Social Frailty Index: Development and validation of an index of social attributes predictive of mortality in older adults.

While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.

MWF of the corpus callosum is a robust measure of remyelination: Results from the ReBUILD trial.

Myelin repair is an unrealized therapeutic goal in the treatment of multiple sclerosis (MS). Uncertainty remains about the optimal techniques for assessing therapeutic efficacy and imaging biomarkers are required to measure and corroborate myelin restoration. We analyzed myelin water fraction imaging from ReBUILD, a double-blind, randomized placebo-controlled (delayed treatment) remyelination trial, that showed a significant reduction in VEP latency in patients with MS. We focused on brain regions rich in myelin. Fifty MS subjects in two arms underwent 3T MRI at baseline and months 3 and 5. Half of the cohort was randomly assigned to receive treatment from baseline through 3 mo, whereas the other half received treatment from 3 mo to 5 mo post-baseline. We computed myelin water fraction changes occurring in normal-appearing white matter of corpus callosum, optic radiations, and corticospinal tracts. An increase in myelin water fraction was documented in the normal-appearing white matter of the corpus callosum, in correspondence with the administration of the remyelinating treatment clemastine. This study provides direct, biologically validated imaging-based evidence of medically induced myelin repair. Moreover, our work strongly suggests that significant myelin repair occurs outside of lesions. We therefore propose myelin water fraction within the normal-appearing white matter of the corpus callosum as a biomarker for clinical trials looking at remyelination.

One-Year Recovery Among Survivors of Prolonged Severe COVID-19: A National Multicenter Cohort.

OBJECTIVES: Understanding the long-term effects of severe COVID-19 illness on survivors is essential for effective pandemic recovery planning. Therefore, we investigated impairments among hospitalized adults discharged to long-term acute care hospitals (LTACHs) for prolonged severe COVID-19 illness who survived 1 year. DESIGN: The Recovery After Transfer to an LTACH for COVID-19 (RAFT COVID) study was a national, multicenter, prospective longitudinal cohort study. SETTING AND PATIENTS: We included hospitalized English-speaking adults transferred to one of nine LTACHs in the United States between March 2020 and February 2021 and completed a survey. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Validated instruments for impairments and free response questions about recovering. Among 282 potentially eligible participants who provided permission to be contacted, 156 (55.3%) participated (median age, 65; 38.5% female; 61.3% in good prior health; median length of stay of 57 d; 77% mechanically ventilated for a median of 26 d; 42% had a tracheostomy). Approximately two-thirds (64%) had a persistent impairment, including physical (57%), respiratory (49%; 19% on supplemental oxygen), psychiatric (24%), and cognitive impairments (15%). Nearly half (47%) had two or more impairment types. Participants also experienced persistent debility from hospital-acquired complications, including mononeuropathies and pressure ulcers. Participants described protracted recovery, attributing improvements to exercise/rehabilitation, support, and time. While considered life-altering with 78.7% not returning to their usual health, participants expressed gratitude for recovering; 99% returned home and 60% of previously employed individuals returned to work. CONCLUSIONS: Nearly two-thirds of survivors of among the most prolonged severe COVID-19 illness had persistent impairments at 1 year that resembled post-intensive care syndrome after critical illness plus debility from hospital-acquired complications.

Development of the UroARC Surgical Calculator: A Novel Risk Calculator for Older Adults Undergoing Surgery for Bladder Outlet Obstruction.

PURPOSE: Bladder outlet obstruction (BOO) is common in older adults. Many older adults who pursue surgery have additional vulnerabilities affecting surgical risk, including frailty. A clinical tool that builds on frailty to predict surgical outcomes for the spectrum of BOO procedures would be helpful to aid in surgical decision-making but does not currently exist. MATERIALS AND METHODS: Medicare beneficiaries undergoing BOO surgery from 2014 to 2016 were identified and analyzed using the Medicare MedPAR, Outpatient, and Carrier files. Eight different BOO surgery categories were created. Baseline frailty was calculated for each beneficiary using the Claims-Based Frailty Index (CFI). All 93 variables in the CFI and the 17 variables in the Charlson Comorbidity Index were individually entered into stepwise logistic regression models to determine variables most highly predictive of complications. Similar and duplicative variables were combined into categories. Calibration curves and tests of model fit, including C statistics, Brier scores, and Spiegelhalter P values, were calculated to ensure the prognostic accuracy for postoperative complications. RESULTS: In total, 212,543 beneficiaries were identified. Approximately 42.5% were prefrail (0.15 ≤ CFI < 0.25), 8.7% were mildly frail (0.25 ≤ CFI < 0.35), and 1.2% were moderately-to-severely frail (CFI ≥0.35). Using stepwise logistic regression, 13 distinct prognostic variable categories were identified as the most reliable predictors of postoperative outcomes. Most models demonstrated excellent model discrimination and calibration with high C statistic and Spiegelhalter P values, respectively, and high accuracy with low Brier scores. Calibration curves for each outcome demonstrated excellent model fit. CONCLUSIONS: This novel risk assessment tool may help guide surgical prognostication among this vulnerable population.

