Imaging of Medical Patients with Acute Kidney Injury: Patterns of Ultrasound Use and the Role of Point-of-care Ultrasound at a Tertiary Care Center
Bibliographic record
Abstract
BACKGROUND: The etiology of acute kidney injury (AKI) can be divided into pre-renal, renal, and post-renal causes. Ultrasound is the test of choice to identify post-renal AKI. While ultrasound is routinely used in the assessment of AKI, obstructive AKI is rare, leading to concerns of potential test overutilization. OBJECTIVE: Our primary aim is to describe patterns of use of imaging in patients admitted to hospital with AKI and to determine whether imaging patterns reflect risk of obstruction. Our secondary aim is to identify the role of point-of-care ultrasound (POCUS) when assessing patients with AKI. DESIGN: This is a retrospective cohort study. PARTICIPANTS: Patients admitted to internal medicine with AKI over a 12-month period at a large tertiary care academic center. MAIN MEASURES: Our outcome variables were radiology-performed ultrasound, computed tomography (CT), or point-of-care ultrasound (POCUS), presence or absence of hydronephrosis and urological intervention. KEY RESULTS: The proportion of patients with imaging was highest among those with a high-risk score and lowest in patients with a low-risk score (66.0% versus 52.2%). For radiology ultrasound specifically, the rate was 19.5% in low-risk patients and 17.7% in high-risk patients. The prevalence of hydronephrosis among patients at low, moderate and high risk for hydronephrosis was 7.1%, 8.5% and 19.7%, respectively and the rate of urological intervention was 1.4%, 1.2% and 3.8%, respectively. In moderate to high-risk patients, POCUS had a sensitivity of 86.7% and specificity of 90.0% for the identification of hydronephrosis. CONCLUSIONS: In our cohort, nearly 20% of radiology ultrasounds are ordered in patients with a low risk of obstructive uropathy, despite low rates of hydronephrosis and hydronephrosis requiring intervention in this group. With a sensitivity of 86.7% and specificity of 90.0% in patients at moderate to high risk of obstruction, POCUS may support clinical decision making in patients with AKI.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".