Predicting Mortality and Adverse Outcomes: Comparing the Frailty Index to General Prognostic Indices.

BACKGROUND: Mortality prediction models are useful to guide clinical decision-making based on prognosis. The frailty index, which allows prognostication and personalized care planning, has not been directly compared with validated prognostic models. OBJECTIVE: To compare the discrimination of mortality, disability, falls, and hospitalization between a frailty index and validated prognostic indices. DESIGN: Secondary Analysis of the National Health and Aging Trends Study. PARTICIPANTS: Seven thousand thirty-three Medicare beneficiaries 65 years or older. MEASUREMENTS: We measured a deficit-accumulation frailty index, Schonberg index, and Lee index at the 2011 baseline assessment. Primary outcome was mortality at 5 years. Secondary outcomes were decline in activities of daily living (ADL), decline in instrumental activities of daily living (IADL), fall, and hospitalization at 1 year. We used C-statistics to compare discrimination between indices, adjusting for age and sex. RESULTS: The study population included 4146 (44.8%) with age ≥ 75 years, with a median frailty index of 0.15 (interquartile range 0.09-0.25). A total of 1385 participants died (14.7%) and 2386 (35.2%) were lost to follow-up. Frailty, Schonberg, and Lee indices predicted mortality similarly: C-statistics (95% confidence interval) were 0.78 (0.77-0.80) for frailty index; 0.79 (0.78-0.81) for Schonberg index; and 0.78 (0.77-0.80) for Lee index. The frailty index had higher C-statistics for decline in ADL function (frailty index, 0.80 [0.78-0.83]; Schonberg, 0.74 [0.72-0.76]; Lee, 0.74 [0.71-0.77]) and falls (frailty index, 0.66 [0.65-0.68]; Schonberg, 0.61 [0.58-0.63]; Lee, 0.61 [0.59-0.63]). C-statistics were similar for decline in IADL function (frailty index, 0.61 [0.59-0.63]; Schonberg, 0.60 [0.59-0.62]; Lee, 0.60 [0.58-0.62]) and hospitalizations (frailty index, 0.68 [0.66-0.70]; Schonberg, 0.68 [0.66-0.69]; Lee, 0.65 [0.63-0.67]). CONCLUSIONS: A deficit-accumulation frailty index performs as well as prognostic indices for mortality prediction, and better predicts ADL disability and falls in community-dwelling older adults. Frailty assessment offers a unifying approach to risk stratification for key health outcomes relevant to older adults.

Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

BACKGROUND: Capturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies. Although it is common to ascertain frailty status utilizing claims-based surrogates (e.g. diagnosis and health service codes) selected according to clinical knowledge, the accuracy of this approach has not yet been examined. We evaluated the accuracy of claims-based surrogates against two clinical definitions of frailty. METHODS: This cross-sectional study was conducted in a Health and Retirement Study subsample of 3097 participants, aged 65 years or older and with at least 12-months of continuous fee-for-service Medicare enrollment. We defined 18 previously utilized claims-based surrogates of frailty from Medicare data and evaluated each against clinical reference standards, ascertained from a direct examination: a deficit accumulation frailty index (FI) (range: 0-1) and frailty phenotype. We also compared the accuracy of the total count of 18 claims-based surrogates with that of a validated claims-based FI model, comprised of 93 claims-based variables. RESULTS: 19% of participants met clinical criteria for the clinical frailty phenotype. The mean clinical FI for our sample was 0.20 (standard deviation 0.13). Hospital Beds and associated supplies was the claims-based surrogate associated with the highest clinical FI (mean FI 0.49). Claims-based surrogates had low sensitivity ranging from 0.01 (cachexia, adult failure to thrive, anorexia) to 0.38 (malaise and fatigue) and high specificity ranging from 0.79 (malaise and fatigue) to 0.99 (cachexia, adult failure to thrive, anorexia) in discriminating the clinical frailty phenotype. Compared with a validated claims-based FI, the total count of claims-based surrogates demonstrated lower Spearman correlation with the clinical FI (0.41 [95% CI 0.38-0.44] versus 0.59 [95% CI, 0.56-0.61]) and poorer discrimination of the frailty phenotype (C-statistics 0.68 [95% CI, 0.66-0.70] versus 0.75 [95% CI, 0.73-0.77]). CONCLUSIONS: Claims-based surrogates, selected according to clinical knowledge, do not accurately capture frailty in Medicare claims data. A simple count of claims-based surrogates improves accuracy but remains inferior to a claims-based FI model.