Association of Race and Ethnicity With Postoperative Gabapentinoid and Opioid Prescribing Trends for Older Adults.

BACKGROUND: Disparities in opioid prescribing by race/ethnicity have been described in many healthcare settings, with White patients being more likely to receive an opioid prescription than other races studied. As surgeons increase prescribing of nonopioid medications in response to the opioid epidemic, it is unknown whether postoperative prescribing disparities also exist for these medications, specifically gabapentinoids. METHODS: We conducted a retrospective cohort study using a 20% Medicare sample for 2013-2018. We included patients ≥66 years without prior gabapentinoid use who underwent one of 14 common surgical procedures. The primary outcome was the proportion of patients prescribed gabapentinoids at discharge among racial and ethnic groups. Secondary outcomes were days' supply of gabapentinoids, opioid prescribing at discharge, and oral morphine equivalent (OME) of opioid prescriptions. Trends over time were constructed by analyzing proportion of postoperative prescribing of gabapentinoids and opioids for each year. For trends by year by racial/ethnic groups, we ran a multivariable logistic regression with an interaction term of procedure year and racial/ethnic group. RESULTS: Of the 494,922 patients in the cohort (54% female, 86% White, 5% Black, 5% Hispanic, mean age 73.7 years), 3.7% received a new gabapentinoid prescription. Gabapentinoid prescribing increased over time for all groups and did not differ significantly among groups (P = 0.13). Opioid prescribing also increased, with higher proportion of prescribing to White patients than to Black and Hispanic patients in every year except 2014. CONCLUSIONS: We found no significant prescribing variation of gabapentinoids in the postoperative period between racial/ethnic groups. Importantly, we found that despite national attention to disparities in opioid prescribing, variation continues to persist in postoperative opioid prescribing, with a higher proportion of White patients being prescribed opioids, a difference that persisted over time.

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery.

INTRODUCTION: Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist. MATERIALS AND METHODS: Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined. RESULTS: In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy. CONCLUSIONS: Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

Patterns of peripartum depression and anxiety during the pre-vaccine COVID-19 pandemic.

BACKGROUND: Pregnant people are vulnerable to new or worsening mental health conditions. This study aims to describe prevalence and course of depression and anxiety symptoms in pregnancy during the pre-vaccine COVID-19 pandemic. METHODS: This is a prospective cohort study of pregnant individuals with known or suspected COVID-19. Participants completed Edinburgh Postnatal Depression Scale (EPDS) and Generalized-Anxiety Disorder-7 (GAD-7) questionnaires, screening tools for depression and anxiety, at 34weeks gestational age, 6-8weeks postpartum, and 6months postpartum. Prevalence of elevated depressive and anxiety symptoms at each visit was described. Univariable logistic regression analysis was used to determine the association between demographic and clinical factors and those with elevated depression or anxiety symptoms. RESULTS: 317 participants were included. The prevalence of elevated antepartum depression symptoms was 14.6%, 10.3%, and 20.6% at 34weeks gestational age, 6-8weeks postpartum, and 6months postpartum, respectively. The rate of elevated anxiety symptoms was 15.1%, 10.0%, and 17.3% at 34weeks gestational age, 6-8weeks postpartum, and 6months postpartum, respectively. A prior history of depression and/or anxiety (p's < 0.03), as well as higher EPDS and GAD-7 scores at enrollment (p's < 0.04) associated with elevated depression and anxiety symptoms throughout pregnancy and the postpartum period. Quarantining during pregnancy was associated with elevated anxiety symptoms at 34weeks gestational age in univariate (P = 0.027) analyses. COVID-19 diagnosis and hospitalization were not associated with elevated depression or anxiety symptoms. CONCLUSIONS: Elevated depression and anxiety symptoms were prevalent throughout pregnancy and the postpartum period, particularly in those with prior depression and/or anxiety and who quarantined. Strategies that target social isolation may mitigate potential adverse consequences for pregnant people, and continued vigilance in recognition of depression and anxiety in pregnancy should be considered.

