Citations

Abstract Objective Flares of autoimmune rheumatic diseases (AIRDs) following COVID-19 vaccination are a particular concern in vaccine-hesitant individuals. Therefore, we investigated the incidence, predictors and patterns of flares following vaccination in individuals living with AIRDs, using global COVID-19 Vaccination in Autoimmune Diseases (COVAD) surveys. Methods The COVAD surveys were used to extract data on flare demographics, comorbidities, COVID-19 history, and vaccination details for patients with AIRDs. Flares following vaccination were identified as patient-reported (a), increased immunosuppression (b), clinical exacerbations (c) and worsening of PROMIS scores (d). We studied flare characteristics and used regression models to differentiate flares among various AIRDs. Results Of 15 165 total responses, the incidence of flares in 3453 patients with AIRDs was 11.3%, 14.8%, 9.5% and 26.7% by definitions a–d, respectively. There was moderate agreement between patient-reported and immunosuppression-defined flares (K = 0.403, P = 0.022). Arthritis (61.6%) and fatigue (58.8%) were the most commonly reported symptoms. Self-reported flares were associated with higher comorbidities (P = 0.013), mental health disorders (MHDs) (P < 0.001) and autoimmune disease multimorbidity (AIDm) (P < 0.001). In regression analysis, the presence of AIDm [odds ratio (OR) = 1.4; 95% CI: 1.1, 1.7; P = 0.003), or a MHD (OR = 1.7; 95% CI: 1.1, 2.6; P = 0.007), or being a Moderna vaccine recipient (OR = 1.5; 95% CI: 1.09, 2.2; P = 0.014) were predictors of flares. Use of MMF (OR = 0.5; 95% CI: 0.3, 0.8; P = 0.009) and glucocorticoids (OR = 0.6; 95% CI: 0.5, 0.8; P = 0.003) were protective. A higher frequency of patients with AIRDs reported overall active disease post-vaccination compared with before vaccination (OR = 1.3; 95% CI: 1.1, 1.5; P < 0.001). Conclusion Flares occur in nearly 1 in 10 individuals with AIRDs after COVID vaccination; people with comorbidities (especially AIDm), MHDs and those receiving the Moderna vaccine are particularly vulnerable. Future avenues include exploring flare profiles and optimizing vaccine strategies for this group.

Background: We developed a deep learning algorithm that detects coronary artery calcium (CAC) score using 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that the output from the CAC-ECG algorithm would be associated with incident atherosclerotic cardiovascular disease (ASCVD) events and that the CAC- ECG would refine the AHA/ACC Pooled Cohort Equation’s (PCE) predictive capabilities. Methods: A community-based cohort of consecutive patients seeking primary care in Olmsted County, MN, between 1998-2000 with passive follow-up via record linkage. Inclusion was identical to the PCE. The original CAC-ECG was developed in 43,210 subjects yielding an AUC of 0.83. Herein, we used the CAC-ECG output to predict a high CAC (≥ 300). Primary outcome was ASCVD defined as fatal and non-fatal myocardial infarction and ischemic stroke, secondary outcome was Major Adverse Cardiovascular Events (MACE) further including PCI, CABG, and mortality. Events were validated in duplicate. Cox proportional hazard models adjusted for variables included in the PCE and were stratified to evaluate the effect of the CAC-ECG on PCE-predicted risk. Follow-up was truncated at 10 years for PCE analyses. Results: We included 24,793 subjects, mean ± SD age 53.9 ± 12.1, 52% women, 95% white. After 16.7±3.7 yrs follow-up, 2,366 (9.5%) had ASCVD and 3,401 (13.7%) had MACE. Risk of ASCVD and MACE increased with CAC-ECG probability quintiles, independent of risk factors, p for trend <0.001 ( Fig. A-B ). The CAC-ECG enhanced the predicted capabilities of the PCE across all ASCVD risk groups ( Fig. C ). Net reclassification improved 13.7% with comparable C-statistic from 0.77 vs. 0.78 for PCE and CAC-ECG. Conclusions: CAC-ECG was associated with ASCVD and MACE and improved PCE predicted risk. The CAC-ECG algorithm could identify individuals at risk in primary prevention; Unlike the PCE, the CAC-ECG can be applied without chart review or performing a computer tomography and may be reliably used retrospectively in cohorts with digitally stored ECGs.

