Citations

Abstract Objective The presence of comorbidities can substantially affect patients’ quality of life, but data regarding their impact on idiopathic inflammatory myopathies (IIMs) are limited. Methods We examined the prevalence of comorbidities in IIM patients, other autoimmune rheumatic diseases (oAIRDs) and healthy controls (HCs), using data from the self-reported COVAD-2 survey. We defined basic multimorbidity (BM) as the presence of ≥2 non-rheumatic chronic conditions and complex multimorbidity (CM) as the presence of ≥3 non-rheumatic chronic conditions affecting ≥3 organ systems. Hierarchical clustering on principal components was performed for grouping. Results Among the COVAD respondents, 1558 IIMs, 4591 oAIRDs and 3652 HCs were analysed. IIMs exhibited a high burden of comorbidities (odds ratio [OR]: 1.62 vs oAIRDs and 2.95 vs HCs, P < 0.01), BM (OR: 1.66 vs oAIRDs and 3.52 vs HCs, P < 0.01), CM (OR: 1.69 vs AIRDs and 6.23 vs HCs, P < 0.01) and mental health disorders (MHDs) (OR: 1.33 vs oAIRDs and 2.63 vs HCs, P < 0.01). Among the IIM patients, those with comorbidities or MHDs had lower PROMIS Global Physical (PGP), PROMIS Global Mental (PGM), and PROMIS Physical Function (SF10) scores, and higher fatigue (F4a) scores (all P < 0.001). PGP, PGM, SF10a and F4a were influenced by age, active disease, BM and MHDs. Four distinct clusters were identified among the IIMs according to comorbidities and PROMIS scores. Conclusion Patients with IIMs have a higher burden of comorbidities that influence physical and mental health, identifiable as clinical clusters for optimized and holistic management approaches.

SummaryBackground and AimsMachine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute‐on‐chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.MethodsClinical data of 1568 patients with ACLF from a tertiary care centre (2015–2023) were subjected to distance‐, density‐ and model‐based clustering algorithms. Final model was selected on best cluster separation, viz. Silhouette width and Dunn's index (for distance‐ or density‐based algorithms) and minimum BIC (for model‐based algorithms). Cluster assignments, patient trajectories and survival were analysed through inferential statistics. Supervised ML models were trained in 70% data that predicted clusters in remaining 30% data followed by an temporal validation.ResultsThe cohort was male‐predominant (87%), aged 44.3 years, with alcohol‐associated hepatitis (62.9%) and survival of 50.5%. Due to poor performance of distance‐ and density‐based algorithms and better explainability, the latent class model (LCM) was selected for exploration. LCM revealed four clusters with distinct trajectories, reversibility and survival (independent of MELD, CLIF‐C ACLF and AARC scores). Cluster1 had patients with none/one organ failure and highest reversibility. Cluster2 had females with viral hepatitis and two organ failures. More‐than‐one acute precipitant, severity, infections, organ failures and irreversibility escalated from clusters 1 to 4. Circulatory and renal failures critically influenced cluster assignments. Incorporating clusters to CLIF‐C ACLF, infection and ACLF definition improved the discriminative accuracy of CLIF‐C‐ACLF by 11%. Extreme gradient boost and decision trees could predict clusters with AUCs of 0.989 (0.979–0.995) and 0.875 (0.865–0.890). MELD, CLIF‐C‐OF, haemoglobin, lactate, CLIF‐C‐ACLF and ALT were critical variables for cluster prediction. Clusters with distinct survival were documented in a temporal validation cohort.ConclusionsML for the first time could identify clusters with distinct phenotypes, trajectories and outcomes in ACLF. Stratification into clusters can address heterogeneity, guide prognosis, recruitment in trials, resource allocation and liver transplant discussions in ACLF.

AbstractStatins are the first line of treatment for both primary and secondary prevention of atherosclerotic cardiovascular disease. Despite the positive effects of statins on cardiovascular events, not all patients can use them at an optimized dose. The reason for this is the skeletal muscle side effects, termed statin‐associated muscle symptoms (SAMS). Despite extensive research, the precise pathophysiology of SAMS remains unclear and multiple mechanisms may contribute to this phenomenon. Various therapeutic options are available for the management of SAMS, ranging from rechallenging with the same or a different statin to utilizing non‐statin therapeutic alternatives in patients intolerant to statins. However, the lack of consensus on the definition of SAMS, the absence of a definitive diagnostic test, and lack of a universally accepted management algorithm pose a great challenge in dealing with this entity. This review aims to explore the various pathophysiological mechanisms involved in SAMS and understand the difference between self‐limited toxic myopathy and immune‐mediated myopathy requiring immunomodulatory therapy. The conundrum of statin withdrawal, tapering, and rechallenge in SAMS will also be explored in detail along with the newer non‐statin therapies that are available.

Abstract Background Delirium is a common complication of older people in hospitals, rehabilitation and long-term facilities. Objective To assess the worldwide use of validated delirium assessment tools and the presence of delirium management protocols. Design Secondary analysis of a worldwide one-day point prevalence study on World Delirium Awareness Day, 15 March 2023. Setting Cross-sectional online survey including hospitals, rehabilitation and long-term facilities. Methods Participating clinicians reported data on delirium, the presence of protocols, delirium assessments, delirium-awareness interventions, non-pharmacological and pharmacological interventions, and ward/unit-specific barriers. Results Data from 44 countries, 1664 wards/units and 36 048 patients were analysed. Validated delirium assessments were used in 66.7% (n = 1110) of wards/units, 18.6% (n = 310) used personal judgement or no assessment, and 10% (n = 166) used other assessment methods. A delirium management protocol was reported in 66.8% (n = 1094) of wards/units. The presence of protocols for delirium management varied across continents, ranging from 21.6% (on 21/97 wards/units) in Africa to 90.4% (235/260) in Australia, similar to the use of validated delirium assessments with 29.6% (29/98) in Africa to 93.5% (116/124) in North America. Wards/units with a delirium management protocol [n = 1094/1664, 66.8%] were more likely to use a validated delirium test than those without a protocol [odds ratio 6.97 (95% confidence interval 5.289–9.185)]. The presence of a delirium protocol increased the chances for valid delirium assessment and, likely, evidence-based interventions. Conclusion Wards/units that reported the presence of delirium management protocols had a higher probability of using validated delirium assessments tools to assess for delirium.

