scholarly journals 2156

2017 ◽  
Vol 1 (S1) ◽  
pp. 24-25
Author(s):  
Caroline Presley ◽  
Marie Griffin ◽  
Jea Young Min ◽  
Robert Greevy ◽  
Christianne Roumie

OBJECTIVES/SPECIFIC AIMS: This study is part of a parent grant evaluating antidiabetic medications and risk for heart failure in an observational cohort of Veterans with type 2 diabetes (T2DM). Confounding by indication remains a concern in many observational studies of medications because difficult to measure confounders such as frailty may influence prescribing of different medications based on patient characteristics. Frailty is a multidimensional syndrome of loss of reserves (energy, physical ability, cognition, health) that gives rise to vulnerability to adverse outcomes. The objective of this study is to determine if frailty is a potential confounder in Veterans with T2DM, that is, independently associated with exposure to a specific antidiabetic medications and hospitalization for decompensated heart failure. METHODS/STUDY POPULATION: We conducted a cross-sectional study of patients with diabetes who were hospitalized within the Veterans Health Administration (VHA) Tennessee Valley Healthcare System from 2002 to 2012. Inclusion criteria were: age 18 years or older, receive regular VHA care (prescription fill or visit at least once every 180 d), a diagnosis of T2DM. A probability sample of HF and non-HF hospitalizations was collected. HF hospitalizations were selected on the basis of meeting either a primary diagnosis code (ICD-9) and/or disease related group (DRG) code for HF. For each hospitalization using a standardized chart abstraction tool, data was abstracted on: antidiabetic medication(s), patient frailty status, and reason for hospitalization (HF or non-HF). Antidiabetic medication regimens were categorized as follows: no medication treatment, metformin alone, sulfonylurea alone, insulin alone, insulin and one oral agent, and all other regimens. Patient frailty status was measured using a modified version of the Canadian Health and Aging frailty index (FI), which generates a score (range 0–1) by dividing the number of deficits present by the number of deficits measured. Established categories for FI scores are: non frail ≤0.10, vulnerable 0.10–0.21, frail 0.22–0.45, and very frail >0.45. Patient frailty status at the time of hospitalization was used as a surrogate for patient frailty at the time of prescription of antidiabetic medication; this is a limitation of this approach. Hospitalizations were classified as HF hospitalizations if 2 major or 1 major and 2 minor Framingham criteria were present. FI was compared across antidiabetic medication regimen categories and hospitalization type using analysis of variance (ANOVA) and Student t-test, respectively. RESULTS/ANTICIPATED RESULTS: Of the 500 hospitalizations reviewed, 430 patients had confirmed diabetes diagnosis, adequate data to calculate FI scores, and were included in this analysis. Patients were on average 66.9 (10.9) years old; 99% male and 75% were White. Overall, 268 patients (62.3%) were categorized as frail or very frail. The mean FI score was 0.23 (SD 0.07). FI scores were highest in patients receiving insulin alone (mean 0.26) compared with patients receiving metformin alone (mean 0.22), sulfonylurea alone (mean 0.23), or no medication (mean 0.22). The lowest mean frailty score was seen in patients taking all other drug combinations, 0.19. The differences across these patient groups were statistically significant with p<0.01. Further, 75% of patients on insulin alone were frail or very frail compared with 68% on sulfonylurea alone, 58% on metformin alone, and 58% on no medication. Framingham criteria for acute HF were present for 318 of 430 patients (74.0%). FI scores were higher in patients hospitalized for HF compared with non-HF hospitalizations (mean 0.24 vs. 0.21, p<0.01). A higher proportion of patients hospitalized for HF were classified as frail or very frail compared with those hospitalized for non-HF diagnosis (66.4% vs. 50.9%, p<0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: This study demonstrates that certain antidiabetic medications are associated with patient frailty. In addition, those patients admitted for HF have higher FI scores than those admitted for non-HF diagnoses. Further investigation is planned to assess the degree to which frailty is captured by traditional covariates used in observational studies.

2015 ◽  
Vol 114 (07) ◽  
pp. 70-77 ◽  
Author(s):  
Al Ozonoff ◽  
Elaine M. Hylek ◽  
Dan R. Berlowitz ◽  
Arlene S. Ash ◽  
Donald R. Miller ◽  
...  

