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 (>0.2). 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.

Validation of electronic diagnostic codes for rapid eye movement sleep behavior disorder.

We investigated the accuracy of International Classification of Diseases (ICD) codes for the identification of Veterans with rapid eye movement (REM) sleep behavior disorder (RBD). The charts of 139 randomly sampled Veterans with ≥1 ICD-9 and ICD-10 code(s) for RBD were reviewed for documentation of a suspected, previous, or current diagnosis; clinical symptoms; and/or empiric treatments for this disorder. Notably, 71 (51.1%) of patients with RBD electronic diagnoses lacked polysomnography (PSG), and 29 (20.9%) had PSG reports without commentary on REM sleep without atonia (RSWA). Sleep centers are therefore encouraged to include a brief sentence in PSG report templates commenting on the presence/absence of RSWA.

Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer.

This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.

The association between OSA and glycemic control in diabetes.

Background: Obstructive sleep apnea (OSA) is the most common sleep-realted respiratory disorder. It is frequently comorbid with cardiovascular, cerebrovascular, and metabolic diseases and is commonly observed in populations with these comorbidities. Investigators aimed to assess the effect of OSA on glycemic control in patients with diabetes. Methods: In this cross-sectional study, 266 adult patients with diabetes mellitus (DM) attending the outpatient endocrinology clinic at the Guilan University of Medical Sciences were enrolled. Patients completed a checklist that included demographic characteristics, factors, and laboratory results in addition to Berlin and STOP-BANG questionnaires to evaluate the risk of OSA. Data were analyzed by independent t-test, Mann-Whitney U test, and Chi-squared or Fisher's exact tests using the Statistical Package for the Social Sciences (SPSS) version 17. Results: A total of 266 patients with DM were enrolled in this study (34.6% males, mean age 47.00 ± 19.04 years). Based on the Berlin Questionnaire, 38.6% of all participants were at high risk of developing OSA. Based on the STOP-BANG Questionnaire (SBQ), 45.1% were at moderate and high risks. Additionally, this questionnaire showed a significant difference between low and moderate-to-severe groups regarding sex, age, body mass index (BMI), neck size, other chronic diseases, types of DM, use of insulin, Berlin Questionnaire, fasting blood sugar (FBS), and mean HbA1c. Conclusions: Based on the SBQ, our results indicated a significant relationship between OSA and glycemic control according to mean HbA1c and FBS. Therefore, by controlling the OSA, we may find a way to acheieve better glycemic control in diabetic patients.

Association Between Poor Sleep Quality and Glycemic Control in Adult Patients with Diabetes Referred to Endocrinology Clinic of Guilan: A Cross-sectional Study.

Background: Diabetes is a prevalent chronic medical comorbid condition worldwide. Diabetes mellitus is associated with various sleep disorders. Objectives: We aimed to determine the prevalence of poor sleep and the main factors of sleep interruptions in patients with diabetes mellitus. We further evaluated the association of sleep interruptions with glycemic control in this cohort. Methods: We conducted a cross-sectional study on 266 patients with type 1 and type 2 diabetes recruited from a university outpatient endocrinology clinic. Patients completed a checklist including demographic and disease-related characteristics in addition to the Pittsburgh Sleep Quality Index (PSQI) to evaluate sleep quality. Using the PSQI cutoff score of 5, we created two subgroups of good sleepers (GS) and poor sleepers (PS). Results: Our results showed that good sleeper and poor sleeper patients with diabetes were significantly different regarding sex, employment status, BMI, presence of diabetes-related complications, HbA1c, and 2-hour postprandial blood sugar (2HPPBS) (all significant at P < 0.05). The most prevalent factors of sleep interruptions were "waking up to use a bathroom", "feeling hot", "pain", "having coughs or snores", and "bad dreams". Among the subjective factors of sleep interruption, problems with sleep initiation, maintenance, or early morning awakenings in addition to having pain or respiratory problems such as coughing or snoring had the most significant associations with HbA1c. Conclusions: Our study showed significant subjective sleep disturbances (both quality and quantity) in patients with diabetes mellitus (both type I and II) and its association with diabetes control. We further identified the main factors that led to sleep interruptions in this cohort.