Background
Frailty, a state of vulnerability to stressors resulting from loss of physiological reserve due to multisystemic dysfunction, is common among hospitalized older adults. Hospital clinicians ...need objective and practical instruments that identify older adults with frailty. The FI‐LAB is based on laboratory values and vital signs and may capture biological changes of frailty that predispose hospitalized older adults to complications. The study's aim was to assess the association of the FI‐LAB versus VA‐FI with hospital and post‐hospital clinical outcomes in older adults.
Methods
Retrospective cohort study was conducted on Veterans aged ≥60 admitted to a VA hospital. We identified acute hospitalizations January 2011‐December‐2014 with 1‐year follow‐up. A 31‐item FI‐LAB was created from blood laboratory tests and vital signs collected within the first 48 h of admission and scores were categorized as low (<0.25), moderate (0.25–0.40), and high (>0.40). For each FI‐LAB group, we obtained odds ratio (OR) and confidence intervals (CI) for hospital and post‐hospital outcomes using multivariate binomial logistic regression. Additionally, we calculated hazard ratios (HR) and CI for all‐cause in‐hospital mortality comparing the high and moderate FI‐LAB group with the low group.
Results
Patients were 1407 Veterans, mean age 72.7 (SD = 9.0), 67.8% Caucasian, 96.1% males, 47.0% (n = 661), 41.0% (n = 577), and 12.0% (n = 169) were in the low, moderate, and high FI‐LAB groups, respectively. Moderate and high scores were associated with prolonged LOS, OR:1.62 (95% CI:1.29–2.03); and 3.36 (95% CI:2.27–4.99), ICU admission, OR:1.40 (95% CI:1.03–1.90); and OR:2.00 (95% CI:1.33–3.02), nursing home placement OR:2.36 (95% CI:1.26–4.44); and 5.99 (95% CI:2.83–12.70), 30‐day readmissions OR:1.74 (95% CI:1.20–2.52); and 2.20 (95% CI:1.30–3.74), 30‐day mortality OR: 2.51 (95% CI:1.01–6.23); and 8.97 (95% CI:3.42–23.53), 6‐month mortality OR:3.00 (95% CI:1.90–4.74); and 6.16 (95% CI:3.55–10.71), and 1‐year mortality OR: 2.66 (95% CI:1.87–3.79); and 4.76 (95% CI:3.00–7.54) respectively. The high FI‐LAB group showed higher risk of in‐hospital mortality, HR:18.17 (95% CI:4.01–80.52) with an area‐under‐the‐curve of 0.843 (95% CI:0.75–0.93).
Conclusions
High and moderate FI‐LAB scores were associated with worse in‐hospital and post‐hospital outcomes. The FI‐LAB may identify hospitalized older patients with frailty at higher risk and assist clinicians in implementing strategies to improve outcomes.
Introduction: Frailty is a state of vulnerability characterized by multisystemic physiological decline. The Pictorial Fit Frail Scale (PFFS) is a practical, image-based assessment that may facilitate ...the assessment of frailty in individuals with inadequate health literacy (HL). Objective: Determine the concurrent validity and feasibility of the PFFS in older Veterans with different levels of HL and cognition. Methods: Cross-sectional study in a geriatric clinic at a Veteran Health Administration (VHA) medical center. Veterans ≥65 years old completed a HL evaluation, PFFS, FRAIL scale and cognitive screening. We assessed the associations between PFFS, FRAIL scale, and VA-Frailty Index (VA-FI), and compared PFFS and FRAIL scale accuracy with a Receiver Operating Characteristic curve, Area Under the Curve (AUC) analysis, using the VA-FI as reference. Results: Eighty-three Veterans, mean age 76.20 (SD = 6.02) years, 65.1% Caucasian, 69.9% had inadequate HL, 57.8% were frail and 20.5% had cognitive impairment. All participants completed the 43 PFFS items. There were positive correlations between PFFS and VA-FI, r = .55 (95% CI: 0.365–0.735, p < .001), and FRAIL scale, r = .673 (95% CI: 0.509–0.836, p < .001). Compared to the VA-FI, the PFFS (AUC = 0.737; 95% CI: 0.629–0.844) and FRAIL scale (AUC = 0.724;95% CI: 0.615–0.824; p < .001) showed satisfactory diagnostic accuracy. Conclusions: The PFFS is valid and feasible in evaluating frailty in older Veterans with different levels of HL and cognition.
Background
The evidence that lower socio‐economic status (SES) may be associated with late‐onset dementia is inconsistent. Differences may be related to how SES was assessed. The area deprivation ...index (ADI) is a validated, composite measure of neighborhood socioeconomic status representing a geographic area level of social deprivation. Previous research has shown an association between ADI and cognitive impairment, but less is known about the Veteran population. The study aim was to determine the cross‐sectional association between the area deprivation index (ADI) and cognitive impairment in a Veteran population.
