COVID-19 Pandemic and Stroke Care in Patients with Dementia Compared to Other Stroke Patients.

OBJECTIVES: The primary objective of this study was to examine the impact of the COVID-19 pandemic on the quality of stroke care for patients with preexisting dementia, compared with patients who had only stroke. The secondary aim was to investigate how the quality of stroke care changed during the pandemic and post-pandemic periods compared with the pre-pandemic period in patients with preexisting dementia. DESIGN: A registry-based, nationwide cohort study in Sweden. SETTING AND PARTICIPANTS: We included patients with a first stroke between 2019 and 2022, both with and without dementia. The study periods were defined as follows: pre-pandemic (January 1, 2019, to February 29, 2020), COVID-19 pandemic (March 1, 2020, to February 24, 2022), and post-COVID-19 pandemic period (February 25, 2022, to September 19, 2022). The outcomes examined were the following quality indicators of stroke care, suggested by the national guideline of stroke care in Sweden: stroke admission site, performance of swallowing assessment, reperfusion treatment, assessment for rehabilitation, and early supported discharge. METHODS: The associations were studied through group comparisons and binary logistic regressions. RESULTS: Of the 21,795 patients with strokes, 1357 had documented preexisting dementia, and 20,438 had stroke without a dementia diagnosis. Throughout all study periods, a significantly lower proportion of patients with stroke with preexisting dementia, compared with stroke-only patients, received reperfusion treatment, assessments for rehabilitation, and early supported discharge from stroke units. In the subgroup of stroke patients with preexisting dementia, no significant associations were found regarding the quality indicators of stroke care before, during, and after the pandemic. CONCLUSIONS AND IMPLICATIONS: Disparities in quality of stroke care were observed between stroke patients with preexisting dementia and those with only stroke during the COVID-19 pandemic. However, there were no statistically significant differences in stroke care for patients with dementia across the pandemic.

Psychiatric Disorders Before and After Dementia Diagnosis.

Importance: Little is known about the specific timing and sequence of incident psychiatric comorbidities at different stages of dementia diagnosis. Objectives: To examine the temporal risk patterns of psychiatric disorders, including depression, anxiety, stress-related disorders, substance use disorders, sleep disorders, somatoform/conversion disorders, and psychotic disorders, among patients with dementia before, at the time of, and after receipt of a diagnosis. Design, Setting, and Participants: This population-based, nationwide cohort study analyzed data from 796 505 participants obtained from 6 registers between January 1, 2000, and December 31, 2017, including the Swedish registry for cognitive/dementia disorders. Patients with dementia were matched on year of birth (±3 years), sex, and region of residence with up to 4 controls. Data were analyzed between March 1, 2023, and August 31, 2023. Exposures: Any cause of dementia and dementia subtypes. Main Outcomes and Measures: Flexible parametric survival models to determine the time-dependent risk of initial diagnosis of psychiatric disorders, from 7 years prior to dementia diagnosis to 10 years after diagnosis. Subgroup analysis was conducted for psychiatric drug use among persons receiving a diagnosis of dementia from January 1, 2011, to December 31, 2012. Results: Of 796 505 patients included in the study (mean [SD] age at diagnosis, 80.2 [8.3] years; 448 869 (56.4%) female), 209 245 had dementia, whereas 587 260 did not, across 7 824 616 person-years. The relative risk of psychiatric disorders was consistently higher among patients with dementia compared with control participants and began to increase from 3 years before diagnosis (hazard ratio, [HR], 1.72; 95% CI, 1.67-1.76), peaked during the week after diagnosis (HR, 4.74; 95% CI, 4.21-5.34), and decreased rapidly thereafter. Decreased risk relative to controls was observed from 5 years after diagnosis (HR, 0.93; 95% CI, 0.87-0.98). The results were similar for Alzheimer disease, mixed dementia, vascular dementia and unspecified dementia. Among patients with dementia, markedly elevated use of psychiatric medications was observed in the year leading up to the dementia diagnosis and peaked 6 months after diagnosis. For example, antidepressant use was persistently higher among patients with dementia compared with controls, and the difference increased from 2 years before dementia diagnosis (15.9% vs 7.9%, P < .001), peaked approximately 6 months after dementia diagnosis (29.1% vs 9.7%, P < .001), and then decreased slowly from 3 years after diagnosis but remained higher than controls 5 years after diagnosis (16.4% vs 6.9%, P < .001). Conclusions and Relevance: The findings of this cohort study that patients with dementia had markedly increased risks of psychiatric disorders both before and after dementia diagnosis highlight the significance of incorporating psychiatric preventative and management interventions for individuals with dementia across various diagnostic stages.

