Abstract 150: Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease
Bibliographic record
Abstract
Introduction: Peripheral artery disease (PAD) is manifested over a continuum of severity with comorbidities that may significantly increase healthcare costs (HC). Little research has been completed to understand the healthcare resource use (HRU) and HC in this population, specifically among severe PAD patients. We sought to understand the economic burden in this population. Methods: We identified severe PAD patients (rest pain, gangrene or ulceration) from an integrated administrative claims and electronic medical records database (Optum + Humedica 2007-15) with over 7 million patients. The first PAD diagnosis was the index date. Patients were required to be age ≥50 at index date, have clinical activity and continuous enrollment in the 6-month pre-index and 12-month or until death post-index periods. Patients with history of intracranial hemorrhage, stroke and transient ischemic attack were removed. We assessed HRU and all-cause annual total HC in the post-index period or until death, and descriptive analyses, means and SDs. Reverse Engineering and Forward Simulation (REFS TM ) models, an ensemble of Bayesian networks, were machine learned to examine baseline demographic and clinical characteristics and their association with post-index natural log all-cause annual total HC among living patients. We assessed effect estimates across the ensemble using Mean Percentage Change in Costs (MPCC) with SD. Results: The final study sample included 3,189 severe PAD patients. Mean number of all-cause hospitalizations was 1.3 (SD 1.8) and the mean length of stay was 8.0 days (SD 18.2). The mean all-cause annual total HC per patient was $56,973 (SD $91,523). Highly predictive factors associated with increased costs were (MPCC, SD): chronic ulcer of leg or foot (1.9, 0.1), chronic kidney diseases (CKD; 1.9, 0.2), cellulitis and abscess (1.8, 0.2), hypertension (1.6, 0.1), and carditis and cardiomyopathy (1.2, 0.1). Conclusion: In this study, the presence of chronic ulcers in the lower extremities and CKD were two factors most predictive of increased all-cause total HC in a geographically diverse population of severe PAD patients.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".