Reducing Unnecessary Noninvasive Testing for Inpatients With Unstable Angina: The RUNIT Protocol
Bibliographic record
Abstract
Background Routine inpatient transthoracic echocardiography (TTE) for patients with unstable angina is common, but it anecdotally adds little value to clinical care. A practice audit at our academic hospital demonstrated that 61.5% of patients with troponin-negative chest pain (TNCP) had normal left ventriculography (LVG) during coronary angiography and normal TTE on the same admission (duplicate testing). Methods We developed the R ed u cing N on- I nvasive T esting (RUNIT) protocol, a clinical algorithm applied by clinical nurses to patient with TNCP. We performed a prospective assessment of rate of duplicate testing before and after intervention. If patients met certain simple clinical criteria, their TTE was cancelled (RUNIT positive). Patients then proceeded to have either coronary angiography with LVG or noninvasive risk stratification. We aimed to reduce duplicate testing by 25% over a 1-year period. Balancing measures included pathology on ordered TTEs, 30-day readmission, length of stay, and number of LVG. Results Among 254 patients admitted with TNCP over 12 months, we reduced duplicate testing from 61.5% (before intervention) to 34% ( P = 0.001). There was no clinical difference in 30-day readmission (0.9% vs 0.7%), and length of stay was significantly shorter in RUNIT positive (3.48 vs 4.16 days, P = 0.02). The majority of duplicate TTEs did not reveal any management-informing pathology. RUNIT-positive patients underwent more LVG than RUNIT-negative patients (78.3% vs 62.8%, P = 0.008). Conclusion We achieved a sustained reduction in reflexive TTE ordering in patients with TNCP, and we discuss the potential of nursing-led interventions to address other areas of low value care in cardiology.
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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 | 0.069 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".