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Record W4205892353 · doi:10.1161/circ.135.suppl_1.p178

Abstract P178: Cost-Effectiveness of Intensive versus Standard Blood Pressure Goals by Baseline Systolic Blood Pressure Levels in SPRINT-Eligible US Adults

2017· article· en· W4205892353 on OpenAlexaff
Brandon K. Bellows, Adam P. Bress, Jordan B. King, Natalia Ruiz‐Negrón, Rachel Hess, Srinivasan Beddhu, Zugui Zhang, Dan R. Berlowitz, William S. Weintraub, Andrew E. Moran

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsBrandon University
Fundersnot available
KeywordsMedicineSprintBlood pressureBaseline (sea)Adverse effectIntensive careInternal medicineCardiologyEmergency medicinePhysical therapyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: In high-risk adults, intensive systolic blood pressure (SBP) goals (<120 mmHg) reduced cardiovascular disease (CVD) events and mortality, when compared to standard SBP goals (<140 mmHg), in the Systolic Blood Pressure Intervention Trial (SPRINT). While average benefit of reduced CVD risk with intensive SBP goals outweighs the average risk of serious adverse events (SAEs) and treatment costs, net intensive SBP goal benefits may vary by baseline SBP. We hypothesized that intensive SBP goals would be cost-effective vs. standard SBP goals when stratified by baseline SBP level in SPRINT-eligible US adults from the payer’s perspective. Methods: Using CVD event, death, and SAE risks, a microsimulation model compared direct healthcare costs and quality-adjusted life-years (QALYs) in the SPRINT intensive and standard SBP goal arms over patients’ remaining lifetimes. Published literature and national estimates were used for model parameters. The model assumed that, after the planned duration of SPRINT, adherence to treatment goals decayed over time. To estimate the effect of baseline SBP on the cost-effectiveness of intensive SBP goals, baseline SBP was stratified into the following groups: 130-139 mmHg, 140-149 mmHg, 150-159 mmHg, 160-169 mmHg, and 170-180 mmHg. Results: With higher baseline SBP, patients in both arms experienced higher rates of CVD events or CVD deaths and accrued higher costs compared with patients starting with lower baseline SBP. However, with higher baseline SBP, there were slightly more incremental QALYs gained with intensive SBP treatment, from 0.29 to 0.31. This led to a modestly better incremental cost-effectiveness ratio (ICER) for intensive vs. standard SBP goals, decreasing from approximately $50,000 to $43,000 per QALY gained as baseline SBP increased. If SPRINT treatment effects and in-trial adherence persisted for the remaining lifetime, the ICER for intensive SBP goals decreased to approximately $25,000-$26,000 per QALY gained across baseline SBP groups. Conclusions: Treatment of all SPRINT-eligible patients led to lifetime cost-effectiveness below accepted willingness-to-pay thresholds and lifetime net benefit and cost-effectiveness varied only modestly by baseline SBP. However, this analysis was based on summary data and did not account for individual patient risks. Multiple predictors, in addition to baseline SBP, may be needed to identify which SPRINT-eligible patients derive maximal benefit from intensive SBP treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.224
GPT teacher head0.412
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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