| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - time-series-forecasting |
| tags: |
| - synthetic-data |
| - healthcare |
| - cardiology |
| - coronary-artery-disease |
| - cad |
| - stable-angina |
| - unstable-angina |
| - acute-coronary-syndrome |
| - acs |
| - nstemi |
| - stemi |
| - post-pci |
| - post-cabg |
| - ccs-class |
| - canadian-cardiovascular-society |
| - angina-classification |
| - syntax-score |
| - ffr |
| - fractional-flow-reserve |
| - ifr |
| - pci |
| - percutaneous-coronary-intervention |
| - cabg |
| - coronary-artery-bypass-graft |
| - stent |
| - des |
| - drug-eluting-stent |
| - bms |
| - bare-metal-stent |
| - everolimus |
| - ees |
| - door-to-balloon |
| - d2b |
| - timi-flow |
| - killip |
| - killip-kimball |
| - grace-score |
| - timi-risk-score |
| - saq |
| - seattle-angina-questionnaire |
| - ischemia-trial |
| - courage |
| - syntax-trial |
| - freedom |
| - ncdr-action |
| - ncdr-cathpci |
| - sts-database |
| - adult-cardiac-surgery |
| - ccta |
| - coronary-ct-angiography |
| - nuclear-stress-test |
| - spect-mpi |
| - mibi |
| - duke-treadmill |
| - echocardiography |
| - lvef |
| - ejection-fraction |
| - hfref |
| - hfpef |
| - rwma |
| - troponin |
| - ck-mb |
| - bnp |
| - nt-probnp |
| - crp |
| - ldl |
| - lp-a |
| - statin |
| - pcsk9 |
| - evolocumab |
| - alirocumab |
| - sglt2-inhibitor |
| - ace-inhibitor |
| - arb |
| - beta-blocker |
| - dapt |
| - aspirin |
| - ticagrelor |
| - clopidogrel |
| - prasugrel |
| - mace |
| - in-stent-restenosis |
| - isr |
| - graft-patency |
| - lima |
| - left-internal-mammary-artery |
| - euroscore-ii |
| - sts-score |
| - agatston |
| - calcium-score |
| - plaque-burden |
| - tcfa |
| - napkin-ring-sign |
| - ehr-synthetic |
| - longitudinal-cohort |
| - clinical-trial-simulation |
| pretty_name: HCCAR005 — Synthetic Coronary Artery Disease Dataset (Sample) |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: hccar005_dataset.parquet |
| --- |
| |
| # HCCAR005 — Synthetic Coronary Artery Disease Dataset (Sample Preview) |
|
|
| **XpertSystems.ai | Synthetic Data Factory | Healthcare / Cardiology Vertical** |
|
|
| A **longitudinal coronary artery disease (CAD) patient dataset** spanning |
| the full spectrum from subclinical disease through acute coronary |
| syndromes through post-revascularization follow-up. 150 patients across |
| **7 CAD stages** (Subclinical, Stable Angina, Unstable Angina, NSTEMI, |
| STEMI, Post-PCI, Post-CABG) followed annually for 10 years — yielding |
| 1,500 visit-level records with 140 features per row covering: |
|
|
| - **CAD anatomy** (3-vessel stenosis %, **FFR per vessel + iFR**, SYNTAX |
| score, plaque burden, lesion length, MLA, Agatston calcium score, |
| plaque type including TCFA) |
| - **Angina assessment** (CCS class, angina frequency/duration/trigger, |
| nitroglycerin response, NYHA functional class, **Seattle Angina |
| Questionnaire 5 domains**, Duke treadmill score, stress test results |
| + modality) |
| - **ACS events** (door-to-balloon, TIMI flow pre/post, thrombus burden, |
| Killip class, GRACE score, TIMI risk score) |
| - **Biomarkers** (Troponin I/T, CK-MB, BNP/NT-proBNP, CRP, visit-level |
| LDL/HDL/Trig with statin effect) |
| - **Interventions** (PCI with DES_EES/BMS, stent type/length/diameter, |
| num_stents, post-PCI FFR/MLA, contrast volume, radiation dose; OR |
| CABG with graft count, LIMA usage, pump time, cross-clamp time; |
| procedural success flag) |
| - **Imaging** (echo LVEF/LVEDV/LVESV, RWMA + territory, E/e', LAVI; |
| CCTA plaque volume + napkin-ring sign; nuclear stress SSS/SDS) |
| - **Medications** (DAPT with P2Y12 selection and duration, statin |
| intensity, beta-blocker, ACEi/ARB, **SGLT2i, PCSK9i**, anticoagulant) |
| - **Outcomes** (MACE flag + component, time-to-MACE, target vessel |
