PREDICTION OF MORTALITY USING CLUSTERS BASED ON CLINICAL AND LAB PARAMETERS: DATA FROM 2072 PATIENTS FROM INSPIRE COHORT

BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.

SYSTEMIC SCLEROSIS AND COVID-19 VACCINE-ASSOCIATED DELAYED ADVERSE EVENTS: INSIGHTS FROM THE COVAD-2 STUDY

BackgroundWe have previously reported short term safety of the COVID-19 vaccination in patients with Systemic sclerosis (SSc) but delayed adverse events (ADEs) (occurring >7 days post-vaccination) are poorly characterized in this rare yet vulnerable disease group.ObjectivesWe analyzed delayed COVID-19 vaccine-related ADEs among patients with SSc, other systemic autoimmune and inflammatory disorders (SAIDs) and healthy controls (HC) using data from the ongoing 2nd global COVID-19 Vaccination in Autoimmune Diseases (COVAD-2) study [1].MethodsThe COVAD-2 study was a cross-sectional, patient self-reporting e-survey utilizing an extensively validated, pilot tested questionnaire, translated into 19 languages, circulated by a group of 157 physicians across 106 countries from February to June 2022.We captured data on demographics, SSc/SAID disease characteristics (including skin subset, treatment history and self-reported disease activity), autoimmune and non-autoimmune comorbidities, COVID-19 infection history and course, and vaccination details including delayed ADEs as defined by the CDC.Delayed ADEs were categorized into local injection site pain/soreness;minor and major systemic ADEs, and hospitalizations. We descriptively analyzed the risk factors for overall and specific ADEs in SSc and SAIDs, and further triangulated clinically significant variables in binominal logistic regression analysis with adjustment for age, gender, ethnicity, comorbidity, and immunosuppressive therapy to analyze the survey responses.ResultsFrom among 17 612 respondents, 10 041 patients (median age 51 (18-58) years, 73.4% females, 44.9% Caucasians) vaccinated against COVID-19 at least once (excluding incomplete responses and trial participants) were included for analysis. Of these, 2.6 % (n=258) had SSc, 63.7% other SAIDs, and 33.7% were HCs. BNT162b2 Pfizer (69.4%) was the most administered vaccine, followed by MRNA-1273 Moderna (32.25%) and ChadOx1 nCOV-19 Oxford/AstraZeneca (12.4%) vaccines.Among the patients with SSc, 18.9% reported minor while 8.5% experienced major delayed ADEs, and 4.6% reported hospitalization. These values were comparable to those of the ADEs reported in other SAIDs and HCs. Patients with SSc reported higher frequency of difficulty in breathing than HCs [OR=2.3 (1.0-5.1), p=0.042].Individuals receiving Oxford/AstraZeneca reported more minor ADEs [OR=2.5 (1.0-6.0), p=0.045];whereas patients receiving Moderna were less likely to develop myalgia and body ache [OR=0.1 (0.02-1.0), p=0.047 and OR=0.2 (0.05-1.0), p=0.044 respectively].Patients with diffuse cutaneous SSc experienced minor ADEs and specifically fatigue more frequently [OR=2.1 (1.1-4.4), p=0.036, and OR=3.9 (1.3-11.7), p=0.015] than those with limited cutaneous SSc. Self-reported active disease pre-vaccination did not confer any increased risk of vaccine ADEs in the adjusted analysis. Unlike our previous observations