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Background Despite significant support system disruptions during the coronavirus 2019 (COVID-19) pandemic, little is known about the relationship between social support and symptom burden among older adults following COVID-19 hospitalization. Methods From a prospective cohort of 341 community-living persons aged ≥60 years hospitalized with COVID-19 between June 2020 and June 2021 who underwent follow-up at 1, 3, and 6 months after discharge, we identified 311 participants with ≥1 follow-up assessment. Social support prehospitalization was ascertained using a 5-item version of the Medical Outcomes Study Social Support Survey (range, 5–25), with low social support defined as a score ≤15. At hospitalization and each follow-up assessment, 14 physical symptoms were assessed using a modified Edmonton Symptom Assessment System inclusive of COVID-19-relevant symptoms. Mental health symptoms were assessed using Patient Health Questionnaire-4. Longitudinal associations between social support and physical and mental health symptoms, respectively, were evaluated through multivariable regression. Results Participants' mean age was 71.3 years (standard deviation, 8.5), 52.4% were female, and 34.2% were of Black race or Hispanic ethnicity. 11.8% reported low social support. Over the 6-month follow-up period, low social support was independently associated with higher burden of physical symptoms (adjusted rate ratio [aRR], 1.26; 95% confidence interval [CI], 1.05–1.52), but not mental health symptoms (aRR, 1.14; 95% CI, 0.85–1.53). Conclusions Low social support is associated with greater physical, but not mental health, symptom burden among older survivors of COVID-19 hospitalization. Our findings suggest a potential need for social support screening and interventions to improve post-COVID-19 symptom management in this vulnerable group. [ABSTRACT FROM AUTHOR]

Background: Most older adults hospitalized with COVID-19 survive their acute illness. The impact of COVID-19 hospitalization on patient-centered outcomes, including physical function, cognition, and symptoms, is not well understood. To address this knowledge gap, we collected longitudinal data about these issues from a cohort of older survivors of COVID-19 hospitalization. Methods: We undertook a prospective study of community-living persons age ≥ 60 years who were hospitalized with COVID-19 from June 2020–June 2021. A baseline interview was conducted during or up to 2 weeks after hospitalization. Follow-up interviews occurred at one, three, and six months postdischarge. Participants completed comprehensive assessments of physical and cognitive function, symptoms, and psychosocial factors. An abbreviated assessment could be performed with a proxy. Additional information was collected from the electronic health record. Results: Among 341 participants, the mean age was 71.4 (SD 8.4) years, 51% were women, and 37% were of Black race or Hispanic ethnicity. Median length of hospitalization was 8 (IQR 6–12) days. All but 4% of participants required supplemental oxygen, and 20% required care in an intensive care unit or stepdown unit. At enrollment, nearly half (47%) reported at least one preexisting disability in physical function, 45% demonstrated cognitive impairment, and 67% were pre-frail or frail. Participants reported a mean of 9 of 14 (SD 3) COVID-19-related symptoms. At the six-month follow-up interview, more than a third of participants experienced a decline from their pre-hospitalization function, nearly 20% had cognitive impairment, and burdensome symptoms remained highly prevalent. Conclusions: We enrolled a diverse cohort of older adults hospitalized with COVID-19 and followed them after discharge. Functional decline was common, and there were high rates of persistent cognitive impairment and symptoms. Future analyses of these data will advance our understanding of patient-centered outcomes among older COVID-19 survivors. [ABSTRACT FROM AUTHOR]

