men.

Abstract Objectives We investigated coronavirus disease 2019 (COVID-19) vaccine safety in pregnant and breastfeeding women with autoimmune diseases (AID) in the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. Methods Delayed-onset (&gt;7 days) vaccine-related adverse events (AE), disease flares and AID-related treatment modifications were analysed upon diagnosis of AID vs healthy controls (HC) and the pregnancy/breastfeeding status at the time of at least one dose of vaccine. Results Among the 9201 participants to the self-administered online survey, 6787 (73.8%) were women. Forty pregnant and 52 breastfeeding patients with AID were identified, of whom the majority had received at least one dose of COVID-19 vaccine (100% and 96.2%, respectively). AE were reported significantly more frequently in pregnant than in non-pregnant patients (overall AE 45% vs 26%, P = 0.01; minor AE 40% vs 25.9%, P = 0.03; major AE 17.5% vs 4.6%, P &lt; 0.01), but no difference was found in comparison with pregnant HC. No difference was observed between breastfeeding patients and HC with respect to AE. Post-vaccination disease flares were reported by 17.5% of pregnant and 20% of breastfeeding patients, and by 18.3% of age- and disease-matched non-pregnant and non-breastfeeding patients (n = 262). All pregnant/breastfeeding patients who experienced a disease flare were managed with glucocorticoids; 28.6% and 20% of them required initiation or change in immunosuppressants, respectively. Conclusion This study provides reassuring insights into the safety of COVID-19 vaccines administered to women with AID during the gestational and post-partum periods, helping overcome hesitant attitudes, as the benefits for the mother and for the fetus by passive immunization appear to outweigh potential risks.

Abstract Background The relationship between body mass index (BMI) and outcomes in patients with nonalcoholic fatty liver disease (NAFLD) is not well defined. This study aimed to assess the presentations, outcomes, and development of liver-related events (LREs) and non-LREs in patients with NAFLD stratified by BMI. Methods Records of NAFLD patients from 2000–2022 were reviewed. Patients were categorized as lean (18.5–22.9 kg/m2), overweight (23–24.9 kg/m2), and obese (&gt;25 kg/m2) based on BMI. Stage of steatosis, fibrosis, and NAFLD activity score were noted in the patients undergoing liver biopsy in each group. Results Out of 1051 NAFLD patients, 127 (12.1%) had normal BMI, 177 (16.8%) and 747 (71.1%) were overweight and obese, respectively. Median [interquartile range] BMI was 21.9 [20.6–22.5], 24.2 [23.7–24.6], and 28.3 [26.6–30.6] kg/m2 in each group, respectively. Prevalence of metabolic syndrome and dyslipidemia were significantly higher in the obese. Obese patients had significantly higher median [interquartile range] liver stiffness (6.4 [4.9–9.4] kPa) than overweight and lean subjects. A higher proportion of obese patients had significant and advanced liver fibrosis. At follow-up, there were no significant differences in the progression of liver disease, new LREs, coronary artery disease, or hypertension across the BMI groups. Overweight and obese patients were more likely to develop new-onset diabetes by follow-up. The mortality rates in the three groups were comparable (0.47, 0.68, and 0.49 per 100 person-years, respectively), with similar causes of death (liver-related vs non-liver-related). Conclusions Patients with lean NAFLD have similar disease severity and rates of progression as the obese. BMI is not a reliable determinant of outcomes in NAFLD patients. Key messages What is already known on this topic What this study adds How this study might affect research, practice, or policy

Abstract Objectives We aimed to explore current practice and interregional differences in the treatment of idiopathic inflammatory myopathies (IIMs). We triangulated these observations considering countries' gross national income (GNI), disease subtypes, and symptoms using patient-reported information. Methods A cross-sectional ancillary analysis of the ‘COVID-19 vaccination in auto-immune disease’ (COVAD) e-survey containing demographic characteristics, IIM subtypes (DM, PM, IBM, anti-synthetase syndrome [ASSD], immune-mediated necrotizing myopathy [IMNM], overlap myopathies [OM]), current symptoms (surrogate for organ involvement) and treatments (corticosteroids [CS], immunomodulators [IM], i.e. antimalarials, immunosuppressants [IS], IVIG, biologic treatments and targeted-synthetic small molecules). Treatments were presented descriptively according to continents, GNI, IIM and organ involvement, and associated factors were analysed using multivariable binary logistic regressions. Results Of 18 851 respondents from 94 countries, 1418 with IIM were analysed (age 61 years, 62.5% females). DM (32.4%), IBM (24.5%) and OM (15.8%) were the most common subtypes. Treatment categories included IS (49.4%), CS (38.5%), IM (13.8%) and IVIG (9.4%). Notably, treatments varied across regions, GNI categories (IS mostly used in higher-middle income, IM in lower-middle income, IVIG and biologics largely limited to high-income countries), IIM subtypes (IS and CS associated with ASSD, IM with OM and DM, IVIG with IMNM, and biologic treatments with OM and ASSD) and disease manifestations (IS and CS with dyspnoea). Most inter-regional treatment disparities persisted after multivariable analysis. Conclusion We identified marked regional treatment disparities in a global cohort of IIM. These observations highlight the need for international consensus-driven management guidelines considering patient-centred care and available resources.

