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Record W4381851920 · doi:10.1101/2023.06.18.23290762

The prevalence of chronic kidney disease in Australian primary care: analysis of a national general practice dataset

2023· preprint· en· W4381851920 on OpenAlexaff
Min Jun, James Wick, Brendon L. Neuen, Sradha Kotwal, Sunil V. Badve, Mark Woodward, John Chalmers, David Peiris, Anthony Rodgers, Kellie Nallaiah, Meg Jardine, Vlado Perkovic, Martin Gallagher, Paul E. Ronksley

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersUniversity of New South WalesFaculty of Medicine and Health, University of SydneyAustralian Commission on Safety and Quality in Health Care
KeywordsMedicineRenal functionKidney diseaseCreatinineUrologyInternal medicineGeneral practiceHealth carePrimary careDemographyFamily medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background There remains substantial variation in the reported prevalence of CKD in Australia. 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. The study included all adults with ≥1 visit to a general practice participating in the MedicineInsight program and ≥1 serum creatinine measurement (with or without a urine albumin-to-creatinine ratio [UACR] measurement) between 1 January 2011 and 31 December 2020; n=2,720,529 patients). The prevalence of CKD was estimated using three definitions: (1): an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m 2 or an eGFR ≥60 mL/min/1.73m 2 with a UACR ≥2.5 mg/mmol for males and ≥3.5 mg/mmol for females (definition 1), (2) two consecutive eGFR measures <60 mL/min/1.73m 2 at least 90 days apart or an eGFR ≥60 mL/min/1.73m 2 with a UACR ≥2.5 mg/mmol for males and ≥3.5 mg/mmol for females (definition 2), and (3) two consecutive eGFR measures <60 mL/min/1.73m 2 at least 90 days part and/or two consecutive UACR measures ≥2.5 mg/mmol for males and ≥3.5 mg/mmol for females at least 90 days apart (definition 3). Patient sociodemographic characteristics including comorbid conditions were assessed across the three definitions. Results The prevalence of CKD in the study cohort progressively increased over the 10-year study period, irrespective of the method used to define CKD. The annual prevalence of CKD varied across the three CKD definitions, with definition 1 resulting in the highest estimates. In 2020, the prevalence of CKD 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 measurements available in the corresponding year. Patients in whom both eGFR and UACR measurements were available mostly had moderate or high risk of CKD progression by local and international CKD guidelines (83.6%, 80.6% and 76.2%, respectively). Comorbid burden in patients with CKD was also frequently observed (coronary heart disease: 28.9%, type 2 diabetes: 38.5%, heart failure: 17.9%; using CKD definition 3). Conclusion 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 including coronary heart disease and diabetes. These findings highlight the need for early detection and effective management to slow progression of CKD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.339
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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