Background: After hospitalization for acute myocardial infarction (AMI), older adults may be at increased risk for falls due to deconditioning, new medications, and worsening health status. Our primary objective was to identify risk factors for falls after AMI hospitalization among adults over age 75. Methods: We used data from the Comprehensive Evaluation of Risk Factors in Older Patients with AMI (SILVER‐AMI) study, a prospective cohort study of 3041 adults age 75 and older hospitalized with AMI at 94 community and academic medical centers across the United States. In‐person interviews and physical assessments, as well as medical record review, were performed to collect demographic, clinical, functional, and psychosocial data. Falls were self‐reported in telephone interviews and medically serious falls (those associated with emergency department use or hospitalization) were determined by medical record adjudication. Backward selection was used to identify predictors of fall risk in logistic regression analysis. Results: A total of 554 (21.6%) participants reported a fall and 191 (6.4%) had a medically serious fall within 6 months of discharge. Factors independently associated with self‐reported falls included impaired mobility, prior fall history, longer hospital stay, visual impairment, and weak grip. Factors independently associated with medically serious falls included older age, polypharmacy, impaired functional mobility, prior fall history, and living alone. Conclusions: Among older patients hospitalized for AMI, falls are common in the 6 months following discharge and associated with demographic, functional, and clinical factors that are readily identifiable. Fall risk should be considered in post‐AMI clinical decision‐making and interventions to prevent falls should be evaluated. [ABSTRACT FROM AUTHOR]

Background: The Pain Management Collaboratory (PMC) is a multi-site network of pragmatic clinical trials (PCTs) focused on nonpharmacological approaches to pain management, conducted in health care systems of the US Department of Defense (DoD) and Department of Veterans Affairs (VA) and co-funded by the National Institutes of Health (NIH). Concerns about potential research-site overlap prompted the PMC investigator community to consider strategies to avert this problem that could negatively affect recruitment and contaminate interventions and thus pose a threat to trial integrity.Methods: We developed a two-step strategy to identify and remediate research-site overlap by obtaining detailed recruitment plans across all PMC PCTs that addressed eligibility criteria, recruitment methods, trial settings, and timeframes. The first, information-gathering phase consisted of a 2-month period for data collection from PIs, stakeholders, and ClinicalTrials.gov . The second, remediation phase consisted of a series of moderated conference calls over a 1-month time period to develop plans to address overlap. Remediation efforts focused on exclusion criteria and recruitment strategies, and they involved collaboration with sponsors and stakeholder groups such as the Military Treatment Facility Engagement Committee (MTFEC). The MTFEC is comprised of collaborating DoD and university-affiliated PIs, clinicians, and educators devoted to facilitating successful pragmatic trials in DoD settings.Results: Of 61 recruitment sites for the 11 PMC PCTs, 17 (28%) overlapped. Four PCTs had five overlapping Military Treatment Facilities (MTFs), and eight PCTs had 12 overlapping VA Medical Centers (VAMCs). We developed three general strategies to avoid research-site overlap: (i) modify exclusion criteria, (ii) coordinate recruitment efforts, and/or (iii) replace or avoid any overlapping sites. Potential overlap from competing studies outside of the PMC was apparent at 26 sites, but we were not able to confirm them as true conflicts.Conclusion: Proactive strategies can be used to resolve the issue of overlapping research sites in the PMC. These strategies, combined with open and impartial mediation approaches that include researchers, sponsors, and stakeholders, provide lessons learned from this large and complex pragmatic research effort. [ABSTRACT FROM AUTHOR]

Older patients presenting with acute myocardial infarction (AMI) often have comorbidities. Our objective was to examine how outcomes differ by cognitive and vision status in older AMI patients. We use data from a prospective cohort study conducted at 94 hospitals in the United States between January 2013 and October 2016 that enrolled men and women aged ≥75 years with AMI. Cognitive impairment (CI) was defined as telephone interview for cognitive status (TICS) score <27; vision impairment (VI) and activities of daily living (ADLs) were assessed by questionnaire. Of 2988 senior AMI patients, 260 (8.7%) had CI but no VI, 858 (28.7%) had VI but no CI, and 251 (8.4%) had both CI/VI. Patients in the VI/CI group were most likely to exhibit geriatric syndromes. More severe VI was associated with lower (worse) scores on the TICS (β −1.53, 95% confidence interval (CI) −1.87 to −1.18). In adjusted models, compared to participants with neither impairment, participants with VI/CI were more likely to die (hazard ratio 1.61, 95% CI 1.10–2.37) and experience ADL decline (odds ratio 2.11, 95% CI 1.39–3.21) at 180 days. Comorbid CIs and VIs were associated with high rates of death and worsening disability after discharge among seniors hospitalized for AMI. Future research should evaluate protocols to accommodate these impairments during AMI presentations and optimize decision-making and outcomes. [ABSTRACT FROM AUTHOR]

