Clinical audit of adherence to hypertension treatment guideline and control rates in hospitals of different sizes in Thailand

A clinical audit of hospitals in Thailand was conducted to assess compliance with the national hypertension treatment guidelines and determine hypertension control rates across facilities of different sizes. Stratified random sampling was used to select sixteen hospitals of different sizes from four provinces. These included community (120 beds) hospitals. Among new cases, the audit determined whether (i) the recommended baseline laboratory assessment was completed, (ii) the initial choice of medication was appropriate based on the patient's cardiovascular risk, and (iii) patients received medication adjustments when indicated. The hypertension control rates at six months and at the last visit were recorded. Among the 1406 patients, about 75% had their baseline glucose and kidney function assessed. Nearly 30% (n = 425/1406) of patients were indicated for dual therapy but only 43% of them (n = 182/425) received this. During treatment, 28% (198/1406) required adjustments in medication but this was not done. The control of hypertension at six months after treatment initiation was 53% varying between 51% in community and 56% in large hospitals (p p < .01). Failure to adjust medication when required was associated with 30% decrease in the odds of hypertension control (OR 0.69, 95% CI 0. 50 to 0.90). Failure to comply with the treatment guidelines regarding adjustment of medication and lost to follow-up are possible target areas to improve hypertension control in Thailand

Investigating radiomic and metabolomic biomarkers of abdominal aortic aneurysm (AAA) growth

Introduction: As there is no viable medical treatment to halt the progression of Abdominal Aortic Aneurysms (AAA), current research focus is on the identification of novel methods to predict AAA growth, allowing for risk stratification and timing of surgical intervention. This DPhil thesis aimed to i) assess the performance of radiomic features in predicting AAA growth and systemic endothelial function, ii) investigate the metabolomic fingerprint of patients with AAA and the performance of a metabolic signature in predicting future AAA growth, iii) investigate metabolic pathways and novel biomarkers for AAA progression. Methods: Radiomics analysis utilized CT scans from a retrospective multicohort sample. Image segmentation and volume reconstruction was done using semi-automated method developed by our research group and radiomics features were extracted using Python software. Model performance was assessed using Receiver Operating Characteristic (ROC) curves and Root Mean Square Error (RMSE). Untargeted metabolomics analysis was conducted using four pairwise experiments: (i) comparison of patients with AAA vs healthy volunteers (HV), (ii)fast vs slow growing AAA, (iii) before vs after AAA open surgical repair, (iv) ILT tissue vs omental tissue. Global metabolomics analysis was done using Atmospheric Pressure Solid Analysis Probe Mass spectrometry. Prediction of future AAA growth was assessed using ROC curves. Targeted metabolomics was conducted using Ion Chromatography and C18 High Performance Liquid Chromatography. Functional metabolite relevance was assessed using the Mummichog software in Metaboanalyst 5.0 platform. Results: Radiomic signature performed marginally better at predicting fast and slow AAA growth compared to AAA diameter [slow growth (Accuracy 77.6%, AUC 0.61 vs accuracy 62.2%, AUC 0.46)] and [fast growth (Accuracy 78.8%, AUC 0.72 vs accuracy 67.3%, AUC 0.51)]. AAA diameter, however, seemed to perform better than radiomic signature at predicting endothelial function using both linear (RMSE 2.145 vs RMSE 2.723) and logistic models (Accuracy 49.3 vs 52.3). On untargeted metabolomics, univariate analysis of metabolites in the four experiments showed Differentially Expressed Metabolites (DEMs) in all four pairwise comparisons and integration using Venn diagrams revealed three metabolites m/z 177.10, m/z 191.15, m/z 362.15 that were (i) significantly elevated in AAA compared to HV, (ii) elevated in fast compared to slow AAA, (iii) increased in AAA patients before surgery compared to after surgery and finally, (iv) elevated in ILT tissue compared to the omental tissue. The metabolic signature performed better than AAA diameter at predicting future growth for fast-growing aneurysms AUC 77.8%, p<0.00001 vs AAA diameter of AUC 62.6%, p=0.057. Pathway-level integration analysis revealed enriched pathways including 3 pathways not previously described in association with AAA. These included glycosphingolipid biosynthesis-ganglio series, Pentose phosphate and mucin type O-glycan biosynthesis. Compound identification revealed metabolites as N-formyl- methionine and citric acid as possible biomarkers of AAA growth. Conclusion: Radiomic and metabolic features correlate with AAA growth and has potential to predict AAA growth. Metabolomic biomarkers offer promise in the search for robust AAA biomarkers and open a wide field for further research and validation of the identified compounds and metabolic pathways

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerised Tomography Images Acquired During the Aneurysm Surveillance Period

OBJECTIVE: We investigated the utility of geometric features for future abdominal aortic aneurysms (AAA) growth prediction. BACKGROUND: Novel methods for growth prediction of AAA are recognised as a research priority. Geometric feature has been applied to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. METHODS: Computerised tomography (CT) scans from patients with infra-renal AAAs were analysed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI) and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. RESULTS: There was no correlation between the geometric measurements and patients' demographic features. APD (spearman r = 0.25, p 5 mm/year) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. CONCLUSIONS: Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway

Illness perceptions, self-care practices, and glycemic control among type 2 diabetes patients in Chiang Mai, Thailand.

