The difference of lipid profiles between psoriasis with arthritis and psoriasis without arthritis and sex-specific downregulation of methotrexate on the apolipoprotein B/apolipoprotein A-1 ratio

Background Methotrexate (MTX) has a protective effect against cardiovascular diseases (CVD), but the mechanism is unclear. Objective To investigate the effect of MTX on lipid profiles and the difference between psoriasis without arthritis (PsO) and psoriatic arthritis (PsA). Methods In this prospective study, we recruited 288 psoriatic patients (136 PsA and 152 PsO) who completed 12 weeks of MTX treatment. Total cholesterol (TC), triglycerides (TG), lipoprotein A [LP(a)], high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and ApoB were measured. Results Compared with sex- and age-matched healthy controls, psoriatic patients had significantly (p < 0.0001) higher levels of proatherogenic lipids and lower levels of anti-atherogenic lipids. PsA patients had a higher ApoB/ApoA1 ratio than PsO patients (p < 0.05). Stepwise regression analysis found a positive correlation between the inflammatory marker hCRP and the Psoriasis Area Severity Index (PASI), ApoB/ApoA1 ratio, BMI, and smoking. ApoB was positively associated with concomitant arthritis, diabetes, and hypertension. MTX decreased the levels of pro-atherogenic and anti-atherogenic lipids. However, a significant reduction of the ApoB/ApoA1 ratio by MTX was only observed in male patients. Conclusion PsA patients had a significantly higher percentage of concomitant disease than PsO. The decrease of MTX on CVD might be related with sex. Trial registration ChiCTR2000036192

Features of the course of coronavirus infection in patients with diabetes mellitus

Diabetesmellitus, overweight and the age of a patient over 65 years old are identified by clinicians as themain factors that can complicate the course of the coronavirus infection and increase the likelihood of fatal outcome. Although in the general human population mortality from coronavirus fluctuateswithin 3–5 %, sometimes very significantly differing in individual countries, this level can reach 15–25 % among patientswith diabetes, especially for those receiving insulin therapy. Diabetes mellitus as a concomitant disease in COVID-19 is considered one of the most significant risk factors for the development of adverse outcomes due to a more severe course of infection in conditions of hyperglycemia and other aggravating factors.

Transition readiness in adolescents with juvenile idiopathic arthritis and their parents: Our single-center experience

Objectives: We aimed to identify characteristics of juvenile idiopathic arthritis (JIA) patients associated with good self-management skills in the transition readiness process and to investigate the readiness of JIA patients and their families for the transition into the adult healthcare system. Patients and methods: Between March 2021 and June 2021, a total of 44 JIA patients (9 males, 35 females; median age: 15.1 years; range, 12.3 to 19.3 years) admitted to the pediatric rheumatology outpatient and inpatient clinics and their parents were included. Transition Readiness Assessment Questionnaire (TRAQ) was cross-culturally adapted. The TRAQ was administered to all JIA patients and their parents at one point. Demographic and clinical data were collected. Results: Fourteen (31.8%) of 44 JIA patients had a concomitant disease, while 10 (22.7%) of them had uveitis. Eleven (25%) of them had a family history of autoimmune diseases. In total, 21 (47.7%) of JIA patients were receiving biologics. There was a strong correlation between older age and total TRAQ scores among patients (ρ=0.799, p<0.001) and a moderate correlation between older patient age and total TRAQ scores among parents (ρ=0.522, p<0.001). Patient and parent total TRAQ scores were strongly correlated (ρ=0.653, p<0.001). There was no significant association of JIA patient characteristics (JIA disease subtypes, disease duration, gender, concomitant diseases, uveitis, family history of autoimmune diseases, number of hospitalizations, and treatment with biologics) with TRAQ scores and JIA patients' and parents' readiness for transition. Conclusion: Transition readiness of JIA patients increases with advancing age. There is no significant difference between transition readiness for JIA patients and their parents.

Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery

Background: This study intended to use a machine learning model to identify critical preoperative and intraoperative variables and predict the risk of several severe complications (myocardial infarction, stroke, renal failure, and hospital mortality) after cardiac valvular surgery.Study Design and Methods: A total of 1,488 patients undergoing cardiac valvular surgery in eight large tertiary hospitals in China were examined. Fifty-four perioperative variables, such as essential demographic characteristics, concomitant disease, preoperative laboratory indicators, operation type, and intraoperative information, were collected. Machine learning models were developed and validated by 10-fold cross-validation. In each fold, Recursive Feature Elimination was used to select key variables. Ten machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC), accuracy (ACC), Youden index, sensitivity, specificity, F1-score, positive predictive value (PPV), and negative predictive value (NPV) were used to compare the prediction performance of different models. The SHapley Additive ex Planations package was applied to interpret the best machine learning model. Finally, a model was trained on the whole dataset with the merged key variables, and a web tool was created for clinicians to use.Results: In this study, 14 vital variables, namely, intraoperative total input, intraoperative blood loss, intraoperative colloid bolus, Classification of New York Heart Association (NYHA) heart function, preoperative hemoglobin (Hb), preoperative platelet (PLT), age, preoperative fibrinogen (FIB), intraoperative minimum red blood cell volume (Hct), body mass index (BMI), creatinine, preoperative Hct, intraoperative minimum Hb, and intraoperative autologous blood, were finally selected. The eXtreme Gradient Boosting algorithms (XGBOOST) algorithm model presented a significantly better predictive performance (AUROC: 0.90) than the other models (ACC: 81%, Youden index: 70%, sensitivity: 89%, specificity: 81%, F1-score:0.26, PPV: 15%, and NPV: 99%).Conclusion: A model for predicting several severe complications after cardiac valvular surgery was successfully developed using a machine learning algorithm based on 14 perioperative variables, which could guide clinical physicians to take appropriate preventive measures and diminish the complications for patients at high risk.

Exploring the Intersection of Chronic Disease, Function, and Social Care in Adult Day Care Clients With Dementia

The interrelationships among dementia, concomitant disease, and social determinants of health are poorly understood and have critical implications for disease course, treatments, and caregiving needs. The aim of this study was to identify patterns of co-occurring chronic conditions among persons with dementia and the relationship of these patterns with clinical characteristics, demographic predictors and functional status. A latent class analysis (LCA) was conducted using data from 53 California adult day centers (n=3,053). Four distinct groups emerged: “dementia only”; “dementia +: &gt; 2; + &gt; 3; + &gt;5 chronic conditions. Having dementia + &gt;5 was associated (p &lt;.001) with greater risk of falls, isolation, medication mismanagement, and reduced likelihood of using an adaptive device. Dementia in day center clients is complicated by clinical conditions, functional decline, and a need for supports that may be lacking. Center staff must be trained and resourced to manage the complex needs of persons with dementia.

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure ( P < 0.05 ). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.

A Comparative Outcome Analysis of COVID-19 and Influenza Infection in Patients with Hematological Malignancies

