Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia

Around the EQUATOR With Clin-STAR: AI-Based Randomized Controlled Trial Challenges and Opportunities in Aging Research

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rapid growth of Artificial Intelligence (AI) based interventions in the past 10 years, the CONSORT-AI extension was created in 2020 to provide guidelines for AI-based randomized controlled trials (RCT). The Clin-STAR "Around the EQUATOR" series features existing reported standards while also highlighting the inherent complexities of research involving research of older participants. In this work, we propose that when designing AI-based RCTs involving older adults, researchers adopt a conceptual framework (CONSORT-AI-5Ms) designed around the 5Ms (Mind, Mobility, Medications, Matters most, and Multi-complexity) of Age-Friendly Healthcare Systems. Employing the 5Ms in this context, we provide a detailed rationale and include specific examples of challenges and potential solutions to maximize the impact and value of AI RCTs in an older adult population. By combining the original intent of CONSORT-AI with the 5Ms framework, CONSORT-AI-5Ms provides a patient-centered and equitable perspective to consider when designing AI-based RCTs to address the diverse needs and challenges associated with geriatric care.

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity

PURPOSE

This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to elucidate the interplay between mental health, lifestyle, and physical activity while comparing the effectiveness of the RSF model against the traditional Cox proportional hazards model in predicting CVD mortality.

METHODS

Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2014 were used for comprehensive depression screening. The Patient Health Questionnaire-9 (PHQ-9) was employed to categorize depression severity levels among participants. The final cohort included 9271 participants, selected after excluding those with incomplete data. Participants were followed up for a median of 7.1 years, and cardiovascular mortality was assessed up to December 31, 2019. We employed the RSF model to predict cardiovascular mortality with high effectiveness and precision. And to ensure comparability, we developed the traditional Cox proportional hazards model using the same set of predictors.

RESULTS

RSF model outperformed the Cox proportional hazards model in predicting cardiovascular mortality among hypertensive patients with varying depression levels. The RSF model's integrated area under the curve (iAUC) scores were 0.842, 0.893, and 0.760 for none, mild, and severe depression, respectively, surpassing the Cox model's scores of 0.826, 0.805, and 0.746.

CONCLUSION

The RSF model provides a more accurate prediction of CVD mortality among hypertensive patients with varying degrees of depression, offering a valuable tool for personalized patient care. Its ability to stratify patients into risk categories can assist healthcare professionals in making informed decisions, underscoring the potential of machine learning in public health and clinical settings. This model demonstrates particular utility in settings where detailed, patient-specific risk assessments are critical for managing long-term health outcomes. Future research should focus on external validation and integration of more diverse variables to enhance predictive power.

Study protocol: type II hybrid effectiveness-implementation study of routine functional status screening in VA primary care

BACKGROUND

Maintaining functional status, defined as the ability to perform daily activities such as bathing, dressing, and preparing meals, is central to older adults' quality of life, health, and ability to remain independent. Identifying functional impairments - defined as having difficulty or needing help performing these activities - is essential for clinicians to provide optimal care to older adults, and on a population level, understanding function can help anticipate service needs. Yet uptake of standardized measurement of functional status into routine patient care has been slow and inconsistent due to the burden posed by current tools. The goal of the Patient-Aligned Care Team (PACT) Functional Status Screening Initiative is to implement and evaluate a patient-centered, low-burden intervention to improve identification and management of functional impairment among older veterans in Veterans Health Administration (VHA) primary care settings.

METHODS

We will conduct a hybrid type 2 implementation-effectiveness cluster-randomized adaptive trial at 8 VHA sites using the Practical, Robust Implementation and Sustainability Model (PRISM) to guide implementation and evaluation. During a Pre-Implementation phase, we will engage clinical partners and develop local adaptations to maximize intervention-setting fit. During an Implementation phase, we will launch a standard bundle of implementation strategies (coalition building, champions, technical assistance) and system-level audit and feedback, identify sites with low uptake, and randomize those sites to receive continued standard vs. enhanced strategies (standard strategies plus clinician-level audit and feedback). The primary implementation outcome is reach (proportion of eligible patients at each site who receive screening/assessment) and the primary effectiveness outcome is appropriate management of impairment (proportion of patients with identified impairments who receive related referrals).