AbstractObjectivesTo compare pain intensity among individuals with idiopathic inflammatory myopathies (IIMs), other systemic autoimmune rheumatic diseases (AIRDs), and without rheumatic disease (wAIDs).MethodsData were collected from the COVID‐19 Vaccination in Autoimmune Diseases (COVAD) study, an international cross‐sectional online survey, from December 2020 to August 2021. Pain experienced in the preceding week was assessed using numeral rating scale (NRS). We performed a negative binomial regression analysis to assess pain in IIMs subtypes and whether demographics, disease activity, general health status, and physical function had an impact on pain scores.ResultsOf 6988 participants included, 15.1% had IIMs, 27.9% had other AIRDs, and 57.0% were wAIDs. The median pain NRS in patients with IIMs, other AIRDs, and wAIDs were 2.0 (interquartile range [IQR] = 1.0–5.0), 3.0 (IQR = 1.0–6.0), and 1.0 (IQR = 0–2.0), respectively (P < 0.001). Regression analysis adjusted for gender, age, and ethnicity revealed that overlap myositis and antisynthetase syndrome had the highest pain (NRS = 4.0, 95% CI = 3.5–4.5, and NRS = 3.6, 95% CI = 3.1–4.1, respectively). An additional association between pain and poor functional status was observed in all groups. Female gender was associated with higher pain scores in almost all scenarios. Increasing age was associated with higher pain NRS scores in some scenarios of disease activity, and Asian and Hispanic ethnicities had reduced pain scores in some functional status scenarios.ConclusionPatients with IIMs reported higher pain levels than wAIDs, but less than patients with other AIRDs. Pain is a disabling manifestation of IIMs and is associated with a poor functional status.

Heterozygous loss-of-function (LOF) mutations in PIK3R1 (encoding phosphatidylinositol 3-kinase [PI3K] regulatory subunits) cause activated PI3Kδ syndrome 2 (APDS2), which has a similar clinical profile to APDS1, caused by heterozygous gain-of-function (GOF) mutations in PIK3CD (encoding the PI3K p110δ catalytic subunit). While several studies have established how PIK3CD GOF leads to immune dysregulation, less is known about how PIK3R1 LOF mutations alter cellular function. By studying a novel CRISPR/Cas9 mouse model and patients’ immune cells, we determined how PIK3R1 LOF alters cellular function. We observed some overlap in cellular defects in APDS1 and APDS2, including decreased intrinsic B cell class switching and defective Tfh cell function. However, we also identified unique APDS2 phenotypes including defective expansion and affinity maturation of Pik3r1 LOF B cells following immunization, and decreased survival of Pik3r1 LOF pups. Further, we observed clear differences in the way Pik3r1 LOF and Pik3cd GOF altered signaling. Together these results demonstrate crucial differences between these two genetic etiologies.

Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.