Method
This is a cross‐sectional study among community‐dwelling Veterans aged ≥50 years enrolled in a VA primary care clinic from July 2019‐May 2020. Patients were mailed questionnaires including sociodemographic, the Self‐Administered Gerocognitive Examination (SAGE). Clinical information was extracted from the electronic health records (EHR). To assess frailty, we used a 31‐item VA Frailty Index (VA‐FI) generated from EHR data matched to the study date. We calculated the ADI from 17 socioeconomic indicators available from the US Census. ADIs were generated for census tracts corresponding to veterans’ addresses. Higher ADI values are consistent with increased deprivation. After adjusting for age, gender, marital status, race, ethnicity, body mass index and frailty, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using binomial logistic regression models with cognitive impairment (MCI and dementia) as the outcome variable and ADI as the independent variable.
Result
Participants were 1060 Veterans (response rate of 20.14%), mean age of 68.39 (SD=8.52) years, 59.0% Caucasian, 69.2% non‐Hispanic, 95.8% male, 12.0% screened positive for MCI and 15.6% for dementia. Among ADI quintile groups, patients in the highest group had a significantly higher proportion of cognitive impairment (40.59%) when compared to lower groups, p<.001. No significant differences were found between quintiles 1 to 4. The highest ADI quintile was the only group associated with cognitive impairment, adjusted OR: 1.916 (95% CI:1.187‐3.093), p=.008.
Conclusion
This study shows a cross‐sectional association between ADI and dementia in community‐dwelling older Veterans. Further investigation of the links between neighborhood social deprivation and cognitive impairment may assist in the development of strategies that lower the incidence of cognitive impairment in these communities.
Few case series have described the simultaneous development of angioedema in patients with coronavirus 19 disease (COVID-19). Most of these reports were described in at-risk patients for developing ...bradykinin angioedema. Therefore, we aim to describe 5 African American patients who developed simultaneous COVID-19 and angioedema.
This was a case series of hospitalized patients with simultaneous angioedema and COVID-19 infection in a single center from May 2020 to February 2022. We used descriptive statistics. The study was approved by the institutional review board.
Their median age was 55 years (range 28–66); all patients were African American, and 3/5 were males. All patients developed angioedema within a week of hospitalization. Two subjects had prior history of ACEI-related angioedema but were not exposed to ACEI recently, whereas 1 subject was on chronic lisinopril therapy for the last 3 years. All patients had orofacial involvement; the most common locations were lips (5/5) and tongue (3/5). None had histaminergic features of angioedema (either skin rash or peripheral eosinophilia). 4/5 subjects had respiratory symptoms and chest imaging features of COVID-19 pneumonia, whereas 3/5 subjects developed severe COVID-19 infection. Most patients were treated with standard combination of H1 and H2 blockers, and corticosteroids. A total of 2/5 subjects were intubated; one patient developed refractory tongue swelling, received tracheostomy for extubation, and died due to COVID-19 pneumonia. The median length of angioedema improvement was 44 hours (range 20–168 hours). The median length of hospital stay was 15 days (range 1–49).
We described 5 cases of angioedema in COVID-19 patients that shared risk factors and features of bradykinin-related angioedema.
Dementia risk in older veterans with frailty Ysea-Hill, Otoniel; Shah, Aakashi; Gomez, Christian ...
Journal of the neurological sciences,
October 2021, 2021-10-00, Letnik:
429
Journal Article
Abstract only Introduction: Atrial fibrillation (AF) is the most common arrhythmia in older adults and has been previously shown to be associated with frailty. There is a lack of data regarding the ...trajectory of patients with new-onset AF and the development of frailty. The study aim was to determine the association between new-onset atrial fibrillation and the development of frailty over time in an older adult population. Methods: This is a retrospective cohort study using propensity score matching (PSM) among Veterans ≥60 years receiving primary care at Miami VAHS between July 2013-June 2014. Socio-demographic and clinical information was obtained from EHR and chart review. Patients were followed from their initial visit until onset of frailty, death, or end of study (June 2020). We assessed baseline and follow-up (FU) frailty using a 30-item VA Frailty Index (VA-FI). Veterans were categorized as non-frail (FI<.21) and frail (FI≥.21). Frail subjects at baseline were excluded. Patients with recent AF diagnosis were matched with controls using PSM with one-to-one nearest neighbor matching without replacement. Matching covariates used to calculate propensity score were age, race, ethnicity, and BMI, with a tolerance level of .01. Veterans were not matched for gender due to very small female population. Using a multivariate cox regression analysis, we determined the risk of developing frailty among patients with AF and controls. Results: After PSM, 415 Veterans with AF and 415 controls were included, mean age 78.58 (SD=8.54), 98.07% males, 76.02% Caucasian, 87.47% Non-Hispanic, 53.86% married, and mean BMI 29.21 (SD=6.85). Median FU was 4.05 (IQR=5.09) years. At the end of FU, 274 (34.99%) Veterans developed frailty, of those, patients with AF showed higher rates of frailty (42.89%, n=178), compared to controls (26.08%, n=96), p<.001. In comparison to controls, Veterans with AF were more likely to develop frailty, HR:3.15 (95%CI:2.42-4.10), p<.001. Conclusions: In this study, the risk of development of frailty was higher in older Veterans with AF, compared to controls without AF. Careful monitoring of older adults with AF for concurrent morbidity may reduce incident frailty in this population.
