Journal of the American Geriatrics Society最新文献

Background: Frailty is an important predictor of health outcomes, characterized by increased vulnerability due to physiological decline. The Clinical Frailty Scale (CFS) is commonly used for frailty assessment but may be influenced by rater bias. Use of artificial intelligence (AI), particularly Large Language Models (LLMs) offers a promising method for efficient and reliable frailty scoring.

Methods: The study utilized seven standardized patient scenarios to evaluate the consistency and reliability of CFS scoring by OpenAI's GPT-3.5-turbo model. Two methods were tested: a basic prompt and an instruction-tuned prompt incorporating CFS definition, a directive for accurate responses, and temperature control. The outputs were compared using the Mann-Whitney U test and Fleiss' Kappa for inter-rater reliability. The outputs were compared with historic human scores of the same scenarios.

Results: The LLM's median scores were similar to human raters, with differences of no more than one point. Significant differences in score distributions were observed between the basic and instruction-tuned prompts in five out of seven scenarios. The instruction-tuned prompt showed high inter-rater reliability (Fleiss' Kappa of 0.887) and produced consistent responses in all scenarios. Difficulty in scoring was noted in scenarios with less explicit information on activities of daily living (ADLs).

Conclusions: This study demonstrates the potential of LLMs in consistently scoring clinical frailty with high reliability. It demonstrates that prompt engineering via instruction-tuning can be a simple but effective approach for optimizing LLMs in healthcare applications. The LLM may overestimate frailty scores when less information about ADLs is provided, possibly as it is less subject to implicit assumptions and extrapolation than humans. Future research could explore the integration of LLMs in clinical research and frailty-related outcome prediction.

Background: High-intensity end-of-life (EOL) care, marked by admission to intensive care units (ICUs) or in-hospital death, can be costly and burdensome. Recent trends in use of ICUs, life-sustaining treatments (LSTs), and noninvasive ventilation (NIV) during EOL hospitalizations among older adults with advanced cancer and patterns of in-hospital death are unknown.

Methods: We used SEER-Medicare data (2003-2017) to identify beneficiaries with advanced solid cancer (summary stage 7) who died within 3 years of diagnosis. We identified EOL hospitalizations (within 30 days of death), classifying them by increasing intensity of care into: (1) without ICU; (2) with ICU but without LST (invasive mechanical ventilation, tracheostomy, gastrostomy, acute dialysis) or NIV; (3) with ICU and NIV but without LST; and (4) with ICU and LST use. We constructed a multinomial regression model to evaluate trends in risk-adjusted hospitalization, overall and across hospitalization categories, adjusting for sociodemographics, cancer characteristics, comorbidities, and frailty. We evaluated trends in in-hospital death across categories.

Results: Of 226,263 Medicare beneficiaries with advanced cancer, 138,305 (61.1%) were hospitalized at EOL [Age, Mean (SD):77.9(7.1) years; 45.5% female]. Overall, EOL hospitalizations remained high throughout, from 78.1% (95% CI: 77.4, 78.7) in 2004 to 75.5% (95% CI: 74.5, 76.2) in 2017. Hospitalizations without ICU use decreased from 49.3% (95% CI: 48.5, 50.2) to 35.0% (95% CI: 34.2, 35.9) while hospitalizations with more intensive care increased, from 23.7% (95% CI: 23.0, 24.4) to 28.7% (95% CI: 27.9, 29.5) for ICU without LST or NIV, 0.8% (95% CI: 0.6, 0.9) to 3.8% (95% CI: 3.4, 4.1) for ICU with NIV but without LST, and 4.3% (95% CI: 4.0, 4.7) to 8.0% (95% CI: 7.5, 8.5) for ICU with LST use. Among those who experienced in-hospital death, the proportion receiving ICU care increased from 46.5% to 65.0%.

Conclusions: Among older adults with advanced cancer, EOL hospitalization rates remained stable from 2004-2017. However, intensity of care during EOL hospitalizations increased as evidenced by increasing use of ICUs, LSTs, and NIV.

Background: Frailty is associated with adverse cardiovascular outcomes independent of age and comorbidities, yet the independent influence of frailty progression on cardiovascular outcomes remains uncertain.