DISCUSSION

Implementing routine measurement of functional status in primary care has the potential to improve identification and management of functional impairment for older veterans. Improved management includes increasing access to services and supports for veterans and family caregivers, reducing potentially preventable acute care utilization, and allowing veterans to live in the least restrictive setting for as long as possible. Implementation will also provide data to inform the delivery of proactive interventions to prevent and delay development of functional impairment and improve quality of life, health, and independence.

TRIAL REGISTRATION

Registered at ClinicalTrials.gov on May 7, 2024, at NCT06404970 ( https://clinicaltrials.gov/ ).

REPORTING GUIDELINES

Standards for Reporting Implementation Studies (Additional file 1).

Near-infrared brain functional characteristics of mild cognitive impairment with sleep disorders

BACKGROUND

Mild cognitive impairment (MCI) has a high risk of progression to Alzheimer's disease. The disease is often accompanied by sleep disorders, and whether sleep disorders have an effect on brain function in patients with MCI is unclear.

AIM

To explore the near-infrared brain function characteristics of MCI with sleep disorders.

METHODS

A total of 120 patients with MCI (MCI group) and 50 healthy subjects (control group) were selected. All subjects underwent the functional near-infrared spectroscopy test. Collect baseline data, Mini-Mental State Examination, Montreal Cognitive Assessment scale, fatigue severity scale (FSS) score, sleep parameter, and oxyhemoglobin (Oxy-Hb) concentration and peak time of functional near-infrared spectroscopy test during the task period. The relationship between Oxy-Hb concentration and related indexes was analyzed by Pearson or Spearmen correlation.

RESULTS

Compared with the control group, the FSS score of the MCI group was higher (t = 11.310), and the scores of Pittsburgh sleep quality index, sleep time, sleep efficiency, nocturnal sleep disturbance, and daytime dysfunction were higher (Z = -10.518, -10.368, -9.035, -10.661, -10.088). Subjective sleep quality and total sleep time scores were lower (Z = -11.592, -9.924). The sleep efficiency of the MCI group was lower, and the awakening frequency, rem sleep latency period, total sleep time, and oxygen desaturation index were higher (t = 5.969, 5.829, 2.887, 3.003, 5.937). The Oxy-Hb concentration at T0, T1, and T2 in the MCI group was lower (t = 14.940, 11.280, 5.721), and the peak time was higher (t = 18.800, 13.350, 9.827). In MCI patients, the concentration of Oxy-Hb during T0 was negatively correlated with the scores of Pittsburgh sleep quality index, sleep time, total sleep time, and sleep efficiency (r = -0.611, -0.388, -0.563, -0.356). It was positively correlated with sleep efficiency and total sleep time (r = 0.754, 0.650), and negatively correlated with oxygen desaturation index (r = -0.561) and FSS score (r = -0.526). All comparisons were P < 0.05.

CONCLUSION

Patients with MCI and sleep disorders have lower near-infrared brain function than normal people, which is related to sleep quality. Clinically, a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect.

Prediction of physiological status, community participation, and daily activity function to sleep quality for outpatient dynapenic older people

BACKGROUND

The global aging population has increased dynapenia prevalence, leading to mobility issues and poor sleep quality among older adults. Despite its impact, research on sleep quality in dynapenic outpatients is limited. This study investigates how physiological status, community participation, and daily activity function influence sleep quality in this group.

METHODS

This cross-sectional study employed purposive sampling to collect data from 192 dynapenic older outpatients in October 2022, assessing their basic attributes, physiological status, community participation, daily activity function, and sleep quality. Data were analyzed using SPSS 25.0 for descriptive statistics, independent samples t-tests, chi-square tests, and logistic regression analysis.

RESULTS

Findings indicated significant correlations between sleep quality and gender (X2 = 11.340, p < .001), occupational status (X2 = 13.378, p < .05), residence (X2 = 6.265, p < .05), medication intake (X2 = 7.250, p < .05), smoking history (X2 = 6.695, p < .01), instrumental activities of daily living (X2 = 12.556, p < .01), activities of daily living (t = 2.74, p < .01), instrumental activities of daily living (t = 3.60, p < .001), skeletal muscle mass (t = 2.94, p < .01), skeletal muscle index (t = 2.65, p < .01), grip strength (t = 3.61, p < .001), and walking speed (t = 2.09, p < .05). Furthermore, the type of occupational status (OR = 6.608, 95% CI = 1.124-3.744, p < .05), medication intake (OR = 3.916, 95% CI = 1.682-9.114, p < .05), and grip strength (OR = 0.891, 95% CI = 0.797-0.996, p < .05) were significant predictors of sleep quality in dynapenic older patients.

