Long-term Survival Benefit From Revascularization Compared With Medical Therapy in Patients With or Without Diabetes Undergoing Myocardial Perfusion Single Photon Emission Computed Tomography
Bibliographic record
Abstract
OBJECTIVE: To explore the long-term association of survival benefit from early revascularization with the magnitude of ischemia in patients with diabetes compared with those without diabetes using a large observational cohort of patients undergoing single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). RESEARCH DESIGN AND METHODS: Of 41,982 patients who underwent stress and rest SPECT-MPI from 1998 to 2017, 8,328 (19.8%) had diabetes. A propensity score was used to match 8,046 patients with diabetes to 8,046 patients without diabetes. Early revascularization was defined as occurring within 90 days after SPECT-MPI. The percentage of myocardial ischemia was assessed from the magnitude of reversible myocardial perfusion defect on SPECT-MPI. RESULTS: Over a median 10.3-year follow-up, the annualized mortality rate was higher for the patients with diabetes compared with those without diabetes (4.7 vs. 3.6%; P < 0.001). There were significant interactions between early revascularization and percent myocardial ischemia in patients with and without diabetes (all interaction P values <0.05). After adjusting for confounding variables, survival benefit from early revascularization was observed in patients with diabetes above a threshold of >8.6% ischemia and in patients without diabetes above a threshold of >12.1%. Patients with diabetes receiving insulin had a higher mortality rate (6.2 vs. 4.1%; P < 0.001), but there was no interaction between revascularization and insulin use (interaction P value = 0.405). CONCLUSIONS: Patients with diabetes, especially those on insulin treatment, had higher mortality rate compared with patients without diabetes. Early revascularization was associated with a mortality benefit at a lower ischemic threshold in patients with diabetes compared with those without diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".