Background: Older adults with acute myocardial infarction (AMI) have higher prevalence of functional impairments, including deficits in cognition, strength, and sensory domains, than their younger counterparts.Objective: To develop and evaluate the prognostic utility of a risk model for 6-month post-AMI mortality in older adults that incorporates information about functional impairments.Design: Prospective cohort study. (ClinicalTrials.gov: NCT01755052).Setting: 94 hospitals throughout the United States.Participants: 3006 persons aged 75 years or older who were hospitalized with AMI and discharged alive.Measurements: Functional impairments were assessed during hospitalization via direct measurement (cognition, mobility, muscle strength) or self-report (vision, hearing). Clinical variables associated with mortality in prior risk models were ascertained by chart review. Seventy-two candidate variables were selected for inclusion, and backward selection and Bayesian model averaging were used to derive (n = 2004 participants) and validate (n = 1002 participants) a model for 6-month mortality.Results: Participants' mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. There were 266 deaths (8.8%) within 6 months. The final risk model included 15 variables, 4 of which were not included in prior risk models: hearing impairment, mobility impairment, weight loss, and lower patient-reported health status. The model was well calibrated (Hosmer-Lemeshow P > 0.05) and showed good discrimination (area under the curve for the validation cohort = 0.84). Adding functional impairments significantly improved model performance, as evidenced by category-free net reclassification improvement indices of 0.21 (P = 0.008) for hearing impairment and 0.26 (P < 0.001) for mobility impairment.Limitation: The model was not externally validated.Conclusion: A newly developed model for 6-month post-AMI mortality in older adults was well calibrated and had good discriminatory ability. This model may be useful in decision making at hospital discharge.Primary Funding Source: National Heart, Lung, and Blood Institute of the National Institutes of Health. [ABSTRACT FROM AUTHOR]

Older patients presenting with acute myocardial infarction (AMI) often have comorbidities. Our objective was to examine how outcomes differ by cognitive and vision status in older AMI patients. We use data from a prospective cohort study conducted at 94 hospitals in the United States between January 2013 and October 2016 that enrolled men and women aged ≥75 years with AMI. Cognitive impairment (CI) was defined as telephone interview for cognitive status (TICS) score <27; vision impairment (VI) and activities of daily living (ADLs) were assessed by questionnaire. Of 2988 senior AMI patients, 260 (8.7%) had CI but no VI, 858 (28.7%) had VI but no CI, and 251 (8.4%) had both CI/VI. Patients in the VI/CI group were most likely to exhibit geriatric syndromes. More severe VI was associated with lower (worse) scores on the TICS (β −1.53, 95% confidence interval (CI) −1.87 to −1.18). In adjusted models, compared to participants with neither impairment, participants with VI/CI were more likely to die (hazard ratio 1.61, 95% CI 1.10–2.37) and experience ADL decline (odds ratio 2.11, 95% CI 1.39–3.21) at 180 days. Comorbid CIs and VIs were associated with high rates of death and worsening disability after discharge among seniors hospitalized for AMI. Future research should evaluate protocols to accommodate these impairments during AMI presentations and optimize decision-making and outcomes. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Patient Health Priorities Identification (PHPI) is a values‐based process in which trained facilitators assist older adults with multiple chronic conditions identify their health priorities. The purpose of this study was to evaluate patients' perceptions of PHPI. DESIGN: Qualitative study using thematic analysis. SETTING: In‐depth semistructured telephone and in‐person interviews. PARTICIPANTS: Twenty‐two older adults who participated in the PHPI process. MEASUREMENTS: Open‐ended questions about patient perceptions of the PHPI process, perceived benefits of the process, enablers and barriers to PHPI, and recommendations for process enhancement. RESULTS: Patient interviews ranged from 9 to 63 minutes (median = 20 min; interquartile range = 15–26). The mean age was 80 years (standard deviation = 7.96), 64% were female, and all patients identified themselves as white. Of the sample, 73% reported no caregiver involvement in their healthcare; 36% lived alone. Most patients felt able to complete the PHPI process with ease. Perceived benefits included increased knowledge and insight into disease processes and treatment options, patient activation, and enhanced communication with family and clinicians. Patients identified several factors that were both enablers and barriers to PHPI including facilitator characteristics, patient demographic and clinical characteristics, social support, relationships between the patient and their primary care provider, and the changing health priorities of the patient. Recommendations for process enhancement included more frequent and flexible facilitator contacts, selection of patients for participation based on specific patient characteristics, clarification of process aims and expectations, involvement of family, written reminders of established health priorities, short duration between facilitation and primary care provider follow‐up, and the inclusion of health‐related tasks in facilitation visits. CONCLUSIONS: Patients found the PHPI process valuable in identifying actionable health priorities and healthcare goals leading to enhanced knowledge, activation, and communication regarding their treatment options and preferences. PHPI may be useful for aligning the healthcare that patients receive with their values‐based priorities. [ABSTRACT FROM AUTHOR]

