Using Veterans Affairs Corporate Data Warehouse to identify 30-day hospital readmissions

Vincent, Brenda M.; Wiitala, Wyndy L.; Burns, Joseph E.; Iwashyna, Theodore J.; Prescott, Hallie C.

doi:10.1007/s10742-018-0178-3

Cited by 30 publications

(34 citation statements)

References 9 publications

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“…Data from VA Corporate Data Warehouse (CDW) were used in this study ( 23 – 26 ). CDW Outpatient and Inpatient Encounters domains were used to collect ICD-10 diagnosis codes, Current Procedural Terminology (CPT) codes, and ICD-10 procedure codes during an outpatient visit or hospitalization ( 27 ). The CDW Outpatient Pharmacy domain was used to collect prescription data and provider data.…”

Section: Methodsmentioning

confidence: 99%

Comparative Effectiveness of the Sodium–Glucose Cotransporter 2 Inhibitor Empagliflozin Versus Other Antihyperglycemics on Risk of Major Adverse Kidney Events

et al. 2020

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OBJECTIVE To examine the comparative effectiveness of the sodium–glucose cotransporter 2 inhibitor (SGLT2i) empagliflozin and other non-SGLT2i antihyperglycemics on the risk of major adverse kidney events (MAKE) of estimated glomerular filtration rate (eGFR) decline >50%, end-stage kidney disease, or all-cause mortality. RESEARCH DESIGN AND METHODS In a cohort study of 379,033 new users of empagliflozin or other non-SGLT2i antihyperglycemics, predefined variables and covariates identified by a high-dimensional variable selection algorithm were used to build propensity scores. Weighted survival analyses were then applied to estimate the risk of MAKE. RESULTS Compared with other antihyperglycemics, empagliflozin use was associated with 0.99 (95% CI 0.51, 1.55) mL/min/1.73 m 2 less annual reduction in eGFR, 0.25 (95% CI 0.16, 0.33) kg/m 2 more annual decrease in BMI, and reduced risk of MAKE (hazard ratio [HR] 0.68 [95% CI 0.64, 0.73]). Empagliflozin use was associated with reduced risk of MAKE in eGFR ≥90, ≥60 to <90, ≥45 to <60, and ≥30 to <45 mL/min/1.73 m 2 (HR 0.70 [95% CI 0.60, 0.82], 0.66 [0.60, 0.73], 0.78 [0.69, 0.89]), and 0.71 [0.55, 0.92], respectively), in participants without albuminuria, with microalbuminuria and macroalbuminuria (HR 0.65 [95% CI 0.57, 0.75], 0.72 [0.66. 0.79], and 0.74 [0.62, 0.88], respectively), and in participants with and without cardiovascular disease (HR 0.67 [95% CI 0.61, 0.74] and 0.76 [0.69, 0.83], respectively). The association was evident in per-protocol analyses, which required continuation of the assigned antihyperglycemic medication (empagliflozin or other antihyperglycemics) during follow-up (HR 0.64 [95% CI 0.60, 0.70]), and in analyses requiring concurrent use of metformin in at least the first 90 days of follow-up (HR 0.63 [0.57–0.69]). CONCLUSIONS Among people with type 2 diabetes, empagliflozin use was associated with eGFR preservation, a greater decline in BMI, and a reduced risk of MAKE compared with other non-SGLT2i antihyperglycemics.

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Section: Methodsmentioning

confidence: 99%

Comparative Effectiveness of the Sodium–Glucose Cotransporter 2 Inhibitor Empagliflozin Versus Other Antihyperglycemics on Risk of Major Adverse Kidney Events

et al. 2020

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“…We used the methods of Vincent et al [11] to define hospitalizations within VA. Briefly, VA data (specifically, the Specialty Transfer table in the Inpatient domain) are organized by the concept of bedded stay rather than the concept of a hospitalization (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Veterans Affairs patient database (VAPD 2014–2017): building nationwide granular data for clinical discovery