Privacy, bias and the clinical use of facial recognition technology: A survey of genetics professionals.

Facial recognition technology (FRT) has been adopted as a precision medicine tool. The medical genetics field highlights both the clinical potential and privacy risks of this technology, putting the discipline at the forefront of a new digital privacy debate. Investigating how geneticists perceive the privacy concerns surrounding FRT can help shape the evolution and regulation of the field, and provide lessons for medicine and research more broadly. Five hundred and sixty-two genetics clinicians and researchers were approached to fill out a survey, 105 responded, and 80% of these completed. The survey consisted of 48 questions covering demographics, relationship to new technologies, views on privacy, views on FRT, and views on regulation. Genetics professionals generally placed a high value on privacy, although specific views differed, were context-specific, and covaried with demographic factors. Most respondents (88%) agreed that privacy is a basic human right, but only 37% placed greater weight on it than other values such as freedom of speech. Most respondents (80%) supported FRT use in genetics, but not necessarily for broader clinical use. A sizeable percentage (39%) were unaware of FRT's lower accuracy rates in marginalized communities and of the mental health effects of privacy violations (62%), but most (76% and 75%, respectively) expressed concern when informed. Overall, women and those who self-identified as politically progressive were more concerned about the lower accuracy rates in marginalized groups (88% vs. 64% and 83% vs. 63%, respectively). Younger geneticists were more wary than older geneticists about using FRT in genetics (28% compared to 56% "strongly" supported such use). There was an overall preference for more regulation, but respondents had low confidence in governments' or technology companies' ability to accomplish this. Privacy views are nuanced and context-dependent. Support for privacy was high but not absolute, and clear deficits existed in awareness of crucial FRT-related discrimination potential and mental health impacts. Education and professional guidelines may help to evolve views and practices within the field.

The Epidemiology of Smoking in Older Adults: A National Cohort Study.

BACKGROUND: Older smokers account for the greatest tobacco-related morbidity and mortality in the USA, while quitting smoking remains the single most effective preventive health intervention for reducing the risk of smoking-related illness. Yet, knowledge about patterns of smoking and smoking cessation in older adults is lacking. OBJECTIVE: Assess trends in prevalence of cigarette smoking between 1998 and 2018 and identify patterns and predictors of smoking cessation in US older adults. DESIGN: Retrospective cohort study PARTICIPANTS: Individuals aged 55+ enrolled in the nationally representative Health and Retirement Study, 1998-2018 MAIN MEASURES: Current smoking was assessed with the question: "Do you smoke cigarettes now?" Quitting smoking was defined as having at least two consecutive waves (between 2 and 4 years) in which participants who were current smokers in 1998 reported they were not currently smoking in subsequent waves. KEY RESULTS: Age-adjusted smoking prevalence decreased from 15.9% in 1998 (95% confidence interval (CI) 15.2, 16.7) to 11.2% in 2018 (95% CI 10.4, 12.1). Among 2187 current smokers in 1998 (mean age 64, 56% female), 56% of those living to age 90 had a sustained period of smoking cessation. Smoking less than 10 cigarettes/day was strongly associated with an increased likelihood of quitting smoking (subdistribution hazard ratio 2.3; 95% CI 1.9, 2.8), compared to those who smoked more than 20 cigarettes/day. CONCLUSIONS: Smoking prevalence among older persons has declined and substantial numbers of older smokers succeed in quitting smoking for a sustained period. These findings highlight the need for continued aggressive efforts at tobacco cessation among older persons.

Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

BACKGROUND: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. OBJECTIVE: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. DESIGN: This was a cohort study. SETTING: Veterans Affairs (VA) EHR data. PARTICIPANTS: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). MEASUREMENTS AND ANALYTIC METHODS: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. RESULTS: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. LIMITATION: Our results should be confirmed in non-VA EHRs. CONCLUSION: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.