Methods: To determine whether frailty progression is associated with adverse cardiovascular outcomes, independent of baseline frailty and age, we evaluated all Medicare Fee-for-Service beneficiaries ≥65 years at cohort inception with continuous enrollment from 2003 to 2015. Linear mixed effects models, adjusted for baseline frailty and age, were used to estimate change in a validated claims-based frailty index (CFI) over a 5-year period. Survival analysis was used to examine frailty progression and risk of adverse health outcomes.

Results: There were 8.9 million unique patients identified, mean age 77.3 ± 7.2 years, 58.7% female, 10.9% non-White race. In total, 60% had frailty progression and 40% frailty regression over median follow-up of 2.4 years. Compared to those with frailty regression, when adjusting for age and baseline CFI, those with frailty progression had a significantly greater risk of incident major adverse cardiovascular and cerebrovascular events (MACCE) (hazard ratio [HR] 1.31, 95% confidence interval [CI] 1.31-1.31), all-cause mortality (HR 1.34, 95% CI 1.34-1.34), acute myocardial infarction (HR 1.08, 95% CI 1.07-1.09), heart failure exacerbation (HR 1.30, 95% CI 1.29-1.30), ischemic stroke (HR 1.14, 95% CI 1.14-1.15). There was also a graded increase in risk of each outcome with more rapid progression, as well as significantly fewer days alive at home (DAH) with more rapid progression compared to the slowest progression group (270.4 ± 112.3 vs. 308.6 ± 93.0 days, rate ratio 0.88, 95% CI 0.87-0.88, p < 0.001).

Conclusions: In this large, nationwide sample of older Medicare beneficiaries, frailty progression, independent of age and baseline frailty, was associated with fewer DAH and a graded risk of MACCE, all-cause mortality, myocardial infarction, heart failure, and ischemic stroke compared to those with frailty regression.

Background: Some vaccines have a small risk of triggering Guillain-Barré syndrome (GBS), an autoimmune disorder where nerve damage leads to paralysis. There is a CDC precaution for patients whose GBS was associated with an influenza or tetanus toxoid-containing vaccine (GBS occurring within 42 days following vaccination).

Methods: We described vaccine patterns before and after a GBS diagnosis with a matched cohort design in a 20% random sample of fee-for-service Medicare enrollees. We defined the index date as an ICD-9-CM or ICD-10-CM GBS diagnosis code in the primary position of an inpatient claim. We matched each GBS patient to five non-GBS comparators on sex, exact age, racial and ethnic category, state of residence and the month of preventive health visits during baseline; used weighting to balance covariates; and measured frequency of vaccines received per 100 people during year before and after the index date using the weighted mean cumulative count (wMCC).

Results: We identified 1567 patients with a GBS diagnosis with at least 1 year of prior continuous enrollment in Medicare A and B that matched to five comparators each. The wMCCs in the 1 year before the index date were similar for both groups, with a wMCC of 74 vaccines/100 people in the GBS group (95% CI 71, 77). Within 1 year after the index date, patients with GBS had received 26 vaccines/100 people (95% CI 23, 28), which was 41 fewer vaccines than matched non-GBS comparators (95% CI -44, -38). Among GBS patients, 11% were diagnosed with GBS within 42 days after a vaccine.

Conclusions: GBS diagnosis has a strong impact on reducing subsequent vaccination even though there is no warning or precaution about future vaccines for most patients diagnosed with GBS. These data suggest discordance between clinical practice and current vaccine recommendations.

Since their inception in 1975, the Department of Veterans Affairs Geriatric Research, Education, and Clinical Centers (GRECCs) have served as incubators of innovation in geriatrics. Their contributions to the VA mission were last reviewed in 2012. Herein, we describe the continuing impact of GRECCs in research, clinical, and educational areas, focusing on the period between 2018 and 2022. GRECC research spans the continuum from bench to bedside, with a growing research portfolio notable for highly influential publications. GRECC education connects healthcare professions trainees and practicing clinicians, as well as Veterans and their caregivers, to engaging learning experiences. Clinical advancements, including age-friendly care, span the continuum of care and leverage technology to link disparate geographical sites. GRECCs are uniquely positioned to serve older adults given their alignment with the largest integrated health system in the United States and their integration with academic health centers. As such, the GRECCs honor Veterans as they age by building VA capacity to care for the increasing number of aging Veterans seeking care from VA. GRECC advancements also benefit non-VA healthcare systems, their academic affiliates, and non-Veteran older adults. GRECCs make invaluable contributions to advancing geriatric and gerontological science, training healthcare professionals, and developing innovative models of geriatric care.

