What Is the Additive Value of Nutritional Deficiency to Va-Fi in the Risk Assessment For Heart Failure Patients?

OBJECTIVES: to assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure. METHODS: A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (\u3e0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32 RESULTS: We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m CONCLUSION: Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF

Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study

Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies

Developing a parsimonious frailty index for older, multimorbid adults with heart failure using machine learning

Frailty is associated with adverse outcomes in heart failure (HF). A parsimonious frailty index (FI) that predicts outcomes of older, multimorbid patients with HF could be a useful resource for clinicians. A retrospective study of veterans hospitalized from October 2015 to October 2018 with HF, aged ≥50 years, and discharged home developed a 10-item parsimonious FI using machine learning from diagnostic codes, laboratory results, vital signs, and ejection fraction (EF) from outpatient encounters. An unsupervised clustering technique identified 5 FI strata: severely frail, moderately frail, mildly frail, prefrail, and robust. We report hazard ratios (HRs) of mortality, adjusting for age, gender, race, and EF and odds ratios (ORs) for 30-day and 1-year emergency department visits and all-cause hospitalizations after discharge. We identified 37,431 veterans (age, 73 ± 10 years; co-morbidity index, 5 ± 3; 43.5% with EF ≤40%). All frailty groups had a higher mortality than the robust group: severely frail (HR 2.63, 95% confidence interval [CI] 2.42 to 2.86), moderately frail (HR 2.04, 95% CI 1.87 to 2.22), mildly frail (HR 1.60, 95% CI 1.47 to 1.74), and prefrail (HR 1.18, 95% CI: 1.07 to 1.29). The associations between frailty and mortality remained unchanged in the stratified analysis by age or EF. The combined (severely, moderately, and mildly) frail group had higher odds of 30-day emergency visits (OR 1.62, 95% CI 1.43 to 1.83), all-cause readmission (OR, 1.75, 95% CI 1.52 to 2.02), 1-year emergency visits (OR 1.70, 95% CI 1.53 to 1.89), rehospitalization (OR 2.18, 95% CI 1.97 to 2.41) than the robust group. In conclusion, a 10-item FI is associated with postdischarge outcomes among patients discharged home after a hospitalization for HF. A parsimonious FI may aid clinical prediction at the point of care

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

Objectives: To assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure.Methods: A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (\u3e0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32nd deficit with the total number of deficits divided by 32. Frailty levels used the same cut-offs as the VA-FI. We compared categories based on VA-FI to those based on VA-FI-Nutrition and estimated the hazard ratio (HR) for post-discharge all-cause mortality over the study period as the primary outcome and other adverse events as secondary outcomes among patients with reduced or preserved ejection fraction in each VA-FI and VA-FI-Nutrition frailty groups.Results: We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m2). In general, VA-FI-Nutrition reclassified 1959 (18.6%) Veterans to a higher frailty level. The VA-FI identified 1,880 (5%) as robust, 8,644 (23%) as prefrail, and 27,077 (72%) as frail. The VA-FI-Nutrition reclassified 382 (20.3%) from robust to prefrail and 1577 (18.2%) from prefrail to frail creating the modified-prefrail and modified-frail categories based on the VA-FI-Nutrition. We observed shorter time-to-death among Veterans reclassified to a higher frailty status vs. those who remained in their original group (Median of 2.8 years (IQR:0.5,6.8) in modified-prefrail vs. 6.3 (IQR:1.8,6.8) years in robust, and 2.2 (IQR:0.7,5.7) years in modified-frail vs. 3.9 (IQR:1.4,6.8) years in prefrail). The adjusted HR in the reclassified groups was also significantly higher in the VA-FI-Nutrition frailty categories with a 38% increase in overall all-cause mortality among modified-prefrail and a 50% increase among modified-frails. Similar trends of increasing adverse events were also observed among reclassified groups for other clinical outcomes.Conclusion: Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF

Caregiver recruitment strategies for interventions designed to optimize transitions from hospital to home: lessons from a randomized trial

Abstract Challenges to recruitment of family caregivers exist and are amplified when consent must occur in the context of chaotic healthcare circumstances, such as the transition from hospital to home. The onset of the COVID-19 pandemic during our randomized controlled trial provided an opportunity for a natural experiment exploring and examining different consent processes for caregiver recruitment. The purpose of this publication is to describe different recruitment processes (in-person versus virtual) and compare diversity in recruitment rates in the context of a care recipient’s hospitalization. We found rates of family caregiver recruitment for in-person versus virtual were 28% and 23%, respectively (p = 0.01). Differences existed across groups with family caregivers recruited virtually being more likely to be younger, white, have greater than high school education, and not be a spouse or significant other to the care recipient, such as a child. Future work is still needed to identify the modality and timing of family caregiver recruitment to maximize rates and enhance the representativeness of the population for equitable impact