Evaluation of the 2020 American Urological Association Microscopic Hematuria Guidelines in Clinical Practice: Retrospective Chart Review Analysis
Bibliographic record
Abstract
Background: Hematuria is one of the most common urologic diseases seen within clinical practice, with a prevalence range of 1.7%-31.1%. In 2020, American Urological Association (AUA) guidelines were revised and recommend that following initial evaluation, clinicians should categorize patients into three tiers (low risk, intermediate risk, and high risk) based on various factors. Recent literature has shown that the AUA guidelines have high clinical utility when compared to other international guidelines such as those outlined by the Hematuria Risk Index, Canadian Urological Association, and Kaiser Permanente; however, this guideline remains unvalidated among the population of "well adults" within the United States. Objective: We used a retrospective study design to evaluate data abstracted from the electronic medical records of patients seen in the Emory Healthcare Executive Health Clinic from September 29, 2017, to January 29, 2021, to investigate the utility of risk stratification as a tool for clinical decision-making. Methods: According to the AUA risk stratification system, patients were stratified into low-risk and intermediate-risk/high-risk groups based on sex, age, smoking history, history of gross hematuria, and red blood cells/high-powered field. The frequencies and percentages of different causes of hematuria across the three risk strata were reported. Results: Of the 882 instances of red blood cells in urine (URBC) ≥3, a total of 368 (41.72%) underwent a repeat analysis within a 6-month time span, 184 (20.86%) within a 12-month time span, and 330 (37.41%) at >12 months. Instances of a URBC <3 (N=1643) were more likely to have no urologic diagnosis-1503 (91.48%) in comparison to 633 (76.27%) for those instances with a URBC >3 (N=830). Ultimately, 23 (100%) participants in the low-risk group had no urologic diagnosis after urinalysis versus 608 (75.62%) in the intermediate-risk/high-risk group (N=804). Conclusions: We found a need for a greater focus on monitoring elevated URBC counts, in accordance with clinical guidelines for managing hematuria in low-risk patients. Future research should examine the impact of risk stratification on clinical decisions and access to care, especially in underserved populations. It should also assess how the new AUA guidelines affect physician referral patterns and explore real-world implementation challenges and facilitators.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".