Datasets:

xpertsystems
/

hccar010-sample

license: cc-by-nc-4.0
language:
  - en
tags:
  - synthetic-data
  - healthcare
  - cardiology
  - neurology
  - stroke
  - stroke-prevention
  - atrial-fibrillation
  - cardiovascular-risk
  - framingham
  - chads2vasc
  - toast-classification
  - xpertsystems
pretty_name: HC-CAR-010 — Stroke Risk Prediction Synthetic Cohort (sample)
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting

HC-CAR-010 — Stroke Risk Prediction Synthetic Cohort

Sample dataset (500 patients × 144 columns) from the XpertSystems.ai Synthetic Data Factory — Cardiology vertical

A fully synthetic cohort designed for stroke risk prediction modeling, spanning the complete primary/secondary prevention pipeline: demographics and family history, comprehensive cardiovascular risk factors, cardiac and inflammatory/coagulation biomarkers, atrial fibrillation phenotyping with CHA₂DS₂-VASc/HAS-BLED, carotid + cardiac + neuroimaging, validated risk scores (Framingham 10-year stroke, SCORE2, Charlson, RRE-90), and acute stroke outcomes (NIHSS, mRS, Barthel, TOAST classification, thrombolysis, thrombectomy, recurrence, 1-year mortality).

Built to be drop-in usable for analytics, modeling, demos, and education while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk.

At a glance


SKU	HC-CAR-010
Vertical	Healthcare → Cardiology/Neurology
Sample size	500 patients × 144 columns
Modules	10 (Demographics, CV Risk Factors, Biomarkers, Coagulation, AFib, Vascular Imaging, Cardiac Imaging, Neuroimaging, Risk Scores, Outcomes)
Reporting standard	Framingham/SCORE2/CHA₂DS₂-VASc/TOAST compatible
Format	CSV
License (sample)	CC-BY-NC-4.0
License (full product)	Commercial — contact XpertSystems.ai
Validation	Grade A+ (10.0/10) across all 6 canonical seeds {42, 7, 123, 2024, 99, 1}

What makes this dataset useful

Stroke prevention modeling sits at the intersection of cardiology, neurology, hematology, and behavioral science — and the data spans EHR, registry, lab, and imaging silos. This synthetic cohort gives you the full stroke risk phenome in one tidy table — including the linked CHA₂DS₂-VASc/HAS-BLED scoring for the AFib subset, the validated Framingham/SCORE2 risk scores, TOAST etiology of ischemic strokes, NIHSS/mRS/Barthel post-stroke outcomes, and structural identities (anticoagulation upgrade for high-risk AFib; thrombolysis only for ischemic; TOAST only for ischemic) — so you can prototype models, build training labs, demo dashboards, or teach stroke epidemiology without paperwork.

Coverage:

CV risk factors — BP (with bp_category staging), lipids (with statin treatment effect modeling), diabetes, lifestyle, comorbidities (CKD, OSA, depression, migraine ± aura)
Cardiac biomarkers — NT-proBNP, BNP, troponin I & T, hs-CRP, D-dimer, fibrinogen, homocysteine, Lp(a), apoB/apoA-1, myeloperoxidase, galectin-3, ST2 — calibrated to comorbidity (HF, prior MI, diabetes, smoking)
Coagulation panel — INR (warfarin-aware), PTT, platelets, MPV, antiphospholipid, Factor V Leiden, Protein C/S, antithrombin III
AFib phenotyping — type (paroxysmal/persistent/permanent/long-standing), detection method, duration, CHA₂DS₂-VASc, CHADS₂, HAS-BLED, LA dimensions, LAA thrombus, cardioversion, ablation
Carotid + vascular imaging — IMT, plaque, % stenosis (categorical), vertebral stenosis, ABI, aortic arch plaque grade, CAC score
Cardiac imaging — LVEF, HFrEF/mrEF/pEF classification, NYHA, LVH, LV mass, E/A, e', PFO, valvular disease
Neuroimaging — Fazekas leukoaraiosis, cerebral microbleed count, silent lacunar infarcts, brain atrophy GCA, MCA & basilar stenosis
Validated risk scores — Framingham 10-yr stroke, SCORE2 10-yr CVD, Charlson, RRE-90, derived risk stratum (Low/Moderate/High/Very_High)
Acute stroke outcomes — event flag, ischemic/hemorrhagic/TIA type, TOAST etiology (Cardioembolic/Large_Artery/Small_Vessel/Other/Undetermined), time-to-event, NIHSS, mRS 90d, Barthel 90d, DWI lesion volume & location, thrombolysis, thrombectomy, ICU, LOS, 90-day recurrence, 1-yr mortality

Calibration anchors (industry-grade)

This cohort is calibrated against named registries, guidelines, and trials — not invented distributions. Selection from the 30-metric scorecard:

Metric	Sample value (seed 42)	Target range	Source
Age median	63.1 yr	55–70	Framingham, ARIC
Hypertension %	63.4%	50–75	AHA cohorts (enriched)
Diabetes %	27.2%	18–35	NHANES ≥40 (enriched)
Current smoker %	28.8%	18–35	Stroke risk cohorts
CKD any stage %	45.2%	35–60	Lin 2018, ARIC
AFib age 70–80	14.3%	10–22	Rotterdam Study (HF-enriched)
AFib overall	11.4%	8–18	High-risk CV cohort
CHA₂DS₂-VASc median (AFib)	4.0	2–5	Stroke-risk AFib cohort
Ischemic % of strokes	86.6%	≥75% (floor)	GBD 2022: 87%
Hemorrhagic % of strokes	8.5%	2–18	GBD 2022: ~10%
NIHSS median (ischemic)	6.0	3–11	SITS-MOST: median 8 acute
1-yr mortality (stroke)	6.1%	2–25	BENCHMARK: 8% post-stroke
90-day recurrence	1.2%	1–9	AHA/ASA ~4%
Carotid IMT mean	0.97 mm	0.80–1.10	MESA enriched
Carotid plaque %	49.6%	30–60	MESA age >50
Statin %	56.8%	40–70	Secondary prevention enriched
Antiplatelet any %	65.8%	55–80	AHA prevention
Antihypertensive in HTN	66.6%	≥60% (floor)	AHA/ACC 2017
BP control in treated	55.0%	45–65	NHANES
Anticoag in AFib	96.5%	≥75% (floor)	AHA/ACC/HRS 2023
DOAC share of AFib anticoag	69.1%	≥50% (floor)	2023 AHA/ACC DOAC-preferred
LDL mean	100.3 mg/dL	85–115	Statin-treated cohort
HDL mean	47.5 mg/dL	42–55	NHANES
PFO %	23.8%	15–35	Hagen 1984 autopsy
HF %	23.2%	12–32	CV risk cohort enriched
Framingham 10-yr risk mean	12.6%	8–18	High-risk cohort
TOAST cardioembolic %	18.3%	8–30	Adams 1993 / Petty 1999

Full 30-metric scorecard ships in validation_report.json and validation_report.md.

Files in this sample

hccar010_sample/
├── hccar010_sample.csv        # 500 patients × 144 columns
├── validation_report.json     # full scorecard (machine-readable)
├── validation_report.md       # full scorecard (human-readable)
├── sweep_summary.json         # 6-seed canonical sweep results
└── README.md                  # this file

Schema (144 columns across 10 modules)

Module 1 — Demographics (14 cols)

patient_id, age_years, sex, race_ethnicity, education_years, insurance_type, urban_rural, family_history_stroke_flag, family_history_cad_flag, prior_stroke_flag, prior_tia_flag, prior_mi_flag, prior_cabg_flag, prior_pci_flag

Module 2 — CV Risk Factors (32 cols)

BP (systolic_bp_mmhg, diastolic_bp_mmhg, pulse_pressure_mmhg, bp_category, hypertension_flag, antihypertensive_flag, antihypertensive_class, bp_control_achieved_flag), lipids (total_cholesterol_mg_dl, ldl_cholesterol_mg_dl, ldl_untreated_mg_dl, hdl_cholesterol_mg_dl, triglycerides_mg_dl, non_hdl_cholesterol_mg_dl, statin_therapy_flag, statin_intensity), diabetes (diabetes_type, diabetes_flag, hba1c_pct, fasting_glucose_mg_dl, diabetes_duration_years), lifestyle (smoking_status, pack_years, alcohol_drinks_per_week, heavy_drinker_flag, physical_activity_mets_week, bmi_kg_m2, obesity_class), comorbidities (sleep_apnea_flag, ckd_stage, egfr_ml_min_173m2, depression_flag, migraine_flag, migraine_with_aura_flag)

Module 3 — Cardiac Biomarkers (16 cols)

bnp_pg_ml, nt_probnp_pg_ml, troponin_i_ng_ml, troponin_t_ng_ml, hs_crp_mg_l, d_dimer_ng_ml, fibrinogen_mg_dl, homocysteine_umol_l, lp_a_mg_dl, apob_mg_dl, apoa1_mg_dl, myeloperoxidase_pmol_l, galectin_3_ng_ml, st2_ng_ml, creatinine_mg_dl, urine_albumin_creatinine_mg_g

Module 4 — Coagulation & Hematology (13 cols)

inr, ptt_seconds, platelet_count_k_ul, mean_platelet_volume_fl, hemoglobin_g_dl, hematocrit_pct, antiphospholipid_antibody_flag, factor_v_leiden_flag, protein_c_activity_pct, protein_s_activity_pct, antithrombin_iii_pct, anticoagulant_therapy, antiplatelet_therapy