in myositis, autoimmune and non-autoimmune comorbidities did not affect the risk of delayed ADEs in SSc. SSc patients with concomitant myositis reported myalgia [OR=3.4 (1.1-10.7), p=0.035] more frequently, while those with thyroid disorders were more prone to report a higher frequency of joint pain [OR=5.5 (1.5-20.2), p=0.009] and dizziness [OR=5.9 (1.3-27.6), p=0.024] than patients with SSc alone. Patients with SSc-interstitial lung disease did not report increased frequency of ADEs.ConclusionA diagnosis of SSc did not confer a higher risk of delayed post COVID-19 vaccine-related ADEs than other SAIDs and HCs. Diffuse cutaneous phenotype and certain co-existing autoimmune conditions including myositis and thyroid disease can increase the risk of minor ADEs. These patients may benefit from pre-vaccination counselling, close monitoring, and early initiation of appropriate care in the post COVID-19 vaccination period.Reference[1]Fazal ZZ, Sen P, Joshi M, Ravichandran N, Lilleker JB, et al. COVAD survey 2 long-term outcomes: unmet need and protocol. Rheumatol Int 2022 Dec;42(12):2151-2158AcknowledgementsCOVAD Study Team.Disclosure of InterestsBo dana Doskaliuk: None declared, Parikshit Sen: None declared, Mrudula Joshi: None declared, Naveen Ravichandran: None declared, Ai Lyn Tan Speakers bureau: Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, UCB, Consultant of: Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, UCB, Samuel Katsuyuki Shinjo: None declared, Sreoshy Saha: None declared, Nelly Ziade Speakers bureau: Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis,Boehringer Ingelheim, Janssen, and Pierre Fabre, Consultant of: Pfizer, Roche, Abbvie, Eli Lilly,NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre, Grant/research support from: Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and.Pierre Fabre, Tulika Chatterjee: None declared, Masataka Kuwana: None declared, Johannes Knitza: None declared, Oliver Distler Speakers bureau: 4P-Pharma, Abbvie, Acceleron, Alcimed, Altavant, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, iQvia, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Redxpharma, Roivant, Sanofi and Topadur, Consultant of: 4P-Pharma, Abbvie, Acceleron, Alcimed, Altavant, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, iQvia, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Redxpharma, Roivant, Sanofi and Topadur, Grant/research support from: 4P-Pharma, Abbvie, Acceleron, Alcimed, Altavant, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, iQvia, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Redxpharma, Roivant, Sanofi and Topadur, Rohit Aggarwal Consultant of: Mallinckrodt, Octapharma, CSL Behring, Bristol Myers-Squibb, EMD Serono, Kezar, Pfizer, AstraZeneca, Alexion, Argenx, Boehringer Ingelheim, Corbus, Janssen, Kyverna, Roivant, Merck, Galapagos, Actigraph, Abbvie, Scipher, Horizontal Therapeutics, Teva, Biogen, Beigene, ANI Pharmaceutical, Nuvig, Capella, CabalettaBio, Grant/research support from: Mallinckrodt, Pfizer, Bristol Myers-Squibb, Q32, EMD Serono, Janssen, Boehringer Ingelheim (BI), Ashima Makol: None declared, Latika Gupta: None declared, Vikas Agarwal: None declared.

