Long-Term Outcomes in Persons with Stage 3 CKD Recruited from Primary Care
Bibliographic record
Abstract
Background: The majority of persons with chronic kidney disease (CKD) are elderly, have moderately reduced glomerular filtration rate (GFR) and are cared for in primary care in the UK. There are few long term studies to describe the risks of adverse outcomes in this under-studied population. Methods: Participants with CKD stage 3 were recruited from primary care in 2008-10. Clinical assessment and investigations were performed at baseline, 1 and 5 years. In 2019-20, electronic records were reviewed to obtain data on deaths and latest available outpatient estimated GFR (eGFR) and urine albumin to creatinine ratio (UACR). CKD progression was defined as a decline in eGFR of ≥25% and progression to a more advanced stage. Results: Participants: 1741 with median (IQR) age 74 (67-79) years, eGFR 53.8 (45.3-61.7) ml/min/1.73m2, UACR 0.3 (0.001-1.5) mg/mmol; 16.9% diabetes at baseline. Outcomes: 680 deaths (39.1%); CKD progression in 430 of 1402 (30.7%) participants after a median 9.8 (9.2-10.0) years; only 24 of 1741 (1.4%) reached CKD stage 5. UACR increased from 0.3 (0.001-1.26) to 1.4 (0.3-5.90) mg/mmol (p<0.001) in 1188 participants with repeat measurements. Changes in KDIGO GFR category are presented in the table. CKD category improved in 161 (11.5%), progressed in 695 (49.6%) and did not change in 546 (38.9%). Logistic regression analysis identified male sex, diabetes status and lower baseline eGFR, higher baseline UACR and systolic blood pressure (SBP) as independent predictors of CKD progression. Cox Proportional Hazards models identified age, male sex, diabetes status, past or current smoking, lower baseline eGFR and higher baseline UACR as independent risk factors for all-cause mortality. Conclusions: CKD progression was observed in a minority of participants and < 2% reached CKD stage 5. The risk of CKD progression was exceeded by the competing risk of death. Our observations confirm that in primary care, persons with CKD require monitoring and interventions to minimise risk of adverse outcomes but few progress to kidney failure. Funding: Commercial Support - Roche, Private Foundation Support - Year 10 CKD Category Baseline CKD Category 1 (n=8; 0.6%) 2 (n=281; 20%) 3a (n=492; 35%) 3b (n=424; 30%) 4 (n=173; 12%) 5 (n=24; 2%) 1 (n=1; 0.1%) 0 1 0 0 0 0 2 (n=475; 34%) 7 176 198 83 11 0 3a (n=632; 45%) 1 93 251 227 56 4 3b (n=275; 20%) 0 10 43 108 95 19 4 (n=19; 1.4%) 0 1 0 6 11 1
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".