| revascularization, in-stent restenosis, graft patency, 30-day |
| readmission, CV death, all-cause mortality, LVEF change) |
|
|
| Calibrated benchmark-first against **ACC/AHA Stable CAD Guidelines** |
| (Fihn et al.), **SYNTAX Trial** (Mohr et al., Serruys et al.), **COURAGE** |
| (Boden et al.), **ISCHEMIA Trial** (Maron et al. 2020), **FREEDOM Trial** |
| (Farkouh et al.), **4th Universal Definition of MI (2018)**, |
| **Killip-Kimball (1967)**, **NCDR ACTION + CathPCI Registries**, **STS |
| Adult Cardiac Surgery Database**, **GRACE Registry** (Granger et al. |
| 2003), **TIMI Risk Score** (Antman et al. 2000), **Seattle Angina |
| Questionnaire** (Spertus et al. 1995), and **KDIGO 2012** CKD staging. |
|
|
| This is the **sample preview** — 150 patients × 10 annual visits over |
| 10 years (1,500 visit records, ~1.1 MB). The full product covers |
| 10,000+ patients with extended procedural detail, full medication |
| titration trajectories, multi-imaging modality co-occurrence, and |
| pre-built scenario configs for **ISCHEMIA replication, FREEDOM |
| DM-CAD cohort, COURAGE invasive vs OMT, EXCEL-style left-main |
| PCI-vs-CABG, and BIOFLOW-V stent comparison studies**. |
|
|
| --- |
|
|
| ## Dataset summary |
|
|
| | Table | Rows (sample) | What it contains | |
| |---|---:|---| |
| | `hccar005_dataset` | 1,500 | One row per patient × annual visit. 140 features across 8 clinical modules (baseline carried forward + angina + ACS + biomarkers + intervention + imaging + medications + outcomes). 150 unique patients × 10 annual visits each | |
|
|
| Provided in **CSV** and **Parquet**. Aggregate to patient level via |
| `groupby('patient_id')` for cross-sectional analysis. Use baseline |
| visit (`visit_number == 1`) for cohort entry analysis. |
|
|
| --- |
|
|
| ## Calibration sources |
|
|
| All ten validation metrics target named clinical / registry standards: |
|
|
| - **ACC/AHA Stable Ischemic Heart Disease Guidelines** (Fihn et al. |
| 2012; 2014 Focused Update) — CCS class definitions, GDMT framework |
| - **ACC/AHA STEMI / NSTE-ACS Guidelines** (Levine et al. 2015; Amsterdam |
| et al. 2014) — D2B targets, primary PCI criteria |
| - **SYNTAX Trial / Score** (Sianos et al. 2005; Mohr et al. 2013) — |
| SYNTAX scoring system, PCI vs CABG decision thresholds (≥33: CABG |
| preferred; 23-32: Heart Team; <22: PCI acceptable) |
| - **COURAGE Trial** (Boden et al. 2007) — invasive vs OMT framework |
| - **ISCHEMIA Trial** (Maron et al. 2020) — stable CAD invasive vs OMT |
| - **FREEDOM Trial** (Farkouh et al. 2012) — DM-CAD revascularization |
| - **EXCEL Trial** (Stone et al. 2016) — left main PCI vs CABG |
| - **4th Universal Definition of MI** (Thygesen et al. 2018) — STEMI/ |
| NSTEMI classification, troponin kinetics |
| - **Killip-Kimball (1967)** — AMI hemodynamic classification |
| - **GRACE Registry** (Granger et al. 2003) — in-hospital mortality |
| prediction, score range [0, 372] |
| - **TIMI Risk Score** (Antman et al. 2000) — 0-7 point UA/NSTEMI score |
| - **Seattle Angina Questionnaire** (Spertus et al. 1995) — 5-domain |
| patient-reported angina assessment (0-100 scale) |
| - **CCS Functional Classification** — angina severity 0-4 |
| - **NCDR ACTION + CathPCI** — door-to-balloon, stent attribute |
| reporting standards |
| - **STS Adult Cardiac Surgery Database** — CABG quality measures, |
| graft count, LIMA usage, pump/cross-clamp times |
| - **EuroSCORE II + STS Mortality Risk** — surgical risk stratification |
| - **KDIGO 2012** — CKD eGFR-based staging |
|
|
| --- |
|
|
| ## Validation scorecard (seed = 42) |
|
|
| 10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1). |
|
|
| | # | Metric | Observed | Target | Tol | Type | Source | |
| |---|---|---:|---:|---:|---|---| |
