Citations

Abstract Objectives The objective of this study was to explore the prevalence, characteristics and risk factors of COVID-19 breakthrough infections (BIs) in idiopathic inflammatory myopathies (IIMs) using data from the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. Methods A validated patient self-reporting e-survey was circulated by the COVAD study group to collect data on COVID-19 infection and vaccination in 2022. BIs were defined as COVID-19 occurring ≥14 days after two vaccine doses. We compared BI characteristics and severity among patients with IIMs, patients with other autoimmune rheumatic and non-rheumatic diseases (AIRD, nrAID), and healthy controls (HCs). Multivariable Cox regression models were used to assess the risk factors for BI, severe BI ,and hospitalizations among patients with IIMs. Results Among the 9449 included responses, BIs occurred in 1447 respondents (15.3%). The median age was 44 years [interquartile range (IQR) 21], 77.4% were female, and 182 BIs (12.9%) occurred among the 1406 patients with IIMs. Multivariable Cox regression among the data for patients with IIMs showed increasing age to be a protective factor for BIs [hazard ratio (HR) = 0.98, 95% CI = 0.97–0.99], and HCQ and SSZ use were risk factors (HR = 1.81, 95% CI = 1.24–2.64, and HR = 3.79, 95% CI = 1.69–8.42, respectively). Glucocorticoid use was a risk factor for a severe BI (HR = 3.61, 95% CI = 1.09–11.8). Non-white ethnicity (HR = 2.61, 95% CI = 1.03–6.59) was a risk factor for hospitalization. Compared with other groups, patients with IIMs required more supplemental oxygen therapy (IIMs = 6.0% vs AIRDs = 1.8%, nrAIDs = 2.2% and HCs = 0.9%), intensive care unit admission (IIMs = 2.2% vs AIRDs = 0.6%, nrAIDs and HCs = 0%), advanced treatment with antiviral or monoclonal antibodies (IIMs = 34.1% vs AIRDs = 25.8%, nrAIDs = 14.6% and HCs = 12.8%) and had more hospitalization (IIMs = 7.7% vs AIRDs = 4.6%, nrAIDs = 1.1% and HCs = 1.5%). Conclusion Patients with IIMs are susceptible to severe COVID-19 BIs. Age and immunosuppressive treatments were related to the risk of BIs.

Abstract Objectives To investigate health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) compared with those with non-IIM autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs) and without autoimmune diseases (controls) using Patient-Reported Outcome Measurement Information System (PROMIS) instrument data obtained from the second COVID-19 vaccination in autoimmune disease (COVAD-2) e-survey database. Methods Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis. Results We analysed responses from 1582 IIM, 4700 non-IIM AIRD and 545 nrAID patients and 3675 controls gathered through 23 May 2022. The median GPH scores were the lowest in IIM and non-IIM AIRD patients {13 [interquartile range (IQR) 10–15] IIMs vs 13 [11–15] non-IIM AIRDs vs 15 [13–17] nrAIDs vs 17 [15–18] controls, P < 0.001}. The median GMH scores in IIM patients were also significantly lower compared with those without autoimmune diseases [13 (IQR 10–15) IIMs vs 15 (13–17) controls, P < 0.001]. Inclusion body myositis, comorbidities, active disease and glucocorticoid use were the determinants of lower GPH scores, whereas overlap myositis, interstitial lung disease, depression, active disease, lower PROMIS Physical Function 10a and higher PROMIS Fatigue 4a scores were associated with lower GMH scores in IIM patients. Conclusion Both physical and mental health are significantly impaired in IIM patients, particularly in those with comorbidities and increased fatigue, emphasizing the importance of patient-reported experiences and optimized multidisciplinary care to enhance well-being in people with IIMs.

The human adult immune system maintains normal T cell counts and compensates for T cell loss throughout life, mainly through peripheral homeostatic proliferation after the ability of the thymus to generate new T cells has rapidly declined at adolescence. This process is mainly driven by STAT5-activating cytokines, most importantly IL-7, and is very effective in maintaining a large naive CD4 + T cell compartment into older age. Here, we describe that naive CD4 + T cells undergo adaptations to optimize IL-7 responses by upregulating the guanine-nucleotide exchange factor PREX1 in older age. PREX1 promotes nuclear translocation of phosphorylated STAT5, thereby supporting homeostatic proliferation in response to IL-7. Through the same mechanism, increased expression of PREX1 also biases naive cells to differentiate into effector T cells. These findings are consistent with the concept that primarily beneficial adaptations during aging, i.e., improved homeostasis, account for unfavorable functions of the aged immune system, in this case biased differentiation.