CONCLUSION

This cross-sectional study reveals significant correlations between sleep quality and key factors such as physiological status, community participation, and daily functional activities in older adults with dynapenia. These findings underscore the importance of addressing these determinants to enhance sleep quality in this population.

Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review

Importance

People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer.

Objective

To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice.

Evidence Review

MEDLINE, Embase, and the Cochrane Library were searched for studies published between January 1, 2004, and September 30, 2024. Studies were included if they created or evaluated mortality prediction models for people who developed kidney failure, whether treated or not treated with kidney replacement with hemodialysis or peritoneal dialysis. Studies including exclusively kidney transplant recipients were excluded. Two reviewers independently extracted data and graded each study at low, high, or unclear risk of bias and applicability using recommended checklists and tools. Reviewers used the Prediction Model Risk of Bias Assessment Tool and followed prespecified questions about study design, prediction framework, modeling algorithm, performance evaluation, and model deployment. Analyses were completed between January and October 2024.

Findings

A total of 7184 unique abstracts were screened for eligibility. Of these, 77 were selected for full-text review, and 50 studies that created all-cause mortality prediction models were included, with 2 963 157 total participants, who had a median (range) age of 64 (52-81) years. Studies had a median (range) proportion of women of 42% (2%-54%). Included studies were at high risk of bias due to inadequate selection of study population (27 studies [54%]), shortcomings in methods of measurement of predictors (15 [30%]) and outcome (12 [24%]), and flaws in the analysis strategy (50 [100%]). Concerns for applicability were also high, as study participants (31 [62%]), predictors (17 [34%]), and outcome (5 [10%]) did not fit the intended target clinical setting. One study (2%) reported decision curve analysis, and 15 (30%) included a tool to enhance model usability.

Conclusions and Relevance

According to this systematic review of 50 studies, published mortality prediction models were at high risk of bias and had applicability concerns for clinical practice. New mortality prediction models are needed to inform treatment decisions in people with kidney failure.

Development and validation of a clinical prediction tool to estimate survival in community-dwelling adults living with dementia: a protocol

INTRODUCTION

A clinical prediction tool to estimate life expectancy in community-dwelling individuals living with dementia could inform healthcare decision-making and prompt future planning. An existing Ontario-based tool for community-dwelling elderly individuals does not perform well in people living with dementia specifically. This study seeks to develop and validate a clinical prediction tool to estimate survival in community-dwelling individuals living with dementia receiving home care in Ontario, Canada.

METHODS AND ANALYSIS

This will be a population-level retrospective cohort study that will use data in linked healthcare administrative databases at ICES. Specifically, data that are routinely collected from regularly administered assessments for home care will be used. Community-dwelling individuals living with dementia receiving home care at any point between April 2010 and March 2020 will be included (N≈200 000). The model will be developed in the derivation cohort (N≈140 000), which includes individuals with a randomly selected home care assessment between 2010 and 2017. The outcome variable will be survival time from index assessment. The selection of predictor variables will be fully prespecified and literature/expert-informed. The model will be estimated using a Cox proportional hazards model. The model's performance will be assessed in a temporally distinct validation cohort (N≈60 000), which includes individuals with an assessment between 2018 and 2020. Overall performance will be assessed using Nagelkerke's R2, discrimination using the concordance statistic and calibration using the calibration curve. Overfitting will be assessed visually and statistically. Model performance will be assessed in the validation cohort and in prespecified subgroups.

ETHICS AND DISSEMINATION

The study received research ethics board approval from the Sunnybrook Health Sciences Centre (SUN-6138). Abstracts of the project will be submitted to academic conferences, and a manuscript thereof will be submitted to a peer-reviewed journal for publication. The model will be disseminated on a publicly accessible website (www.projectbiglife.com).

TRIAL REGISTRATION NUMBER

NCT06266325 (clinicaltrials.gov).

