Survival Analysis of Patients with Alzheimer's Disease: A Study Based on Data from the Korean National Health Insurance Services' Senior Cohort Database

Clinical profile and survival analysis of Alzheimer's disease patients in a Brazilian cohort

OBJECTIVE

To investigate the clinical and epidemiological characteristics of a large sample of patients with dementia due to Alzheimer's disease (AD) who were followed up at a cognitive neurology outpatient clinic.

METHODS

Retrospective, longitudinal, and descriptive design. We collected data from patients with dementia due to AD who visited the outpatient clinic of the SARAH Network of Rehabilitation Hospitals in Rio de Janeiro, Brazil, between May 2009 and June 2019. The evaluated characteristics included age of onset, sex, education, family history, comorbidities, time until diagnosis, and survival rates.

RESULTS

Overall, 1434 patients were evaluated, 74% of whom were women, with a mean age at symptom onset of 72.7 years and 75.8 at diagnosis. A positive family history was reported in 602 patients, with a first-degree relative in 86.3% of them. Hypertension was the most prevalent comorbidity, affecting 61.2% of the sample, and 16.2% were classified as having early-onset AD. The mean survival rate for the sample population was 112.8 months (9.4 years). The sample population was positively affected by dyslipidaemia.

CONCLUSIONS

This study presents a clinical and epidemiological analysis of a large and diverse group of patients with AD. The study confirms previous observations such as a higher prevalence of AD in women, low education among sufferers, and the presence of a family history. The study also found that comorbidities significantly affected patient survival and provides new data on the survival rates of patients with early and late AD in the Brazilian population.

Mortality Risks and Causes of Death by Dementia Types in a Japanese Cohort with Dementia: NCGG-Stories

BACKGROUND

Prognosis-related information regarding dementia needs to be updated, as changes in medical and long-term care environments for patients with dementia in recent decades may be improving the prognosis of the disease.

OBJECTIVE

We aimed to investigate the mortality, cause of death, and prognostic factors by types of dementia in a Japanese clinic-based cohort.

METHODS

The National Center for Geriatrics and Gerontology-Life Stories of People with Dementia consists of clinical records and prognostic data of patients who visited the Memory Clinic in Japan. Patients who attended the clinic between July 2010 and September 2018, or their close relatives, were asked about death information via a postal survey. A cohort of 3,229 patients (mean age, 76.9; female, 1,953) was classified into six groups: normal cognition (NC), mild cognitive impairment (MCI), Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration. A Cox proportional hazards model was employed to compare the mortality of each type of dementia, MCI, and NC.

RESULTS

Patients with all types of dementia and MCI had higher mortality rates than those with NC (hazard risks: 2.61-5.20). The most common cause of death was pneumonia, followed by cancer. In the MCI, AD, and DLB groups, older age, male sex, and low cognitive function were common prognostic factors but not presence of apolipoprotein E ɛ4 allele.

CONCLUSION

Our findings suggest important differences in the mortality risk and cause of death among patients with dementia, which will be useful in advanced care planning and policymaking.

Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE

Introduction

Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.

Methods

After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.

Results

Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.

Discussion

Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.

Time-to-event prediction using survival analysis methods for Alzheimer's disease progression

INTRODUCTION

Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.

METHODOLOGIES

We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.

RESULTS

The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.

CONCLUSIONS

The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Independent effects of amyloid and vascular markers on long-term functional outcomes: An 8-year longitudinal study of subcortical vascular cognitive impairment

BACKGROUND AND PURPOSE

Subcortical vascular cognitive impairment (SVCI) is characterized by the presence of cerebral small vessel disease (CSVD) markers. Some SVCI patients also show Alzheimer's disease and cerebral amyloid angiopathy markers. However, the effects of these imaging markers on long-term clinical outcomes have not yet been established. The present study, therefore, aimed to determine how these imaging markers influence functional disability and/or mortality.

METHODS

We recruited 194 participants with SVCI from the memory clinic and followed them up. All participants underwent brain magnetic resonance imaging at baseline, and 177 (91.2%) participants underwent beta-amyloid (Aβ) positron emission tomography. We examined the occurrence of ischemic or hemorrhagic strokes. We also evaluated functional disability and mortality using the modified Rankin scale. To determine the effects of imaging markers on functional disability or mortality, we used Fine and Gray competing regression or Cox regression analysis.

RESULTS

During a 8.6-year follow-up period, 46 of 194 patients (23.7%) experienced a stroke, 110 patients (56.7%) developed functional disabilities and 75 (38.6%) died. Aβ positivity (subdistribution hazard ratio [SHR] = 2.73), greater white matter hyperintensity (WMH) volume (SHR = 3.11) and ≥3 microbleeds (SHR = 2.29) at baseline were independent predictors of functional disability regardless of the occurrence of stroke. Greater WMH volume (hazard ratio = 2.07) was an independent predictor of mortality.

CONCLUSIONS

Our findings suggest that diverse imaging markers may predict long-term functional disability and mortality in patients with SVCI, which in turn may provide clinicians with a more insightful understanding of the long-term outcomes of SVCI.