Introduction: Patients with hematological malignancies and concomitant SARS-CoV-2 infection suffer from a more severe course of their infection than patients without underlying concomitant disease. Similar observations have been made for concomitant influenza infections. The aim of this retrospective study is to compare the clinical courses of COVID-19 and seasonal influenza in patients with hematological malignancies. Methods: In this retrospective, single center analysis all patients with hematological malignancies aged 18 years and older were included with a laboratory confirmed SARS-CoV-2 or influenza A or B infection who were admitted or were already under treatment at the Department of Oncology and Hematology or at the Department of Stem Cell Transplantation at the University Medical Center Hamburg-Eppendorf, Germany, between January 2012 and January 2021. Primary and secondary endpoints of this study are the rate of acute respiratory distress syndrome (ARDS) and virus-associated 30- and 90-day mortalities. The retrospective data collection was performed in accordance with local legal requirements and was reviewed and approved by the Ethics Committee of the Medical Council of Hamburg. Results: A total of 79 patients were included in this study. 29 patients had laboratory confirmed SARS-CoV-2 infection and 50 patients had influenza A or B infection. 69% in the COVID-19 group and 68% in the influenza group were male. Median age in the COVID-19 group were 59 years vs 58.5 years in the influenza group. Distribution of hematological malignancies in the COVID-19 group was as follows: 59% had acute leukemia (AL), 24% malignant lymphoma, 14% multiple myeloma (MM) and 3% myelodysplastic syndrome (MDS). 89% of the patients with concomitant SARS-CoV-2 diagnosis were currently under treatment with chemotherapy, CD20 or CD38 antibody-therapy, underwent allogeneic stem cell transplantation (SCT) or received CAR-T-cells shortly before (&lt; 2 months) or during SARS-CoV-2 positivity. In the influenza group, 60% had AL, 8% lymphoma, 24% MM and 8% MDS or myeloproliferative neoplasm. 84% of these patients were under treatment with chemotherapy, CD33-, CD38- or SLAMF7-directed antibodies or underwent allogeneic SCT shortly before or during infection with seasonal influenza. At the time of infection, 41% of all SARS-CoV-2 positive patients were in refractory or relapsed setting compared to 42% in the influenza group whereas 28% in the COVID-19 and 36% in the influenza cohort were in complete remission. At the time of SARS-CoV-2 detection 38% of patients had grade IV neutropenia (defined as neutrophil count &lt;0.5 x 10 9/L) with a median duration of 3.5 days which is comparable to 33% of patients and a median neutropenia duration of three days in the influenza group. The incidence of ARDS was significantly higher in the COVID-19 group compared to the influenza group (48% vs. 14%, p = 0.001). Furthermore, virus infection related 30-day and 90-day mortality was significantly higher in the COVID-19 group (28% vs. 8%, p = 0.026 and 41% vs. 12%, p = 0.005). In the COVID-19 group, a duration of aplasia ≥ 7 days had no negative impact on 90-day mortality or development of an ARDS (p = 0.599 and 0.982 respectively) whereas in the patients infected with influenza A or B, an aplasia ≥ 7 days had a negative impact on 90-day mortality and development of ARDS (p &lt; 0.001 each). Conclusion: Based on our results, we conclude that comparable to the general population, infections with SARS-CoV-2 result in a significantly higher rate of ARDS and a significantly higher 30- and 90-day mortality compared to influenza A or B infections in patients with underlying hematological malignancies. Disclosures Weisel: Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; GSK: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Research Funding. Bokemeyer: Gilead Sciences: Research Funding; Bayer Schering Pharma: Consultancy; Merck Serono: Consultancy, Other: Travel accomodation ; AOK Health insurance: Consultancy; Alexion Pharmaceuticals: Research Funding; Agile Therapeutics: Research Funding; ADC Therapeutics: Research Funding; Abbvie: Research Funding; GSO: Consultancy; Lilly/ImClone: Consultancy; Amgen: Research Funding; Apellis Pharmaceuticals: Research Funding; Astellas: Research Funding; BerGenBio: Research Funding; Blueprint Medicine: Research Funding; Boehringer Ingelheim: Research Funding; Celgene: Research Funding; Daiichi Sankyo: Research Funding; Eisai: Research Funding; Gylcotope GmbH: Research Funding; GlaxoSmithKline: Research Funding; Inside: Research Funding; IO Biotech: Research Funding; Isofol Medical: Research Funding; Janssen-Cilag: Research Funding; Sanofi: Consultancy, Honoraria, Other: Travel accomodation; Merck KGaA: Honoraria; Roche: Honoraria, Research Funding; Merck Sharp Dohme: Consultancy, Honoraria; AstraZeneca: Honoraria, Research Funding; BMS: Honoraria, Other: Travel accomodation, Research Funding; Bayer: Honoraria, Research Funding; Karyopharm Therapeutics: Research Funding; Lilly: Research Funding; Millenium: Research Funding; MSD: Research Funding; Nektar: Research Funding; Rafael Pharmaceuticals: Research Funding; Springworks Therapeutics: Research Funding; Taiho Pharmaceutical: Research Funding; Pfizer: Other. Fiedler: Novartis: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Daiichi Sanyko: Consultancy, Other: Meeting attendance, Preparation of information material; Stemline: Consultancy; Servier: Consultancy, Other: Meeting attendance, Preparation of information material; MorphoSys: Consultancy, Honoraria; Jazz: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material; Celgene: Consultancy, Honoraria; Ariad/Incyte: Honoraria; Amgen: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material, Patents & Royalties, Research Funding; Abbvie: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material. Modemann: Teva: Other: Travel accomodation; Novartis: Other: Travel accomodation; Jazz Pharmaceuticals: Other: Travel accomodation; Gilead: Other: Travel accomodation; Incyte: Other: Travel accomodation; Servier: Honoraria, Other: Travel accomodation; Pfizer: Other: Travel accomodation; Amgen: Other: Travel accomodation; Daiichi Sankyo: Research Funding; Abbvie: Honoraria, Other: Travel accomodation.

Results of a Single Center Survey on Vaccination Against COVID-19 in Patients with Chronic Myeloid Leukemia