Top Ten Tips Palliative Care Clinicians Should Know About Hospice Live Discharge

Hospice care is designed to support the medical and psychosocial needs of individuals with serious illness and their caregivers through the dying process. Some individuals, though, leave hospice prior to death, generally referred to as disenrollment or a "live discharge." Live discharge from hospice is a common and often distressing issue for hospice patients, their caregivers, and also for hospice professionals and agencies. This paper discusses common issues surrounding live discharge that clinicians and other healthcare professionals should consider when dealing with live discharge in their own clinical practices. Where applicable, we provide practical steps for hospice and palliative care clinicians to better support patients and families through this critical care transition. Further, we offer strategic directions interprofessional clinicians can take to affect systemic change to improve live discharge experiences.

External Validation of the Charlson Comorbidity Index-based Model for Survival Prediction in Thai Patients Diagnosed with Dementia

BACKGROUND

The Charlson Comorbidity Index (CCI) is commonly employed for predicting mortality. Nonetheless, its performance has rarely been evaluated in patients with dementia. This study aimed to examine the predictive capability of the CCI-based model for survival prediction in Thai patients diagnosed with dementia.

METHODS

An external validation study was conducted using retrospective data from adults with dementia who had visited the outpatient departments at Maharaj Nakorn Chiang Mai Hospital between 2006 and 2012. The data obtained from electronic medical records included age, gender, date of dementia diagnosis and death, types of dementia, and comorbidities at the time of dementia diagnosis. The discriminative ability and calibration of the CCI-based model were estimated using Harrell's C Discrimination Index and visualized with calibration plot. As the initial performance did not meet satisfaction, model updating and recalibration were performed.

RESULTS

Of 702 patients, 56.9% were female. The mean age at dementia diagnosis was 75.22 (SD 9.75) year-old. During external validation, Harrell's C-statistic of the CCI-based model was 0.58 (95% CI, 0.54-0.61). The model showed poor external calibration. Model updating was subsequently performed. All updated models demonstrated a modest increase in Harrell's C-statistic. Temporal recalibration did not significantly improve the calibration of any of the updated models.

CONCLUSION

The CCI-based model exhibited fair discriminative ability and poor calibration for predicting survival in Thai patients diagnosed with dementia. Despite attempts at model updating, significant improvements were not achieved. Therefore, it is important to consider the incorporation of other influential prognostic factors.

Holistic and Personalized Strategies for Managing in Elderly Type 2 Diabetes Patients

Due to increased life expectancy and lifestyle changes, the prevalence of diabetes among the elderly in Korea is continuously rising, as is the associated public health burden. Diabetes management in elderly patients is complicated by age-related physiological changes, sarcopenia characterized by loss of muscle mass and function, comorbidities, and varying levels of functional, cognitive, and mobility abilities that lead to frailty. Moreover, elderly patients with diabetes frequently face multiple chronic conditions that elevate their risk of cardiovascular diseases, cancer, and mortality; they are also prone to complications such as hyperglycemic hyperosmolar state, diabetic ketoacidosis, and severe hypoglycemia. This review examines the characteristics of and management approaches for diabetes in the elderly, and advocates for a comprehensive yet personalized strategy.

Prognoses Associated With Palliative Performance Scale Scores in Modern Palliative Care Practice

Importance

The Palliative Performance Scale (PPS) is one of the most widely used prognostic tools for patients with serious illness. However, current prognostic estimates associated with PPS scores are based on data that are over a decade old.

Objective

To generate updated prognostic estimates by PPS score, care setting, and illness category, and examine how well PPS predicts short- and longer-term survival.

Design, Setting, and Participants

This prognostic study was conducted at a large academic medical center with robust inpatient and outpatient palliative care practices using electronic health record data linked with data from California Vital Records. Eligible participants included patients who received a palliative care consultation between January 1, 2018, and December 31, 2020. Data analysis was conducted from November 2022 to February 2024.

Exposure

Palliative care consultation with a PPS score documented.

Main Outcomes and Measures

The primary outcomes were predicted 1-, 6-, and 12-month mortality and median survival of patients by PPS score in the inpatient and outpatient settings, and performance of the PPS across a range of survival times. In subgroup analyses, mortality risk by PPS score was estimated in patients with cancer vs noncancer illnesses and those seen in-person vs by video telemedicine in the outpatient setting.

