Assessing patterns of chronic kidney disease care in Australian primary care: a retrospective cohort study of a national general practice dataset
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) monitoring and cardiovascular risk management are essential in reducing disease progression and cardiovascular events. This study aimed to understand CKD monitoring and management practices in Australian primary care. Methods: We conducted a retrospective, population-based cohort study of adults who attended general practices participating in MedicineInsight between 1 January 2011 and 30 June 2020 and met diagnostic criteria for CKD. Care quality was assessed in the 18-months following identification of CKD. Core monitoring was defined as at least one assessment of all the following measurements: blood pressure, estimated glomerular filtration rate (eGFR), urine albumin creatinine ratio (UACR), lipid profile, and HbA1c in patients with diabetes. Cardiovascular risk management comprised medication prescription (ACEi/ARB and statin), blood pressure target achievement and LDL cholesterol <2 mmol/L. Modified Poisson regression models adjusted for socio-demographic and clinical characteristics were used to identify patient factors associated with completion of monitoring and medication prescription. Findings: CKD was identified in 140,780 patients, of which 34.2% received core monitoring within 18 months of CKD identification. Measurement of the individual components of the core monitoring outcome varied: blood pressure (88.7%), eGFR (86.0%), UACR (41.1%), lipids (70.9%) and HbA1c (85.5%). ACEi/ARB were prescribed in 65.2% of the cohort and 54.4% were prescribed a statin. Blood pressure targets of <140/90 mmHg and <130/80 mmHg were achieved in 57.9% and 29.3% of patients, respectively. LDL target of <2 mmol/L was achieved in 38.8% of patients. Older age, comorbid diabetes and hypertension were associated with a greater likelihood of monitoring and medication prescription. Interpretation: In this large, population-based study, we observed substantial variation in CKD risk monitoring and the management of cardiovascular risk in patients with CKD. We identified several priority areas for CKD management in primary care including need for improvement in albuminuria monitoring. Funding: University of New South Wales Scientia Program and Boehringer Ingelheim Eli Lilly Alliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".