Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aß) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aß plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.

Characterizing Treatment Non-responders vs. Responders in Completed Alzheimer's Disease Clinical Trials.

Alzheimer's disease (AD) patients have varying responses to AD drugs and there may be no single treatment for all AD patients. Trial after trial shows that identifying non-responsive and responsive subgroups and their corresponding moderators will provide better insights into subject selection and interpretation in future clinical trials. We aim to extensively investigate pre-treatment features that moderate treatment effect of Galantamine, Bapineuzumab, and Semagacestat from completed trial data. We obtained individual-level patient data from ten randomized clinical trials. Six Galantamine trials and two Bapineuzumab trials were from Yale University Open Data Access Project and two Semagacestat trials were from the Center for Global Clinical Research Data. We included a total of 10,948 subjects. The trials were conducted worldwide from 2001 to 2012. We estimated treatment effect using causal forest modeling on each trial. Finally, we identified important pre-treatment features that determine treatment efficacy and identified responsive or nonresponsive subgroups. As a result, patient's pre-treatment conditions that determined the treatment efficacy of Galantamine differed by dementia stages, but we consistently observed that non-responders in Galantamine trials had lower BMI (25 vs 28, P < .001) and increased ages (74 vs 68, P < .001). Responders in Bapineuzumab and Semagacestat trials had lower Aß42 levels (6.41 vs 6.53 pg/ml, P < .001) and smaller whole brain volumes (983.13 vs 1052.78 ml, P < .001). 6 'positive' treatment trials had subsets of patients who had, in fact, not responded. 4 "negative" treatment trials had subsets of patients who had, in fact, responded. This study suggests that analyzing heterogeneity in treatment effects in "positive" or "negative" trials may be a very powerful tool for identifying distinct subgroups that are responsive to treatments, which may significantly benefit future clinical trial design and interpretation.

The Impact of Routine Vaccinations on Alzheimer's Disease Risk in Persons 65 Years and Older: A Claims-Based Cohort Study using Propensity Score Matching.

BACKGROUND: Accumulating evidence suggests that adult vaccinations can reduce the risk of developing Alzheimer's disease (AD) and Alzheimer's disease related dementias. OBJECTIVE: To compare the risk for developing AD between adults with and without prior vaccination against tetanus and diphtheria, with or without pertussis (Tdap/Td); herpes zoster (HZ); or pneumococcus. METHODS: A retrospective cohort study was performed using Optum's de-identified Clinformatics® Data Mart Database. Included patients were free of dementia during a 2-year look-back period and were≥65 years old by the start of the 8-year follow-up period. We compared two similar cohorts identified using propensity score matching (PSM), one vaccinated and another unvaccinated, with Tdap/Td, HZ, or pneumococcal vaccines. We calculated the relative risk (RR) and absolute risk reduction (ARR) for developing AD. RESULTS: For the Tdap/Td vaccine, 7.2% (n = 8,370) of vaccinated patients and 10.2% (n = 11,857) of unvaccinated patients developed AD during follow-up; the RR was 0.70 (95% CI, 0.68-0.72) and ARR was 0.03 (95% CI, 0.02-0.03). For the HZ vaccine, 8.1% (n = 16,106) of vaccinated patients and 10.7% (n = 21,417) of unvaccinated patients developed AD during follow-up; the RR was 0.75 (95% CI, 0.73-0.76) and ARR was 0.02 (95% CI, 0.02-0.02). For the pneumococcal vaccine, 7.92% (n = 20,583) of vaccinated patients and 10.9% (n = 28,558) of unvaccinated patients developed AD during follow-up; the RR was 0.73 (95% CI, 0.71-0.74) and ARR was 0.02 (95% CI, 0.02-0.03). CONCLUSION: Several vaccinations, including Tdap/Td, HZ, and pneumococcal, are associated with a reduced risk for developing AD.

Do vaccinations influence the development of Alzheimer disease?

A growing literature supports a protective association between vaccines targeting an array of pathogens (e.g., influenza, pneumococcus, herpes zoster) and the risk of Alzheimer disease (AD). This article discusses the potential underlying mechanisms for this apparent protective effect of immunizations against infectious pathogens on the risk of AD; explores the basic and pharmacoepidemiologic evidence for this association, with particular attention paid to important methodological variations among the epidemiologic studies; and reviews the remaining uncertainties regarding the effects of anti-pathogen vaccines on Alzheimer disease and all-cause dementia, with recommendations for future directions to address those uncertainties.

Population-Based Mini-Mental State Examination Norms in Adults of Mexican Heritage in the Cameron County Hispanic Cohort.

BACKGROUND: Accurately identifying cognitive changes in Mexican American (MA) adults using the Mini-Mental State Examination (MMSE) requires knowledge of population-based norms for the MMSE, a scale which has widespread use in research settings. OBJECTIVE: To describe the distribution of MMSE scores in a large cohort of MA adults, assess the impact of MMSE requirements on their clinical trial eligibility, and explore which factors are most strongly associated with their MMSE scores. METHODS: Visits between 2004-2021 in the Cameron County Hispanic Cohort were analyzed. Eligible participants were ≥18 years old and of Mexican descent. MMSE distributions before and after stratification by age and years of education (YOE) were assessed, as was the proportion of trial-aged (50-85- year-old) participants with MMSE <24, a minimum MMSE cutoff most frequently used in Alzheimer's disease (AD) clinical trials. As a secondary analysis, random forest models were constructed to estimate the relative association of the MMSE with potentially relevant variables. RESULTS: The mean age of the sample set (n = 3,404) was 44.4 (SD, 16.0) years old and 64.5% female. Median MMSE was 28 (IQR, 28-29). The percentage of trial-aged participants (n = 1,267) with MMSE <24 was 18.6% overall and 54.3% among the subset with 0-4 YOE (n = 230). The five variables most associated with the MMSE in the study sample were education, age, exercise, C-reactive protein, and anxiety. CONCLUSION: The minimum MMSE cutoffs in most phase III prodromal-to-mild AD trials would exclude a significant proportion of trial-aged participants in this MA cohort, including over half of those with 0-4 YOE.

Outpatient Infection Prevention: A Practical Primer.

As more patients seek care in the outpatient setting, the opportunities for health care-acquired infections and associated outbreaks will increase. Without uptake of core infection prevention and control strategies through formal initiation of infection prevention programs, outbreaks and patient safety issues will surface. This review provides a step-wise approach for implementing an outpatient infection control program, highlighting some of the common pitfalls and high-priority areas.