IDDF2023-ABS-0045 Spatiotemporal immune landscape of Mas-driven pro-inflammatory microenvironment with hyperactivated glycolysis in acetaminophen-induced liver injury

IDDF2023-ABS-0045 Figure 1 IDDF2023-ABS-0045 Figure 2 IDDF2023-ABS-0045 Figure 3 IDDF2023-ABS-0045 Figure 4

PERICARDITIS AFTER COVID-19 VACCINATION: A CASE SERIES

BackgroundCOVID-19 vaccination campaigns successfully impacted on viral spreading and in particular on clinical course of the disease. However, secondary to a highly extended vaccination program, several local and systemic adverse events associated with mRNA COVID-19 vaccines have been reported. Pericarditis and myocarditis are examples of cardiac complications related to these vaccines. In particular, cases of pericarditis have occurred after mRNA COVID-19 vaccination (mostly secondary to vaccination with Moderna than Pfizer-BioNTech), especially in male adolescents and young adults, more often after the second dose. The incidence is approximately of 1-2 cases/100.000.ObjectivesAim of our study was to study the clinical profile of pericarditis occurred within 30 days after COVID-19 vaccines in our clinic.MethodsWe present a case series of patients who developed pericarditis after COVID-19 vaccination in the Department of Internal Medicine at Fatebenefratelli Hospital in Milan, followed from December 1, 2021 to April 15, 2022.ResultsTwenty-five individuals, of which 18 (72%) were women and 7 (28%) were males, had vaccine related pericarditis. Two patients were vaccinated with AstraZeneca, 2 with Moderna, the remaining with Pfizer-BioNTech. Median age was of 42 years. Of all patients, one subject was affected by constrictive effusive pericarditis, while another required treatment of pericarditis with Anakinra, switched to Canakinumab after severe skin reactions, because of failure of therapeutic response to first-line treatments.Two patients required hospital admission, in one case for a transient constrictive pericarditis. In the remaining cases clinical symptoms associated with post-vaccines pericarditis were mild and didn't require hospitalization.Chest pain was reported in 100% of cases, whereas pericardial effusion (in one case larger than 10 mm) was evidenced in 30% of subjects. Eighty percent of patients experienced tachycardia, whereas 90% reported asthenia.An increase in indices of inflammation (CRP) was documented in 50% of patients, usually mild.With regard to therapy, 90% of patients were treated with NSAIDs, 95% with colchicine, while 50% of cases required treatment with low-dose steroids.ConclusionCOVID-19 vaccination induces a particular form of pericarditis, often insidious and very troublesome, but with good prognosis. The clinical phenotype showed less typical chest pain, often normal indices of inflammation and little or no instrumental changes, but patients often experimented tachycardia and functional limitation. With regard to therapy, we used NSAIDs at adequate dosages to control the clinical condition, or low-dose colchicine. Low doses of cortisone (e.g., prednisone 5-10 mg a day) were useful in the presence of marked asthenia or systemic symptoms. Beta-blockers or ivabradine were used in the presence of tachycardia.References[1]Barda N, Children 2021, 8(7), 607;Safety of the BNT162b2 mRNA Covid-19 in a Nationwide setting. N Engl J med 2021;385:1078-1090.[2]Diaz GA, Myocarditis and Pericarditis After Vaccination for COVID-19. JAMA 2021;326 (12): 1210-1212.[3]Bibhuti D, Myocarditis and Pericarditis Following mRNA COVID-19 Vaccination: What Do We Know So Far?. Children 2021, 8(7), 607.[4]Giacomo Maria Viani, Patrizia Pedrotti, Romano Seregni, and Brucato Antonio;Effusive–constrictive pericarditis after the second dose of BNT162b2 vaccine (Comirnaty): a case report;European Heart Journal - Case Reports (2022) 6(2), 1–6.[5]Francesco Perna, Elena Verecchia, Gaetano Pinnacchio, Laura Gerardino, Antonio Brucato, and Raffaele Manna;Rapid resolution of severe pericardial effusion using anakinra in a patient with COVID-19 vaccine-related acute pericarditis relapse:a case report;European Heart Journal - Case Reports (2022) 6, 1–6.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

The complex syndrome of functional neurological disorder.

BACKGROUND: Patients with functional neurological disorders (FND) often present with multiple motor, sensory, psychological and cognitive symptoms. In order to explore the relationship between these common symptoms, we performed a detailed clinical assessment of motor, non-motor symptoms, health-related quality of life (HRQoL) and disability in a large cohort of patients with motor FND. To understand the clinical heterogeneity, cluster analysis was used to search for subgroups within the cohort. METHODS: One hundred fifty-two patients with a clinically established diagnosis of motor FND were assessed for motor symptom severity using the Simplified Functional Movement Disorder Rating Scale (S-FMDRS), the number of different motor phenotypes (i.e. tremor, dystonia, gait disorder, myoclonus, and weakness), gait severity and postural instability. All patients then evaluated each motor symptom type severity on a Likert scale and completed questionnaires for depression, anxiety, pain, fatigue, cognitive complaints and HRQoL. RESULTS: Significant correlations were found among the self-reported and all objective motor symptoms severity measures. All self-reported measures including HRQoL correlated strongly with each other. S-FMDRS weakly correlated with HRQoL. Hierarchical cluster analysis supplemented with gap statistics revealed a homogenous patient sample which could not be separated into subgroups. CONCLUSIONS: We interpret the lack of evidence of clusters along with a high degree of correlation between all self-reported and objective measures of motor or non-motor symptoms and HRQoL within current neurobiological models as evidence to support a unified pathophysiology of 'functional' symptoms. Our results support the unification of functional and somatic syndromes in classification schemes and for future mechanistic and therapeutic research.