Plasma neurofilament light chain levels suggest neuroaxonal stability following therapeutic remyelination in people with multiple sclerosis.

BACKGROUND: Chronic demyelination is a major contributor to axonal vulnerability in multiple sclerosis (MS). Therefore, remyelination could provide a potent neuroprotective strategy. The ReBUILD trial was the first study showing evidence for successful remyelination following treatment with clemastine in people with MS (pwMS) with no evidence of disease activity or progression (NEDAP). Whether remyelination was associated with neuroprotection remains unexplored. METHODS: Plasma neurofilament light chain (NfL) levels were measured from ReBUILD trial's participants. Mixed linear effect models were fit for individual patients, epoch and longitudinal measurements to compare NfL concentrations between samples collected during the active and placebo treatment period. RESULTS: NfL concentrations were 9.6% lower in samples collected during the active treatment with clemastine (n=53, geometric mean=6.33 pg/mL) compared to samples collected during treatment with placebo (n=73, 7.00 pg/mL) (B=-0.035 [-0.068 to -0.001], p=0.041). Applying age- and body mass index-standardised NfL Z-scores and percentiles revealed similar results (0.04 vs 0.35, and 27.5 vs 33.3, p=0.023 and 0.042, respectively). Higher NfL concentrations were associated with more delayed P100 latencies (B=1.33 [0.26 to 2.41], p=0.015). In addition, improvement of P100 latencies between visits was associated with a trend for lower NfL values (B=0.003 [-0.0004 to 0.007], p=0.081). Based on a Cohen's d of 0.248, a future 1:1 parallel-arm placebo-controlled study using a remyelinating agent with comparable effect as clemastine would need 202 subjects per group to achieve 80% power. CONCLUSIONS: In pwMS, treatment with the remyelinating agent clemastine was associated with a reduction of blood NfL, suggesting that neuroprotection is achievable and measurable with therapeutic remyelination. TRIAL REGISTRATION NUMBER: NCT02040298.

Predicting Life Expectancy to Target Cancer Screening Using Electronic Health Record Clinical Data.

BACKGROUND: Guidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry. OBJECTIVE: To develop and internally validate a life expectancy calculator from structured EHR data. DESIGN: Retrospective cohort study using national Veteran's Affairs (VA) EHR databases. PATIENTS: Veterans aged 50+ with a primary care visit during 2005. MAIN MEASURES: We assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration. KEY RESULTS: In 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration. CONCLUSIONS: We developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.

COVID-19 severity and age increase the odds of delirium in hospitalized adults with confirmed SARS-CoV-2 infection: a cohort study.

BACKGROUND: Despite recognition of the neurologic and psychiatric complications associated with SARS-CoV-2 infection, the relationship between coronavirus disease 19 (COVID-19) severity on hospital admission and delirium in hospitalized patients is poorly understood. This study sought to measure the association between COVID-19 severity and presence of delirium in both intensive care unit (ICU) and acute care patients by leveraging an existing hospital-wide systematic delirium screening protocol. The secondary analyses included measuring the association between age and presence of delirium, as well as the association between delirium and safety attendant use, restraint use, discharge home, and length of stay. METHODS: In this single center retrospective cohort study, we obtained electronic medical record (EMR) data using the institutional Epic Clarity database to identify all adults diagnosed with COVID-19 and hospitalized for at least 48-h from February 1-July 15, 2020. COVID-19 severity was classified into four groups. These EMR data include twice-daily delirium screenings of all patients using the Nursing Delirium Screening Scale (non-ICU) or CAM-ICU (ICU) per existing hospital-wide protocols. RESULTS: A total of 99 patients were diagnosed with COVID-19, of whom 44 patients required ICU care and 17 met criteria for severe disease within 24-h of admission. Forty-three patients (43%) met criteria for delirium at any point in their hospitalization. Of patients with delirium, 24 (56%) were 65 years old or younger. After adjustment, patients meeting criteria for the two highest COVID-19 severity groups within 24-h of admission had 7.2 times the odds of having delirium compared to those in the lowest category [adjusted odds ratio (aOR) 7.2; 95% confidence interval (CI) 1.9, 27.4; P = 0.003]. Patients > 65 years old had increased odds of delirium compared to those < 45 years old (aOR 8.7; 95% CI 2.2, 33.5; P = 0.003). Delirium was associated with increased odds of safety attendant use (aOR 4.5; 95% CI 1.0, 20.7; P = 0.050), decreased odds of discharge home (aOR 0.2; 95% CI 0.06, 0.6; P = 0.005), and increased length of stay (aOR 7.5; 95% CI 2.0, 13; P = 0.008). CONCLUSIONS: While delirium is common in hospitalized patients of all ages with COVID-19, it is especially common in those with severe disease on hospital admission and those who are older. Patients with COVID-19 and delirium, compared to COVID-19 without delirium, are more likely to require safety attendants during hospitalization, less likely to be discharged home, and have a longer length of stay. Individuals with COVID-19, including younger patients, represent an important population to target for delirium screening and management as delirium is associated with important differences in both clinical care and disposition.

