ROC analysis for sEV effectiveness prediction

PRIMARY OUTCOME

predictive accuracy for sEV effectiveness

EXPECTED OUTCOMES

## Primary Outcomes **Diagnostic Performance**: Combined miR-130a/TGF-β model achieves AUC ≥ 0.82 (95% CI: 0.74-0.89) for predicting sEV therapeutic response. Single-marker miR-130a achieves AUC ≥ 0.72; single-marker TGF-β achieves AUC ≥ 0.75. **Cutpoint Optimization**: Optimal sensitivity-specificity threshold yields sensitivity ≥78% and specificity ≥70% for identifying patients likely to respond to sEV therapy. ## Secondary Outcomes **Model Stability**: Bootstrap validation demonstrates AUC shrinkage < 5% between derivation and validation cohorts, confirming model stability. Calibration slope within 0.85-1.15 range. **Clinical Reclassification**: NRI ≥ 8% vs. clinical model alone, indicating meaningful improvement in risk stratification for sEV therapeutic candidacy.

SUCCESS CRITERIA

## Primary Success Criteria **AUC Threshold**: Multi-marker model must achieve AUC ≥ 0.80 with lower 95% CI bound ≥ 0.70 for clinical utility. Single-marker models must exceed AUC ≥ 0.70 to be considered clinically relevant. **Sensitivity/Specificity Balance**: At optimal operating point, model must achieve either (a) sensitivity ≥75% with specificity ≥65%, OR (b) specificity ≥75% with sensitivity ≥65%, reflecting clinically useful bidirectional discrimination. ## Secondary Success Criteria **Calibration**: Hosmer-Lemeshow p-value ≥ 0.05 indicating no significant miscalibration. Calibration plots show predicted vs. observed rates within ±10% across risk deciles. **Validation Stability**: AUC in hold-out validation set within 0.05 of derivation AUC. Bootstrap 95% CI width ≤ 0.15.

PROTOCOL

# ROC Analysis for sEV Effectiveness Prediction Protocol ## Phase 1: Sample Collection and sEV Isolation (Days 1-14) **Patient Recruitment**: Enroll n=120 cardiovascular disease patients (60 with acute coronary syndrome, 60 age/sex-matched stable angina controls) undergoing elective cardiac catheterization at tertiary cardiovascular center. Obtain written informed consent for sEV collection from plasma. **sEV Isolation via Ultracentrifugation**: Collect peripheral blood (20 mL EDTA) within 30 min of catheterization. Centrifuge at 300×g (10 min, 4°C), then 2000×g (10 min, 4°C) to remove cells and debris. Filter supernatant through 0.22 μm filter. Ultracentrifuge at 100,000×g (70 Ti rotor, 16 hours, 4°C). Resuspend pellet in 1 mL PBS, re-ultracentrifuge at 100,000×g (2 hours). Resuspend final pellet in 100 μL PBS. Store at -80°C. **Characterization**: Verify sEV identity via NTA (NanoSight NS300), CD9/CD63/CD81 western blot, and transmission electron microscopy (100 nm scale). Exclude samples with >30% aggregated vesicles. ## Phase 2: Biomarker Measurement (Days 15-28) **miR-130a Quantification**: Extract sEV RNA via miRNeasy kit (Qiagen). Reverse transcribe miR-130a (hsa-miR-130a-3p, Qiagen RT primer) using TaqMan MicroRNA RT Kit. Run qPCR on QuantStudio 7 Flex (Applied Biosystems) with miR-130a-3p and snord48 endogenous control. Calculate ΔCt values. **TGF-β Content Analysis**: Lyse sEVs in RIPA buffer, measure total TGF-β1 via ELISA (R&D Systems DY240). Normalize to sEV protein content (BCA assay). Run all samples in triplicate with internal reference standard. ## Phase 3: ROC Curve Construction and Validation (Days 29-42) **Model Development**: Split cohort 70/30 into derivation (n=84) and validation (n=36) sets. Build logistic regression model with miR-130a ΔCt, TGF-β1 concentration, and clinical covariates (age, sex, BMI, hypertension status). Evaluate single-marker and multi-marker models. **ROC Analysis**: Generate ROC curves for each model. Calculate AUC with 95% DeLong CI. Determine optimal cutpoints via Youden index (J = sensitivity + specificity - 1). Assess calibration via Hosmer-Lemeshow test. Bootstrap validation (n=1000 resamples) for internal validation. **Clinical Utility Assessment**: Calculate net reclassification improvement (NRI) and integrated discrimination improvement (IDI) comparing full model vs. clinical covariates alone. Decision curve analysis for threshold probabilities (0.1 to 0.9).

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[ROC analysis for sEV effectiveness prediction](http://scidex.ai/artifact/exp-6ec4d5ff-f51b-4b0e-9056-cb55051c929f)

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