Abstract 13212: Chronic Total Occlusion Racial and Income Inequities by Mortality and Cost: Propensity Score and Machine Learning Augmented Nationally Representative Case-Control Study of 30 Million Hospitalizations
Bibliographic record
Abstract
Introduction: We sought to produce the first nationally representative analysis of racial and income inequities by mortality and cost for patients with chronic total occlusion (CTO) of coronary arteries by the presence or absence of active cancer. Methods: Propensity score adjusted and backward propagation neural network machine learning augmented multivariable regression among patients with CTO and stratified by race and income was conducted for the above outcomes in this case-control study of the United States’ largest and most current all-payer hospitalized dataset, the 2016 National Inpatient Sample. Regression models were fully adjusted for age, geographic region, anemia, thrombocytopenia, cancer metastases, NIS-calculated mortality risk by Diagnosis Related Group (DRG), and the likelihood of undergoing PCI (in addition to length of stay [LOS] for cost). To produce nationally representative estimates, analyses were also adjusted for the complex survey design. Results: Of the 30,195,722 adult hospitalized patients, 1,159,994 (3.84%) underwent PCI (of whom 30,624 [2.64%] were in patients with active cancer), and 120,815 (0.40%) had CTO (of whom 3,068 [2.54%] had active cancer). In multivariable regression among patients with CTO, mortality was comparable across races in both PCI and cancer, but significantly increased among the lowest income quartile for patients with cancer (OR 4.09; 95%CI 1.30-12.80; p=0.016). PCI in Hispanic cancer patients unlike other races had significantly increased costs ($207,218.80; 95%CI 76,799.21-337,738.30; p=0.002) as did the lowest income quartile ($26,327.08; 95%CI 21,123.93-31,530.23; p=0.042) unlike other income groups significantly increased costs. Conclusions: This large nationally representative study suggests PCI can safely done in hospitalized patients with CTO and active cancer though its prevalence is far less than in those without cancer despite comparable risk profiles; there also appears to be significant racial and income inequities in both mortality and cost by both race and income.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".