| | 1 | `prior_cabg_flag_equals_post_cabg_stage_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Structural | |
| | 2 | `prior_mi_requires_acs_or_post_stage_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACS history consistency | |
| | 3 | `door_to_balloon_in_stemi_only_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | NCDR ACTION | |
| | 4 | `d2b_met_flag_matches_d2b_under_90_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACC/AHA STEMI | |
| | 5 | `pci_stent_attributes_consistent_with_arm_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | NCDR CathPCI | |
| | 6 | `cabg_attributes_consistent_with_arm_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | STS Database | |
| | 7 | `hfref_flag_matches_lvef_under_40_rate` | 0.999 | 0.99 | ±0.01 | FLOOR | ACC/AHA HF | |
| | 8 | `cv_death_implies_mortality_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Survival monotonicity | |
| | 9 | `mace_component_matches_mace_flag_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | MACE composite | |
| | 10 | `risk_scores_in_published_ranges_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Multiple guidelines | |
|
|
| --- |
|
|
| ## Schema highlights (140 cols total) |
|
|
| ### Identity & visit (6 cols) |
| `patient_id` (HC-CAR-XXXXXXX), `site_id`, `visit_number` (1-10), |
| `visit_date`, `age_at_visit`, `years_from_baseline`. |
|
|
| ### Patient baseline (49 cols) |
| `cad_stage` (Subclinical / StableAngina / UnstableAngina / NSTEMI / |
| STEMI / PostPCI / PostCABG), `sex`, `age_at_baseline`, `bmi`, |
| `systolic_bp_mmhg`, `heart_rate_bpm`, `smoking_history`, |
| **comorbidities** (`diabetes_flag`, `hypertension_flag`, |
| `hyperlipidemia_flag`, `ckd_flag`, `ckd_stage`, `heart_failure_flag`, |
| `afib_flag`, `pad_flag`, `prior_mi_flag`, `prior_pci_flag`, |
| `prior_cabg_flag`), `egfr_ml_min_1_73m2`, `creatinine_mg_dl`, |
| **baseline lipids** (`ldl_mg_dl`, `hdl_mg_dl`, `triglycerides_mg_dl`, |
| `lp_a_nmol_l`, `hba1c_pct`, `hemoglobin_g_dl`), **CAD anatomy** |
| (`num_vessels_diseased`, `lm_disease_flag`, `syntax_score`, |
| `culprit_vessel`, `stenosis_pct_lad`, `stenosis_pct_lcx`, |
| `stenosis_pct_rca`, `ffr_lad`, `ffr_lcx`, `ffr_rca`, `ifr_value`, |
| `plaque_burden_pct`, `lesion_length_mm`, `reference_vessel_diameter_mm`, |
| `mla_mm2`, `calcium_score_agatston`, `plaque_type`, |
| `annual_stenosis_progression_pct`), `intervention_arm` (OMT / PCI_BMS / |
| PCI_DES / CABG / Hybrid), `euroscore_ii`, `sts_score_mortality_pct`. |
|
|
| ### Angina (16 cols) |
| `angina_class_ccs` (0-4), `angina_type` (Stable / Unstable / Silent / |
| Mixed), `angina_frequency_per_week`, `angina_duration_min`, |
| `angina_trigger`, `nitroglycerin_response`, `dyspnea_nyha_class` (1-4), |
| `ischemic_burden_pct_lv`, `stress_test_result`, `duke_treadmill_score`, |
| `stress_test_modality`, **SAQ 5 domains** (`saq_physical_limitation`, |
| `saq_angina_stability`, `saq_angina_frequency`, |
| `saq_treatment_satisfaction`, `saq_quality_of_life`). |
|
|
| ### ACS (10 cols) |
| `acs_type` (None / UA / NSTEMI / STEMI), `symptom_onset_to_door_min`, |
| `door_to_balloon_min`, `door_to_balloon_met_flag`, `thrombus_burden`, |
| `timi_flow_pre` (0-3), `timi_flow_post` (0-3), `killip_class` (1-4), |
| `grace_score` (0-372), `timi_risk_score` (0-7). |
|
|
| ### Biomarkers (9 cols) |
| `troponin_i_ng_ml`, `troponin_t_ng_ml`, `ck_mb_ng_ml`, `bnp_pg_ml`, |
| `nt_probnp_pg_ml`, `crp_mg_l`, `ldl_mg_dl_visit`, `hdl_mg_dl_visit`, |
| `triglycerides_mg_dl_visit`. |
|
|
| ### Intervention (15 cols) |
| `intervention_type`, `pci_target_vessel`, `stent_type` (DES_EES / BMS), |
| `stent_length_mm`, `stent_diameter_mm`, `post_pci_ffr`, |
| `post_pci_mla_mm2`, `num_stents_deployed`, `total_stent_length_mm`, |
| `cabg_grafts`, `lima_used_flag`, `cabg_pump_time_min`, |
| `cabg_xclamp_time_min`, `contrast_volume_ml`, |
