Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data
Bibliographic record
Abstract
Ben Li,1– 4,* Farah Shaikh,2,* Abdelrahman Zamzam,2 Ravel Raphael,2 Muzammil H Syed,2 Houssam K Younes,5 Rawand Abdin,6 Mohammad Qadura1– 3,7 1Department of Surgery, University of Toronto, Toronto, Ontario, Canada; 2Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; 3Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; 4Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada; 5Heart, Vascular, & Thoracic Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 6Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 7Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada*These authors contributed equally to this workCorrespondence: Mohammad Qadura, University of Toronto, Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-076, Toronto, Ontario, M5B 1W8, Canada, Tel +1 416-864-5154, Email mohammad.qadura@utoronto.caPurpose: Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data.Methods: We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers.Results: The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84.Conclusion: Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.Plain Language Summary: Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes an inflammatory protein panel to inform prognosis of PAD may improve predictive accuracy. We developed predictive models for 2-year major adverse limb events (MALE) using clinical characteristics (demographics, comorbidities, and medications) and a panel of 5 PAD-specific inflammatory biomarkers (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI) that achieved excellent performance on an independent validation cohort (AUROC 0.84). The models developed through this study may support PAD risk-stratification and targeted management strategies.Keywords: inflammatory biomarkers, predictive model, prognosis, peripheral artery disease
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".