Introduction Despite the availability of vaccination against COVID 19 for all population categories since January 2021, it is moving slowly in Russia. Patients (pts) with chronic myeloid leukemia (CML) usually lead a normal life with social interactions. In the context of the COVID 19 pandemic, we find it important to identify the factors of adherence to vaccination and clarify the concerns. Objective: To determine the proportion of CML pts willing to consider vaccination against COVID 19, adherence factors and reasons for not vaccinating. Materials and methods. A survey on the attitude to vaccination against COVID 19 was prospectively carried out among all pts with CML consulted at the outpatient department of National Research Center for Hematology (Moscow, Russia) who agreed to participate. The key questions included considerations for and against vaccination, socio-demographic and clinical characteristics, lifestyle, comorbidities and history of COVID 19. Results. Within 4 months (from March 15 to July 19, 2021), 172 CML pts completed the questionnaire. CML chronic phase, advanced phase and blast crisis were in 167 (97%), 4 (2%) and 1(1%) respectively. In total, 141 (82%) pts were on therapy with 1 st, 2 nd and &gt;3 rd therapy line in 77 (55%), 33 (23%) and 31 (22%) pts, respectively. Thirty one (18%) had no therapy: 6 (3.5%) newly diagnozed, 25 (14.5%) in a treatment-free remission. A deep and major molecular response was in 77 (45%) and 30 (17%) pts, respectively. Presence and absence of molecular response MR2 was in 20 (12%) and 45 (26%) pts respectively. The median age of pts was 46 years (range 19-82), 75(44%) were males. Married 108 (63%), 70 (41%) lived with elderly relatives, 35 (20%) with children. A higher education was in 123 (72%) pts, 123 (72%) could not work remotely and 46 (27%) had interactions to people by work. Any comorbidity was in 89 (52%) pts, 42(24%) had &gt;1 concomitant disease, 48 (28%) had cardiovascular diseases, 44 (26%) had an obesity. A history of COVID 19 was in 41 (24%) pts and in the close circle of 74 (43%) pts. Vaccination was supported by 94(55%) pts (with 29 (17%) already vaccinated) and not supported by 76 (44%) pts, 2 (1%) pts did not answer. Among those supporting vaccination vs not supporting there was significantly more males (52% vs 33%, p=0,012), married pts (73% vs 49%, p&lt;0,001) and pts with higher education (88% vs 51%, p=0,006). Other factors (age, comorbidities, obesity, profession-related features, COVID 19 in pts or their environment, living with elderly relatives or children, therapy and treatment response) were not significant. Less pts were against vaccination in June-July 2021 before the 3 rd outbreak of COVID 19 compared to spring period (33% vs 50%, p=0,045). The two most common reasons to avoid vaccination were the fear of complications in 37(49%) pts and waiting for additional data in 19(25%) (Fig.1). Notably, 7 (9%) pts considered CML as a contraindication to vaccination. Among those supporting vaccination, 55(59%) preferred to choose the GamCovidVac (Sputnik V) vaccine, 20(21%) had no preference (Fig.2). Out of 32 pts who gave the rationale for the Sputnik V choice 19(59%) noted its best availability, study or popularity (Fig.3). Among 23 pts with additional questions 12 (52%) wondered about the possibility of vaccination with CML diagnosis and 6 (26%) asked help with a vaccine choice. Conclusion: Despite access to vaccines against COVID 19 with proven efficacy and safety, almost half of CML pts (44%) do not support vaccination. Socio-demographic factors such as gender, education, marriage status appeared to be significant for this decision. Considering the frequent concerns of the possibility of vaccination with CML diagnosis as well as the fear of complications, hematologists should provide a relevant clarifying information on these issues. Figure 1 Figure 1. Disclosures Chelysheva: Pfizer: Speakers Bureau; Novartis Pharma: Speakers Bureau; Pharmstandart: Speakers Bureau; Bristol Myers Squibb: Speakers Bureau. Petrova: Pfizer: Speakers Bureau; Novartis Pharma: Speakers Bureau. Gurianova: Pfizer: Speakers Bureau. Kokhno: Novartis Pharma: Speakers Bureau; Bristol Myers Squibb: Speakers Bureau. Turkina: Novartis Pharma: Speakers Bureau; Pfizer: Speakers Bureau; Pharmstandart: Speakers Bureau; Bristol Myers Squibb: Speakers Bureau.

Hereditary Spherocytosis Caused by a Novel Compound Heterozygous Mutation of the SPTA1 Gene and Autoimmune Hepatitis in a Pediatric Patient

Objective: Patients suffering from both hereditary spherocytosis (HS) and autoimmune hepatitis (AIH) are very rare. We analyzed the clinical and genetic characteristics of a seven-year-old girl with yellow sclerae and abnormal liver function tests, but no further symptoms. Methods: Blood samples were collected from the proband, her parents, and her paternal grandmother, and analyzed using routine laboratory tests, as well as subjected to next-generation and Sanger sequencing.Results: Compound heterozygous mutations of the spectrin alpha, erythrocytic 1 (SPTA1) gene were identified in the proband. Thec.134G>A (p.R45K) and c.6544G>C (p.D2182H) mutations were inherited from her mother and father, respectively. The proband’s father and paternal grandmother had the same mutation. Neither mutation is described in the Human Gene Mutation Database. Conclusions: HS has clinical manifestations similar to AIH, it may be difficult to diagnose when it coexists with AIH. When laboratory results cannot be explained by autoimmune liver disease alone, the possibility of a concomitant disease should be considered. Pedigree investigation and genetic analyses might be required to arrive at the final diagnosis.