External Causes of Death From Death Certificates in Patients With Dementia.

OBJECTIVES: We aim to analyze the risk of death from specific external causes, including falls, complications of medical and surgical care, unintentional injuries, and suicide, in dementia patients. DESIGN: Swedish nationwide cohort study integrating 6 registers from May 1, 2007, through December 31, 2018, including the Swedish Registry for Cognitive/Dementia Disorders (SveDem). SETTING AND PARTICIPANTS: Population-based study. Patients diagnosed with dementia from 2007 to 2018 and up to 4 controls matched on year of birth (±3 years), sex, and region of residence. METHODS: The exposures of this study were diagnosis of dementia and dementia subtypes. Number of deaths and causes of mortality were obtained from death certificates compiled into the Cause of Death Register. Hazard ratios (HRs) and 95% CIs were estimated using Cox and flexible models, adjusted for sociodemographics, medical and psychiatric disorders. RESULTS: The study population included 235,085 patients with dementia [96,760 men (41.2%); mean age 81.5 (SD 8.5) years] and 771,019 control participants [341,994 men (44.4%); mean age 79.9 (SD 8.6) years], over 3,721,687 person-years. Compared with control participants, patients with dementia presented increased risk for unintentional injuries (HR 3.30, 95% CI 3.19-3.40) and falls (HR 2.67, 95% CI 2.54-2.80) during old age (≥75 y), and suicide (HR 1.56, 95% CI 1.02-2.39) in middle age (<65 y). Suicide risk was 5.04 times higher (HR 6.04, 95% CI 4.22-8.66) in patients with both dementia and 2 or more psychiatric disorders relative to controls (incidence rate per person-years, 1.6 vs 0.3). For dementia subtypes, frontotemporal dementia had the highest risks of unintentional injuries (HR 4.28, 95% CI 2.80-6.52) and falls (HR 3.83, 95% CI 1.98-7.41), whereas subjects with mixed dementia were less likely to die from suicide (HR 0.11, 95% CI 0.03-0.46) and complications of medical and surgical care (HR 0.53, 95% CI 0.40-0.70) compared to controls. CONCLUSIONS AND IMPLICATIONS: Suicide risk screening and psychiatric disorders management in early-onset dementia and early interventions for unintentional injuries and falls prevention in older dementia patients should be provided.

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study. Sixty variables were considered as potential predictors of mortality risk, such as age at dementia diagnosis, dementia type, sex, body mass index (BMI), mini-mental state examination (MMSE) score, time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications, comorbidities, and some specific medications for chronic comorbidities (e.g., cardiovascular disease). We applied sparsity-inducing penalties for three ML algorithms and identified twenty important variables for the binary classification task in mortality risk prediction and fifteen variables to predict time to death. Area-under-ROC curve (AUC) measure was used to evaluate the classification algorithms. Then, an unsupervised clustering algorithm was applied on the set of twenty-selected variables to find two main clusters which accurately matched surviving and dead patient clusters. A support-vector-machines with an appropriate sparsity penalty provided the classification of mortality risk with accuracy = 0.7077, AUROC = 0.7375, sensitivity = 0.6436, and specificity = 0.740. Across three ML algorithms, the majority of the identified twenty variables were compatible with literature and with our previous studies on SveDem. We also found new variables which were not previously reported in literature as associated with mortality in dementia. Performance of basic dementia diagnostic work-up, time from referral to initiation of work-up, and time from initiation of work-up to diagnosis were found to be elements of the diagnostic process identified by the ML algorithms. The median follow-up time was 1053 (IQR = 516-1771) days in surviving and 1125 (IQR = 605-1770) days in dead patients. For prediction of time to death, the CoxBoost model identified 15 variables and classified them in order of importance. These highly important variables were age at diagnosis, MMSE score, sex, BMI, and Charlson Comorbidity Index with selection scores of 23%, 15%, 14%, 12% and 10%, respectively. This study demonstrates the potential of sparsity-inducing ML algorithms in improving our understanding of mortality risk factors in dementia patients and their application in clinical settings. Moreover, ML methods can be used as a complement to traditional statistical methods.