Clustering Analysis Identified Three Long COVID Phenotypes and Their Association with General Health Status and Working Ability.

BACKGROUND/AIM: This study aimed to distinguish different phenotypes of long COVID through the post-COVID syndrome (PCS) score based on long-term persistent symptoms following COVID-19 and evaluate whether these symptoms affect general health and work ability. In addition, the study identified predictors for severe long COVID. METHOD: This cluster analysis included cross-sectional data from three cohorts of patients after COVID-19: non-hospitalized (n = 401), hospitalized (n = 98) and those enrolled at the post-COVID outpatient's clinic (n = 85). All the subjects responded to the survey on persistent long-term symptoms and sociodemographic and clinical factors. K-Means cluster analysis and ordinal logistic regression were used to create PCS scores that were used to distinguish patients' phenotypes. RESULTS: 506 patients with complete data on persistent symptoms were divided into three distinct phenotypes: none/mild (59%), moderate (22%) and severe (19%). The patients with severe phenotype, with the predominating symptoms were fatigue, cognitive impairment and depression, had the most reduced general health status and work ability. Smoking, snuff, body mass index (BMI), diabetes, chronic pain and symptom severity at COVID-19 onset were factors predicting severe phenotype. CONCLUSION: This study suggested three phenotypes of long COVID, where the most severe was associated with the highest impact on general health status and working ability. This knowledge on long COVID phenotypes could be used by clinicians to support their medical decisions regarding prioritizing and more detailed follow-up of some patient groups.

Organization of immunobiological therapy for severe bronchial asthma in the Sverdlovsk region

BACKGROUND: Biologicals use in severe asthma (SA) is associated with targeted therapy (TT) availability problem. Ensuring the availability of biologicals can be resolved within the territorial compulsory medical insurance program (TCMIP) in day-stay or round-the-clock hospital. AIMS: This study aimed to develop and implement a program for immunobiological therapy (IBT) introduction for SA in Sverdlovsk Region (SR). MATERIALS AND METHODS: Program for introduction of IBT for SA was developed in SR in 2018 to provide patients with expensive biologicals within the TCMIP. Program includes the following: SA prevalence study in SR;practitioners training in differential diagnosis of SA;organization of affordable therapy for patients with SA;registration of patients with SA creation and maintenance;and selection and management of patients with SA in accordance with federal clinical guidelines. RESULT(S): Atopic phenotype in SA was detected in 5%, eosinophilic - in 2.3% of all analyzed cases of asthma (n=216). Practitioners of SR were trained in differential diagnosis of SA. Orders of the Ministry of Health of SR were issued as follows: regulating the procedure for referring patients with SA to IBT, with a list of municipal medical organizations providing IBT in a day-stay or round-the-clock hospital;approving regional registration form of patients with SA requiring biologicals use;ungrouping of clinical and statistical groups of day-stay hospital was depending on INN and dosage of biologicals;and selecting patients with SA for TT and including them in the regional register. Initiating of TT in round-the-clock hospital and continuation therapy in day-stay hospital provides a significant savings in compulsory medical insurance funds. CONCLUSION(S): IBT introduction for SA in SR is carried out within the framework of the developed program. Principle of decentralization brings highly specialized types of medical care closer to patients making it possible to provide routine medical care in "allergology-immunology" profile in the context of restrictions caused by coronavirus disease 2019 pandemic.Copyright © 2020 Pharmarus Print Media All rights reserved.

Radiological Patterns and Lung Ultrasound

The early diagnosis of coronavirus disease 2019 (COVID-19) is one of the crucial points in order to reduce virus spread, also containing morbidity and mortality of the pandemic. Despite the utility of specific molecular tests (such as real time polymerase chain reaction, RT-PCR), imaging is considered one of the key strategies for an early diagnostic typing of the disease, and to individualize patient management [1-3]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.