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort.

BACKGROUND: Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index. METHODS: This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots. RESULTS: In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration. CONCLUSIONS: Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Association of Race and Ethnicity With Incidence of Dementia Among Older Adults.

Importance: The racial and ethnic diversity of the US, including among patients receiving their care at the Veterans Health Administration (VHA), is increasing. Dementia is a significant public health challenge and may have greater incidence among older adults from underrepresented racial and ethnic minority groups. Objective: To determine dementia incidence across 5 racial and ethnic groups and by US geographical region within a large, diverse, national cohort of older veterans who received care in the largest integrated health care system in the US. Design, Setting, and Participants: Retrospective cohort study within the VHA of a random sample (5% sample selected for each fiscal year) of 1 869 090 participants aged 55 years or older evaluated from October 1, 1999, to September 30, 2019 (the date of final follow-up). Exposures: Self-reported racial and ethnic data were obtained from the National Patient Care Database. US region was determined using Centers for Disease Control and Prevention (CDC) regions from residential zip codes. Main Outcomes and Measures: Incident diagnosis of dementia (9th and 10th editions of the International Classification of Diseases). Fine-Gray proportional hazards models were used to examine time to diagnosis, with age as the time scale and accounting for competing risk of death. Results: Among the 1 869 090 study participants (mean age, 69.4 [SD, 7.9] years; 42 870 women [2%]; 6865 American Indian or Alaska Native [0.4%], 9391 Asian [0.5%], 176 795 Black [9.5%], 20 663 Hispanic [1.0%], and 1 655 376 White [88.6%]), 13% received a diagnosis of dementia over a mean follow-up of 10.1 years. Age-adjusted incidence of dementia per 1000 person-years was 14.2 (95% CI, 13.3-15.1) for American Indian or Alaska Native participants, 12.4 (95% CI, 11.7-13.1) for Asian participants, 19.4 (95% CI, 19.2-19.6) for Black participants, 20.7 (95% CI, 20.1-21.3) for Hispanic participants, and 11.5 (95% CI, 11.4-11.6) for White participants. Compared with White participants, the fully adjusted hazard ratios were 1.05 (95% CI, 0.98-1.13) for American Indian or Alaska Native participants, 1.20 (95% CI, 1.13-1.28) for Asian participants, 1.54 (95% CI, 1.51-1.57) for Black participants, and 1.92 (95% CI, 1.82-2.02) for Hispanic participants. Across most US regions, age-adjusted dementia incidence rates were highest for Black and Hispanic participants, with rates similar among American Indian or Alaska Native, Asian, and White participants. Conclusions and Relevance: Among older adults who received care at VHA medical centers, there were significant differences in dementia incidence based on race and ethnicity. Further research is needed to understand the mechanisms responsible for these differences.

Incidence of Severe COVID-19 Illness Following Vaccination and Booster With BNT162b2, mRNA-1273, and Ad26.COV2.S Vaccines.