SummaryAmong patients receiving oral anticoagulation for atrial fibrillation (AF), heart failure (HF) is associated with poor anticoagulation control. However, it is not known which patients with heart failure are at greatest risk of adverse outcomes. We evaluated 62,156 Veterans Health Administration (VA) patients receiving warfarin for AF between 10/1/06–9/30/08 using merged VA-Medicare dataset. We predicted time in therapeutic range (TTR) and rates of adverse events by categorising patients into those with 0, 1, 2, or 3+ of five putative markers of HF severity such as aspartate aminotransferase (AST)> 80 U/l, alkaline phosphatase> 150 U/l, serum sodium< 130 mEq/l, any receipt of metolazone, and any inpatient admission for HF exacerbation. These risk categories predicted TTR: patients without HF (referent) had a mean TTR of 65.0 %, while HF patients with 0, 1, 2, 3 or more markers had mean TTRs of 62.2 %, 57.2 %, 53.5 %, and 50.7 %, respectively (p< 0.001). These categories also discriminated for major haemorrhage well; compared to patients without HF, HF patients with increasing severity had hazard ratios of 1.84, 3.06, 3.52 and 5.14 respectively (p< 0.001). However, although patients with HF had an elevated hazard for bleeding compared to those without HF, these categories did not effectively discriminate risk of ischaemic stroke across HF. In conclusion, we developed a HF severity model using easily available clinical characteristics that performed well to risk-stratify patients with HF who are receiving anticoagulation for AF with regard to major haemorrhage.


2018 ◽  
Vol 74 (8) ◽  
pp. 1282-1288 ◽  
Author(s):  
Caroline A Presley ◽  
Jonathan Chipman ◽  
Jea Young Min ◽  
Carlos G Grijalva ◽  
Robert A Greevy ◽  
...  

Abstract Background It is unknown whether observational studies evaluating the association between antidiabetic medications and mortality adequately account for frailty. Our objectives were to evaluate if frailty was a potential confounder in the relationship between antidiabetic medication regimen and mortality and how well administrative and clinical electronic health record (EHR) data account for frailty. Methods We conducted a retrospective cohort study in a single Veterans Health Administration (VHA) healthcare system of 500 hospitalizations—the majority due to heart failure—of Veterans who received regular VHA care and initiated type 2 diabetes treatment from 2001 to 2008. We measured frailty using a modified frailty index (FI, >0.21 frail). We obtained antidiabetic medication regimen and time-to-death from administrative sources. We compared FI among patients on different antidiabetic regimens. Stepwise Cox proportional hazards regression estimated time-to-death by demographic, administrative, clinical EHR, and FI data. Results Median FI was 0.22 (interquartile range 0.18, 0.27). Frailty differed across antidiabetic regimens (p < .001). An FI increase of 0.05 was associated with an increased risk of death (hazard ratio 1.45, 95% confidence interval 1.32, 1.60). Cox proportional hazards model for time-to-death including demographic, administrative, and clinical EHR data had a c-statistic of 0.70; adding FI showed marginal improvement (c-statistic 0.72). Conclusions Frailty was associated with antidiabetic regimen and death, and may confound that relationship. Demographic, administrative, and clinical EHR data, commonly used to balance differences among exposure groups, performed moderately well in assessing risk of death, with minimal gain from adding frailty. Study design and analytic techniques can help minimize potential confounding by frailty in observational studies.


BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e020455 ◽  
Author(s):  
Caroline A Presley ◽  
Jea Young Min ◽  
Jonathan Chipman ◽  
Robert A Greevy ◽  
Carlos G Grijalva ◽  
...  

ObjectivesWe aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system.DesignValidation study.SettingVeterans Health Administration—Tennessee Valley Healthcare SystemParticipantsWe identified and reviewed a stratified, random sample of hospitalisations between 2001 and 2012 within a single VHA healthcare system of adults who received regular VHA care and were initiated on an antidiabetic medication between 2001 and 2008. We sampled 500 hospitalisations; 400 hospitalisations that fulfilled algorithm criteria, 100 that did not. Of these, 497 had adequate information for inclusion. The mean patient age was 66.1 years (SD 11.4). Majority of patients were male (98.8%); 75% were white and 20% were black.Primary and secondary outcome measuresTo determine if a hospitalisation was due to HF, we performed chart abstraction using Framingham criteria as the referent standard. We calculated the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for the overall algorithm and each component (primary diagnosis code (ICD-9), DRG code or both).ResultsThe algorithm had a PPV of 89.7% (95% CI 86.8 to 92.7), NPV of 93.9% (89.1 to 98.6), sensitivity of 45.1% (25.1 to 65.1) and specificity of 99.4% (99.2 to 99.6). The PPV was highest for hospitalisations that fulfilled both the ICD-9 and DRG algorithm criteria (92.1% (89.1 to 95.1)) and lowest for hospitalisations that fulfilled only DRG algorithm criteria (62.5% (28.4 to 96.6)).ConclusionsOur algorithm, which included primary discharge diagnosis and DRG codes, demonstrated excellent PPV for identification of hospitalisations due to decompensated HF among patients with diabetes in the VHA system.