BACKGROUND: Diabetes Self-Management Education (DSME) is a fundamental aspect of diabetes care, but no standard program exists in Thailand. Understanding current patterns of illness perceptions (concerns) and self-management practices among patients with diabetes in Thailand is vital to develop culturally tailored DSME programs. This study sought to explore the association between reported self-management practices and diabetes perceptions on glycemic control among patients with type 2 diabetes in Chiang Mai Province, Thailand. Specifically, the study examined whether the association between illness perceptions and diabetes control was mediated by self-management. METHODS: This was a cross-sectional study conducted among type 2 diabetes patients on outpatient care and follow-up in four districts hospitals in Chiang Mai, Thailand. Illness perceptions was measured by the Brief Illness Perceptions Questionnaire (BIPQ). Self-management practices were measured by Summary Diabetes Self-Care activities (SDSCA). For illness perceptions and self-management practices, patients were classified into two groups, high level and low level based on the median values. Univariate and multivariable analyses were done to determine the association between the determinant factors: self-care practices and illness perceptions and the outcome of interest- good glycemic control (HbA1c < 7%). RESULTS: Of the 200 participants recruited into the study, 180 completed the questionnaire. Only 35% of participants had good glycemic control (HBA1c < 7.0). Both illness perceptions and self-management practices were independently linked to glycemic control. Among illness perceptions, a sense of personal control was strongly associated with good glycemic control (p = 0.01). For self-management, appropriate diet (p = 0.03) and medication adherence (p = 0.05) were associated with good glycemic control. After adjustments for key baseline characteristics, patients with high levels of illness perceptions were less likely to achieve glycemic control (OR 0.55, 95% CI 0.29 to 1.14, p = 0.11) and those with high level of self-management were more likely to achieve glycemic control (OR 2.11, 95% CI 1.04 to 4.30, p = 0.04). The effect size for illness perception attenuated when further adjusted for levels of self-management (OR 0.88, 95% CI 0.39 to 1.96, p = 0.75) while the effect size for self-management and glycemic control did not materially change (OR 2.30, 95% CI 1.06 to 5.02, p = 0.04). CONCLUSION: Illness perceptions and self-management practices are associated with glycemic control. Future culturally tailored interventions in Thailand aimed at improving glycemic should focus on personal control, improving diet and treatment adherence as these are more likely to help improve diabetes control as demonstrated in this study

Process evaluation protocol of a cluster randomised trial for a scalable solution for delivery of Diabetes Self-Management Education in Thailand (DSME-T).

INTRODUCTION: Type 2 diabetes mellitus is a major global challenge, including for Thai policy-makers, as an estimated 4 million people in Thailand (population 68 million) have this condition. Premature death and disability due to diabetes are primarily due to complications which can be prevented by good risk factor control. Diabetes Self-Management Education (DSME) programmes provide patients with diabetes with the necessary knowledge and skills to effectively manage their disease. Currently, a trial is being conducted in Thailand to evaluate the effectiveness, defined as HbA1c<7 at 12 months after enrolment, of a culturally tailored DSME in Thailand. A process evaluation can provide further interpretation of the results from complex interventions as well as insight into the success of applying the programme into a broader context. METHODS AND ANALYSIS: The aim of the process evaluation is to understand how and why the intervention was effective or ineffective and to identify contextually relevant strategies for future successful implementation. For the process evaluation, the design will be a mixed-method study collecting data from nurse providers, and village health volunteers (community health workers) as well as patients. This will be conducted using observations, interviews and focus groups from the three purposively selected groups at the beginning and end of trial. Quantitative data will be collected through surveys conducted at the beginning, during 6-month follow-up, and at the end of trial. The mixed-methods analysis will be triangulated to assess differences and similarities across the various data sources. The overall effectiveness of the intervention will be examined using multilevel analysis of repeated measures. ETHICS AND DISSEMINATION: Study approved by the Chiang Mai University Research Ethics Committee (326/2018) and the London School of Hygiene & Tropical Medicine (16113/RR/12850). Results will be published in open access, peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: NCT03938233