Abstract Background/Aims Flares following COVID-19 vaccination are an emerging concern among patients with rare rheumatic disease like idiopathic inflammatory myositis (IIMs), whereas data and understanding of this is rather limited. We aimed to study the prevalence, characteristics and determinants of IIM flares following COVID-19 vaccination. Methods CoVAD (COVID-19 Vaccination In Autoimmune Diseases) surveys are global patient self-reported e-surveys from 109 countries conducted in 2021 and 2022. Flares of IIM were defined by 4 definitions; a. patient self-reported, b. physician and immunosuppression (IS) denoted, c. sign directed (new erythematous rash, or worsening myositis or arthritis), d. MCID worsening of PROMISPF10a score between the patients who had taken both surveys. Descriptive statistics and multivariate regression were used to describe the predictors of flare. Cox-regression analysis was used to differentiate flares by IIM subtypes. Results Among the 1,278 IIM patients, aged 63 (50-71) years, 276 (21.5%) were dermatomyositis, 237 (18.5%) IBM, 899 (70.3%) were female and most were Caucasian (80.8%). Flares of IIM were seen in 123/1278 (9.6%), 163/1278 (12.7%), 112/1278 (8.7%), and 16/96 (19.6%) by definitions a-d respectively with median time to flare being 71.5 (10.7-235) days. Muscle weakness (69.1%), and fatigue (56.9%) were the most common symptoms of flare. The predictors of self-reported flare were: inactive/disease in remission prior to first dose of vaccine (OR = 4.3, 95%CI=2.4-7.6), and anxiety disorder (OR = 2.2, 95%CI=1.1-4.7). Rituximab use (OR = 0.3, 95%CI=0.1-0.7) and IBM (OR = 0.3, 95%CI=0.1-0.7) were protective. Physician defined flares were seen more often in females, mixed ethnicity, and those with asthma, ILD, and anxiety disorder (OR ranging 1.6-7.0, all p &lt; 0.05). Notably, overlap myositis (OM) had higher HR for flare compared to polymyositis (HR = 2.3, 95%CI=1.2-4.4, p = 0.010). Conclusion Nearly one in ten individuals with IIM develop flares after vaccination, more so among women, those with overlap myositis, and inactive disease prior to vaccination. Formal definition of flares in IIM is needed. Disclosure L. Gupta: None. N. R: None. Z. Griger: None. V. Agarwal: None. P. Sen: None. M. Joshi: None. S. Saha: None. K. Jagtap: None. O. Distler: Honoraria; Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon (Curzion), Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Mitsubishi Tanabe, Novartis, Roche, Roivant, Sanofi, Serodapharm, Topadur and UCB. E. Nikiphorou: Honoraria; Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly. Grants/research support; Pfizer and Lilly. A. Tan: Honoraria; Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, and UCB. S. Shinjo: None. N. Ziade: Honoraria; Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre. M. Milchert: None. I. Parodis: Honoraria; Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis and F. Hoffmann-La Roche AG. M. Kuwana: None. A. Makol: None. J. Pauling: None. C. Wincup: None. J. Lilleker: Honoraria; Sanofi Genzyme, Roche, and Biogen. A. Nune: None. J. Day: Grants/research support; CSL Limited. H. Chinoy: Consultancies; Novartis, Eli Lilly, Orphazyme, Astra Zeneca. Honoraria; UCB, and Biogen. Grants/research support; Eli Lilly and UCB. V. Agarwal: None. R. Aggarwal: Consultancies; Bristol Myers-Squibb, Pfizer, Genentech, Octapharma, CSL Behring, Mallinckrodt, AstraZeneca, Corbus, Kezar, Abbvie, Janssen, Alexion, Argenx, Q32, EMD-Serono, Boehringer Ingelheim, and Roivant.

