Clinical decision support systems in the care of hospitalised patients with diabetes

This thesis explored the role of health informatics (decision support systems) in caring for hospitalised patients with diabetes through a systematic review and by analysing data from University Hospital Birmingham, UK. Findings from the thesis: 1) highlight the potential role of computerised physician order entry system in improving guideline based anti-diabetic medication prescription in particular insulin prescription, and their effectiveness in contributing to better glycaemic control; 2) quantify the occurrence of missed discharge diagnostic codes for diabetes using electronic prescription data and suggests 60% of this could be potentially reduced using an algorithm that could be introduced as part of the information system; 3) found that hypoglycaemia and foot disease in hospitalised diabetes patients were independently associated with higher in-hospital mortality rates and longer length of stay; 4) quantify the hypoglycaemia rates in non-diabetic patients and proposes one method of establishing a surveillance system to identify non diabetic hypoglycaemic patients; and 5) introduces a prediction model that may be useful to identify patients with diabetes at risk of poor clinical outcomes during their hospital stay

The heritability of premenstrual syndrome

We aimed to determine (1) the prevalence of premenstrual syndrome in a sample of twins and (2) the relative contribution of genes and environment in premenstrual syndrome. A group of 193 subjects inclusive of same gender twins (n = 176) and females from opposite sex twin sets (n = 17) entered the study. Heritability analysis used same gender twin data only. The probandwise concordance rate for the presence or absence of premenstrual syndrome was calculated and the heritability of premenstrual syndrome was assessed by a quantitative genetic model fitting approach using MX software. The prevalence of premenstrual syndrome was 43.0% and 46.8% in monozygotic and dizygotic twins, respectively. The probandwise concordance for premenstrual syndrome was higher in monozygotic (0.81) than in dizygotic twins (0.67), indicating a strong genetic effect. Quantitative genetic modeling found that a model comprising of additive genetic (A) and unique environment (E) factors provided the best fit (A: 95%, E: 5%). No association was found between premenstrual symptom and the following variables: belonging to the opposite gender twin set, birth weight, being breast fed and vaccination. These results established a clear genetic influence in premenstrual syndrome

Bayesian Logistic Regression Model on Risk Factors of Type 2 Diabetes Mellitus

This research evaluates the risk of diabetes among 581 men and women with factors such as age, ethnicity, gender, physical activity, hypertension, body mass index, family history of diabetes, and waist circumference by applying the logistic regression model to estimate the coefficients of these variables. Significant variables determined by the logistic regression model were then estimated using the Bayesian logistic regression (BLR) model. A flat non-informative prior, together with a non- informative non- flat prior distribution were used. These results were compared with those from the frequentist logistic regression (FLR) based on the significant factors. It was shown that the Bayesian logistic model with the non-informative flat prior distribution and frequentist logistic regression model yielded similar results, while the non-informative  non-flat model showed a different result compared to the (FLR) model. Hence, non-informative but not perfectly flat prior yielded better model than the maximum likelihood estimate (MLE) and Bayesian with the flat prior. Keywords: Bayesian approach,  Binary  logistic regression,  Parameter estimate,  Prior,  MCMC.

Clinically meaningful and lasting HbA1c improvement rarely occurs after 5 years of type 1 diabetes: an argument for early, targeted and aggressive intervention following diagnosis.

AIMS/HYPOTHESIS: Our objectives were to explore whether the phenomenon of HbA1c 'tracking' occurs in individuals with type 1 diabetes, how long after diagnosis does tracking take to stabilise, and whether there is an effect of sex and age at diagnosis on tracking. METHODS: A total of 4525 individuals diagnosed with type 1 diabetes between 1 January 1995 and 1 May 2015 were identified from The Health Improvement Network (THIN) database. Mixed models were applied to assess the variability of HbA1c levels over time with random effects on general practices (primary care units) and individuals within practices. RESULTS: 4525 individuals diagnosed with type 1 diabetes were identified in THIN over the study period. The greatest difference in mean HbA1c measurement (-7.0 [95% CI -8.0, -6.1] mmol/mol [0.6%]) was seen when comparing measurements made immediately after diagnosis (0-1 year since diagnosis) with those at 10 or more years (the reference category). The mean difference in HbA1c for the successive periods compared with 10 or more years after diagnosis declined and was no longer statistically significant after 5 years. In the stratified analysis using sex and age group there was considerable heterogeneity with adult onset type 1 diabetes appearing to track earlier and at a lower mean HbA1c. CONCLUSIONS/INTERPRETATION: In individuals with type 1 diabetes, glycaemic control measured by HbA1c settles onto a long-term 'track' and this occurs on average by 5 years following diagnosis. Age at diagnosis modifies both the rate at which individuals settle into their track and the absolute HbA1c tracking level for the next 10 years

How migraine and its associated treatment impact on pregnancy outcomes:Umbrella Review with Updated Systematic Review and Meta-Analysis

Background: Migraine is common in reproductive aged women. Understanding the impact of migraine and associated treatments on pregnancy outcomes remains very important. An umbrella review of systematic reviews, with or without meta-analyses, examined the link between migraine and pregnancy outcomes. Methods: We systematically searched Medline, Embase and Cochrane to 27th October 2022. Quality appraisal was carried out using the AMSTAR2 tool. An established framework was used to determine whether included reviews were eligible for update. Results: Four studies met review criteria. Migraine was reported to be associated with increased odds ratio (OR) of pre-eclampsia, low birth weight and peripartum mental illness (pooled OR 3.54 (2.24-5.59)). Triptan-exposed women had increased odds of miscarriage compared to women without migraine (pooled OR 3.54 (2.24-5.59)). In updated meta-analyses, migraine was associated with an increased odds of pre-eclampsia and preterm birth (pooled OR 2.05 (1.47-2.84) and 1.26 (1.21-1.32) respectively).Conclusion: Migraine is associated with increased odds of pre-eclampsia, peripartum mental illness and preterm birth. Further investigation of the relationship between migraine and placental abruption, low birth weight and small for gestational age is warranted, as well as the relationship between migraine, triptans and miscarriage risk.Systematic Review Registration: Prospero CRD4202235763