Mobility Device Use and Frailty Progression in Community-Dwelling Older Adults With Mobility Limitations.

Objective: Examine the association between mobility device use and changes in a frailty index (FI) over one year in community-dwelling older adults with mobility limitations. Methods: Analyses utilized 2015-2016 data from the National Health and Aging Trends Study community-dwelling older adults (n = 3934). We calculated a validated 40-item deficit accumulation frailty index (FI) in 2015 and 2016 and compared one year change in FI in older adults with/without canes or walkers using multivariable logistic regression. Analyses were repeated with stratification by baseline frailty. Results: Device use was not associated with worsening frailty in the overall cohort, but was associated with worsening frailty in non-frail individuals when stratified by baseline frailty. Discussion: Device use does not worsen frailty in individuals who are frail at baseline. Device users who were not frail at baseline experienced worsening frailty suggesting additional contributing factors to their frailty aside from mobility limitations.

Claims-Based Frailty Index and Its Relationship With Commonly Used Clinical Frailty Measures.

BACKGROUND: The relationship of claims-based frailty index (CFI), a validated measure to identify frail individuals using Medicare data, and frailty measures used in clinical practice has not yet been fully explored. METHODS: We identified community-dwelling participants of the 2015 National Health and Aging Trends Study (NHATS) whose CFI scores could be calculated using linked Medicare claims. We calculated 9 commonly used clinical frailty measures from their NHATS in-person examination: Study of Osteoporotic Fracture Index (SOF), FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indicator (TFI), Groningen Frailty Indicator (GFI), Edmonton Frail Scale (EFS), and 40-item Frailty Index (FI). Using equipercentile method, CFI scores were linked to clinical frailty measures. C-statistics and test characteristics of CFI to identify frailty as defined by each clinical frailty measure were calculated. RESULTS: Of the 3 963 older adults, 44.5% were ≥75 years, 59.4% were female, and 82.3% were non-Hispanic White. A CFI of 0.25 was equipercentile to the following clinical frailty measure scores: SOF 1.4, FRAIL 1.8, Phenotype 1.8, CFS 5.4, VES-13 5.7, TFI 4.6, GFI 5.0, EFS 6.0, and FI 0.26. The C-statistics of using CFI to identify frailty as defined by each clinical measure were ≥0.70, except for CFS and VES-13. The optimal CFI cutpoints to identify frailty per clinical frailty measure ranged from 0.212 to 0.242, with sensitivity and specificity of 0.37-0.83 and 0.66-0.84, respectively. CONCLUSIONS: Understanding the relationship of CFI and commonly used clinical frailty measures can enhance the interpretability and potential utility of CFI.

Frailty and Time at Home After Post-Acute Care in Skilled Nursing Facilities.