Objectives: While patients’ health priorities should inform healthcare, strategies for doing so are lacking for patients with multiple conditions. We describe challenges to, and strategies that support, patients’ priorities-aligned decision-making. Design: Participant observation qualitative study. Setting: Primary care and cardiology practices in Connecticut. Participants: Ten primary care clinicians, five cardiologists, and the Patient Priorities implementation team (four geriatricians, physician expert in clinician training, behavioral medicine expert). The patients discussed were ≥ 66 years with >3 chronic conditions and ≥10 medications or saw ≥ two specialists. Exposure: Following initial training and experience in providing Patient Priorities Care, the clinicians and Patient Priorities implementation team participated in 21 case-based, group discussions (10 face-to-face;11 telephonic). Using emergent learning (i.e. learning which arises from interactions among the participants), participants discussed challenges, posed solutions, and worked together to determine how to align care options with the health priorities of 35 patients participating in the Patient Priorities Care pilot. Main outcomes: Challenges to, and strategies for, aligning decision-making with patient’s health priorities. Results: Categories of challenges discussed among participants included uncertainty, complexity, and multiplicity of problems and treatments; difficulty switching to patients’ priorities as the focus of decision-making; and differing perspectives between patients and clinicians, and among clinicians. Strategies identified to support patient priorities-aligned decision-making included starting with one thing that matters most to each patient; conducting serial trials of starting, stopping, or continuing interventions; focusing on function (i.e. achieving patient’s desired activities) rather than eliminating symptoms; basing communications, decision-making, and effectiveness on patients’ priorities not solely on diseases; and negotiating shared decisions when there are differences in perspectives. Conclusions: The discrete set of challenges encountered and the implementable strategies identified suggest that patient priorities-aligned decision-making in the care of patients with multiple chronic conditions is feasible, albeit complicated. Findings require replication in additional settings and determination of their effect on patient outcomes. [ABSTRACT FROM AUTHOR]