Abstract Objective COVID-19 vaccines have a favorable safety profile in patients with autoimmune rheumatic diseases (AIRDs) such as idiopathic inflammatory myopathies (IIMs); however, hesitancy continues to persist among these patients. Therefore, we studied the prevalence, predictors and reasons for hesitancy in patients with IIMs, other AIRDs, non-rheumatic autoimmune diseases (nrAIDs) and healthy controls (HCs), using data from the two international COVID-19 Vaccination in Autoimmune Diseases (COVAD) e-surveys. Methods The first and second COVAD patient self-reported e-surveys were circulated from March to December 2021, and February to June 2022 (ongoing). We collected data on demographics, comorbidities, COVID-19 infection and vaccination history, reasons for hesitancy, and patient reported outcomes. Predictors of hesitancy were analysed using regression models in different groups. Results We analysed data from 18 882 (COVAD-1) and 7666 (COVAD-2) respondents. Reassuringly, hesitancy decreased from 2021 (16.5%) to 2022 (5.1%) (OR: 0.26; 95% CI: 0.24, 0.30, P &lt; 0.001). However, concerns/fear over long-term safety had increased (OR: 3.6; 95% CI: 2.9, 4.6, P &lt; 0.01). We noted with concern greater skepticism over vaccine science among patients with IIMs than AIRDs (OR: 1.8; 95% CI: 1.08, 3.2, P = 0.023) and HCs (OR: 4; 95% CI: 1.9, 8.1, P &lt; 0.001), as well as more long-term safety concerns/fear (IIMs vs AIRDs – OR: 1.9; 95% CI: 1.2, 2.9, P = 0.001; IIMs vs HCs – OR: 5.4 95% CI: 3, 9.6, P &lt; 0.001). Caucasians [OR 4.2 (1.7–10.3)] were likely to be more hesitant, while those with better PROMIS physical health score were less hesitant [OR 0.9 (0.8–0.97)]. Conclusion Vaccine hesitancy has decreased from 2021 to 2022, long-term safety concerns remain among patients with IIMs, particularly in Caucasians and those with poor physical function.

Abstract The role of IL 1 in the activation of IL 4-producing murine T cell clones was investigated by using a calcium ionophore (ionomycin) or a phorbol ester (12-O-tetradecanoylphorbol 13-acetate; TPA) as T cell receptor-independent costimuli. The use of these pharmacologic agents to investigate IL 1-mediated T cell activation revealed two distinct mechanisms of activation. IL 1 in combination with ionomycin (iono/rIL 1) stimulated a proliferative response that was associated with the production of IL 4 as measured by lymphokine bioassay and mRNA studies. Furthermore, inhibition of this proliferative response with an anti-IL 4 monoclonal antibody or cyclosporine indicated that IL 4 functions as an autocrine growth factor. In contrast, IL 1 synergized with TPA (TPA/rIL 1) to induce proliferation in the absence of either IL 4 or IL 2 gene transcription or lymphokine secretion. The IL 4-independence of this activation mechanism was further supported by the failure of both anti-IL 4 antibodies and cyclosporine to inhibit the response. In addition, activation by TPA/rIL 1 caused no detectable alteration in cytoplasmic calcium levels. Both IL 4-dependent and IL 4-independent activation responses were associated with the expression of functional receptors for IL 2 as well as IL 4. Characterization of these activation responses suggests that the synergistic activity of IL 1 during T cell activation is multipotential. The nature of an IL 1-dependent T cell growth response, therefore, may vary depending on the balance of intracellular signals generated concurrently through the T cell receptor complex and other regulatory surface molecules.

Abstract Stimulation of NK cell-mediated cytotoxicity involves the coupling of proximal Src and Syk family protein tyrosine kinases to downstream effectors. However, the mechanisms linking these second messenger pathways are incompletely understood. Here, we describe a key role for the LAT (p36) adaptor protein in human NK cell activation. LAT is tyrosine phosphorylated upon stimulation of NK cells through FcγRIII receptors and following direct contact with NK-sensitive target cells. This NK stimulation induces the association of LAT with several phosphotyrosine-containing proteins. In addition to the biochemical evidence showing LAT involvement in NK cell activation, a genetic model shows that LAT is required for FcR-dependent phosphorylation of phospholipase C-γ. Furthermore, overexpression of LAT in NK cells leads to increased Ab-dependent cell-mediated cytotoxicity and “natural cytotoxicity,” thus demonstrating a functional role for LAT in NK cells. These data suggest that LAT is an important adaptor protein for the regulation of human NK cell-mediated cytotoxicity.