Citations

ABSTRACT Premature physeal closure occurs following trauma, cancer, or infection. Current treatments have poor success rates. With recent pediatric donor tissue availability, physeal allograft transfer (PAT) can now be considered. The purpose of this study was to study the safety and feasibility of PAT in a large animal model. The aim of this study is to gather foundational data to inform future studies into the efficacy of PAT. Physeal defects were created in the distal femur of nine female domestic swine and treated with PAT from two male donor pigs, cementation, or bone autograft. Viability was assessed. After 3 months, physes were visualized using CT and MRI. Integration, tissue composition, donor DNA presence, and microscopic appearance were evaluated. Physeal allografts demonstrated 93% viability after procurement and preservation. All animals reached the 3‐months study endpoint without gross deformations. No physeal bars formed in any group. Cystic changes were seen in experimental and control femurs. All groups showed disorganized tissue architecture without growth plate recapitulation. High allografts viability and structural integrity after procurement support the potential of this treatment. Although no gross deformities were found, transferred physes demonstrate poor integration and incomplete repair. The lack of physeal bar formation in the control group limits the reliability of the animal model for studying physeal allograft transfer. Lastly, this study was designed as a feasibility study and lacks power to compare treatment effects statistically. The efficacy of PAT for preventing growth arrest remains undetermined.

Abstract Delirium is a state of acute confusion characterized by deficits in attention that occurs commonly in hospitalized older adults and is associated with adverse outcomes. Delirium may have significant implications for cognitive health, however the relationship of delirium to mild cognitive impairment (MCI) and dementia is not fully understood. To evaluate the impact of delirium on cognitive health trajectories, we leveraged the Mayo Clinic Study of Aging (MCSA), a population-based cohort study of cognitive health and aging in residents of Olmsted County, Minnesota, who are between the ages of 30 and 89 and without a previous diagnosis of dementia at enrollment. Emergency Department and hospital encounters were identified for all patients enrolled in the MCSA, and clinical notes following each encounter up to 30 days were analyzed using a previously validated natural language processing (NLP) algorithm for delirium detection. The association between delirium within 30 days of hospitalization and the development of MCI or dementia was assessed using Cox proportional hazards regression, adjusted for patient age, sex, and level of education. Initial MCSA assessment indicated normal cognition for all individuals in this cohort. We found that delirium was significantly associated with development of MCI and dementia, with 42 individuals experiencing delirium within 30 days of hospitalization; hazard ratio of 10.07 (95% CI 4.14, 24.49) for developing MCI and 19.10 (95% CI 8.40, 43.47) for dementia compared to those without delirium. Our results suggest that hospitalization with an episode of delirium can significantly impact cognitive trajectories in older adults.

Abstract Over 50% of older adults in the US take ≥5 medications, with prevalence reaching 76.8% in the emergency department (ED) setting. However, little is known about their medication self-efficacy (the ability to understand and follow medication regimens), which may influence adherence and adverse drug events when new prescriptions are added during ED visits. Our objective was to determine whether polypharmacy and potentially inappropriate medication (PIM) are associated with self-efficacy. We performed a logistic regression analysis using 5-site multicenter ED survey data linked to electronic health records from patients aged ≥65 years with baseline ED visits during 2019-2021. The predictors were polypharmacy (≥5 medications) and PIM use (≥1 high-risk medication, GEMRx criteria) of prescriptions taken within 1 year of the ED visit. The outcome was low medication self-efficacy (< 11 points MUSE-taking score) assessed at the ED visit. Covariates included insurance, education, marital status, race/ethnicity, caregiver support, dementia status, and frailty. Among 921 patients, 249 (27.0%) had polypharmacy and 66 (7.2%) had PIM use. Low medication self-efficacy was reported by 76 (37.8%) with polypharmacy and 26 (12.9%) with PIM use versus 125 (62.2%) and 40 (5.6%) without. Both polypharmacy (aOR 6.12, 95% CI 2.05-18.22, p = 0.001) and PIM use (aOR 33.65, 95% CI 5.81-214.35, p < 0.001) were associated with greater adjusted odds of low medication self-efficacy. Frailty modified this association, with weaker effects in more frail patients (OR 5.51, 95% CI 2.57-11.8, p = 0.003). These findings suggest that the ED is an important setting to identify older adults who may struggle with medication use.

Abstract Depressive symptoms and poor quality of life (QoL) are common among over 65% of older adults living with multimorbidity. Community-based interventions have the potential for scalability and may be more accessible for older adults with complex needs compared to interventions delivered in healthcare settings. This scoping review aims to describe the literature on community-based interventions targeting depressive symptoms and QoL for adults with multimorbidity. Six databases were searched for studies published in 2015 or later, focusing on interventions in community-based settings (e.g., community-based organizations, senior centers, retirement communities). Studies focusing on a single condition or an index condition with comorbidities were excluded. Of 7,829 screened studies, 173 articles were included in full-text review. About half of the studies were conducted in high-income English-speaking countries and 11% in low and middle-income countries (LMIC). Study samples consisted mostly of older adults with varied multimorbidity profiles that included diabetes, cardiovascular disease, mental health disorders, and frailty. Nearly 40% of studies reported physical activity and patient education interventions, while peer-led/community health worker interventions were the least reported intervention. This review highlights a gap in studies conducted in LMIC where the burden of multimorbidity is high and resources are limited, and a gap in interventions that are community member-led. More research is needed to understand how these scalable, cost-effective community-based interventions may address the needs of multimorbidity patients in LMIC and how leveraging community members may strengthen interventions in addressing depressive symptoms and QoL in patients with multimorbidity.