OBJECTIVES: To examine the association of a claims-based frailty index with time at home, defined as the number of days alive and spent out of hospital or skilled nursing facility (SNF). DESIGN: Cohort Study. SETTING AND PARTICIPANTS: A 5% Medicare random sample of fee-for-service beneficiaries, who had continuous part A and B enrollment in the prior 6 months, that were discharged from a short SNF admission in 2014‒2016. METHODS: Frailty was measured with a validated claims-based frailty index (CFI) (range: 0‒1, higher scores indicating worse frailty) and categorized into nonfrail (CFI <0.25), mild frailty (CFI 0.25‒0.34), and moderate-to-severe frailty (CFI ≥0.35). We measured home time in the 6 months following SNF discharge (range: 0‒182 days with higher values representing more days at home and thus a better outcome). We used logistic regression to assess the association between frailty and short home time, defined as <173 days, adjusting for age, sex, race, region, a comorbidity index, clinical SNF admission characteristics in the Minimum Data Set, and SNF characteristics. RESULTS: In our sample of 144,708 beneficiaries (mean age, 80.8 years, 64.9% female, 85.9% white) who were discharged to community after SNF stay, the mean CFI was 0.26 (standard deviation, 0.07). The mean home time was 165.6 (38.1) days in nonfrail, 154.4 (47.4) days in mild frailty, 145.0 (52.0) days in moderate-to-severe frailty group. After full model adjustments, moderate to severe frailty was associated with a 1.71 (95% CI 1.65‒1.78) higher odds of having short time at home in the 6 months following SNF discharge. CONCLUSION AND IMPLICATIONS: Higher CFI is associated with short time at home in Medicare beneficiaries who are discharged to the community after post-acute SNF stay. Our results support the utility of CFI in identifying SNF patients who need additional resources and interventions to prevent health decline and poor quality of life.

Restricted mean survival time versus conventional effect summary for treatment decision-making: A mixed-methods study.

BACKGROUND: Treatment effect is typically summarized in terms of relative risk reduction or number needed to treat ("conventional effect summary"). Restricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in event-free days. Older adults' preference between the two effect summary measures has not been studied. METHODS: We conducted a mixed methods study using a quantitative survey and qualitative semi-structured interviews. For the survey, we enrolled 102 residents with hypertension at five senior housing facilities (mean age 81.3 years, 82 female, 95 white race). We randomly assigned respondents to either RMST-based (n = 49) or conventional decision aid (n = 53) about the benefits and harms of intensive versus standard blood pressure-lowering strategies and compared decision conflict scale (DCS) responses (range: 0 [no conflict] to 100 [maximum conflict]; <25 is associated with implementing decisions). We used a purposive sample of 23 survey respondents stratified by both their random assignment and DCS from the survey. Inductive qualitative thematic analysis explored complementary perspectives on preferred ways of summarizing treatment effects. RESULTS: The mean (standard deviation) total DCS was 22.0 (14.3) for the conventional decision aid group and 16.7 (14.1) for the RMST-based decision aid group (p = 0.06), but the proportion of participants with a DCS <25 was higher in the RMST-based group (26 [49.1%] vs 34 [69.4%]; p = 0.04). Qualitative interviews suggested that, regardless of effect summary measure, older individuals' preference depended on their ability to clearly comprehend quantitative information, clarity of presentation in the visual aid, and inclusion of desired information. CONCLUSIONS: When choosing a blood pressure-lowering strategy, older adults' perceived uncertainty may be reduced with a time-based effect summary, although our study was underpowered to detect a statistically significant difference. Given highly variable individual preferences, it may be useful to present both conventional and RMST-based information in decision aids.

Balancing Benefit and Harm of Intensive Blood Pressure Lowering Based on Individual Outcome Profile Analysis.

BACKGROUND: Intensive blood pressure lowering prevents major adverse cardiovascular events, but some patients experience serious adverse events. Examining benefit-harm profiles may be more informative than analyzing major adverse cardiovascular events and serious adverse events separately. METHODS: We analyzed data from the Systolic Blood Pressure Intervention Trial (n = 9361), comparing intensive treatment (systolic blood pressure target <120 mm Hg) to standard treatment (<140 mm Hg). A 4-year hierarchical outcome profile was defined for each participant: 1) alive with neither major adverse cardiovascular events nor serious adverse events (most desirable); 2) alive with serious adverse events only; 3) alive with major adverse cardiovascular events only; 4) alive with both events; and 5) deceased (least desirable). We compared 4-year outcome profiles between the treatment groups in the entire population and by frailty subgroups defined using physical frailty phenotype (non-frail, pre-frail, and frail). RESULTS: The proportion who died were lower with intensive treatment than standard treatment (5% vs 6%). A higher proportion of the intensive treatment group was alive with serious adverse events and no major adverse cardiovascular events (36% vs 33%), and a lower proportion were alive with both events (6% vs 5%) than the standard treatment group. The outcome profiles were more favorable among those with physical frailty phenotype who were treated with intensive treatment vs standard treatment, but outcome profiles were similar between the treatment groups among non-frail or pre-frail participants. CONCLUSIONS: This post hoc proof-of-concept analysis demonstrates the utility of the outcome profile analysis that simultaneously examines the benefit and harm of the treatment.