Background
Early identification of individuals at risk for dementia represents an important challenge for the design and implementation of prevention strategies. Machine learning (ML) algorithms ...might help to identify dementia risk from claims‐based electronic health records (EHR). The study aim was to develop and validate a new method based on ML to identify Veterans at risk for dementia using EHR claims‐based data.
Method
We propose a ML–based probabilistic method to evaluate dementia risk within 10 years, based on information from claims‐based EHR data as part of a retrospective cohort study. We identified veterans without baseline dementia from January 2000 to December 2009 and followed them until December 2019 for dementia onset (ICD codes). A ML model was established using veteran’s EHR data to predict dementia occurrence during follow‐up. The features for the modeling included 72 EHR associated with dementia in previous studies including socio‐demographic, medical and mental morbidities, vital signs, medications, hospitalizations and laboratory data. ML algorithms including Linear Discrimination, Random Forest Classifier, Ensemble Bootstrapping, and Multilayer Perception (MLP) neural network. MLP provided more robust prediction outcomes and was employed for the final dementia modeling and prediction.
Result
Data comes from 7202 veterans, mean age 54.89 (SD=6.64, range 40‐65) years, 90.3% male, 49.3% Caucasian, and 72.1% Non‐Hispanic. Over a median follow up of 9.82 (IQR=3.44) years, 786 (10.9%) of these veterans developed dementia. The training and testing sets at a ratio of 9:1 were pseudo‐randomly extracted from the original EHR database. The performance was determined on the testing dataset using the neural network model learned from the training dataset. The results from three experiments of modeling and testing are (Overall Accuracy/Sensitivity/Specificity): (1) 84.2%/12.8%/92.9%, (2) 85.7%/13.9%/94.5%, and (3) 87.5%/17.7%/96.1%. The modeling and prediction were implemented by missing data for some variables. The experimental results are: (1) 82.4%/9.84%/89.1%, (2) 82.9%/12.2%/91.1%, (3) 83.1%/16.5%/92.0%.
Conclusion
Though the model prediction specificity is acceptable (above 90%), the sensitivity of the prediction is low (less than 20%). Exploring better modeling algorithms and treatment methods for unbalanced samples may improve prediction. Future research could benefit from ML techniques that evaluate the optimal combination of variables that best predict dementia.
Background
Frailty, a clinical syndrome characterized by vulnerability to stressors resulting from multisystemic loss of physiological reserve. The use of benzodiazepines in older adults has been ...associated with confusion, sedation, and cognitive impairment, which in turn may lead to frailty.
Aims
The purpose of this study was to determine the cross-sectional association between frailty and chronic past or current use of benzodiazepine drugs among older US Veterans.
Methods/design
This is a cross-sectional study of community-dwelling older Veterans who had determinations of frailty. Benzodiazepine prescription data were obtained via EHR. A 31-item VA Frailty Index (VA-FI) was generated at the time of the assessment. We categorized Veterans into robust (FI ≤ 0.10), pre-frail (FI 0.10–0.21), and Frail (FI ≥ 0.21). After adjusting for sociodemographic characteristics, we calculated ORs and 95% CIs using a binomial logistic regression (BLR) model to assess the cross-sectional association between benzodiazepine use and frailty.
Results
Population sample consisted of 17,423 Veterans, mean age 75.53 (SD = 8.03) years, 70.80% Caucasian, 97.34% male, 14,545 (83.50%) patients were non-users of benzodiazepine drugs, 2408 (13.80%) had a past use, and 470 (2.70%) were current users. In BLR, individuals with past (OR 2.51, 95% CI 2.30–2.74,
p
< .001) or current (OR 2.36, 95% CI 1.96–2.83,
p
< .001) use showed a higher association with frailty as compared to individuals who were non-users.
Conclusions
The use of benzodiazepine was cross-sectionally associated with frailty in older Veterans. These results suggest that screening for frailty in patients with past or current exposure to benzodiazepine medications may be necessary for proper management.
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