AbstractINTRODUCTIONPatients with dementia with Lewy bodies (DLB) may have Alzheimers disease (AD) pathology that can be detected by plasma biomarkers. Our objective was to evaluate plasma biomarkers of AD and their association with positron emission tomography (PET) biomarkers of amyloid and tau deposition in the continuum of DLB, starting from prodromal stages of the disease.METHODSThe cohort included patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD), mild cognitive impairment with Lewy bodies (MCI‐LB), or DLB, with a concurrent blood draw and PET scans.RESULTSAbnormal levels of plasma glial fibrillary acidic protein (GFAP) were found at the prodromal stage of MCI‐LB in association with increased amyloid PET. Abnormal levels of plasma phosphorylated tau (p‐tau)‐181 and neurofilament light (NfL) were found at the DLB stage. Plasma p‐tau‐181 showed the highest accuracy in detecting abnormal amyloid and tau PET in patients with DLB.DISCUSSIONThe range of AD co‐pathology can be detected with plasma biomarkers in the DLB continuum, particularly with plasma p‐tau‐181 and GFAP.

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

Abstract A variety of terms, such as “antiepileptic,” “anticonvulsant,” and “antiseizure” have been historically applied to medications for the treatment of seizure disorders. Terminology is important because using terms that do not accurately reflect the action of specific treatments may result in a misunderstanding of their effects and inappropriate use. The present International League Against Epilepsy (ILAE) position paper used a Delphi approach to develop recommendations on English‐language terminology applicable to pharmacological agents currently approved for treating seizure disorders. There was consensus that these medications should be collectively named “antiseizure medications”. This term accurately reflects their primarily symptomatic effect against seizures and reduces the possibility of health care practitioners, patients, or caregivers having undue expectations or an incorrect understanding of the real action of these medications. The term “antiseizure” to describe these agents does not exclude the possibility of beneficial effects on the course of the disease and comorbidities that result from the downstream effects of seizures, whenever these beneficial effects can be explained solely by the suppression of seizure activity. It is acknowledged that other treatments, mostly under development, can exert direct favorable actions on the underlying disease or its progression, by having “antiepileptogenic” or “disease‐modifying” effects. A more‐refined terminology to describe precisely these actions needs to be developed.

Abstract Background Long-term corticosteroid (CS) use is not recommended for patients (pts) with ulcerative colitis (UC). Receiving early advanced therapy (AT) may help limit CS overuse, but there is a need to identify pt populations who may benefit most from receiving early AT as monotherapy. Previous post hoc analyses of the phase 3 True North (TN) study (NCT02435992) demonstrated ozanimod (OZA) efficacy in 5-aminosalicylate (5-ASA)–exposed, AT-naive, immunomodulator (IMM)–naive pts. This analysis further assessed the efficacy of OZA induction therapy with and without concomitant CS in this pt population by baseline (BL) endoscopic disease severity. Methods In TN, pts in Cohort 1 (C1) were randomised to OZA or placebo (PBO); pts in Cohort 2 (C2) received open-label OZA through Week (W) 10. Pts were on stable doses of oral 5-ASA and/or CS for ≥2 wk before screening and continuing through W10. 5-ASA–exposed, AT-naive, IMM-naive pts were grouped as moderate (Mayo endoscopy subscore [MES]=2 at BL) or severe (MES=3 at BL). Data were analysed in all moderate and severe pts and by concomitant CS use. Clinical remission, clinical response, endoscopic improvement, mucosal healing, and histologic remission were assessed at W10. Results Of the 464 5-ASA–exposed, AT-naive, IMM-naive pts, 237 had moderate (PBO, n=47; OZA C1, n=108; OZA C2], n=82) and 227 had severe (PBO, n=54; OZA C1, n=97; OZA C2, n=76) BL endoscopic disease. BL pt characteristics were generally similar between moderate and severe pts with or without concomitant CS use. Overall, OZA was significantly more effective in moderate pts and numerically more effective in severe pts vs PBO in C1, with numerically greater treatment differences in moderate vs severe pts (Figure 1A). The moderate and severe groups each had 51 pts (~22%) on concomitant CS. In moderate pts, numerically greater OZA vs PBO C1 treatment differences were observed in those without concomitant CS use vs those with concomitant CS use for clinical remission and clinical response; treatment differences were significant for all endpoints in pts without concomitant CS use (Figure 1B). In contrast, severe pts with concomitant CS use demonstrated greater OZA vs PBO C1 treatment differences vs those without concomitant CS use for all endpoints (Figure 1C). Results in OZA C2 were generally similar to OZA C1. Conclusion OZA treatment was efficacious in 5-ASA–exposed, AT-naive, IMM-naive pts with UC, with numerically greater efficacy in moderate pts than severe pts. Additionally, the results suggest that concomitant CS may not provide additional benefit over OZA monotherapy in moderate pts. These findings support OZA positioning after 5-ASA, but before CS, in pts with moderate UC.

