METHODS: Data from a retrospective review of 13-year S.suis patient records in a tertiary hospital in Chiang Mai, Northern, Thailand was obtained. Univariate and multivariate logistic regressions were employed to develop a predictive model. The clinical risk score was constructed from the coefficients of significant predictors. Area under the receiver operator characteristic curve (AuROC) was identified to verify the model discriminative performance. Bootstrap technique with 1000-fold bootstrapping was used for internal validation.
KEY RESULTS: Among 133 patients, the incidence of hearing loss was 31.6% (n = 42). Significant predictors for S. suis hearing loss were meningitis, raw pork consumption, and vertigo. The predictive score ranged from 0-4 and correctly classified 81.95% patients as being at risk of S.suis hearing loss. The model showed good power of prediction (AuROC: 0.859; 95%CI 0.785-0.933) and calibration (AuROC: 0.860; 95%CI 0.716-0.953).
CONCLUSIONS: To our best knowledge, this is the first risk scoring system development for S.suis hearing loss. We identified meningitis, raw pork consumption and vertigo as the main risk factors of S.suis hearing loss. Future studies are needed to optimize the developed scoring system and investigate its external validity before recommendation for use in clinical practice.
Methods: People diagnosed with type 2 diabetes (n=218) were selected from three health care centers, located in different cities of Pakistan. Disease knowledge and self-care practices were assessed by Urdu versions of Diabetes Knowledge Questionnaire (DKQ) and Diabetes Self-Management Questionnaire (DSMQ), using a cross-sectional design. Chi-square and correlation analysis were applied to explore the relationship of disease knowledge with glycemic control and self-care practices. Linear regression was used to explore the predictors for disease knowledge.
Results: Majority of the sample was >45-60 years old (48.8%), suffering from type 2 diabetes mellitus for <5 years (49.5%) and had poor glycemic control (HbA1C≥7%; n=181 participants). Disease knowledge was significantly associated (p<0.05) with patient's gender, level of education, family history of diabetes, nature of euglycemic therapy, and glycemic control. Correlation matrix showed strongly inverse correlations of DKQ with glycated hemoglobin levels (r=-0.62; p<0.001) and strongly positive with DSMQ sum scale (r=0.63; p<0.001). PWD having university-level education (β=0.22; 95% Confidence Interval (CI) 0.189, 0.872; p<0.01), doing job (β=0.22; 95% CI 0.009, 0.908]; p=0.046), and use of oral hypoglycemic agents in combination with insulin (β=-0.16; 95% CI [-1.224, -0.071]; p=0.028) were the significant predictors for disease knowledge.
Conclusion: Disease knowledge significantly correlated with glycated hemoglobin levels and self-care activities of PWD. These findings will help in designing patient-tailored diabetes educational interventions for yielding a higher probability of achieving target glycemic control.