Community Pharmacy-Based eGFR Screening for Early Detection of CKD in High Risk Patients
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a condition presenting with long-term slow progression of structural and/or functional damage to the kidneys. Early detection is key to preventing complications and improving outcomes. Point-of-care estimated glomerular filtration rate (eGFR) screening technology allows for detection of abnormal kidney function in the community pharmacy setting. OBJECTIVE: To evaluate the effectiveness of a community pharmacist-directed point-of-care screening program and to identify the prevalence of CKD in high-risk patients. DESIGN: Quantitative observational. SETTING: Four community pharmacies in British Columbia over a 6-month period. PATIENTS: In all, 642 participants with at least one CKD risk factor were identified and screened. Mean age was 60 years and females accounted for 55% of the study population. MEASUREMENTS: Serum creatinine was measured from peripheral blood using the HeathTab® screening system (Piccolo® Renal Function Panel with the Piccolo® blood chemistry analyzer). eGFR was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. METHODS: screening system. Once results were available, the pharmacist conducted a comprehensive medication review with the patient and recommended certain follow-up actions if appropriate. RESULTS: CKD risk factor included diabetes (30%), hypertension (45%), cardiovascular disease (12%), family history of kidney disease (13%), age over 55 years (68%), and an Aboriginal, Asian, South Asian, or African ethnic background (82%). A total of 11.5% of patients had eGFR values lower than 60 mL/min (abnormal renal function) and 34% had an eGFR between 60 and 89 mL/min (minimally reduced renal function). Overall pharmacists' actions included blood pressure check (98%), education on CKD and risk factors (89%), medication review (72%), and physician follow-up (38%). Limitations included lack of follow-up beyond the 3-month study period prevented medical confirmation of CKD and limited the ability to quantify the impact of pharmacist interventions on the clinical outcomes of patients with low eGFR. CONCLUSION: These results illustrate the prevalence of abnormal renal function among undiagnosed, high-risk patients in the community. Pharmacists, as the most accessible healthcare practitioners, are ideally positioned to utilize novel point-of care technologies to improve access to CKD screening, facilitate follow-up, and increase awareness around the importance of early detection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".