Predicting medication adherence in older Hispanic patients with type 2 diabetes

Dropout risk and effectiveness of retention strategies in the Memory Advancement by Intranasal Insulin in Type 2 Diabetes (MemAID) Clinical Trial

BACKGROUND

Effective recruitment and retention strategies are essential in clinical trials.

METHODS

The MemAID trial consisted of 12 visits during 24 weeks of intranasal insulin or placebo treatment and 24 weeks of post-treatment follow-up in older people with and without diabetes. Enhanced retention strategies were implemented mid study to address high drop-out rate. Baseline variables used in Cox regression models to identify dropout risk factors were: demographics and social characteristics, functional measures, metabolic and cardiovascular parameters, and medications.

RESULTS

244 participants were randomized; 13 (5.3%) were discontinued due to adverse events. From the remaining 231 randomized participants, 65 (28.1%) dropped out, and 166 (71.9%) did not. The Non-retention group included 95 participants not exposed to retention strategies, of which 43 (45.2%) dropped out. The Retention group included 136 participants exposed to enhanced retention strategies, of which 22 (16.2%) dropped out. Dropout risk factors included being unmarried, a longer diabetes duration, using oral antidiabetics as compared to not using, worse executive function and chronic pain. After adjusting for exposure to retention strategies, worse baseline executive function composite score (p = 0.001) and chronic pain diagnosis (p = 0.032) were independently associated with a greater risk of dropping out. The probability of dropping out decreased with longer exposure to retention strategies and the dropout rate per month decreased from 4.1% to 1.8% (p = 0.04) on retention strategies.

CONCLUSIONS

Baseline characteristics allow prediction of dropping out from a clinical trial in older participants. Retention strategies has been effective at minimizing the impact of dropout-related risk factors.

TRIAL REGISTRATION

Clinical trials.gov NCT2415556 3/23/2015 (www.

CLINICALTRIALS

gov).

Cognitive impairment, perceived medication adherence, and high-risk medication use in patients with reduced kidney function: A cross-sectional analysis

Background and Aims

Reduced estimated glomerular filtration rate (eGFR < 60 ml/min/1.73 m2) is a risk factor for cognitive impairment (CI) and medication nonadherence. However, the association between CI and medication adherence in adults with reduced eGFR has not been adequately examined. Our pragmatic objectives were to assess the cross-sectional relationship between CI and self-reported medication adherence, medication number, and use of potentially high-risk medications among adults with reduced eGFR.

Methods

An observational cohort study of the epidemiology of CI in community-dwelling adults aged 45 years or older with reduced eGFR.

Results

Our analytic cohort consisted of 420 participants (202 with CI; mean age: 69.7 years) with reduced eGFR, at least one prescription medication, and nonmissing medication adherence data. Participants with CI had four times greater unadjusted odds of reporting good medication adherence than participants without CI (self-report of missing medications <4 days/month; odds ratio [OR]: 4.04, 95% confidence interval [CI]:​​​​​ 1.62-10.10). This difference persisted following adjustment for demographic factors and comorbidities (OR: 5.50, 95% CI: 1.86-16.28). Participants with CI were no more likely than participants without CI to report forgetfulness as a reason for missing medication doses. Participants with CI were, on average, taking more total (mean: 13.3 vs. 11.5, median: 12 vs. 11) and more high-risk (mean: 5.0 vs. 4.2, median: 5 vs. 4) medications than those without CI; these differences were attenuated and no longer significant following adjustment for demographics and comorbidities.

Conclusion

Given the well-documented association between CI and medication nonadherence, better self-reported medication adherence among those with CI may represent perceptions of adherence rather than actual adherence. Participants with CI were, on average, taking more total and more high-risk medications than those without CI, suggesting a possible increased risk for adverse drug events. Our results highlight the potential risks of relying on self-reported medication adherence in reduced eGFR patients with CI.

Dementia and Adherence to Anti-Diabetic Medications: A Meta-Analysis

Introduction Diabetes mellitus (DM) and dementia (DN) are common morbid disorders with high mortality, the two disorders shared the pathogenesis of proinflammation and insulin resistance. Polypharmacy is expected when DM and DN co-exist and medication adherence is essential to an effective self-care and management plan. This meta-analysis aimed to assess medication persistence among patients with diabetes and cognitive impairment (CogImp). Methods We systematically searched the literature through PubMed, Medline, Cochrane library, and the first 100 articles published in Google Scholar. We included articles publishes in English and conducted on humans, no limitation was set to the date of publication, all the articles were approached from the first published up to March 15, 2021. The keywords used were Dementia, cognitive impairment, cognitive decline, cognitive dysfunction, diabetes self-care, compliance to anti-diabetic drugs, and medication adherence. One hundred-seventy-six were identified, the 12 full texts screened, only four fulfilled the inclusion and exclusion criteria. Results The studies were published in Europe, the United States, and Asia (all were observational). The results showed no effects of dementia on medication adherence, P-value of 0.41, odd ratio: 1.09, 95% CI: 0.89-1.32, Chi-square for heterogeneity: 12.15, I² = 75%, and standard difference = 3. The P-value for heterogeneity was 0.007. The studies included 2,556 patients and 1,854 events. Conclusion No association was found between dementia and compliance to anti-diabetic medications. Further prospective studies are needed to solve the issue.

Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer's Disease

OBJECTIVE

To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer's disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).

METHODS

dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for "10 after 11." A digital pen and custom software recorded patient's drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.

RESULTS

Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25-125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.

CONCLUSION

Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

Association Between Cognitive Tests and Antiretroviral Medication Adherence in Older Adults With HIV

BACKGROUND

One of the fastest growing populations living with HIV is older adults especially those 65 years of age or older. Current antiretroviral therapy (ART) has prolonged life expectancy of persons with HIV. However, for therapy to be effective, patients need to be adherent. Over time, older persons with HIV may experience HIV-associated neurocognitive disorders or other factors that could affect ART adherence. The use of expedient cognitive tests that help measure medication adherence may be useful for the optimal care of these patients.

OBJECTIVE

To investigate the association between cognitive tests and ART adherence.

METHODS

This was a prospective study evaluating patients 65 years of age or older with HIV. Cognitive tests used included the Executive Clock-Drawing Task (CLOX) 1 and 2, Trail Making Test parts A and B, and Grooved Pegboard Test (GPB). The medication event monitoring system cap over 1 month was used as the primary measure for adherence.

RESULTS

CLOX 1 and GPB were significantly related to adherence ( P < 0.05). Comparison of the magnitude of each measure's relation to adherence suggests that the GPB is a better indicator of ability to adhere ( R = 0.514 vs R = 0.381). Conclusion and Relevance: CLOX 1 and GPB demonstrated an association with adherence in patients 65 years of age or older with HIV. Although the use of these tests to measure adherence in older persons with HIV seems promising, more research is needed to ascertain their ultimate utility.