Background: Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment. Methods: We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes. Results: Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set. Conclusions: We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Distinct phenotypes of multisystem inflammatory syndrome in children: a cohort study.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a severe disease with an unpredictable course and a substantial risk of cardiogenic shock. Our objectives were to (a) compare MIS-C phenotypes across the COVID-19 pandemic, (b) identify features associated with intensive care need and treatment with biologic agents. METHODS: Youth aged 0-18 years, fulfilling the World Health Organization case definition of MIS-C, and admitted to the Alberta Children's Hospital during the first four waves of the COVID-19 pandemic (May 2020-December 2021) were included in this cohort study. Demographic, clinical, biochemical, imaging, and treatment data were captured. RESULTS: Fifty-seven MIS-C patients (median age 6 years, range 0-17) were included. Thirty patients (53%) required intensive care. Patients in the third or fourth wave (indicated as phase 2 of the pandemic) presented with higher peak ferritin (µg/l, median (IQR) = 1134 (409-1806) vs. 370 (249-629), P = 0.001), NT-proBNP (ng/l, median (IQR) = 12,217 (3013-27,161) vs. 3213 (1216-8483), P = 0.02) and D-dimer (mg/l, median (IQR) = 4.81 (2.24-5.37) vs. 2.01 (1.27-3.34), P = 0.004) levels, and higher prevalence of liver enzyme abnormalities (n(%) = 17 (68) vs. 11 (34), P = 0.02), hypoalbuminemia (n(%) = 24 (100) vs. 25 (81), P = 0.03) and thrombocytopenia (n(%) 18 (72) vs. 11 (34), P = 0.007) compared to patients in the first two waves (phase 1). These patients had a higher need of non-invasive/mechanical ventilation (n(%) 4 (16) vs. 0 (0), P = 0.03). Unsupervised clustering analyses classified 47% of the patients in the correct wave and 74% in the correct phase of the pandemic. NT-proBNP was the only significant contributor to the need for intensive care in all applied multivariate regression models. Treatment with biologic agents was significantly associated with peak CRP (mg/l (median, IQR = 240.9 (132.9-319.4) vs. 155.8 (101.0-200.7), P = 0.02) and ferritin levels (µg/l, median (IQR) = 1380 (509-1753) vs. 473 (280-296)). CONCLUSIONS: MIS-C patients in a later stage of the pandemic displayed a more severe phenotype, reflecting the impact of distinct SARS-CoV-2 variants. NT-proBNP emerged as the most crucial feature associated with intensive care need, underscoring the importance of monitoring.

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact.

BACKGROUND: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. METHODS: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. RESULTS: Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81-5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50-3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92-2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. CONCLUSIONS: The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study.

Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype's development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics.

Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients: Results from a Cohort Observational Study.

Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020-2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients' care, justifying different therapy responses and inconsistencies identified across different randomized control trial results.

SARS-CoV-2 infection: pathogenesis, immune responses, diagnosis

COVI D-19 has emerged as the most alarming infection of the present time instigated by the virus SARS-CoV-2. In spite of advanced research technologies, the exact pathophysiology and treatment of the condition still need to be explored. However, SARS-CoV-2 has several structural and functional similarities that resemble SARS-CoV and MERS-CoV which may be beneficial in exploring the possible treatment and diagnostic strategies for SARS-CoV-2. This review discusses the pathogen phenotype, genotype, replication, pathophysiology, elicited immune response and emerging variants of SARSCoV- 2 and their similarities with other similar viruses. SARS-CoV-2 infection is detected by a number of diagnostics techniques, their advantages and limitations are also discussed in detail. The review also focuses on nanotechnology-based easy and fast detection of SARS-CoV-2 infection. Various pathways which might play a vital role during SARS-CoV-2 infection have been elaborately discussed since immune response plays a major role during viral infections.