A crosswalk of commonly used frailty scales.

BACKGROUND: Several validated scales have been developed to measure frailty, yet the direct relationship between these measures and their scores remains unknown. To bridge this gap, we created a crosswalk of the most commonly used frailty scales. METHODS: We used data from 7070 community-dwelling older adults who participated in National Health and Aging Trends Study (NHATS) Round 5 to construct a crosswalk among frailty scales. We operationalized the Study of Osteoporotic Fracture Index (SOF), FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indictor (TFI), Groningen Frailty Indicator (GFI), Edmonton Frailty Scale (EFS), and 40-item Frailty Index (FI). A crosswalk between FI and the frailty scales was created using the equipercentile linking method, a statistical procedure that produces equivalent scoring between scales according to percentile distributions. To demonstrate its validity, we determined the 4-year mortality risk across all scales for low-risk (equivalent to FI <0.20), moderate-risk (FI 0.20 to <0.40), and high-risk (FI ≥0.40) categories. RESULTS: Using NHATS, the feasibility of calculating frailty scores was at least 90% for all nine scales, with the FI having the highest number of calculable scores. Participants considered frail on FI (cutpoint of 0.25) corresponded to the following scores on each frailty measure: SOF 1.3, FRAIL 1.7, Phenotype 1.7, CFS 5.3, VES-13 5.5, TFI 4.4, GFI 4.8, and EFS 5.8. Conversely, individuals considered frail according to the cutpoint of each frailty measure corresponded to the following FI scores: 0.37 for SOF, 0.40 for FRAIL, 0.42 for Phenotype, 0.21 for CFS, 0.16 for VES-13, 0.28 for TFI, 0.21 for GFI, and 0.37 for EFS. Across frailty scales, the 4-year mortality risks between the same categories were similar in magnitude. CONCLUSION: Our results provide clinicians and researchers with a useful tool to directly compare and interpret frailty scores across scales.

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology.

Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.

Frailty and Differences in Self-Reported Sexual Functioning Among Older Females and Males in National Social life, Health and Aging Project.

OBJECTIVE: To understand the association of frailty with females' and males' self-reported sexual functioning. METHODS: Logistic regression on 5 domains of sexual function by frailty status (robust, pre-frail, frail) were analyzed from 2058 respondents to National Social Life, Health, and Aging Project (2010-2011). RESULTS: Females had similar frailty profiles to males, but more often reported low overall sexual functioning (12.9% v. 4.0%). Compared to robust, pre-frail and frail males had higher odds of sexual function-related: anxiety (pre-frail OR 1.91 95% CI [1.33, 2.74]; frail OR 2.13 95% CI [1.03, 4.41]), negative changes (pre-frail: OR 1.40, 95% CI [1.00, 1.96]; frail: OR 2.42, 95% CI [1.51, 3.89]), and erectile dysfunction (pre-frail: OR 1.81, 95% CI [1.23,2.68]; frail: 2.00, 95% CI [1.00,4.02]); frail females had 1.69 times higher odds (95% CI [1.16,2.48]) of negative changes. DISCUSSION: Frailty may be a clinical indicator of sexual functioning decline for males more than females.