Results

Overall, 4779 patients (mean [SD] age, 63.5 [14.8] years; 2437 female [51.0%] and 2342 male [49.0%]) had a palliative care consultation with a PPS score documented. Of these patients, 2276 were seen in the inpatient setting and 3080 were seen in the outpatient setting. In both the inpatient and outpatient settings, 1-, 6-, and 12-month mortality were higher and median survival was shorter for patients with lower PPS scores. Prognostic estimates associated with PPS scores were substantially longer (2.3- to 11.7-fold) than previous estimates commonly used by clinicians. The PPS had good ability to discriminate between patients who lived and those who died in the inpatient setting (integrated time-dependent area under the curve [iAUC], 0.74) but its discriminative ability was lower in the outpatient setting (iAUC, 0.67). The PPS better predicted 1-month survival than longer-term survival. Mortality rates were higher for patients with cancer than other serious illnesses at most PPS levels.

Conclusions and Relevance

In this prognostic study, prognostic estimates associated with PPS scores were substantially longer than previous estimates commonly used by clinicians. Based on these findings, an online calculator was updated to assist clinicians in reaching prognostic estimates that are more consistent with modern palliative care practice and specific to the patient's setting and diagnosis group.

Improving measurement of functional status among older adults in primary care: A pilot study

Despite its importance for clinical care and outcomes among older adults, functional status-the ability to perform basic activities of daily living (ADLs) and instrumental ADLs (IADLs)-is seldom routinely measured in primary care settings. The objective of this study was to pilot test a person-centered, interprofessional intervention to improve identification and management of functional impairment among older adults in Veterans Affairs (VA) primary care practices. The four-component intervention included (1) an interprofessional educational session; (2) routine, standardized functional status measurement among patients aged ≥75; (3) annual screening by nurses using a standardized instrument and follow-up assessment by primary care providers; and (4) electronic tools and templates to facilitate increased identification and improved management of functional impairment. Surveys, semi-structured interviews, and electronic health record data were used to measure implementation outcomes (appropriateness, acceptability and satisfaction, feasibility, fidelity, adoption/reach, sustainability). We analyzed qualitative interviews using rapid qualitative analysis. During the study period, all 959 eligible patients were screened (100% reach), of whom 7.3% (n = 58) reported difficulty or needing help with ≥1 ADL and 11.8% (n = 113) reported difficulty or needing help with ≥1 IADL. In a chart review among a subset of 50 patients with functional impairment, 78% percent of clinician notes for the visit when screening was completed had content related to function, and 48% of patients had referrals ordered to address impairments (e.g., physical therapy) within 1 week. Clinicians highly rated the quality of the educational session and reported increased ability to measure and communicate about function. Clinicians and patients reported that the intervention was appropriate, acceptable, and feasible to complete, even during the COVID pandemic. These findings suggest that this intervention is a promising approach to improve identification and management of functional impairment for older patients in primary care. Broader implementation and evaluation of this intervention is currently underway.

Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool

OBJECTIVES

 To assess primary care physicians' (PCPs) perception of the need for serious illness conversations (SIC) or other palliative care interventions in patients flagged by a machine learning tool for high 1-year mortality risk.

METHODS

 We surveyed PCPs from four Brigham and Women's Hospital primary care practice sites. Multiple mortality prediction algorithms were ensembled to assess adult patients of these PCPs who were either enrolled in the hospital's integrated care management program or had one of several chronic conditions. The patients were classified as high or low risk of 1-year mortality. A blinded survey had PCPs evaluate these patients for palliative care needs. We measured PCP and machine learning tool agreement regarding patients' need for an SIC/elevated risk of mortality.

RESULTS

 Of 66 PCPs, 20 (30.3%) participated in the survey. Out of 312 patients evaluated, 60.6% were female, with a mean (standard deviation [SD]) age of 69.3 (17.5) years, and a mean (SD) Charlson Comorbidity Index of 2.80 (2.89). The machine learning tool identified 162 (51.9%) patients as high risk. Excluding deceased or unfamiliar patients, PCPs felt that an SIC was appropriate for 179 patients; the machine learning tool flagged 123 of these patients as high risk (68.7% concordance). For 105 patients whom PCPs deemed SIC unnecessary, the tool classified 83 as low risk (79.1% concordance). There was substantial agreement between PCPs and the tool (Gwet's agreement coefficient of 0.640).