Relationships between sensory reactivity, restricted and repetitive behaviors, and autonomic nervous system activity in autistic and non-autistic individuals

The current dissertation includes seven chapters. Chapter 1 includes my professional background and describes the experiences that led me to study restricted and repetitive behaviors. It also briefly describes my personal journey as an international graduate student. Chapter 2 is devoted to the rich literature that this dissertation is based on. The literature review covers both the foundational and most recent findings in the fields of sensory reactivity, restricted and repetitive behaviors, and autonomic activity, as well as the known relationships between these areas in autistic and non-autistic individuals. In Chapter 3, I describe in detail the methodology used in the current dissertation, including a description of the participants and the study design and analysis choices. Chapters 4 through 6 describe three experimental studies examining different aspects of the relationships between sensory reactivity, restricted and repetitive behaviors, and autonomic activity. Chapter 4 presents findings from a study conducted with both children and adults examining the pupil light reflex as it relates to levels of autistic traits in both age groups. Chapter 5 presents findings from a remote questionnaire study using caregiver-report measures that examines the relationships among sensory reactivity, restricted and repetitive behaviors, and adaptive behaviors in non-autistic children. Chapter 6 extends the questionnaire study of Chapter 5, presenting findings from an in-person study with a subset of children (limited due to restrictions related to COVID-19) that aimed to examine the role of autonomic activity in the relationship between sensory reactivity and restricted and repetitive behaviors in order to begin uncovering the potential mechanisms underlying this relationship. Finally, Chapter 7 is devoted to an overarching conclusion, potential implications, and a description of future plans for my own line of research, which include examining new questions in autistic populations and then extending these questions into the general, broader, population. The motivation behind the research presented in this dissertation is to better understand behaviors that are associated with and prevalent in autism and are also highly stigmatized. Research showing that autistic traits vary widely in the general population can speak to and contribute to the increasing awareness and acceptance of the autistic experience, which is just different, not less. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

T cell phenotypes in COVID-19 - a living review.

COVID-19 is characterized by profound lymphopenia in the peripheral blood, and the remaining T cells display altered phenotypes, characterized by a spectrum of activation and exhaustion. However, antigen-specific T cell responses are emerging as a crucial mechanism for both clearance of the virus and as the most likely route to long-lasting immune memory that would protect against re-infection. Therefore, T cell responses are also of considerable interest in vaccine development. Furthermore, persistent alterations in T cell subset composition and function post-infection have important implications for patients' long-term immune function. In this review, we examine T cell phenotypes, including those of innate T cells, in both peripheral blood and lungs, and consider how key markers of activation and exhaustion correlate with, and may be able to predict, disease severity. We focus on SARS-CoV-2-specific T cells to elucidate markers that may indicate formation of antigen-specific T cell memory. We also examine peripheral T cell phenotypes in recovery and the likelihood of long-lasting immune disruption. Finally, we discuss T cell phenotypes in the lung as important drivers of both virus clearance and tissue damage. As our knowledge of the adaptive immune response to COVID-19 rapidly evolves, it has become clear that while some areas of the T cell response have been investigated in some detail, others, such as the T cell response in children remain largely unexplored. Therefore, this review will also highlight areas where T cell phenotypes require urgent characterisation.

Phenotypic and genotypic characterization of Candida auris, an emerging pathogen isolated from blood

Introduction: Candida auris has been reported from various health care settings and has recently gained importance because of its intrinsic resistance to many classes of antifungal agents and to disinfection. The outbreak potential and high mortality associated with Candida auris infection reinforces the need for speciation. Routine conventional methods are cumbersome and automated systems are unable to confirm up to species level. Materials and Methods: Candida auris isolates from consecutive non-repetitive blood cultures over a 1-year period were speciated based on phenotypic, physiological and biochemical tests and VITEK. Molecular confirmation was done by PCR-RFLP and MALDI-TOF. Anti- fungal susceptibility test was performed according to CLSI guidelines (2021), using suitable controls. Virulence factors such as production of Hemolysin, Phospholipase, Esterase and Bio-film production were demonstrated. RT-PCR was used to screen the COVID-19 status using SD-Biosensor kit. Baseline data and clinical history were collected and analysed. Results: Of 3632 blood cultures (0.77%), 28 Candida sp. were isolated including 9 Candida auris, (9/28, 32.14%). Of these 8 were from COVID-19 positive patients (88.89%), while 1 was from COVID-19 negative patient (11.11%). Two patients survived, while the remaining 7 patients succumbed to the disease. Conclusion: The increasing incidence of Candidiasis especially during the COVID-19 pandemic has raised the concern for early speciation. Through multi-modal strategies such as quick and correct identification, active surveillance, guided reporting, stringent infection control measures and correct use of anti-fungals through proper susceptibility testing, we can prevent the occurrence and spread of new Candida auris cases in the future.