Abstract Background/Aims Post COVID-19 syndrome (PCS) is an emerging cause of morbidity and poor quality of life in COVID-19 survivors. We aimed to assess the prevalence, risk factors, outcomes, and association with disease flares of PCS in patients with autoimmune rheumatic diseases (AIRDs) and non-rheumatic autoimmune diseases (nrAIDs), both vulnerable groups understudied in the current literature using data from the 2nd COVID-19 Vaccination in Autoimmune Diseases (COVAD) global multicentre patient self-reported e-survey. Methods The survey was circulated from February to July 2022 by the international COVAD Study Group (157 collaborators from 106 countries), and demographics, comorbidities, AIRD/nrAID status, COVID-19 history, vaccination details, and PROMIS physical and mental function were recorded. PCS was defined as symptom resolution time &gt;90 days following acute COVID-19. Predictors of PCS were analysed using regression models for the different groups. Results 7666 total respondents completed the survey. Of these, 2650 respondents with complete responses had positive COVID-19 infection, and 1677 (45.0% AIRDs, 12.5% nrAIDs, 42.5% HCs) completed the survey &gt;90 days post acute COVID-19. Of these, 136 (8.1%) had PCS. Prevalence of PCS was higher in AIRDs (10.8%) than healthy controls HCs (5.3%) (OR: 2.1; 95%CI: 1.4-3.1, p = 0.002). Across the entire cohort, a higher risk of PCS was seen in women (OR: 2.9; 95%CI: 1.1-7.7, p = 0.037), patients with long duration of AIRDs/nrAIDs (OR 1.01; 95%CI: 1.0-1.02, p = 0.016), those with comorbidities (OR: 2.8; 95%CI: 1.4-5.7, p = 0.005), and patients requiring oxygen supplementation for severe acute COVID-19 (OR: 3.8; 95%CI: 1.1-13.6, p = 0.039). Among patients with AIRDs, comorbidities (OR 2.0; 95%CI: 1.08-3.6, p = 0.026), and advanced treatment (OR: 1.9; 95%CI: 1.08-3.3, p = 0.024), or intensive care (OR: 3.8; 95%CI: 1.01-14.4, p = 0.047) for severe COVID-19 were risk factors for PCS. Notably, patients who developed PCS had poorer PROMIS global physical [15 (12-17) vs 12 (9-15)] and mental health [14 (11-16) vs 11 (8-14)] scores than those without PCS. Conclusion Individuals with AIRDs have a greater risk of PCS than HCs. Associated comorbid conditions, and advanced treatment or intensive care unit admission for severe COVID-19 confer a higher risk of PCS. It is imperative to identify risk factors for PCS for immediate multidisciplinary management in anticipation of poor physical and mental health. Disclosure L. Gupta: None. P. Sen: None. N. R: None. M. Joshi: None. S. Saha: None. K. Jagtap: None. V. Agarwal: None. O. Distler: Consultancies; Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon (Curzion), Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Mitsubishi Tanabe, Novartis, Roche, Roivant, Sanofi, Serodapharm, Topadur and UCB. E. Nikiphorou: Honoraria; Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly. Grants/research support; Pfizer and Lilly. A. Tan: Honoraria; Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, and UCB. S. Shinjo: None. N. Ziade: Honoraria; Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre. M. Milchert: None. I. Parodis: Honoraria; Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis and F. Hoffmann-La Roche AG. M. Kuwana: None. A. Makol: None. J.D. Pauling: None. C. Wincup: None. J. Lilleker: Honoraria; Sanofi Genzyme, Roche, and Biogen. A. Nune: None. J. Day: Grants/research support; CSL Limited. H. Chinoy: Consultancies; Novartis, Eli Lilly, Orphazyme, Astra Zeneca. Honoraria; UCB, and Biogen. Grants/research support; Eli Lilly and UCB. V. Agarwal: None. R. Aggarwal: Consultancies; Bristol Myers-Squibb, Pfizer, Genentech, Octapharma, CSL Behring, Mallinckrodt, AstraZeneca, Corbus, Kezar, Abbvie, Janssen, Alexion, Argenx, Q32, EMD-Serono, Boehringer Ingelheim, and Roivant.