Differences in End-of-Life Care between COVID-19 Inpatient Decedents with English Proficiency and Limited English Proficiency.

Background: Patients with limited English proficiency (LEP) experience lower quality end-of-life (EOL) care. This inequity may have been exacerbated during the COVID-19 pandemic. Objective: Compare health care utilization, EOL, and palliative care outcomes between COVID-19 decedents with and without LEP during the pandemic's first wave in Massachusetts. Methods: Retrospective cohort study of adult inpatients who died from COVID-19 between February 18, 2020 and May 18, 2020 at two academic and four community hospitals within a greater Boston health care system. We performed multivariable regression adjusting for patient sociodemographic variables and hospital characteristics. Primary outcome was place of death (intensive care unit [ICU] vs. non-ICU). Secondary outcomes included hospital and ICU length of stay and time to initial palliative care consultation. Results: Among 337 patients, 89 (26.4%) had LEP and 248 (73.6%) were English proficient. Patients with LEP were less often white (24 [27.0%] vs. 193 [77.8%]; p < 0.001); were more often Hispanic or Latinx (40 [45.0%] vs. 13 [5.2%]; p < 0.001); and less often had a medical order for life-sustaining treatment (MOLST) on admission (15 [16.9%] vs. 120 [48.4%]; p < 0.001) versus patients with English proficiency. In the multivariable analyses, LEP was not independently associated with ICU death, ICU length of stay, or time to palliative care consultation, but was independently associated with increased hospital length of stay (mean difference 4.12 days; 95% CI, 1.72-6.53; p < 0.001). Conclusions: Inpatient COVID-19 decedents with LEP were not at increased risk of an ICU death, but were associated with an increased hospital length of stay versus inpatient COVID-19 decedents with English proficiency.

Changes in a Frailty Index and Association with Mortality.

BACKGROUND: Although frailty status is dynamic, whether improvements in frailty predict mortality is unknown. OBJECTIVE: Describe 1-year changes in a frailty index (FI) and association with 48-month mortality. DESIGN: Secondary analysis of the National Health in Aging Trends Study. SETTING: Community. PARTICIPANTS: Five thousand six hundred and seventy two Medicare beneficiaries 65 and older (3,267 (55.8%) females). MEASUREMENTS: A 40-item deficit accumulation FI was measured in 2011 and 2012, based on multidomain assessment including comorbidities, activities of daily living, physical tasks, cognition, and performance testing. We categorized 2011 FI into robust (FI < 0.15), pre-frail (FI = 0.15-0.24), mild frailty (FI = 0.25-0.34), and moderate to severe frailty (FI ≥ 0.35). Change in frailty was calculated as the FI change from 2011 to 2012, categorized as either absolute (>0.045 decrease, 0.015-0.045 decrease, ±0.015 change, 0.015-0.045 increase, >0.045 increase) or proportional change (>20% decrease, 5-20% decrease, ±5% change, 5-20% increase, 20% increase). We measured the association of FI change with 4-year mortality using Cox regression. RESULTS: From 2011 to 2012, mean FI increased by 0.02 (standard deviation 0.07), with 58.6% having an increase. Over 4 years, 1,039 participants (13.6%) died. After adjusting for age and sex, compared to stable frailty (±0.015), both absolute (>0.045) and proportional (>20%) increases in frailty were associated with higher mortality among pre-frail participants (hazard ratio (HR) = 2.35, 95% confidence interval (CI) = (1.45-3.79) and HR (95% CI) = 3.32 (1.76-6.26), respectively), participants with mild frailty (HR (95% CI) = 1.96 (1.35-2.85) and 2.03 (1.37-3.02)) and moderate or severe frailty (HR (95% CI) = 1.99 (1.48-2.67) and 1.94 (1.43-2.63)) but not robust participants (HR (95% CI)= 1.48 (0.86-2.54), HR (95% CI) = 1.62 (0.80-3.28)). However, decreases in FI were not significantly associated with decreased risk of mortality. CONCLUSIONS: Increasing deficit accumulation FI over 1 year is associated with increased mortality risk. While decreasing FI occurs, we did not find evidence to support reduced mortality risk.