AbstractBackgroundDespite the overall safety and efficacy of COVID‐19 vaccinations, rare cases of systemic autoimmune diseases (SAIDs) have been reported post‐vaccination. This study used a global survey to analyze SAIDs in susceptible individuals' post‐vaccination.MethodsA cross‐sectional study was conducted among participants with self‐reported new‐onset SAIDs using the COVID‐19 Vaccination in Autoimmune Diseases (COVAD) 2 study dataset—a validated, patient‐reported e‐survey—to analyze the long‐term safety of COVID‐19 vaccines. Baseline characteristics of patients with new‐onset SAIDs and vaccinated healthy controls (HCs) were compared after propensity score matching based on age and sex in a 1:4 ratio.ResultsOf 16 750 individuals, 74 (median age 52 years, 79.9% females, and 76.7% Caucasians) had new‐onset SAID post‐vaccination, mainly idiopathic inflammatory myopathies (IIMs) (n = 23, 31.51%), arthritis (n = 15; 20.53%), and polymyalgia rheumatica (PMR) (n = 12, 16.40%). Higher odds of new‐onset SAIDs were noted among Caucasians (OR = 5.3; 95% CI = 2.9–9.7; p < .001) and Moderna vaccine recipients (OR = 2.7; 95% CI = 1.3–5.3; p = .004). New‐onset SAIDs were associated with AID multimorbidity (OR = 1.4; 95% CI = 1.1–1.7; p < .001), mental health disorders (OR = 1.6; 95% CI = 1.3–1.9; p < .001), and mixed race (OR = 2.2; 95% CI = 1.2–4.2; p = .010), where those aged >60 years (OR = 0.6; 95% CI = 0.4–0.8; p = .007) and from high/medium human development index (HDI) countries (compared to very high HDI) reported fewer events than HCs.ConclusionThis study reports a low occurrence of new‐onset SAIDs following COVID‐19 vaccination, primarily IIMs, PMR, and inflammatory arthritis. Identified risk factors included pre‐existing AID multimorbidity, mental health diseases, and mixed race. Revaccination was well tolerated by most patients; therefore, we recommend continuing COVID‐19 vaccination in the general population. However, long‐term studies are needed to understand the autoimmune phenomena arising post‐vaccination.

AbstractBackgroundThe pandemic presented unique challenges for individuals with autoimmune and rheumatic diseases (AIRDs) due to their underlying condition, the effects of immunosuppressive treatments, and increased vaccine hesitancy.ObjectivesThe COVID‐19 vaccination in autoimmune diseases (COVAD) study, a series of ongoing, patient self‐reported surveys were conceived with the vision of being a unique tool to gather patient perspectives on AIRDs. It involved a multinational, multicenter collaborative effort amidst a global lockdown.MethodsLeveraging social media as a research tool, COVAD collected data using validated patient‐reported outcomes (PROs). The study, comprising a core team, steering committee, and global collaborators, facilitated data collection and analysis. A pilot‐tested, validated survey, featuring questions regarding COVID‐19 infection, vaccination and outcomes, patient demographics, and PROs was circulated to patients with AIRDs and healthy controls (HCs).DiscussionWe present the challenges encountered during this international collaborative project, including coordination, data management, funding constraints, language barriers, and authorship concerns, while highlighting the measures taken to address them.ConclusionCollaborative virtual models offer a dynamic new frontier in medical research and are vital to studying rare diseases. The COVAD study demonstrates the potential of online platforms for conducting large‐scale, patient‐focused research and underscores the importance of integrating patient perspective into clinical care. Care of patients is our central motivation, and it is essential to recognize their voices as equal stakeholders and valued partners in the study of the conditions that affect them.

AbstractVascularity plays a pivotal role in the progression of breast lesions and may be associated with their aggressiveness and likelihood of being malignant. Contrast-enhanced imaging techniques are necessary to evaluate vascularity due to the limited sensitivity of conventional color Doppler techniques, in which motion artifacts are eliminated using wall filters. However, in this process, low-flow signals from small vessels also get removed unintentionally. Advancements in technology have revolutionized the way ultrasound images are generated, resulting in tremendous improvements in Doppler imaging techniques. The new, ultrasound-based noncontrast microvascular imaging techniques overcome the limitations of conventional Doppler, and are highly sensitive for detecting microvessels and low flow. The resultant high Doppler sensitivity leads to detection of vascularity in more breast lesions. It is important for radiologists to understand the imaging principles and the clinical implications of the new techniques, to optimally utilize them and aid correct diagnosis. Angio-PLUS is one such recent advancement, which uses unfocused or plane waves and three-dimensional wall filtering to analyze tissue motion in time, space, and amplitude domains that effectively distinguish between blood flow and tissue. The information is beneficial for assessing the lesion vascularity without using contrast. This article aims to explain the Doppler imaging techniques, their clinical applications, scanning methods, and review the common Doppler-based diagnostic criteria used in the evaluation of breast lesions.

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 &lt; 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 &lt; 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.