Count Neipperg (1775-1829), the morganatic husband of Maria Luigia of Habsburg, Napoleon's former wife, presented with typical heart failure symptoms and died of bilateral bronchopneumonia. Neipperg's case is an example of the conflict in the medical field, which led to the birth of modern evidence-based medicine (EBM), and although Neipperg died almost 200 years ago, his case presents the same critical issues that more complex geriatric patients face today. First, the attending physicians provided divergent opinions without reaching an agreement. For example, Francesco Rossi correctly diagnosed heart disease by evaluating the patient's signs and symptoms, a clinical approach that is an early example of modern EBM. By contrast, Giacomo Tommasini made a misdiagnosis based on the philosophical principles of John Brown's vitalist theory, as reworded by Giovanni Rasori. Second, Tommasini's medical report also includes evidence of the Geriatric 5Ms for older patient care, such as multi-complexity, multimorbidity, medication, mobility, and the mind. Moreover, both physicians considered "what matters most" for the patient and his family. Third, the Count's status and political role were identified as the social and structural determinants of health (SSDoH) and used to justify the exceptional intensity of the health care provided. Subsequently, the ante litteram application of EBM and a clinical evaluation based on Geriatrics 5Ms principles anticipate current multidisciplinary management focused on the patient rather than a single disease. The systematic revision of past clinical cases not examined before could open new windows in the dissemination of the geriatric methodology and discipline.

Although family caregivers are increasingly recognized for their essential role in helping vulnerable adults live in the community for as long as possible, their priorities and perspectives have not been well-integrated into quality assessments of home- and community-based services (HCBS). Our overall goal was to identify measurement gaps to guide monitoring and improve HCBS. Caregiver-specific measurement priorities were identified during a multi-level stakeholder engagement process that included 34 Veterans, 24 caregivers, and 39 facility leaders, clinicians, and staff across four VA healthcare systems. We mapped items from national quality measure sets for HCBS identified during an environmental scan onto the stakeholder-identified measurement priorities. Only 5 of 11 non-VA measure sets and three of four VA measure sets explicitly included caregiver-specific items that were aligned with or relevant to stakeholders' measurement priorities. Six of 14 stakeholder-identified priorities were not reflected in any measure sets, such as those that explicitly assess caregiver-reported experience with services that directly or indirectly support their role as caregivers within HCBS. Although family caregivers fulfill a critical role in helping adults with complex medical needs live independently for as long as possible, their priorities and perspectives have not been well-integrated into quality assessments of HCBS. Measures that acknowledge caregivers' roles and incorporate their priorities can help healthcare systems to better monitor and improve HCBS quality, thereby enabling Veterans to remain in the community as long as possible.

Background: There is increasing recognition of the importance of maximizing program-setting fit in scaling and spreading effective programs. However, in the context of hospital-based mobility programs, there is limited information on how settings could consider local context and modify program characteristics or implementation activities to enhance fit. To fill this gap, we examined site-initiated adaptations to STRIDE, a hospital-based mobility program for older Veterans, at eight Veterans Affairs facilities across the United States.

Methods: STRIDE was implemented at eight hospitals in a stepped-wedge cluster randomized trial. During the pre-implementation phase, sites were encouraged to adapt program characteristics to optimize implementation and align with their hospital's resources, needs, and culture. Recommended adaptations included those related to staffing models, marketing, and documentation. To assess the number and types of adaptations, multiple data sources were reviewed, including implementation support notes from site-level support calls and group-based learning collaborative sessions. Adaptations were classified based on the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME), including attention to what was adapted, when, why, and by whom. We reviewed the number and types of adaptations across sites that did and did not sustain STRIDE, defined as continued program delivery during the post-implementation period.

Results: A total of 25 adaptations were reported and classified across seven of the eight sites. Adaptations were reported across five areas: program documentation (n = 13), patient eligibility criteria (n = 5), program enhancements (n = 3), staffing model (n = 2), and marketing and recruitment (n = 2). More than one-half of adaptations were planned. Adaptations were common in both sustaining and non-sustaining sites.

Conclusions: Adaptations were common within a program designed with flexible implementation in mind. Identifying common areas of planned and unplanned adaptations within a flexible program such as STRIDE may contribute to more efficient and effective national scaling. Future research should evaluate the relationship between adaptations and program implementation.