| `radiation_dose_kerma_mgy`, `procedural_success_flag`. |
|
|
| ### Imaging (13 cols) |
| `echo_lvef_pct`, `echo_lv_edv_ml`, `echo_lv_esv_ml`, `echo_rwma_flag`, |
| `echo_rwma_territory`, `echo_e_e_prime_ratio`, `echo_lavi_ml_m2`, |
| `lvef_hfref_flag`, `ccta_plaque_volume_mm3`, `ccta_napkin_ring_flag`, |
| `nuclear_sss`, `nuclear_sds`, `nuclear_lvef_stress_pct`. |
|
|
| ### Medications (12 cols) |
| `aspirin_flag`, `p2y12_inhibitor` (Ticagrelor / Clopidogrel / Prasugrel |
| / None), `dapt_duration_months`, `statin_flag`, `statin_intensity` |
| (None / Low / Moderate / High), `beta_blocker_flag`, `ace_arb_flag`, |
| `sglt2_inhibitor_flag`, `pcsk9_inhibitor_flag`, `nitrate_use_flag`, |
| `anticoagulant_use`, `medication_adherence_pct`. |
|
|
| ### Outcomes (11 cols) |
| `mace_event_flag`, `mace_component` (MI / Stroke / CV_Death / |
| HF_Hospitalization / None), `time_to_mace_days`, |
| `target_vessel_revascularization_flag`, `in_stent_restenosis_flag`, |
| `graft_patency_flag`, `hospitalization_cv_flag`, |
| `readmission_30d_flag`, `mortality_flag`, `cv_death_flag`, |
| `lvef_change_pct`. |
|
|
| --- |
|
|
| ## Suggested use cases |
|
|
| - **SYNTAX Score → revascularization strategy ML** — train a Heart- |
| Team-style classifier (PCI vs CABG vs OMT) from SYNTAX score, LM |
| involvement, comorbidities, EuroSCORE II, STS score |
| - **FFR / iFR-guided PCI candidate selection** — classifier for |
| significant ischemia (FFR ≤ 0.80) from angiographic features |
| - **CCTA plaque characterization ML** — predict TCFA (TCFA flag in |
| plaque_type) and napkin-ring sign from CCTA volume features |
| - **In-stent restenosis prediction** — classifier for `in_stent_restenosis_flag` |
| from stent characteristics, lesion features, DM status (DES vs BMS |
| comparison) |
| - **Door-to-balloon prediction & quality improvement** — predict D2B |
| time from arrival pattern features; useful for NCDR ACTION quality |
| benchmarking |
| - **GRACE / TIMI risk score validation** — train ML to reproduce or |
| improve published risk models |
| - **DAPT duration optimization** — uplift modeling for prolonged vs |
| short DAPT given DAPT score, bleeding risk, stent type |
| - **MACE survival ML** — Cox / random survival forest on |
| `mace_event_flag` + `time_to_mace_days` with right-censoring |
| - **CABG graft patency prediction** — model `graft_patency_flag` |
| from LIMA usage, pump time, baseline LVEF |
| - **HFrEF post-MI prediction** — classifier for `lvef_hfref_flag` |
| from baseline + intervention features |
| - **Statin response prediction** — model `ldl_mg_dl_visit` from |
| baseline LDL + statin intensity (50% reduction for non-OMT vs |
| 15% for OMT in this generator) |
| - **PCSK9i candidate identification** — predict `pcsk9_inhibitor_flag` |
| prescribing patterns for population health intervention |
| - **SAQ-based outcome prediction** — train regressors for the 5 SAQ |
| domains (physical limitation, frequency, stability, treatment |
| satisfaction, QoL) from clinical features |
| - **Procedural success prediction** — classifier for |
| `procedural_success_flag` in PCI (post-PCI FFR ≥ 0.80) vs CABG |
| - **Cardio-renal-metabolic phenotyping** — unsupervised clustering |
| on comorbidity + biomarker patterns |
| - **ISCHEMIA / COURAGE cohort simulation** — filter to specific |
| eligibility criteria (stable angina, no LM disease, etc.) and |
| simulate trial cohorts |
|
|
| --- |
|
|
| ## Loading examples |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("xpertsystems/hccar005-sample", split="train") |
| print(ds.shape) |
| ``` |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar005-sample", "hccar005_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Patient-level cohort distribution |
| print(df.drop_duplicates("patient_id")["cad_stage"] |
| .value_counts(normalize=True).round(3)) |
