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README.md

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+---
+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.

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