scholarly journals Racial/Ethnic Differences in 30-Day Mortality for Heart Failure and Pneumonia in the Veterans Health Administration Using Claims-based, Clinical, and Social Risk-adjustment Variables

Medical Care ◽  
2021 ◽  
Vol 59 (12) ◽  
pp. 1082-1089
Author(s):  
Gabriella C. Silva ◽  
Lan Jiang ◽  
Roee Gutman ◽  
Wen-Chih Wu ◽  
Vincent Mor ◽  
...  
Author(s):  
Taona P. Haderlein ◽  
Michelle S. Wong ◽  
Kenneth T. Jones ◽  
Ernest M. Moy ◽  
Anita Yuan ◽  
...  

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.


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

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.


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