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Abstract 8: Get With The Guidelines-Stroke Program Participation and Clinical Outcomes for Medicare Beneficiaries

2013· article· en· W2507502210 on OpenAlexaff
Sarah Song, Gregg C. Fonarow, Wenqin Pan, DaiWai M. Olson, Adrian F. Hernandez, Eric D. Peterson, Mathew J. Reeves, Eric E. Smith, Lee H. Schwamm, Jeffrey L. Saver

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)Emergency medicineHazard ratioAcute strokeMedical emergencyEmergency departmentInternal medicineConfidence intervalNursing

Abstract

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Background: Get With The Guidelines (GWTG)-Stroke is a national, hospital-based quality improvement program developed by the American Heart Association. While studies have shown a beneficial effect of hospital participation in GWTG-Stroke upon processes of care, whether there are associated improvements in clinical outcomes has not been previously investigated. Methods: From among all acute care US hospitals, we matched 366 hospitals that joined the GWTG-Stroke program between April 2004 and December 2007, with 366 hospitals that did not. Matching was based on ischemic stroke case volume, calendar year, baseline hospital post-stroke 1-year all-cause mortality rates, teaching status, and geographic region. Outcomes of all acute ischemic stroke (AIS) patients admitted to the study hospitals were abstracted from the CMS administrative claims database (65 years and older). Outcomes at matched hospitals were compared in the PRE-GWTG-Stroke period (-540 to -181 days before program launch), RUN-UP period (-180- to -1 day), EARLY period (0 to 180 days) and SUSTAINED period (181 to 540 days). Additional analysis was performed of the entire BEFORE (-540 to -1 days) and AFTER periods (0 to 540 days). The main analytical approach was stratified Cox proportional hazard modeling, with matched site ID at stratum. We adjusted for patient characteristics (age, gender, race, medical history) and hospital characteristics (rural vs. urban, # beds, annual IS discharges.) Results: The study analyzed 88,584 AIS admissions at the 366 GWTG-Stroke hospitals and 85,401 admissions at the 366 matched non-GWTG-Stroke hospitals. In adjusted analysis comparing BEFORE and AFTER periods, GWTG-Stroke hospitals achieved reduced 30 day mortality (30M - HR 0.911, p<0.0001), reduced 1 year mortality (1YM - HR 0.902, p<0.0001), reduced 30 day all-cause rehospitalization (HR 0.956, p=0.013), reduced 30 day stroke rehospitalization (HR 0.927, p=0.038), and reduced 1 year all-cause rehospitalization (HR 0.972, p=0.007). Conversely, matched, non-GWTG-Stroke hospitals showed only reduced 30M (HR 0.954, p=0.010) between the BEFORE and AFTER periods. Comparing the degree of change at GWTG-Stroke with non-GWTG Stroke hospitals, there were greater improvements in discharge to home (DCH), 30M, and 1YM at GWTG-Stroke hospitals in each of the intervention periods: EARLY: DCH, HR 1.090, p<0.0001; 30M, HR 0.894, p=0.0006; 1YM, HR 0.889, p<0.0001; SUSTAINED: DCH, HR 1.097, p<0.0001; 30M, HR 0.934, p=0.004; 1YM, HR 0.918, p<0.0001. Conclusions: Hospitals joining the GWTG-Stroke quality improvement program between 2004-2008 achieved significantly greater improvement in stroke patient outcomes than matched hospitals not joining the program, with lower all-cause mortality at 30 days and 1 year and higher rates of discharge directly to home.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.104
GPT teacher head0.403
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2013
Admission routes1
Has abstractyes

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