Smoking and Adverse Outcomes in Patients With CKD: The Study of Heart and Renal Protection (SHARP)
Bibliographic record
Abstract
BACKGROUND: The absolute and relative importance of smoking to vascular and nonvascular outcomes in people with chronic kidney disease (CKD), as well its relevance to kidney disease progression, is uncertain. STUDY DESIGN: Observational study. SETTING & PARTICIPANTS: 9,270 participants with CKD enrolled in SHARP. PREDICTOR: Baseline smoking status (current, former, and never). OUTCOMES: Vascular events, site-specific cancer, ESRD, rate of change in estimated glomerular filtration rate (eGFR), and cause-specific mortality. RESULTS: At baseline, 1,243 (13%) participants were current smokers (median consumption, 10 cigarettes/day); 3,272 (35%), former smokers; and 4,755 (51%), never smokers. Median follow-up was 4.9 years. Vascular event rates were 36% higher for current than never smokers (2,317 events; relative risk [RR], 1.36; 95% CI, 1.19-1.55), reflecting increases in both atherosclerotic (RR, 1.49; 95% CI, 1.26-1.76) and nonatherosclerotic (RR, 1.25; 95% CI, 1.05-1.50) events. Cancer was 37% higher among current smokers (632 events; RR, 1.37; 95% CI, 1.07-1.76), with the biggest RRs for lung (RR, 9.31; 95% CI, 4.37-19.83) and upper aerodigestive tract (RR, 4.87; 95% CI, 2.10-11.32) cancers. For 6,245 patients not receiving dialysis at baseline, ESRD incidence did not differ significantly between current and never smokers (2,141 events; RR, 1.02; 95% CI, 0.89-1.17), nor did estimated rate of change in eGFR (current smokers, -1.77±0.14 [SE]; never smokers, -1.70±0.07mL/min/1.73m(2) per year). All-cause mortality was 48% higher among current smokers (2,257 events; RR, 1.48; 95% CI, 1.30-1.70), with significant increases in vascular (RR, 1.35; 95% CI, 1.07-1.69) and nonvascular (RR, 1.60; 95% CI, 1.34-1.91) causes of death, especially cancer (RR, 2.32; 95% CI, 1.58-3.40) and respiratory (RR, 2.25; 95% CI, 1.51-3.35) mortality. LIMITATIONS: Smoking status not assessed during follow-up. CONCLUSIONS: In this study of patients with CKD, smoking significantly increased the risks for vascular and nonvascular morbidity and mortality, but was not associated with kidney disease progression. The associations with vascular and neoplastic disease are in keeping with those observed in the general population and are likely modifiable by cessation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".