Importance: Evidence describing the incidence of severe COVID-19 illness following vaccination and booster with BNT162b2, mRNA-1273, and Ad26.COV2.S vaccines is needed, particularly for high-risk populations. Objective: To describe the incidence of severe COVID-19 illness among a cohort that received vaccination plus a booster vaccine dose. Design, Setting, and Participants: Retrospective cohort study of adults receiving care at Veterans Health Administration facilities across the US who received a vaccination series plus 1 booster against SARS-CoV-2, conducted from July 1, 2021, to May 30, 2022. Patients were eligible if they had received a primary care visit in the prior 2 years and had documented receipt of all US Food and Drug Administration-authorized doses of the initial mRNA vaccine or viral vector vaccination series after December 11, 2020, and a subsequent documented booster dose between July 1, 2021, and April 29, 2022. The analytic cohort consisted of 1 610 719 participants. Exposures: Receipt of any combination of mRNA-1273 (Moderna), BNT162b2 (Pfizer-BioNTech), and Ad26.COV2.S (Janssen/Johnson & Johnson) primary vaccination series and a booster dose. Main Outcomes and Measures: Outcomes were breakthrough COVID-19 (symptomatic infection), hospitalization with COVID-19 pneumonia and/or death, and hospitalization with severe COVID-19 pneumonia and/or death. A subgroup analysis of nonoverlapping populations included those aged 65 years or older, those with high-risk comorbid conditions, and those with immunocompromising conditions. Results: Of 1 610 719 participants, 1 100 280 (68.4%) were aged 65 years or older and 132 243 (8.2%) were female; 1 133 785 (70.4%) had high-risk comorbid conditions, 155 995 (9.6%) had immunocompromising conditions, and 1 467 879 (91.1%) received the same type of mRNA vaccine (initial series and booster). Over 24 weeks, 125.0 (95% CI, 123.3-126.8) per 10 000 persons had breakthrough COVID-19, 8.9 (95% CI, 8.5-9.4) per 10 000 persons were hospitalized with COVID-19 pneumonia or died, and 3.4 (95% CI, 3.1-3.7) per 10 000 persons were hospitalized with severe pneumonia or died. For high-risk populations, incidence of hospitalization with COVID-19 pneumonia or death was as follows: aged 65 years or older, 1.9 (95% CI, 1.4-2.6) per 10 000 persons; high-risk comorbid conditions, 6.7 (95% CI, 6.2-7.2) per 10 000 persons; and immunocompromising conditions, 39.6 (95% CI, 36.6-42.9) per 10 000 persons. Subgroup analyses of patients hospitalized with COVID-19 pneumonia or death by time after booster demonstrated similar incidence estimates among those aged 65 years or older and with high-risk comorbid conditions but not among those with immunocompromising conditions. Conclusions and Relevance: In a US cohort of patients receiving care at Veterans Health Administration facilities during a period of Delta and Omicron variant predominance, there was a low incidence of hospitalization with COVID-19 pneumonia or death following vaccination and booster with any of BNT162b2, mRNA-1273, or Ad26.COV2.S vaccines.

Infant Outcomes Following Maternal Infection With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): First Report From the Pregnancy Coronavirus Outcomes Registry (PRIORITY) Study.

Infant outcomes after maternal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not well described. In a prospective US registry of 263 infants, maternal SARS-CoV-2 status was not associated with birth weight, difficulty breathing, apnea, or upper or lower respiratory infection through 8 weeks of age.

Comparative Outcomes for Pelvic Organ Prolapse Surgery among Nursing Home Residents and Matched Community Dwelling Older Adults.

PURPOSE: We compared short and long-term outcomes between nursing home residents and matched community dwelling older adults undergoing surgery for pelvic organ prolapse. MATERIALS AND METHODS: This retrospective cohort study evaluates women 65 years old or older undergoing different types of pelvic organ prolapse repairs (anterior/posterior, apical and colpocleisis) between 2007 and 2012 using Medicare claims and the Minimum Data Set for Nursing Home Residents. Long-stay nursing home residents were identified and propensity score matched (1:2) to community dwelling older individuals based on procedure type, age, race and Charlson score. Generalized estimating equation models were created to determine the relative risk of hospital length of stay 3 or more days, 30-day complications and 1-year mortality between the 2 groups. Kaplan-Meier curves were created comparing 1-year mortality between groups. RESULTS: There were 799 nursing home residents and 1,598 matched community dwelling older adults who underwent pelvic organ prolapse surgery and were included in our analyses. Nursing home residents demonstrated statistically significant increased risk for hospital length of stay 3 or more days (38.9% vs 18.6%, adjusted RR 2.1, 95% CI 1.8-2.4), 30-day complications (15.1% vs 3.8%, aRR 3.9, 95% CI 2.9-5.3) and 1-year mortality (11.1% vs 3.2%, aRR 3.5, 95% CI 2.5-4.8) compared to community dwelling older adults. Kaplan-Meier curves illustrated similar survival findings at 1 year (11.1%, 95% CI 9.0-13.3 vs 3.2%, 95% CI 2.3-4.1, p <0.0001). CONCLUSIONS: Despite matching on several characteristics, nursing home residents demonstrated worse short and long-term outcomes compared to community dwelling older adults, suggesting other key vulnerabilities exist that contribute additional surgical risk in this population.

