Patterns of follow-up testing of abnormal eGFR and UACR for the detection of chronic kidney disease in Australian primary care: analysis of a national general practice dataset
Bibliographic record
Abstract
OBJECTIVE: To evaluate the patterns of abnormal estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR) follow-up testing for the detection of chronic kidney disease (CKD) in Australian general practices. DESIGN: Retrospective, population-based observational study. SETTING AND PARTICIPANTS: 2 717 966 adults who visited a MedicineInsight participating general practice between 1 January 2012 and 31 December 2020, had ≥1 serum creatinine measurement (with or without a UACR measurement) and did not have CKD at baseline. MAIN OUTCOME MEASURE: ; UACR≥2.5 mg/mmol in males, ≥3.5 mg/mmol in females) incident result. Multivariable logistic regression was used to identify patient factors associated with receiving appropriate follow-up testing. RESULTS: A total of 220 841 and 114 889 patients with an abnormal incident eGFR and UACR result, respectively, were identified. Nearly half (45.0%) of the patients with an abnormal eGFR result and over two-thirds (69.7%) of the patients with an abnormal UACR result did not have a follow-up test within 6 months. Patient factors associated with a higher likelihood of follow-up eGFR testing included indicators of poorer baseline health and greater CKD risk, such as comorbid diabetes (adjusted OR 1.36, 95% CI 1.32 to 1.40) or more severe incident eGFR (adjusted ORs for eGFR categories 30-44, 15-29 and <15 mL/min/1.73 m², respectively, vs eGFR 45-59 mL/min/1.73 m²: 1.51 (1.47 to 1.55), 1.85 (1.76 to 1.95) and 1.62 (1.47 to 1.78)). Higher incident UACR level was associated with greater follow-up UACR testing (adjusted OR for severely increased albuminuria vs moderately increased albuminuria (OR 1.42, 95% CI 1.37 to 1.48). CONCLUSIONS: In this large, population-based study, we observed substantial gaps in the follow-up of abnormal eGFR and UACR for the detection of CKD in primary care settings. Effective strategies to optimise follow-up testing for CKD detection are needed.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".