CONCLUSIONS

 A machine learning mortality prediction tool offers promise as a clinical decision aid, helping clinicians pinpoint patients needing palliative care interventions.

The Impact of Implementation of Palliative, Non-Operative Management on Mortality of Operatively Treated Geriatric Hip Fracture Patients: A Retrospective Cohort Study

(1) Background: Hip fracture patients with very limited life expectancy can opt for non-operative management (NOM) within a palliative care context. The implementation of NOM in the palliative context may affect the mortality of the operatively treated population. This retrospective cohort study aimed to determine whether the operatively treated geriatric hip fracture population would have a lower in-hospital mortality rate and fewer postoperative complications after the introduction of NOM within a palliative care context for patients with very limited life expectancy. (2) Methods: Data from 1 February 2019 to 1 February 2022 of patients aged 70 years or older were analyzed to give a comparison between patients before and after implementation of NOM within a palliative care context. (3) Results: Comparison between 550 patients before and 485 patients after implementation showed no significant difference in in-hospital or 1-year mortality rates (2.9% vs. 1.4%, p = 0.139; 22.4% vs. 20.2%, p = 0.404, respectively). Notably, post-implementation, fewer patients had prior dementia diagnoses (15% vs. 21%, p = 0.010), and intensive care unit admissions decreased (3.5% vs. 1.2%, p = 0.025). (4) Conclusions: The implementation of NOM within a palliative care context did not significantly reduce mortality or complications. However, NOM within palliative care is deemed a more patient-centered approach for geriatric hip fracture patients with very limited life expectancy.

What influences life expectancy in people with dementia? Social support as an emerging protective factor

BACKGROUND

The aim of this study was to investigate the role of support from the social environment for the life expectancy in people with dementia beyond well-established individual demographic and clinical predictors over a period of up to 8 years.

METHODS

The analyses are based on data from 500 community-dwelling individuals in Germany who tested positive for dementia and were followed up for up to 8 years. Life expectancy was examined in relation to perceived social support as well as well-established socio-demographic (age, sex) and clinical predictors (cognitive status, functional status, comorbidities), using Cox regressions.

RESULTS

Greater support from the social environment reduced the risk of mortality (hazard ratio [HR]: 0.78; 95% confidence interval [CI]: 0.63-0.98), with the role of emotional support being particularly important. Furthermore, higher age was associated with an increased mortality risk (HR: 1.08; 95% CI: 1.05-1.11), while female sex (HR: 0.64; 95% CI: 0.48-0.85) and higher cognitive (HR: 0.96; 95% CI: 0.93-0.98) and functional status (HR: 0.91; 95% CI: 0.86-0.97) were associated with higher life expectancy.

CONCLUSION

Our study provides novel evidence that less support from the social environment, especially emotional support, is a risk factor for shorter life expectancy in people with dementia-beyond known clinical factors. Not only the clinical and caregiving needs but also their psychosocial needs of individuals with dementia should be emphasised.

Gender-specific association of the accumulation of chronic conditions and disability in activities of daily living with depressive symptoms

BACKGROUND

In the era of rapid aging with a rising prevalence of multimorbidity, complex interactions between physical and psychological conditions have challenged the health care system. However, little is known about the association of the accumulation of chronic conditions and disability in activities of daily living with depressive symptoms, especially in developed countries.

METHODS

This population-based cohort study used data from the Health and Retirement Study. A total of 22,335 middle-aged and older adults participated in the 2014 (T1), 2016 (T2), and 2018 (T3) waves of the cohort were included. The accumulation of chronic conditions and disability were defined as the number of chronic diseases and the five activities of daily living. Depressive symptoms were measured by the Center for Epidemiologic Studies Depression Scale. A longitudinal mediation model with a cross-lagged panel model was run. As robust check, the models were applied with a longer follow-up period (from 2012 to 2018). Additionally, results were estimated in China.

RESULTS

Bidirectional associations have been found among the accumulation of chronic conditions, disability, and depressive symptoms, especially between disability and depression. Disability (T2) mediated 11.11 % and 16.87 % of the association between the accumulation of chronic conditions (T1) and depression (T3) for men and women in the United States. The results were consistent in robust analysis.

CONCLUSIONS

This study found that men and women routinely experienced disability and depressive symptoms because of the accumulation of chronic conditions. In terms of depressive symptoms, women were more sensitive to the accumulation of chronic conditions through disability.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.Communicated by Ramaswamy H. Sarma.