| ``` |
|
|
| ```python |
| # SYNTAX score → revascularization strategy |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar005-sample", "hccar005_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| patients = df.drop_duplicates("patient_id") |
| |
| # Heart Team-style decision validation |
| syntax_tier = pd.cut(patients["syntax_score"], |
| bins=[0, 22, 32, 60], |
| labels=["Low (<23)", "Intermediate (23-32)", "High (≥33)"]) |
| print(pd.crosstab(syntax_tier, patients["intervention_arm"], normalize="index").round(2)) |
| ``` |
|
|
| ```python |
| # DES vs BMS in-stent restenosis comparison |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar005-sample", "hccar005_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # First-year PCI cohort |
| pci_v1 = df[(df["intervention_arm"].isin(["PCI_DES", "PCI_BMS"])) & (df["visit_number"] == 1)] |
| print("ISR rate by stent type:") |
| print(pci_v1.groupby("intervention_arm").agg( |
| n=("patient_id", "count"), |
| isr_rate_pct=("in_stent_restenosis_flag", lambda x: x.mean() * 100), |
| mean_stent_length=("stent_length_mm", "mean"), |
| procedural_success_pct=("procedural_success_flag", lambda x: x.mean() * 100), |
| ).round(2)) |
| ``` |
|
|
| ```python |
| # Seattle Angina Questionnaire (SAQ) by CCS class |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar005-sample", "hccar005_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| saq_cols = ["saq_physical_limitation", "saq_angina_frequency", |
| "saq_angina_stability", "saq_treatment_satisfaction", |
| "saq_quality_of_life"] |
| print("SAQ domains by CCS class (mean):") |
| print(df.groupby("angina_class_ccs")[saq_cols].mean().round(1)) |
| ``` |
|
|
| ```python |
| # MACE event analysis (aggregate to patient-level) |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar005-sample", "hccar005_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Per-patient any-MACE flag over follow-up |
| patient_outcomes = df.groupby("patient_id").agg( |
| any_mace=("mace_event_flag", "max"), |
| any_mortality=("mortality_flag", "max"), |
| cv_death=("cv_death_flag", "max"), |
| arm=("intervention_arm", "first"), |
| syntax=("syntax_score", "first"), |
| ) |
| print("MACE rates by intervention arm:") |
| print(patient_outcomes.groupby("arm").agg( |
| n=("any_mace", "count"), |
| any_mace_pct=("any_mace", lambda x: x.mean() * 100), |
| mortality_pct=("any_mortality", lambda x: x.mean() * 100), |
| mean_syntax=("syntax", "mean"), |
| ).round(2)) |
| ``` |
|
|
| --- |
|
|
| ## Limitations and honest disclosures |
|
|
| This sample is calibrated for **structural fidelity, not bit-exact reproduction |
| of any specific CAD registry archive.** Specifically: |
|
|
| - **Visit-level outcomes (MACE, mortality, ISR, graft patency, readmission) |
| are FRESH RANDOM SAMPLES per visit**, NOT cumulative carry-forward. The |
| same patient can have `mace_event_flag=1` at visit 3 and `mace_event_flag=0` |
| at visit 7 (with the visit 3 event implicitly recovered from). For |
| patient-level event analysis, use `groupby('patient_id').max()` on |
| the binary outcome flags. |
| - **MACE per-visit rate (~13-14%) compounds over 10 visits to very high |
| cumulative rates** — patient-level any-MACE will exceed real-world |
| CAD cohort 5-year MACE (~15-25%). Disclosed; for absolute-rate |
| calibration use the full product or scale down per-visit hazard. |
| - **Imaging (echo LVEF, RWMA, CCTA, nuclear stress) is computed for ALL |
| visits regardless of clinical indication.** In real practice, serial |
| imaging is reserved for clinical change or pre-procedure planning. |
| Treat as "what the result would be if imaging were performed." |
| - **Patient baseline is FIXED at visit 1** (cad_stage, comorbidities, |
| intervention_arm, baseline lipids, baseline anatomy). The generator |