Older adults with multiple chronic conditions (MCCs) receive care that is fragmented and burdensome, lacks evidence, and most importantly is not focused on what matters most to them. An implementation feasibility study of Patient Priorities Care (PPC), a new approach to care that is based on health outcome goals and healthcare preferences, was conducted. This study took place at 1 primary care and 1 cardiology practice in Connecticut and involved 9 primary care providers (PCPs), 5 cardiologists, and 119 older adults with MCCs. PPC was implemented using methods based on a practice change framework and continuous plan‐do‐study‐act (PDSA) cycles. Core elements included leadership support, clinical champions, priorities facilitators, training, electronic health record (EHR) support, workflow development and continuous modification, and collaborative learning. PPC processes for clinic workflow and decision‐making were developed, and clinicians were trained. After 10 months, 119 older adults enrolled and had priorities identified; 92 (77%) returned to their PCP after priorities identification. In 56 (46%) of these visits, clinicians documented patient priorities discussions. Workflow challenges identified and solved included patient enrollment lags, EHR documentation of priorities discussions, and interprofessional communication. Time for clinicians to provide PPC remains a challenge, as does decision‐making, including clinicians' perceptions that they are already doing so; clinicians' concerns about guidelines, metrics, and unrealistic priorities; and differences between PCPs and patients and between PCPs and cardiologists about treatment decisions. PDSA cycles and continuing collaborative learning with national experts and peers are taking place to address workflow and clinical decision‐making challenges. Translating disease‐based to priorities‐aligned decision‐making appears challenging but feasible to implement in a clinical setting. See related editorial by William B. Applegate et al. [ABSTRACT FROM AUTHOR]

Objectives: To develop a values‐based, clinically feasible process to help older adults identify health priorities that can guide clinical decision‐making. Design: Prospective development and feasibility study. Setting: Primary care practice in Connecticut. Participants: Older adults with 3 or more conditions or taking 10 or more medications (N=64). Intervention: The development team of patients, caregivers, and clinicians used a user‐centered design framework—ideate → prototype → test →redesign—to develop and refine the value‐based patient priorities care process and medical record template with trained clinician facilitators. Measurements: We used descriptive statistics of quantitative measures (percentage accepted invitation and completed template, duration of process) and qualitative analysis of barriers and enablers (challenges and solutions identified, facilitator perceptions). Results: We developed and refined a process for identifying patient health priorities that was typically completed in 35 to 45 minutes over 2 sessions; 64 patients completed the process. Qualitative analyses were used to elucidate the characteristics and training needed for the patient priorities facilitators, as well as perceived benefits and challenges of the process. Refinements based on our experience and feedback include streamlining the process for greater feasibility, balancing fidelity to the process while customizing to individuals, encouraging patients to share their priorities with their clinicians, and simplifying the template transmitted to clinicians. Conclusion: Trained facilitators conducted this process in a busy primary care practice, suggesting that patient priorities identification is feasible and acceptable, although testing in additional settings is necessary. We hope to show that clinicians can align care with patients' health priorities. See related editorial by William B. Applegate et al [ABSTRACT FROM AUTHOR]

The authors report on the use of a Patient Priorities Care (PPC) approach to medical care for older patients with multiple chronic conditions. They discuss the use of PPC in clinical care, the perceived benefits and difficulties of discussing priorities with the patient, and how the clinicians felt about the process.

Background/Objectives Timely administration of antiischemic therapies improves outcomes in individuals with acute myocardial infarction ( AMI). Prior literature on delays in AMI care has largely focused on in-hospital delay ('door to balloon' time). Our objective was to identify factors associated with prehospital delay in a contemporary national cohort of older adults with AMI. Design Cross-sectional analysis of data from the Comprehen SIVe Evaluation of Risk Factors in Older Patients with Acute Myocardial Infarction ( SILVER- AMI) study, an observational study of older adults hospitalized for AMI. Setting U.S. academic and community hospitals (N = 94). Participants Individuals aged 75 and older hospitalized for AMI (N = 2,500). Measurements Prehospital delay was defined as symptom duration of 6 hours or longer before hospital presentation and was obtained according to participant or caregiver report during AMI hospitalization. Potential predictors of delay from demographic, clinical presentation, comorbid conditions, function, and social support domains were obtained through in-person assessment during the index hospitalization and medical record abstraction. Results Nonwhite race (adjusted odds ratio ( aOR) = 1.54, P = .002), atypical symptoms ( aOR = 1.41, P = .001), and heart failure ( HF) ( aOR = 1.35, P = .006 for HF) were significantly associated with delay. Conclusion In contrast with younger AMI populations, female sex and diabetes mellitus were not associated with delay in this older cohort, but factors from other domains (nonwhite race, atypical symptoms, and HF) were significantly associated with delay. These results can be used to customize future public health efforts to encourage early presentation for older adults with AMI. [ABSTRACT FROM AUTHOR]