Prediction of survival of persons with advanced dementia using the advanced dementia prognostic tool: A 2-year prospective study

BACKGROUND

In this prospective study, we evaluated the usefulness of the advanced dementia prognostic tool (ADEPT) for estimating the 2-year survival of persons with advanced dementia (AD) in China.

METHODS

The study predicted the 2-year mortality of 115 persons with AD using the ADEPT score.

RESULTS

In total, 115 persons with AD were included in the study. Of these persons, 48 died. The mean ADEPT score was 13.0. The AUROC for the prediction of the 2-year mortality rate using the ADEPT score was 0.62. The optimal threshold of the ADEPT score was 11.2, which had an AUROC of 0.63, specificity of 41.8, and sensitivity of 83.3.

CONCLUSIONS

The ADEPT score based on a threshold of 11.2 may serve as a prognostic tool to determine the 2-year survival rate of persons with AD in Chongqing, China. However, further studies are needed to explore the nature of this relationship.

Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study

OBJECTIVE

The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age.

DESIGN

Prospective, multicentre, observational study.

SETTING

Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered.

PARTICIPANTS

Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions.

RESULTS

A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality.

CONCLUSIONS

This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation.

Outcomes and cost analysis of patients with dementia in the intensive care unit: a population-based cohort study

BACKGROUND

Dementia is a neurological syndrome affecting the growing elderly population. While patients with dementia are known to require significant hospital resources, little is known regarding the outcomes and costs of patients admitted to the intensive care unit (ICU) with dementia.

METHODS

We conducted a population-based retrospective cohort study of patients with dementia admitted to the ICU in Ontario, Canada from 2016 to 2019. We described the characteristics and outcomes of these patients alongside those with dementia admitted to non-ICU hospital settings. The primary outcome was hospital mortality but we also assessed length of stay (LOS), discharge disposition, and costs.

RESULTS

Among 114,844 patients with dementia, 11,341 (9.9%) were admitted to the ICU. ICU patients were younger, more comorbid, and had less cognitive impairment (81.8 years, 22.8% had ≥ 3 comorbidities, 47.5% with moderate-severe dementia), compared to those in non-ICU settings (84.2 years, 15.0% had ≥ 3 comorbidities, 54.1% with moderate-severe dementia). Total mean LOS for patients in the ICU group was nearly 20 days, compared to nearly 14 days for the acute care group. Mortality in hospital was nearly three-fold greater in the ICU group compared to non-ICU group (22.2% vs. 8.8%). Total healthcare costs were increased for patients admitted to ICU vs. those in the non-ICU group ($67,201 vs. $54,080).

CONCLUSIONS

We find that patients with dementia admitted to the ICU have longer length of stay, higher in-hospital mortality, and higher total healthcare costs. As our study is primarily descriptive, future studies should investigate comprehensive goals of care planning, severity of illness, preventable costs, and optimizing quality of life in this high risk and vulnerable population.

Changes in the Use of Long-Term Medications Following Incident Dementia Diagnosis

Importance

Dementia is a life-altering diagnosis that may affect medication safety and goals for chronic disease management.

Objective

To examine changes in medication use following an incident dementia diagnosis among community-dwelling older adults.

Design, Setting, and Participants

In this cohort study of adults aged 67 years or older enrolled in traditional Medicare and Medicare Part D, patients with incident dementia diagnosed between January 2012 and December 2018 were matched to control patients based on demographics, geographic location, and baseline medication count. The index date was defined as the date of first dementia diagnosis or, for controls, the date of the closest office visit. Data were analyzed from August 2021 to June 2023.

Exposure

Incident dementia diagnosis.

Main Outcomes and Measures

The main outcomes were overall medication counts and use of cardiometabolic, central nervous system (CNS)-active, and anticholinergic medications. A comparative time-series analysis was conducted to examine quarterly changes in medication use in the year before through the year following the index date.