| does NOT model CAD progression to higher-stenosis or stage transitions |
| longitudinally. For genuine CAD progression ML, augment with a |
| trajectory model. |
| - **ACS events fire ONLY at visit 1** (the index visit). The generator |
| does NOT model NEW ACS events at later visits — every visit_number > 1 |
| has `acs_type = 'None'`. For longitudinal ACS incidence ML, use the |
| full product or augment with a recurrent-event model. |
| - **Stent fields are populated ONLY at visit 1 for PCI patients.** They |
| are NOT carried forward to follow-up visits — `stent_type`, `stent_length_mm`, |
| `num_stents_deployed`, `post_pci_ffr` are all NaN at visits 2-10 even |
| for PCI patients. For longitudinal PCI follow-up modeling, join the |
| visit-1 stent data to all subsequent visits manually. |
| - **CABG fields similarly populated only at visit 1**, and the `Hybrid` |
| intervention arm goes through the PCI path in the generator (so |
| `cabg_grafts` is NaN for Hybrid patients despite the arm label |
| including "CABG"). |
| - **The generator has a `hasattr(p, 'angina_class_ccs')` check** in the |
| imaging module (line 505) that ALWAYS returns False because `p` is a |
| dict (not an object with attributes). So `nuclear_sss` calculation |
| never incorporates CCS — it always falls through to the default |
| N(10, 6) distribution. Disclosed; if SSS-vs-CCS correlation matters |
| for your ML, augment. |
| - **eGFR uses a simplified formula** — the lambda |
| `creatinine = clip(9.5 / egfr, 0.5, 5.0)` (line 112) is the INVERSE |
| derivation (creatinine from eGFR, not eGFR from creatinine). It is |
| approximately correct (consistent with simplified CKD-EPI without |
| sex/age/race), but NOT the full published formula. For accurate eGFR |
| research, recompute from creatinine + age + sex + race using the |
| modern 2021 NKF-ASN refit. |
| - **HCCAR005 lacks racial/ethnic information** — the generator does |
| not assign race/ethnicity (unlike HCCAR001 / HCCAR003 / HCCAR004). |
| Disparities research will need augmentation. |
| - **GRACE score formula is simplified** — the generator uses |
| `grace = 20 + age*1.4 + killip*10 + (30 if STEMI) + ck*8` |
| (line 330) as an approximation, NOT the full Granger et al. 2003 |
| logistic regression with all 8 published variables. Values are in |
| the published range [0, 372] but absolute calibration differs from |
| GRACE 2.0. Use for relative risk stratification, not absolute |
| in-hospital mortality probability. |
| - **Statin lipid effect is FIXED** at 50% LDL reduction for non-OMT |
| patients and 15% for OMT patients (line 382). Real-world response |
| varies widely (Rosuvastatin 40mg ~55%, Atorvastatin 80mg ~52%, |
| Pravastatin 20mg ~24%). The `statin_intensity` field (None / Low / |
| Moderate / High) is randomly assigned and NOT linked to the LDL |
| reduction magnitude. For statin response ML, augment with intensity- |
| specific effects. |
| - **PCSK9i prescribing is independent of LDL response** in the |
| generator. Real-world PCSK9i is reserved for patients failing to |
| reach LDL goals on maximally tolerated statin + ezetimibe. The |
| generator fires `pcsk9_inhibitor_flag` at 15% baseline rate if |
| LDL > 100, ignoring statin trial. |
| - **Time-to-MACE is a Weibull sample** with shape=1.8, scale=2000 days |
| (line 598), NOT linked to actual visit when MACE was flagged. Use |
| the visit-level `mace_event_flag` for incident analysis, not |
| `time_to_mace_days` for survival models. |
| - **CSV serialization converts None to NaN** when reading via |
| `pd.read_csv` default behavior. Use `keep_default_na=False` or work |
| with the Parquet file (which preserves nullable types correctly). |
| - **ISCHEMIA / COURAGE eligibility is NOT enforced** — the generator |
| produces a heterogeneous CAD cohort. Filter to your own inclusion |
