The Prevalence of CKD in Australian Primary Care: Analysis of a National General Practice Dataset
Bibliographic record
Abstract
Background: CKD prevalence in Australia varies substantially across reports. Using a large, nationally-representative general practice data source in Australia, we determined the contemporary prevalence and staging of CKD in Australian primary care. Methods: We performed a retrospective, community-based observational study using healthcare data from MedicineInsight, a national general practice data source in Australia. We included all adults with ≥1 visit to a general practice participating in MedicineInsight and ≥1 serum creatinine measurement (with or without a UACR measure) between 2011-2020; n=2,720,529 patients. CKD prevalence was estimated using 3 definitions: (1): an eGFR (mL/min/1.73m2) <60 or an eGFR ≥60 with a UACR (mg/mmol) >2.5 for M and >3.5 for F, (2) 2 consecutive eGFR measures <60, ≥90 days apart or an eGFR ≥60 with a UACR >2.5 for M and >3.5 for F and (3) 2 consecutive eGFR measures <60, ≥90 days part and/or 2 consecutive UACR measures >2.5 for M and >3.5 for F ≥90 days apart. Patient characteristics were assessed across the 3 definitions. Results: CKD prevalence progressively increased over the 10-year study period, irrespective of the method used to define CKD. The annual prevalence of CKD varied across the 3 CKD definitions, with definition 1 resulting in the highest estimates. In 2020, CKD prevalence in the study cohort was 8.4% (n=123,988), 4.7% (n=69,110) and 3.1% (n=45,360) using definitions 1, 2 and 3, respectively. The number of patients with UACR measurements was low such that, among those identified as having CKD in 2020, only 3.8%, 3.2% and 1.5% respectively, had both eGFR and UACR measures available in the corresponding year. Patients in whom both eGFR and UACR measurements were available mostly had moderate or high risk of CKD progression (83.6%, 80.6% and 76.2%, respectively). Comorbid burden in patients with CKD was also frequently observed. Conclusions: In this large, nationally-representative study, we observed an increasing trend in CKD prevalence in primary care settings in Australia. Most patients with CKD were at moderate to high risk of CKD progression with a significant comorbid burden. These findings highlight the need for early detection and effective management to slow progression of CKD. Funding: Commercial Support - This study was supported by an unrestricted research grant from Boehringer Ingelheim.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| 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".