Wang¹,

Vincent²,

Wiitala³

et al. 2019

BMC Med Res Methodol

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Background To study patient physiology throughout a period of acute hospitalization, we sought to create accessible, standardized nationwide data at the level of the individual patient-facility-day. This methodology paper summarizes the development, organization, and characteristics of the Veterans Affairs Patient Database 2014–2017 (VAPD 2014–2017). The VAPD 2014–2017 contains acute hospitalizations from all parts of the nationwide VA healthcare system with daily physiology including clinical data (labs, vitals, medications, risk scores, etc.), intensive care unit (ICU) indicators, facility, patient, and hospitalization characteristics. Methods The VA data structure and database organization represents a complex multi-hospital system. We define a single-site hospitalization as one or more consecutive stays with an acute treating specialty at a single facility. The VAPD 2014–2017 is structured at the patient-facility-day level, where every patient-day in a hospital is a row with separate identification variables for facility, patient, and hospitalization. The VAPD 2014–2017 includes daily laboratory, vital signs, and inpatient medication. Such data were validated and verified through lab value range and comparison with patient charts. Sepsis, risk scores, and organ dysfunction definitions were standardized and calculated. Results We identified 565,242 single-site hospitalizations (SSHs) in 2014; 558,060 SSHs in 2015; 553,961 SSHs in 2016; and 550,236 SSHs in 2017 at 141 VA hospitals. The average length of stay was four days for all study years. In-hospital mortality decreased from 2014 to 2017 (1.7 to 1.4%), 30-day readmission rates increased from 15.3% in 2014 to 15.6% in 2017; 30-day mortality also decreased from 4.4% in 2014 to 4.1% in 2017. From 2014 to 2017, there were 107,512 (4.8%) of SSHs that met the Center for Disease Control and Prevention’s Electronic Health Record-based retrospective definition of sepsis. Conclusion The VAPD 2014–2017 represents a large, standardized collection of granular data from a heterogeneous nationwide healthcare system. It is also a direct resource for studying the evolution of inpatient physiology during both acute and critical illness. Electronic supplementary material The online version of this article (10.1186/s12874-019-0740-x) contains supplementary material, which is available to authorized users.

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“…2020; discharged March 1-July 1, 2020) in the VA's Corporate Data Warehouse. 1 Definitions included definite hospitalizations for COVID-19, in which the patient was diagnosed during hospitalization, and probable hospitalizations, in which the patient was diagnosed during the 14 days preceding or 7 days following hospitalization. 2,3 We also identified comparison cohorts of hospitalizations for non-COVID pneumonia and heart failure during the same time frame, using the Agency for Healthcare Research and Quality's Clinical Classification Software Refined diagnosis groupings.…”

mentioning

confidence: 99%

“…5 We applied weights from coarsened exact matching to balance 6 We measured readmission and death to 60 days after discharge among survivors of COVID-19 hospitalizations, determined the most common readmission diagnoses, and measured use of ICU, mechanical ventilation, and vasopressors during readmission. 1 We compared characteristics of COVID-19 survivors who experienced vs did not experience 60-day readmission or death using Pearson χ 2 and Wilcoxon rank sum tests. We compared rates of outcomes between matched survivors of COVID-19 and control hospitalizations using Rao-Scott-corrected χ 2 tests.…”

mentioning

confidence: 99%

Readmission and Death After Initial Hospital Discharge Among Patients With COVID-19 in a Large Multihospital System

Donnelly

Wang

Iwashyna

et al. 2021

JAMA

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203

208

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Using Veterans Affairs Corporate Data Warehouse to identify 30-day hospital readmissions

Cited by 30 publications

References 9 publications

Comparative Effectiveness of the Sodium–Glucose Cotransporter 2 Inhibitor Empagliflozin Versus Other Antihyperglycemics on Risk of Major Adverse Kidney Events

Comparative Effectiveness of the Sodium–Glucose Cotransporter 2 Inhibitor Empagliflozin Versus Other Antihyperglycemics on Risk of Major Adverse Kidney Events

Veterans Affairs patient database (VAPD 2014–2017): building nationwide granular data for clinical discovery

Readmission and Death After Initial Hospital Discharge Among Patients With COVID-19 in a Large Multihospital System

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