Restricted mean survival time versus conventional measures for treatment decision-making.

BACKGROUND/OBJECTIVES: Restricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in the event-free days. It remains uncertain whether communicating treatment benefit and harm using RMST-based summary is more effective than conventional summary based on absolute and relative risk reduction. We compared the effect of RMST-based approach and conventional approach on decisional conflict using an example of intensive versus standard blood pressure-lowering strategies. DESIGN: On-line survey. SETTING: A convenience sample of patients in the United States. PARTICIPANTS: Two hundred adults aged 65 and older with hypertension requiring anti-hypertensive treatment (response rate 85.5%). INTERVENTIONS: Participants were randomly assigned to either RMST-based summary or conventional summary about the benefit and harm of blood pressure-lowering strategies. MEASUREMENTS: Decisional Conflict Scale (DCS), ranging from 0 (no conflict) to 100 (high conflict), and preference for intensive blood pressure-lowering strategy. RESULTS: Participants assigned to RMST-based approach (n = 100) and conventional approach (n = 100) had similar age (mean [standard deviation, SD]: 72.3 [5.6] vs 72.8 [5.5] years) and proportions of female (50 [50.0%] vs 61 [61.0%]) and white race (92 [92.0%] vs 92 [92.0%]). The mean (SD) DCS score was 25.2 (15.0) for RMST-based approach and 25.6 (14.1) for conventional approach (p = 0.84). The number (%) of participants who preferred intensive strategy was 10 (10.0%) for RMST-based approach and 14 (14.0%) for conventional approach (p = 0.52). The results were consistent in subgroups defined by age, sex, education level, cardiovascular disease status, and predicted mortality risk categories. CONCLUSION: In a sample of relatively healthy older adults with hypertension, RMST-based approach was as effective as conventional approach on decisional conflict about choosing a blood pressure-lowering strategy. This study provides proof-of-concept evidence that RMST-based approach can be used in conjunction with absolute and relative risk reduction for communicating treatment benefit and harm in a decision aid.

Beyond the Health Deficit Count: Examining Deficit Patterns in a Deficit-Accumulation Frailty Index.

BACKGROUND/OBJECTIVES: Exploring deficit patterns among frail people may reveal subgroups of different prognostic importance. DESIGN: Analysis of National Health and Aging Trends Study. SETTING: Community. PARTICIPANTS: Community dwelling older adults with mild to moderate frailty (deficit-accumulation frailty index (FI) of 0.25-0.40) (n = 1821). MEASUREMENTS: Latent class analysis identified distinct clinical subgroups based on comorbidity (range: 0-10), National Health and Aging Trends Study dementia classification, and short physical performance battery (SPPB) (range: 0-12). Survival analyses compared 5-year mortality by subgroups. RESULTS: Three latent classes existed: Class 1 (n = 831, mean FI = 0.30) had 2.7% probable dementia, high comorbidities (mean = 3.6), and low physical impairment (SPPB mean = 9.9); Class 2 (n = 734, mean FI = 0.32) had 6.9% probable dementia, low comorbidities (mean = 2.8), and moderate physical impairment (SPPB mean = 6.2); Class 3 (n = 256, mean FI = 0.34) had 20.7% probable dementia, low comorbidities (mean = 2.4), and high physical impairment (SPPB mean = 2.0). Compared to Class 1, Classes 2 and 3 experienced higher 5-year mortality (C2: 1.28 (95% confidence intervals (CI) = 1.00-1.62); C3: 1.87 (95% CI = 1.29-2.73)). CONCLUSION: Deficit patterns among the mild-to-moderately frail provide additional prognostic information and highlight opportunities for preventive interventions.

Risk Factors, Presentation, and Course of Coronavirus Disease 2019 in a Large, Academic Long-Term Care Facility.