Results

The study included 266 675 adults with incident dementia and 266 675 control adults; in both groups, 65.1% were aged 80 years or older (mean [SD] age, 82.2 [7.1] years) and 67.8% were female. At baseline, patients with incident dementia were more likely than controls to use CNS-active medications (54.32% vs 48.39%) and anticholinergic medications (17.79% vs 15.96%) and less likely to use most cardiometabolic medications (eg, diabetes medications, 31.19% vs 36.45%). Immediately following the index date, the cohort with dementia had a greater increase in mean number of medications used (0.41 vs -0.06; difference, 0.46 [95% CI, 0.27-0.66]) and in the proportion of patients using CNS-active medications (absolute change, 3.44% vs 0.79%; difference, 2.65% [95% CI, 0.85%-4.45%]) owing to an increased use of antipsychotics, antidepressants, and antiepileptics. The cohort with dementia also had a modestly greater decline in use of anticholinergic medications (quarterly change in use, -0.53% vs -0.21%; difference, -0.32% [95% CI, -0.55% to -0.08%]) and most cardiometabolic medications (eg, quarterly change in antihypertensive use: -0.84% vs -0.40%; difference, -0.44% [95% CI, -0.64% to -0.25%]). One year after diagnosis, 75.2% of the cohort with dementia were using 5 or more medications (2.8% increase).

Conclusions and Relevance

In this cohort study of Medicare Part D beneficiaries, following an incident dementia diagnosis, patients were more likely to initiate CNS-active medications and modestly more likely to discontinue cardiometabolic and anticholinergic medications compared with the control group. These findings suggest missed opportunities to reduce burdensome polypharmacy by deprescribing long-term medications with high safety risks or limited likelihood of benefit or that may be associated with impaired cognition.

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis

OBJECTIVE

Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk.

METHODS

This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed. Latent class analysis was performed using 16 common chronic conditions to identify mortality risk among potentially different latent classes. Logistic regression was performed to determine the adjusted association of the determined latent classes with the 5-year mortality rate.

RESULTS

With adjustment for age, a three-class model was identified, with 42.7% of participants classified as "low comorbidity class (cluster 1)", 44.2% as "cardiometabolic multimorbidity class (cluster 2)", and 13.1% as "FRINGED class (cluster 3, characterized by FRacture, Infection, NasoGastric feeding, and bleEDing over upper gastrointestinal tract)." The incidence of 5-year mortality was 17.6% in cluster 1, 26.7% in cluster 2, and 59.6% in cluster 3. Compared with cluster 1, the odds ratio for mortality was 9.828 (95% confidence interval [CI]=6.708-14.401; p<0.001) in cluster 2 and 1.582 (95% CI=1.281-1.953; p<0.001) in cluster 3.

CONCLUSION

Among patients with dementia, the risk for 5-year mortality was highest in the subpopulation characterized by fracture, urinary and pulmonary infection, upper gastrointestinal bleeding, and nasogastric intubation, rather than cancer or cardiometabolic comorbidities. These findings may improve decision-making and advance care planning for patients with dementia.

Validation of a Multivariate Prediction Model of the Clinical Progression of Alzheimer's Disease in a Community-Dwelling Multiethnic Cohort

BACKGROUND

The major aims of the three Predictors Studies have been to further our understanding of Alzheimer's disease (AD) progression sufficiently to predict the length of time from disease onset to major disease outcomes in individual patients with AD.

OBJECTIVES

To validate a longitudinal Grade of Membership (L-GoM) prediction algorithm developed using clinic-based, mainly white patients from the Predictors 2 Study in a statistically representative community-based sample of Hispanic (N = 211) and non-Hispanic (N = 62) older adults (with 60 males and 213 females) from the Predictors 3 Study and extend the algorithm to mild cognitive impairment (MCI).

METHODS

The L-GoM model was applied to data collected at the initial Predictors 3 visit for 150 subjects with AD and 123 with MCI. Participants were followed annually for up to seven years. Observed rates of survival and need for full-time care (FTC) were compared to those predicted by the algorithm.

RESULTS

Initial MCI/AD severity in Predictors 3 was substantially higher than among clinic-based AD patients enrolled at the specialized Alzheimer's centers in Predictors 2. The observed survival and need for FTC followed the L-GoM model trajectories in individuals with MCI or AD, except for N = 32 subjects who were initially diagnosed with AD but reverted to a non-AD diagnosis on follow-up.

CONCLUSION

These findings indicate that the L-GoM model is applicable to community-dwelling, multiethnic older adults with AD. They extend the use of the model to the prediction of outcomes for MCI. They also justify release of our L-GoM calculator at this time.