Objective: Risk stratification models support clinical decision making in acute myocardial infarction (AMI) care. Existing models were developed using data from younger populations, potentially limiting accuracy and relevance in older adults. We describe physician-perceived risk factors, views of existing models, and preferences for future model development in older adults. Method: Qualitative study using semi-structured telephone interviews and the constant comparative method. Results: Twenty-two physicians from 14 institutions completed the interviews. Median age was 37, and median years of clinical experience was 11.5. Perceived predictors included cardiovascular, comorbid, functional, and social risk factors. Physicians viewed models as easy to use, yet neither inclusive of risk factors nor predictive of non-mortality outcomes germane to clinical decision making in older adults. Ideal models included multidimensional risk domains and operational requirements. Discussion: Physicians reported limitations of available risk models when applied to older adults with AMI. New models are needed to guide AMI treatment in this population. [ABSTRACT FROM AUTHOR]

Background While older adults (age 75 and over) represent a large and growing proportion of patients with acute myocardial infarction (AMI), they have traditionally been under-represented in cardiovascular studies. Although chronological age confers an increased risk for adverse outcomes, our current understanding of the heterogeneity of this risk is limited. The Comprehensive Evaluation of Risk Factors in Older Patients with AMI (SILVER-AMI) study was designed to address this gap in knowledge by evaluating risk factors (including geriatric impairments, such as muscle weakness and cognitive impairments) for hospital readmission, mortality, and health status decline among older adults hospitalized for AMI. Methods/Design SILVER-AMI is a prospective cohort study that is enrolling 3000 older adults hospitalized for AMI from a recruitment network of approximately 70 community and academic hospitals across the United States. Participants undergo a comprehensive in-hospital assessment that includes clinical characteristics, geriatric impairments, and health status measures. Detailed medical record abstraction complements the assessment with diagnostic study results, in-hospital procedures, and medications. Participants are subsequently followed for six months to determine hospital readmission, mortality, and health status decline. Multivariable regression will be used to develop risk models for these three outcomes. Discussion SILVER-AMI will fill critical gaps in our understanding of AMI in older patients. By incorporating geriatric impairments into our understanding of post-AMI outcomes, we aim to create a more personalized assessment of risk and identify potential targets for interventions. Trial registration Trial registration number: NCT01755052. [ABSTRACT FROM AUTHOR]

Background: While older adults (age 75 and over) represent a large and growing proportion of patients with acute myocardial infarction (AMI), they have traditionally been under-represented in cardiovascular studies. Although chronological age confers an increased risk for adverse outcomes, our current understanding of the heterogeneity of this risk is limited. The Comprehensive Evaluation of Risk Factors in Older Patients with AMI (SILVER-AMI) study was designed to address this gap in knowledge by evaluating risk factors (including geriatric impairments, such as muscle weakness and cognitive impairments) for hospital readmission, mortality, and health status decline among older adults hospitalized for AMI.Methods/design: SILVER-AMI is a prospective cohort study that is enrolling 3000 older adults hospitalized for AMI from a recruitment network of approximately 70 community and academic hospitals across the United States. Participants undergo a comprehensive in-hospital assessment that includes clinical characteristics, geriatric impairments, and health status measures. Detailed medical record abstraction complements the assessment with diagnostic study results, in-hospital procedures, and medications. Participants are subsequently followed for six months to determine hospital readmission, mortality, and health status decline. Multivariable regression will be used to develop risk models for these three outcomes.Discussion: SILVER-AMI will fill critical gaps in our understanding of AMI in older patients. By incorporating geriatric impairments into our understanding of post-AMI outcomes, we aim to create a more personalized assessment of risk and identify potential targets for interventions.Trial Registration: Trial Registration Number: NCT01755052 . [ABSTRACT FROM AUTHOR]