Abstract P497: Re-analysis of the Systolic Blood Pressure Intervention Trial (SPRINT): The Renal Consequences of Intensive versus Standard Blood Pressure Lowering
Bibliographic record
Abstract
Background: BP management guidelines suggest that persons with CKD should be treated to a SBP ≤ 140 mmHg. SPRINT compared this target to intensive SBP lowering (≤ 120 mmHg) in persons with and without CKD and found a reduced rate of CV events and all-cause mortality (ACM). However intensive therapy was associated with an increased risk of AKI. We extrapolated the results of SPRINT over a lifetime horizon to determine whether in the long-term, the benefit in terms of the primary outcome would be less economically attractive when the risks of more frequent AKI and subsequent CKD progression were considered. Methods: We re-configured the CKD Simulator, a Markov model of CKD progression, AKI events, fatal and non-fatal CV events, and ESRD. We recalibrated the model to be representative of the SPRINT cohort and compared intensive vs. standard blood pressure control among 10 million simulated persons with and without CKD over their lifetimes. Marginal treatment costs were calculated and hazard ratios for AKI, CV events and ACM observed in SPRINT were applied to the monthly probabilities of these events in the intensive SBP arm. Results: Lifetime average, discounted, costs per person associated with intensive vs. standard SBP lowering were predicted to be $35,811 and $30,584, respectively. Quality-adjusted, discounted average lifespans were 196.05 and 190.47 months, respectively. The cost of each quality-adjusted life-year gained by adopting intensive over standard BP lowering would be $11,220, significantly below the accepted cost-effectiveness threshold of $50,000. Intensive SBP control would reduce the lifetime incidence of at least one CV event by 5.5%, but increase the incidence of at least one AKI episode and ESRD by 1.7% and 0.7%, respectively. These differences were associated with average lifetime cost savings per person of $459 for CV events, but losses of $161 and $2,889 for AKI and ESRD. Discussion: Intensive SBP management would be cost-effective and associated with a significant lifetime reduction in CV events. However, there would be an increase in the lifetime risk of AKI and ESRD, contributing to 58% of the total increase in cost of intensive relative to usual SBP control. Intensive SBP lowering should be adopted judiciously in persons at high risk of ESRD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".