Abstract Older adults with dementia account over 15% of intensive care unit (ICU) admissions and face higher risks of functional decline and complications. We systematically evaluated the difference in ICU-related outcomes in patients with and without dementia, (e.g., mortality, resuscitation and code status decisions, complications, duration of life support and hospital stay). Searches were performed in MEDLINE, EMBASE, SCOPUS, and Web of Science between 2003 and 2024. A random-effects meta-analysis was done of odds ratios (OR) and mean differences (MD) and their corresponding 95% confidence intervals (CI) were calculated. Fifteen studies, including 1,231,037 patients (12% with dementia), met the inclusion criteria. Adjusted analysis showed that patients with dementia had a higher risk of ICU mortality (OR = 1.05, 95% CI 1.00–1.11), 30-day mortality (OR = 1.54, 95% CI 1.47–1.62) and 12-month mortality (OR = 1.95, 95% CI 1.88–2.02). They were less likely to receive mechanical ventilation (OR = 0.73, 95% CI 0.62–0.85, I2 0.0%) and more likely to develop delirium (OR = 4.80, 95% CI 1.81–12.72). In patients with dementia, ICU stays were slightly shorter (MD = –1 day, 95% CI –1.7 to –0.4), and discharge home was less common (OR = 0.40, 95% CI 0.39–0.42). Older adults with dementia experienced greater risk of mortality and delirium, alongside less aggressive intervention use and healthcare utilization (i.e., shorter ICU stays). These findings underscore the need for prospective studies to optimize ICU decision-making and post-discharge care for this vulnerable population.

Abstract Background Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an “AI label feedback loop” in a clinically implemented AI-augmented kidney stone composition test. Methods An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. Results V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. Conclusions The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.

Abstract Delirium is a common yet underdiagnosed medical emergency involving acute deficits in cognition and attention. It affects over 20% of hospitalized patients and up to 80% of ICU patients, with increased prevalence in older adults. Delirium is associated with increased mortality, prolonged hospitalization, long-term cognitive decline, and substantial healthcare costs. However, current screening tools are subjective and difficult to implement consistently. Traditional EEGs show diagnostic utility; however, they are costly and require specialized interpretation. We conducted a prospective pilot study evaluating a point-of-care, single-channel EEG device for delirium detection in hospitalized older adults. EEG spectra were utilized to calculate a ‘bispectral EEG’ (BSEEG) score reflecting the ratio of slow to fast brain waves. Twenty-two postoperative patients (mean age 75; 59.09% female) were assessed daily for up to seven days using both the BSEEG and the gold standard 3-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM). ROC analysis was utilized to determine an optimal threshold BSEEG score to indicate delirium positivity. BSEEG scores were significantly higher in the 3D-CAM-positive group than in the 3D-CAM-negative group (1.60 and 1.45, respectively; p < 0.05). A BSEEG threshold of 1.5 demonstrated 77.78% sensitivity and 56.10% specificity for delirium. In contrast, route clinical screenings (Delirium Triage Screen and Brief CAM) showed 0.00% sensitivity and 96.30% specificity, and failed to detect all true delirium cases. Although further validation in larger cohorts is needed, these findings suggest that the point-of-care single-channel EEG is a promising, non-invasive screening tool for delirium screening, particularly in fast-paced clinical settings.

Abstract Caregivers for people with neurodegenerative disease (PWNDs) often experience mental and physical health problems, especially when caring for PWNDs who have deficits in emotional functioning. We studied 65 PWNDs and their caregivers both during active caregiving and after caregiving had ended to determine: (a) how caregiver health changes after caregiving ends; and (b) whether PWNDs’ emotional functioning predicts these changes. PWND emotional functioning was assessed in the laboratory by measuring their generation of preparatory physiological activity (e.g., increased heart rate) to an upcoming emotional event (i.e., emotion eliciting films). Caregiver mental and physical health were measured using the Short-Form Health Survey (SF36) during active caregiving and again after caregiving had ended. Results indicated that, overall, caregivers reported increases in mental health, (t(36) = 2.58, p = .014, 95% CI [1.31, 10.93]) but decreases in physical health (t(36) = -2.35, p = .025, 95% CI [-7.26, -0.53]) across the two measurement periods. Using latent change score models, PWNDs’ preparatory physiological response impairments were associated with better caregiver mental health trajectories (β = -0.37, SE = 0.04, p = .003) such that smaller PWND preparatory responses predicted greater improvements in caregiver mental health (β = 9.92, SE = 2.84, p < .001). There was no association with changes in caregiver physical health. Smaller preparatory physiological responses in PWNDs may indicate diminished emotional responding, which is linked with lower caregiver mental health during active caregiving. Once caregiving ends, these caregivers may be most likely to “rebound” and show improvements in mental health.