The association of antidiabetic medications and Mini-Mental State Examination scores in patients with diabetes and dementia

Background The effect of antidiabetic medication on cognitive function is unclear. We analyzed the association between five antidiabetic drugs and change in Mini-Mental State Examination (MMSE) scores in patients with diabetes and dementia. Methods Using the Swedish Dementia Registry and four supplementary Swedish registers/databases, we identified 1873 patients (4732 observations) with diagnosis of type 2 diabetes (diabetes) and Alzheimer’s disease or mixed-pathology dementia who were followed up at least once after dementia diagnosis. Use of metformin, insulin, sulfonylurea, thiazolidinediones (TZD), and dipeptidyl-peptidase-4 inhibitors (DPP-4i) was identified at baseline. Prevalent-user, incident-user, and drug-drug cohorts were sampled, and propensity-score matching was used to analyze comparable subjects. Beta coefficients with 95% confidence intervals (CI) from the random intercept and slope linear mixed-effects models determined the association between the use of antidiabetic medications and decline in MMSE score points between the follow-ups. Inverse-probability weighting was used to account for patient dropout. Results Compared to non-users, prevalent users of metformin (beta 0.89, 95% CI 0.44; 1.33) and DPP-4i (0.72, 0.06; 1.37) experienced a slower cognitive decline with time. Secondly, compared to DPP-4i, the use of insulin (−1.00, −1.95; −0.04) and sulfonylureas (−1.19; −2.33; −0.04) was associated with larger point-wise decrements in MMSE with annual intervals. Conclusions In this large cohort of patients with diabetes and dementia, the use of metformin and DPP-4i was associated with a slower decline in MMSE scores. Further examination of the cognitive effects of metformin and incretin-based medications is warranted.

Fusion of Multi-Modality Biomedical Images Using Deep Neural Networks

With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, a novel image fusion model is designed to fuse multi-modality medical images. The proposed model model suffers from hyper-parameters tuning issue, therefore, a multi-objective differential evolution (MDE) is used to optimize the initial parameters of the proposed model. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. The performance of the proposed model is validated by comparing the proposed model models with nine competitive models over fifteen benchmark images. Performance analysis reveal that the proposed model outperforms the competitive fusion models.

Weighing and Weight Exposure

Regular, open weighing is a critical component of evidence-based treatments for eating disorders. Weight provides critical information for violating patients’ harm expectancies, and weighing offers a powerful learning opportunity for coping with anxiety. However, many clinicians collude with their patients in avoiding open weighing. The clinician’s job is to tolerate their own anxiety about things such as patient dropout while asking their patients to do likewise with their anxiety about knowing their weight. Therefore, clinician and patient need to learn the same thing—tolerance of uncertainty. This chapter details the rationale and methods for using exposure in the form of open weighing, so that the patient can overcome their fear of uncontrollable weight gain.

Patient Dropout from Opioid Substitution Treatment

Opioid Substitution Treatment (OST) is an established treatment for opioid dependence. In New Zealand, OST programs are regulated by the Ministry of Health (2014) and Methadone and Buprenorphine/Naloxone (Suboxone) are the primary medications. Retention on OST is a key indicator for stabilisation of patients with opioid dependence. The purpose of the present research was to study dropout rates and identify factors associated with the dropout of patients from OST at the Community Alcohol and Drug Service (CADS), Hamilton, from 1st January 2013 to 30th April 2014. A retrospective clinical audit of patients on OST was conducted. There were 150 patients on OST in Hamilton under the CADS team during the period of study. Nine patients dropped out during the study period. Sixty-four patients were randomly selected from the remaining 141 patients who remained on treatment as a comparison group and for the study sample to be approximately half of the overall population of 150 patients. File review was conducted and potential predictors of dropout were identified. Thirty-five independent variables were selected and dropout was the dependent variable. The statistical programme SPSS22 was used to analyse the data. Fisher’s exact test was used and four variables were identified as being associated with dropout: history of intravenous drug use; (Fisher’s exact p = 0.05); history of lifetime imprisonment (Fisher’s exact p =0.05); other medications prescribed, (Fisher’s exact p = 0.04); and opioid type prescribed during the study, i.e. methadone or Suboxone. Patients on Suboxone dropped out more than those on methadone, (Fisher’s exact p = 0.00). The overall dropout rate was 6%, which was less than the rates of 15-85% found in previous studies. The limitations of the study were that it was retrospective and the number of dropouts was small. Furthermore, only patient factors associated with dropout were included in the study and service factors were not included.

Readiness to Start Treatment and Obstacles to Adherence

Effective evidence-based psychotherapeutic regimens for psychogenic nonepileptic seizures (PNES) are available, but several obstacles still contribute to poor adherence to treatment. This chapter reviews the three stages at which patient dropout tends to occur in clinical practice and in studies. Patient-related, provider-related, and systemic causes of nonadherence are reviewed. Patient-related factors include a failure to accept or understand the diagnosis, psychiatric comorbidities, and ambivalence about change. Provider-related and systemic factors include a shortage of behavioral health specialists, gaps in care between neurologists and mental health providers, a lack of familiarity with the disorder, and stigmatization of patients. The chapter concludes with a review of potential interventions to address obstacles to treatment, including an integrated treatment team with joint presentation of the diagnosis, rapid and streamlined transition into psychotherapy, motivational interviewing, and engagement of patients’ family members and support systems.

Abstract TP72: Pilot Trial of Enoxaparin versus Aspirin in Cancer Patients with Stroke: the TEACH Study

Introduction: Anticoagulants are often used in cancer patients with acute ischemic stroke (AIS) because of concerns for hypercoagulability. However, cancer patients are also prone to bleeding. No prospective study has compared different antithrombotic strategies in this population. We aimed to perform the first randomized trial comparing anticoagulation to antiplatelet therapy in cancer patients with AIS. Methods: We conducted a pilot multicenter randomized trial in adult patients with active systemic cancer and MRI-confirmed AIS. Patients were randomized to subcutaneous enoxaparin (1 mg/kg twice daily) or oral aspirin (81 mg daily) for 6 months. Antithrombotic therapy beyond 6 months was dictated by treating clinicians. Exclusion criteria included clear indications for anticoagulant (e.g., DVT) or antiplatelet (e.g., recent stents) therapy, high-risk conditions for bleeding, symptomatic carotid stenosis, and severe hematologic derangements. Patients were followed at clinical visits at 1, 3, and 6 months and by chart review from 6 months to 1 year. The primary outcome was feasibility, defined as an enrollment rate among eligible patients for which the lower bound of the 95% confidence interval (CI) exceeds 30%. Secondary feasibility outcomes included patient dropout and crossover rates. Results: In this interim analysis, 469 patients with cancer and stroke were screened from January 2013 to April 2016 and 49 (10.4%) were eligible. Frequent exclusionary reasons were clear indications for anticoagulation (n=128) and inactive cancer (n=83). We enrolled 20 of the 49 eligible patients, equating to an enrollment rate of 41% (95% CI 27-55%). Enrollment failures primarily occurred because of patient aversion to injections (n=11), patient or physician preference for anticoagulation (n=9), and patient aversion to trial participation (n=7). Thus far, there have been no dropouts but 6 patients crossed over from the enoxaparin arm to the aspirin arm. Major bleeding occurred in 3 patients in the aspirin arm and 1 in the enoxaparin arm; no patients had intracranial hemorrhage. Conclusion: A randomized trial comparing anticoagulation to antiplatelet therapy in cancer patients with AIS may be feasible and safe, particularly with an oral anticoagulant.