Development of Clinical Decision Models for the Prediction of Systemic Lupus Erythematosus and Sjogren's Syndrome Overlap.

OBJECTIVE: To explore the clinical features of patients with systemic lupus erythematosus and Sjögren's syndrome overlap (SLE-SS) compared to concurrent SLE or primary SS (pSS) patients, we utilized a predictive machine learning-based tool to study SLE-SS. METHODS: This study included SLE, pSS, and SLE-SS patients hospitalized at Nanjing Drum Hospital from December 2018 to December 2020. To compare SLE versus SLE-SS patients, the patients were randomly assigned to discovery cohorts or validation cohorts by a computer program at a ratio of 7:3. To compare SS versus SLE-SS patients, computer programs were used to randomly assign patients to the discovery cohort or the validation cohort at a ratio of 7:3. In the discovery cohort, the best predictive features were determined using a least absolute shrinkage and selection operator (LASSO) logistic regression model among the candidate clinical and laboratory parameters. Based on these factors, the SLE-SS prediction tools were constructed and visualized as a nomogram. The results were validated in a validation cohort, and AUC, calibration plots, and decision curve analysis were used to assess the discrimination, calibration, and clinical utility of the predictive models. RESULTS: This study of SLE versus SLE-SS included 290 patients, divided into a discovery cohort (n = 203) and a validation cohort (n = 87). The five best characteristics were selected by LASSO logistic regression in the discovery cohort of SLE versus SLE-SS and were used to construct the predictive tool, including dry mouth, dry eye, anti-Ro52 positive, anti-SSB positive, and RF positive. This study of SS versus SLE-SS included 266 patients, divided into a discovery cohort (n = 187) and a validation cohort (n = 79). In the discovery cohort of SS versus SLE-SS, by using LASSO logistic regression, the eleven best features were selected to build the predictive tool, which included age at diagnosis (years), fever, dry mouth, photosensitivity, skin lesions, arthritis, proteinuria, hematuria, hypoalbuminemia, anti-dsDNA positive, and anti-Sm positive. The prediction model showed good discrimination, good calibration, and fair clinical usefulness in the discovery cohort. The results were validated in a validation cohort of patients. CONCLUSION: The models are simple and accessible predictors, with good discrimination and calibration, and can be used as a routine tool to screen for SLE-SS.

Prediction of diagnosis results of rheumatoid arthritis patients based on autoantibodies and cost-sensitive neural network.

OBJECTIVES: To analyze and evaluate the effectiveness of the detection of single autoantibody and combined autoantibodies in patients with rheumatoid arthritis (RA) and related autoimmune diseases and establish a machine learning model to predict the disease of RA. METHODS: A total of 309 patients with joint pain as the first symptom were retrieved from the database. The effectiveness of single and combined antibodies tests was analyzed and evaluated in patients with RA, a cost-sensitive neural network (CSNN) model was used to integrate multiple autoantibodies and patient symptoms to predict the diagnosis of RA, and the ROC curve was used to analyze the diagnosis performance and calculate the optimal cutoff value. RESULTS: There are differences in the seropositive rate of autoimmune diseases, the sensitivity and specificity of single or multiple autoantibody tests were insufficient, and anti-CCP performed best in RA diagnosis and had high diagnostic value. The cost-sensitive neural network prediction model had a sensitivity of up to 0.90 and specificity of up to 0.86, which was better than a single antibody and combined multiple antibody detection. CONCLUSION: In-depth analysis of autoantibodies and reliable early diagnosis based on the neural network could guide specialized physicians to develop different treatment plans to prevent deterioration and enable early treatment with antirheumatic drugs for remission. Key Points • There are differences in the seropositive rate of autoimmune diseases. • This is the first study to use a cost-sensitive neural network model to diagnose RA disease in patients. • The diagnosis effect of the cost-sensitive neural network model is better than a single antibody and combined multiple antibody detection.

Global, regional and national trends in sex- and age-specific disability-adjusted life years of musculoskeletal disorders, 1990-2019.

OBJECTIVE: To quantify the temporal trend of sex- and age-specific disability-adjusted life years (DALYs) for musculoskeletal (MSK) disorders by region and cause. METHODS: Data were collected from the Global Burden of Diseases Study 2019. The estimated annual percentage change (EAPC) by sex, age, region and cause was calculated to examine the temporal trend of the age-standardized DALYs rate (ASDR). The sociodemographic index (SDI) and risk exposures were also examined. RESULTS: Between 1990 and 2019, the global ASDR for MSK disorders remained almost stable by sex and age group but decreased among females ages 0-14 years (EAPC = -0.27). Such age and sex patterns were nearly the same by SDI, except for high SDI regions, where ASDR increased in all subgroups except those ages 15-49 years. The trend in ASDR of MSK disorders for females and males ages 50-74 and ≥75 years increased in ∼80% of countries and territories. The greatest increase was in El Salvador for males ages 15-49 years (EAPC = 1.30), followed by Nicaragua. The association between EAPC and SDI was positive in developing regions, particularly among females ages 15-49 years, and negative in developed regions. A decreasing trend in ASDR was mainly driven by the decrease in low back pain, while the increasing trend was largely due to other MSK disorders and gout across sexes and age groups. CONCLUSIONS: There are great disparities in the age- and sex-specific trends in ASDR by cause on the global, regional and national levels. More differentiated prevention and management strategies are needed for MSK disorders.

Incidence trend of five common musculoskeletal disorders from 1990 to 2017 at the global, regional and national level: results from the global burden of disease study 2017.

OBJECTIVE: To assess cause-specific incidence and its trend of musculoskeletal (MSK) disorders at global, regional and national levels. METHODS: Data on MSK disorders were downloaded from the Global Burden of Disease 2017 study website. Estimated annual percentage change (EAPC) was calculated to quantify the temporal trend in age-standardised incidence rate (ASR) of MSK disorders, by age, sex, region and cause. RESULTS: Between 1990 and 2017 incident cases of MSK disorders increased globally by 58% from 211.80 million to 334.74 million, with a decreasing ASR of 0.18% annually (95% CI -0.21% to -0.15%). The ASR decreased for low back pain (LBP), remained stable for neck pain (NP), and increased for rheumatoid arthritis (RA), osteoarthritis (OA) and gout, with EAPCs (95% CI) of -0.24 (-0.29 to -0.20), -0.09 (-0.13 to -0.05), 0.36 (0.28 to 0.43), 0.32 (0.28 to 0.36) and 0.22 (0.21 to 0.23), respectively. It appears women have higher increase in EAPC than men for RA (1.3 times) and gout (1.6 times). The absolute EAPC was strikingly high in high or high-middle sociodemographic index (SDI) regions for overall, LBP and gout, and in low SDI regions for NP. EAPC was significantly associated with baseline ASR for LBP (nonlinear), RA (ρ=-0.41) and gout (ρ=-0.42), also with 2017 human development index for LBP (ρ=-0.53) and gout (ρ=0.15). CONCLUSIONS: Globally, MSK disorders remain a public health burden. The ASR is decreasing for MSK disorders overall, mainly in high-middle SDI regions, but increasing for RA, OA and gout.