Preoperative Medical Testing and Falls in Medicare Beneficiaries Awaiting Cataract Surgery.

PURPOSE: Delaying cataract surgery is associated with an increased risk of falls, but whether routine preoperative testing delays cataract surgery long enough to cause clinical harm is unknown. We sought to determine whether the use of routine preoperative testing leads to harm in the form of delayed surgery and falls in Medicare beneficiaries awaiting cataract surgery. DESIGN: Retrospective, observational cohort study using 2006-2014 Medicare claims. PARTICIPANTS: Medicare beneficiaries 66+ years of age with a Current Procedural Terminology claim for ocular biometry. METHODS: We measured the mean and median number of days between biometry and cataract surgery, calculated the proportion of patients waiting ≥ 30 days or ≥ 90 days for surgery, and determined the odds of sustaining a fall within 90 days of biometry among patients of high-testing physicians (testing performed in ≥ 75% of their patients) compared with patients of low-testing physicians. We also estimated the number of days of delay attributable to high-testing physicians. MAIN OUTCOME MEASURES: Incidence of falls occurring between biometry and surgery, odds of falling within 90 days of biometry, and estimated delay associated with physician testing behavior. RESULTS: Of 248 345 beneficiaries, 16.4% were patients of high-testing physicians. More patients of high-testing physicians waited ≥ 30 days and ≥ 90 days to undergo surgery (31.4% and 8.2% vs. 25.0% and 5.5%, respectively; P < 0.0001 for both). Falls before surgery in patients of high-testing physicians increased by 43% within the 90 days after ocular biometry (1.0% vs. 0.7%; P < 0.0001). The adjusted odds ratio of falling within 90 days of biometry in patients of high-testing physicians versus low-testing physicians was 1.10 (95% confidence interval [CI], 1.03-1.19; P = 0.008). After adjusting for surgical wait time, the odds ratio decreased to 1.07 (95% CI, 1.00-1.15; P = 0.06). The delay associated with having a high-testing physician was approximately 8 days (estimate, 7.97 days; 95% CI, 6.40-9.55 days; P < 0.0001). Other factors associated with delayed surgery included patient race (non-White), Northeast region, ophthalmologist ≤ 40 years of age, and low surgical volume. CONCLUSIONS: Overuse of routine preoperative medical testing by high-testing physicians is associated with delayed surgery and increased falls in cataract patients awaiting surgery.

Engaging Diverse Older Adults With Cognitive Impairment and Caregivers in Advance Care Planning: A Pilot Study of the Interactive PREPARE Website.

INTRODUCTION: Engaging patients with cognitive impairment in advance care planning (ACP), including completing advance directives and naming health care proxies, before they lose decision-making capacity is important. METHODS: We determined the feasibility of the PREPAREforYourCare.org ACP program among 20 diverse older adults with mild-to-moderate cognitive impairment and their caregivers in a 1-week, pre-post pilot. We examined ease-of-use, satisfaction, and feasibility using validated scales, and change in ACP Engagement scores, including knowledge, contemplation, self-efficacy, and readiness subscales (5-point scales), from baseline to 1-week. RESULTS: Participants were on average 70 years old (SD=9.0), 45% Spanish-speaking, 60% had limited health literacy, and 15% felt comfortable using the internet. Patients and caregivers rated PREPARE a mean of 8.6 (SD=1.6) and 9.4 (SD=1.1) on the 10-point ease-of-use scale, 4.7 (SD=0.4) and 4.7 (SD=0.3) on the 5-point satisfaction scale, and 4.9 (SD=0.4) and 4.8 (SD=0.6) on the 5-point feasibility scale, respectively. ACP engagement scores increased for 16 of 20 (80%) patients (P=0.03) and 16 of 20 (80%) caregivers (P=0.18). Caregivers experienced increased knowledge (3.8 to 4.7, P=0.002) and self-efficacy (3.6 to 4.5, P=0.034) for ACP. DISCUSSION: The PREPARE website was feasible and may facilitate ACP engagement among diverse older adults with cognitive impairment and their caregivers.