OBJECTIVE: To describe clinical characteristics and risk factors associated with coronavirus disease 2019 (COVID-19) in long-stay nursing home residents. DESIGN AND PARTICIPANTS: Retrospective cohort study (March 16, 2020 to May 8, 2020). SETTING: Academic long-term chronic care facility (Boston, MA). PARTICIPANTS: Long-term care residents. METHODS: Patient characteristics and clinical symptoms were obtained via electronic medical records and Minimum Data Set. Staff residence was inferred by zip codes. COVID-19 infection was confirmed by polymerase chain reaction testing using nasopharyngeal swabs. Residents were followed until discharge from facility, death, or up to 21 days. Risks of COVID-19 infection were modeled by generalized estimating equation to estimate the relative risk (RR) and 95% confidence intervals (CI) of patient characteristics and staff community of residence. RESULTS: Overall 146 of 389 (37.5%) long-stay residents tested positive for COVID-19. At the time of positive test, 66 of 146 (45.5%) residents were asymptomatic. In the subsequent illness course, the most common symptom was anorexia (70.8%), followed by delirium (57.6%). During follow-up, 44 (30.1%) of residents with COVID-19 died. Mortality increased with frailty (16.7% in pre-frail, 22.2% in moderately frail, and 50.0% in frail; P < .001). The proportion of residents infected with COVID-19 varied across the long-term care units (range: 0%‒90.5%). In adjusted models, male sex (RR 1.80, 95% CI 1.07, 3.05), bowel incontinence (RR 1.97, 95% CI 1.10, 3.52), and staff residence remained significant predictors of COVID-19. For every 10% increase in the proportion of staff living in a high prevalence community, the risk of testing positive increased by 6% (95% CI 1.04, 1.08). CONCLUSIONS AND IMPLICATIONS: Among long-term care residents diagnosed with COVID-19, nearly one-half were asymptomatic at the time of diagnosis. Predictors of COVID-19 infection included male sex, bowel incontinence, and staff residence in a community with a high burden of COVID-19. Universal testing of patients and staff in communities with high COVID-19 rates is essential to mitigate outbreaks.

Changes in Predictive Performance of a Frailty Index with Availability of Clinical Domains.

OBJECTIVES: Determine the effects of missing data in frailty identification and risk prediction. DESIGN: Analysis of the National Health in Aging Trends Study. SETTING: Community. PARTICIPANTS: About 6206 older adults. MEASUREMENTS: A 41-variable frailty index (FI) was constructed with the following domains: comorbidities, activities of daily living (ADLs), instrumental activities of daily living, self-reported physical limitations, physical performance, and neuropsychiatric tests. We evaluated discrimination after removing single and multiple domains, comparing C-statistics for predicting 5-year risk of mortality and 1-year risks of disability and falls. RESULTS: The full FI yielded a mean of .18 and C-statistics of .72 (95% confidence interval, .70-.74) for mortality, .80 (.77-.82) for disability, and .66 (.64-.68) for falls. Removal of any single domain shifted the FI distribution, resulting in a mean FI ranging from .13 (removing comorbidities) to .20 (removing ADLs) and frailty prevalence (FI ≥ .25) from 16.0% to 28.7%. Among robust participants models missing ADLs misclassified most often, (19% as pre-frail). Among pre-frail and frail participants missing comorbidities misclassified most often(69.2% from pre-frail to robust, 24% from frail to pre-frail, and 4.9% from frail to robust). Removal of any single domain minimally changed C-statistics: mortality, .71-.73; disability, .79-.80; and falls, .64-.66. Removing neuropsychiatric testing and physical performance yielded comparable C-statistics of .70, .78, and .66 for mortality, ADLs, and falls, respectively. However, removal of three or four domains based on likely availability decreased C-statistics for mortality (.69, .66),disability (.75, .70), and falls (.64, .63), respectively. CONCLUSION: While FI discrimination is robust to missing information in any single domain, risk prediction is affected by absence of multiple domains. This work informs the application of FI as a clinical and research tool. J Am Geriatr Soc 68:1771-1777, 2020.