Prediabetes is a risk factor for ischemic stroke in atrial fibrillation (AF) patients, yet, its impact on recurrent stroke in AF patients remains understudied. Using the 2018 National Inpatient Sample, we investigated the link between Prediabetes and recurrent stroke in AF patients with prior stroke or transient ischemic attack (TIA). Among 18,905 non-diabetic AF patients, 480 (2.5%) had prediabetes. The prediabetic group, with a median age of 78, exhibited a two-fold higher risk of recurrent stroke compared to the non-prediabetic cohort (median age 82), as evidenced by both unadjusted (OR 2.14, 95% CI 1.72–2.66) and adjusted (adjusted for socio-demographics/comorbidities, OR 2.09, 95% CI 1.65–2.64, p < 0.001). The prediabetes cohort, comprising more male and Black patients, demonstrated associations with higher Medicaid enrollment, admissions from certain regions, and higher rates of hyperlipidemia, smoking, peripheral vascular disease, obesity, and chronic obstructive pulmonary disease (all p < 0.05). Despite higher rates of home health care and increased hospital costs in the prediabetes group, the adjusted odds of all-cause mortality were not statistically significant (OR 0.55, 95% CI 0.19–1.56, p = 0.260). The findings of this study suggest that clinicians should be vigilant in managing prediabetes in AF patients, and strategies to prevent recurrent stroke in this high-risk population should be considered.

Abstract Cytopenias represent the most common side effect of CAR T-cell therapy (CAR-T) and can predispose for severe infectious complications. Current grading systems, such as the Common Terminology Criteria for Adverse Events (CTCAE), neither reflect the unique quality of post–CAR-T neutrophil recovery, nor do they reflect the inherent risk of infections due to protracted neutropenia. For this reason, a novel EHA/EBMT consensus grading was recently developed for Immune Effector Cell-Associated HematoToxicity (ICAHT). In this multicenter, observational study, we applied the grading system to a large real-world cohort of 549 patients treated with BCMA- or CD19-directed CAR-T for refractory B-cell malignancies (112 multiple myeloma [MM], 334 large B-cell lymphoma [LBCL], 103 mantle cell lymphoma [MCL]) and examined the clinical sequelae of severe (≥3°) ICAHT. The ICAHT grading was strongly associated with the cumulative duration of severe neutropenia (r = 0.92, P &lt; .0001), the presence of multilineage cytopenias, and the use of platelet and red blood cell transfusions. We noted an increased rate of severe ICAHT in patients with MCL vs those with LBCL and MM (28% vs 23% vs 15%). Severe ICAHT was associated with a higher rate of severe infections (49% vs 13%, P &lt; .0001), increased nonrelapse mortality (14% vs 4%, P &lt; .0001), and inferior survival outcomes (1-year progression-free survival: 35% vs 51%, 1-year overall survival: 52% vs 73%, both P &lt; .0001). Importantly, the ICAHT grading demonstrated superior capacity to predict severe infections compared with the CTCAE grading (c-index 0.73 vs 0.55, P &lt; .0001 vs nonsignificant). Taken together, these data highlight the clinical relevance of the novel grading system and support the reporting of ICAHT severity in clinical trials evaluating CAR-T therapies.

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.