Chronic kidney disease in type 2 diabetes: Does an abnormal urine albumin-to-creatinine ratio need to be retested?
Bibliographic record
Abstract
OBJECTIVE: To determine the positive predictive value (PPV) of a single random abnormal urine albumin-to-creatinine ratio (ACR) compared with repeat test results in patients with type 2 diabetes to diagnose chronic kidney disease (CKD). DESIGN: Retrospective, longitudinal secondary data analysis using Calgary Laboratory Services data. SETTING: Calgary, Alta. PARTICIPANTS: Patients aged 21 and older with a new diagnosis of diabetes in the study period from January 2008 to December 2015 and with a first abnormal urine ACR followed by another ACR test completed within 120 days. MAIN OUTCOME MEASURES: The PPV of an abnormal urine ACR (2 to 20 mg/mmol) to diagnose CKD was calculated. A test result was considered a true positive if a subsequent positive test result (≥ 2 mg/mmol) was identified within 120 days of the first positive test result and a false positive if 2 subsequent negative test results were identified within the same time period. The relationship between the first and second urine ACR values to assess the probability of the second urine ACR being abnormal (≥ 2 mg/mmol) based on the values of the first abnormal urine ACR was also explored. RESULTS: The PPV of the first abnormal urine ACR between 2 and 20 mg/mmol to diagnose CKD was calculated at 96.80% (95% CI 95.37% to 98.21%). Additionally, there was increased predictive probability of the second urine ACR being abnormal at higher values of the first urine ACR (2 to 20 mg/mmol). The data were further analyzed to exclude test results with a new or changed prescription of angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker medications around the time of the first urine ACR test to focus results on screening and not treatment response. With these exclusions, the PPV for first urine ACR between 2 and 20 mg/mmol to diagnose CKD was calculated as 96.23% (95% CI 94.13% to 98.32%). CONCLUSION: The first random abnormal urine ACR has a good PPV for the diagnosis of CKD in patients with type 2 diabetes, so multiple random urine ACR tests might not be necessary to diagnose patients with type 2 diabetes as having persistent microalbuminuria and CKD. A simpler diagnostic model for diagnosing renal disease might improve patient compliance, efficiency of testing, and implementation of health interventions. Reduced testing would also be expected to result in reduced cost from a health care expenditure perspective.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".