Cognition, Frailty, and Functional Outcomes of Transcatheter Aortic Valve Replacement.

BACKGROUND: Cognitive impairment and frailty are highly prevalent in older adults undergoing transcatheter aortic valve replacement. This study aimed to investigate the relationship of cognitive impairment and frailty with functional recovery after transcatheter aortic valve replacement. METHODS: This was a single-center prospective cohort study of 142 patients who were ≥70 years old and underwent transcatheter aortic valve replacement for aortic stenosis. Prior to transcatheter aortic valve replacement, cognitive impairment was defined as Mini-Mental State Examination score <24 points (range: 0-30), and moderate-to-severe frailty was defined as a deficit-accumulation frailty index ≥0.35 (range: 0-1). The functional status composite score, the number of 22 daily and physical tasks that a patient could perform independently, measured at baseline and 1, 3, 6, 9, and 12 months postoperatively were analyzed using linear mixed-effects model. RESULTS: The mean age was 84.2 years; 74 subjects were women (51.8%). Patients with moderate-to-severe frailty and cognitive impairment (n = 27, 19.0%) had the lowest functional status at baseline and throughout 12 months, while patients with mild or no frailty and no cognitive impairment (n = 48, 33.8%) had the best functional status. Patients with cognitive impairment alone (n = 19, 13.4%) had better functional status at baseline than those with moderate-to-severe frailty alone (n = 48, 33.8%), but their functional status scores merged and remained similar during the follow-up. CONCLUSIONS: Preoperative cognitive function plays a vital role in functional recovery after transcatheter aortic valve replacement, regardless of baseline frailty status. Impaired cognition may increase functional decline in the absence of frailty, whereas intact cognition may mitigate the detrimental effects of frailty. Cognitive assessment should be routinely performed prior to transcatheter aortic valve replacement.

Physical Performance and Risk of Postoperative Delirium in Older Adults Undergoing Aortic Valve Replacement.

BACKGROUND: Delirium is a major risk factor for poor recovery after surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). It is unclear whether preoperative physical performance tests improve delirium prediction. OBJECTIVE: To examine whether physical performance tests can predict delirium after SAVR and TAVR, and adapt an existing delirium prediction rule for cardiac surgery, which includes Mini-Mental State Examination (MMSE), depression, prior stroke, and albumin level. DESIGN: Prospective cohort, 2014-2017. SETTING: Single academic center. SUBJECTS: A total of 187 patients undergoing SAVR (n=77) or TAVR (n=110). METHODS: The Short Physical Performance Battery (SPPB) score was calculated based on gait speed, balance, and chair stands (range: 0-12 points, lower scores indicate poor performance). Delirium was assessed using the Confusion Assessment Method. We fitted logistic regression to predict delirium using SPPB components and risk factors of delirium. RESULTS: Delirium occurred in 35.8% (50.7% in SAVR and 25.5% in TAVR). The risk of delirium increased for lower SPPB scores: 10-12 (28.2%), 7-9 (34.5%), 4-6 (37.5%) and 0-3 (44.1%) (p-for-trend=0.001). A model that included gait speed <0.46 meter/second (OR, 2.7; 95% CI, 1.2-6.4), chair stands time ≥11.2 seconds (OR, 3.5; 95% CI, 1.0-12.4), MMSE <24 points (OR, 2.9; 95% CI, 1.3-6.4), isolated SAVR (OR, 5.4; 95% CI, 2.1-13.8), and SAVR and coronary artery bypass grafting (OR, 15.8; 95% CI, 5.5-45.7) predicted delirium better than the existing prediction rule (C statistics: 0.71 vs 0.61; p=0.035). CONCLUSION: Assessing physical performance, in addition to cognitive function, can help identify high-risk patients for delirium after SAVR and TAVR.