| criteria for trial-replication ML. |
|
|
| The full HCCAR005 product addresses these by genuine CAD progression |
| modeling (stenosis evolution, stage transitions), longitudinal stent |
| carry-forward, recurrent ACS event modeling, full CKD-EPI 2021 formula, |
| race/ethnicity assignment with disparities encoding, intensity-specific |
| statin response curves, PCSK9i trial-stepped prescribing, and pre-built |
| scenario configs (ISCHEMIA replication, COURAGE invasive-vs-OMT, |
| FREEDOM DM-CAD, EXCEL left-main PCI-vs-CABG, BIOFLOW-V stent |
| comparison). Contact us for the licensed commercial release. |
|
|
| --- |
|
|
| ## Companion datasets |
|
|
| This is the fifth SKU in our **Healthcare / Cardiology** vertical. The |
| five-SKU set now covers the full cardiology clinical continuum: |
|
|
| - [**HCCAR001**](https://huggingface.co/datasets/xpertsystems/hccar001-sample) |
| Heart Failure Dataset — chronic HF with GDMT and devices |
| - [**HCCAR002**](https://huggingface.co/datasets/xpertsystems/hccar002-sample) |
| Acute MI Dataset — STEMI/NSTEMI/UA with serial troponin kinetics |
| - [**HCCAR003**](https://huggingface.co/datasets/xpertsystems/hccar003-sample) |
| Hypertension Dataset — longitudinal HTN cohort with ABPM, GDMT, MACE |
| - [**HCCAR004**](https://huggingface.co/datasets/xpertsystems/hccar004-sample) |
| Atrial Fibrillation Dataset — CHA2DS2-VASc/HAS-BLED, DOACs, ablation |
| - [**HCCAR005**](https://huggingface.co/datasets/xpertsystems/hccar005-sample) |
| Coronary Artery Disease Dataset (you are here) — full spectrum from |
| subclinical CAD through acute events through revascularization |
|
|
| **Pair HCCAR005 + HCCAR002** for acute-on-chronic CAD (HCCAR002 has the |
| serial troponin detail; HCCAR005 has the longitudinal trajectory). |
| **Pair HCCAR005 + HCCAR001** for ischemic cardiomyopathy progression. |
| **Pair HCCAR005 + HCCAR003** for HTN-driven CAD progression studies. |
|
|
| - [**Healthcare / Neurology**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Insurance & Risk**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Energy & Climate**](https://huggingface.co/xpertsystems) (8 SKUs) |
| - [**Manufacturing**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Oil & Gas**](https://huggingface.co/xpertsystems) (17 SKUs) |
|
|
| For the broader catalog, see https://huggingface.co/xpertsystems |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{xpertsystems_hccar005_sample_2026, |
| author = {XpertSystems.ai}, |
| title = {HCCAR005 Synthetic Coronary Artery Disease Dataset (Sample Preview)}, |
| year = 2026, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/xpertsystems/hccar005-sample} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Contact |
|
|
| - **Web:** https://xpertsystems.ai |
| - **Email:** pradeep@xpertsystems.ai |
| - **Full product catalog:** Cardiology (5 SKUs), Neurology (10 SKUs), |
| Insurance & Risk (10 SKUs), Energy & Climate (8 SKUs), Manufacturing |
| (10 SKUs), Oil & Gas (17 SKUs), and more. |
|
|
| **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) |
| **Full product License:** Commercial — please contact for pricing. |
|
|
| **Important medical disclaimer:** This dataset contains SYNTHETIC patient |
| records only. No data was derived from any real patient, EHR archive, |
| or clinical registry. The dataset is intended for ML model development, |
| benchmarking, and education — NOT for clinical decision support, patient |
| counseling, or medical research conclusions. All clinical thresholds |
| (SYNTAX score tiers, D2B target, HFrEF definition, CCS classification, |
| revascularization criteria) are sourced from published guidelines; |
| users are responsible for verifying against current ACC/AHA/ESC/STS |
| guidelines for clinical applications. |
|
|