Causal association of the brain structure with the susceptibility, hospitalization, and severity of COVID-19: A large-scale genetic correlation study.

Brain structure is related to its ability to resist external pathogens. Furthermore, there are several abnormal anatomical brain events and central system symptoms associated with COVID-19. This study, which was conducted based on genetic variables, aimed to identify the causal association between brain structure and COVID-19 phenotypes. We performed a two-sample bidirectional Mendelian randomization analysis using genetic variables obtained from large genome-wide association studies as instruments to identify the potential causal effects of various brain imaging-derived phenotypes (BIDPs) traits on susceptibility, hospitalisation, and severity of COVID-19. We explored the genetic correlations of 1325 BIDPs with the susceptibility, hospitalisation, and severity of COVID-19 using Linkage Disequilibrium Score Regression. We observed a causal relationship between increased cortical thickness of the left inferior temporal area and an increased risk of increased COVID-19 infection (p = 4.29 × 10-4) and hospitalisation (p = 3.67 × 10-3). Moreover, the larger total surface area of the whole brain was negatively correlated with the risk of hospitalisation for COVID-19. Furthermore, there was a significant causal association between increased cerebrospinal fluid volume and decreased severity of COVID-19 (p = 3.74 × 10-3). In a conclusion, we provide new insights into the causal association between BIDPs and COVID-19 phenotypes, which may help elucidate the aetiology of COVID-19.

Unusual biclonal IgA plasma cell myeloma with aberrant expression of high-risk immunophenotypes: first report of a new diagnostic and clinical challenge.

IgA plasma cell myeloma (PCM) has been linked to molecular abnormalities that confer a higher risk for adverse patient outcomes. However, since IgA PCM only accounts for approximately 20% of all PCM, there are very few reports on high-risk IgA PCM. Moreover, no such reports are found on the more infrequent biclonal IgA PCM. Hence, we present a 65-year-old Puerto Rican female with acute abdominal pain, concomitant hypercalcemia, and acute renal failure. Protein electrophoresis with immunofixation found high IgA levels and detected a biclonal IgA gammopathy with kappa specificity. Histomorphologically, bone marrow showed numerous abnormal plasma cells (32%) replacing over 50% of the marrow stroma. Immunophenotyping analysis detected CD45-negative plasma cells aberrantly expressing CD33, CD43, OCT-2, and c-MYC. Chromosomal analysis revealed multiple abnormalities including the gain of chromosome 1q. Thus, we report on an unusual biclonal IgA PCM and the importance of timely diagnosing aggressive plasma cell neoplasms.

A Differential Therapeutic consideration for use of Corticosteroids according to Established COVID-19 Clinical Phenotypes in Critically ill Patients.

OBJECTIVE: To determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes. DESIGN: A secondary analysis derived from multicenter, observational studySetting: Critical Care UnitsPatients: Adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain. INTERVENTIONS: corticosteroids vs no corticosteroidsMain variables of interest: Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). We performed a Multivariate analysis after propensity optimal full matching (PS) for whole population and weighted Cox regression (HR) and Fine-Gray analysis(sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes. RESULTS: A total of 2,017 patients were analyzed, 1171(58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR:1.0,95%CI:0.98-1.15). Corticosteroids were administered in 298/537(55.5%) patients of "A" phenotype and their use was not associated with ICU mortality (HR=0.85[0.55-1.33]). A total of 338/623(54.2%) patients in "B" phenotype received corticosteroids. No effect of corticosteroids on ICU mortality was observed when HR was performed (0.72[0.49-1.05]). Finally, 535/857(62.4%) patients in "C" phenotype received corticosteroids. In this phenotype HR (0.75[0.58-0.98]) and sHR (0.79[0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality. CONCLUSION: Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate dose. Only patients with the highest inflammatory levels could benefit from steroid treatment.

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

BACKGROUND: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19. METHODS: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked. RESULTS: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype. CONCLUSIONS: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.