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Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student's t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.
RESUMO
OBJECTIVES: To assess disease-related knowledge of rheumatoid arthritis (RA) patients PATIENTS AND METHODS: Consecutive RA patients were invited from the rheumatology departments of BSMM University, Dhaka, Bangladesh. The Bangla version of the Patient Knowledge Questionnaire (B-PKQ) was used. Correlations between the B-PKQ scores and clinical-demographic data were measured using Pearson's correlation coefficient. Impact of independent variables on the level of knowledge about RA was analyzed through multiple regression analysis. Possible explanatory variables included the following: age, disease duration, formal education level, and Bangla Health Assessment Questionnaire (B-HAQ) score. Analysis of variance (ANOVA) was used to test the difference between demographical, clinical, and socioeconomic variables. For statistical analysis, SPSS statistics version 20 was used. RESULTS: A total of 168 RA patients could be included. The mean B-PKQ score was 9.84 (range 1-20) from a possible maximum of 30. The mean time for answering the questionnaire was 24.3 min (range 15-34). Low scores were observed in all domains but the lowest were in medications and joint protection/energy conservation. Knowledge level was higher (15.5) in 6 patients who had RA education before enrollment. B-PKQ showed positive correlation with education level (r = 0.338) and negative correlation with HAQ (r = -0.169). The B-PKQ showed no correlation with age, disease duration, having first degree family member with RA, education from other sources (neighbor, RA patient, nurses), or information from mass media. CONCLUSIONS: Disease-related knowledge of Bangladeshi RA patients was poor in all domains. Using these findings, improved education and knowledge will result in better disease control.Key Points⢠Little is known about the knowledge of RA patients regarding their disease and its treatment in Bangladesh and in developing countries in general.⢠We found that the knowledge of Bangladeshi RA patients regarding their disease was poor in all domains; it correlated positive with education level and negative with function (HAQ), but showed no correlation with age or disease duration.⢠The findings of this study can be used for improving current patient education programs by health professionals and through mass media.⢠Better disease control of RA may be achieved by improving patient knowledge in a developing country like Bangladesh, but also in other parts of the world.