2020 ◽  
pp. 117-127
Author(s):  
Bindiya G. Patel ◽  
Suhong Luo ◽  
Tanya M. Wildes ◽  
Kristen M. Sanfilippo

PURPOSE Age-associated cumulative decline across physiologic systems results in a diminished resistance to stressors, including cancer and its treatment, creating a vulnerable state known as frailty. Frailty is associated with increased risk of adverse outcomes in patients with cancer. Identification of frailty in administrative data can allow for assessment of prognosis and facilitate control for confounding variables. The purpose of this study was to assess frailty from claims-based data using the accumulation of deficits approach in veterans with multiple myeloma (MM). METHODS From the Veterans Administration Central Cancer Registry, we identified patients who were diagnosed with MM between 1999 and 2014. Using the accumulation of deficits approach, we calculated a Frailty Index (FI) using 31 health-associated deficits and categorized scores into five groups: nonfrail (FI, 0 to 0.1), prefrail (FI, 0.11 to 0.20), mild frailty (FI, 0.21 to 0.30), moderate frailty (FI, 0.31 to 0.40), and severe frailty (FI, > 0.4). We used Cox proportional hazards regression analysis to assess association between FI score and mortality while adjusting for potential confounders. RESULTS We calculated an FI for 3,807 veterans age 65 years or older. Among the cohort, 28.7% were classified as nonfrail, 41.3% prefrail, 21.6% mildly frail, 6.6% moderately frail, and 1.7% severely frail. Frailty was strongly associated with mortality independent of age, race, MM treatment, body mass index, or statin use. Higher FI score was associated with higher mortality with hazard ratios of 1.33 (95% CI, 1.21 to 1.47), 1.97 (95% CI, 1.70 to 2.20), 2.86 (95% CI, 2.45 to 3.34), and 3.22 (95% CI, 2.46 to 4.22) for prefrail, mildly frail, moderately frail, and severely frail, respectively. CONCLUSION Frailty status is a significant predictor of mortality in older veterans with MM. Assessment of frailty status using the readily available electronic medical records data in administrative data allows for assessment of prognosis.


2020 ◽  
pp. 174239532096636
Author(s):  
Cindie Slightam ◽  
Rashmi Risbud ◽  
Timothy C Guetterman ◽  
Andrea L Nevedal ◽  
Karin M Nelson ◽  
...  

Objective Heart Failure (HF) care requires substantial care coordination between patients, patients’ informal caregivers, and clinicians, but few studies have examined recommendations from all three perspectives. The objective of this study was to understand and identify shared recommendations to improve HF self-care from the perspective of VA persons with HF, their caregiving partners, and clinicians. Methods Secondary data analysis from a study of semi-structured interviews with 16 couples (persons with HF and their caregiving partners) and 13 clinicians (physicians, nurses, other specialists) from a large Veterans Affairs (VA) hospital. Interviews were double-coded, and analyzed for themes around commonly used or recommended self-care strategies. Results Three themes emerged: (1) Couples and clinicians believe that improvements are still needed to existing HF education, especially the need to be tailored to learning style and culture, (2) Couples and clinicians believe that technology can facilitate better HF self-care, and (3) Couples and clinicians believe that caregiving partners are part of the self-care team, and should be involved in care management to support the person with HF. Discussion Recommendations from couples and clinicians address barriers to HF self-care and encourage patient-centered care.


2012 ◽  
Vol 110 (9) ◽  
pp. 1342-1349 ◽  
Author(s):  
Li Wang ◽  
Brian Porter ◽  
Charles Maynard ◽  
Christopher Bryson ◽  
Haili Sun ◽  
...  

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