Module 5 — Atrial Fibrillation (12 cols)

afib_flag, afib_type (Paroxysmal/Persistent/Permanent/Long_Standing), afib_duration_years, afib_detection_method, chads2_score, chads2vasc_score, hasbled_score, left_atrial_diameter_mm, left_atrial_volume_ml, left_atrial_appendage_thrombus_flag, cardioversion_flag, ablation_flag

Module 6 — Carotid & Vascular Imaging (12 cols)

carotid_imt_right_mm, carotid_imt_left_mm, carotid_plaque_flag, carotid_stenosis_right_pct, carotid_stenosis_left_pct, carotid_stenosis_category (None/Mild/Moderate/Severe/Occluded), vertebral_artery_stenosis_flag, ankle_brachial_index, pad_flag, aortic_arch_plaque_grade, coronary_artery_calcium_score, cac_category

Module 7 — Cardiac Imaging (11 cols)

echo_lvef_pct, heart_failure_flag, heart_failure_type (HFrEF/HFmrEF/HFpEF), nyha_class, echo_lv_hypertrophy_flag, echo_lv_mass_index_g_m2, echo_e_a_ratio, echo_e_prime_cm_s, patent_foramen_ovale_flag, valvular_disease_flag, valvular_disease_type

Module 8 — Neuroimaging (9 cols)

leukoaraiosis_grade_fazekas, cerebral_microbleed_count, silent_lacunar_infarct_count, brain_atrophy_grade_gca, mca_stenosis_pct, basilar_artery_stenosis_pct, ct_brain_performed_flag, mri_brain_performed_flag, cta_performed_flag

Module 9 — Risk Scores (5 cols)

framingham_stroke_10yr_risk_pct, score2_10yr_cvd_risk_pct, charlson_comorbidity_index, rre_90_recurrence_risk_pct, stroke_risk_stratum (Low/Moderate/High/Very_High)

Module 10 — Outcomes (18 cols)

stroke_event_flag, stroke_type_at_event (Ischemic/Hemorrhagic/TIA/NA), stroke_etiology_toast (TOAST 5-class), time_to_stroke_days, stroke_event_date, thrombolysis_tpa_flag, thrombectomy_flag, icu_admission_flag, hospital_los_days, nihss_score, mrs_90_day, barthel_index_90_day, dwi_lesion_volume_ml, dwi_lesion_location, recurrent_stroke_90d_flag, mortality_1yr_flag, cause_of_death, index_date

Use cases

Primary stroke prevention modeling — train classifiers using demographics, risk factors, biomarkers, imaging → stroke_event_flag.
CHA₂DS₂-VASc validation analytics — verify the implementation, stratify AFib patients by score, model anticoagulation decisions.
TOAST classification ML — multi-class etiology models using AFib + carotid stenosis + lacunar count → ischemic stroke subtype.
Survival analysis — Cox PH on time_to_stroke_days with full competing-risks setup (recurrence, mortality).
NIHSS → mRS outcome prediction — acute severity to 90-day functional outcome (Barthel/mRS).
Carotid stenosis → stroke pipeline — model the chain from IMT → plaque → stenosis category → ischemic stroke risk.
AFib anticoagulation appropriateness — HAS-BLED vs CHA₂DS₂-VASc net clinical benefit decision modeling.
GDMT gap analytics — measure preventive therapy gaps (statin %, antiplatelet %, BP control %) for cohort-level QI dashboards.
Real-world thrombolysis/thrombectomy uptake — acute stroke workflow benchmarking.
Teaching & training — neurology + cardiology residents, ML-for-healthcare courses, stroke prevention bootcamps.

Loading examples

pandas

import pandas as pd
df = pd.read_csv("hccar010_sample.csv")
print(df.shape)         # (500, 144)
print(df["stroke_risk_stratum"].value_counts())

Hugging Face `datasets`

from datasets import load_dataset
ds = load_dataset("xpertsystems/hccar010-sample")
df = ds["train"].to_pandas()

Stroke prediction model

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

features = [
    "age_years","hypertension_flag","diabetes_flag","afib_flag",
    "smoking_status","systolic_bp_mmhg","ldl_cholesterol_mg_dl",
    "hdl_cholesterol_mg_dl","hba1c_pct","bmi_kg_m2",
    "carotid_imt_right_mm","carotid_plaque_flag",
    "framingham_stroke_10yr_risk_pct","chads2vasc_score",
    "nt_probnp_pg_ml","hs_crp_mg_l","d_dimer_ng_ml",
]
X = pd.get_dummies(df[features], columns=["smoking_status"])
y = df["stroke_event_flag"]
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.25, random_state=42)
clf = GradientBoostingClassifier(random_state=42).fit(X_tr, y_tr)
auc = roc_auc_score(y_te, clf.predict_proba(X_te)[:, 1])
print(f"AUC: {auc:.3f}")

TOAST etiology stratification

ischemic = df[df["stroke_type_at_event"] == "Ischemic"]
toast = ischemic["stroke_etiology_toast"].value_counts(normalize=True)
print(toast)
# Adams 1993 expected: Large_Artery 16%, Cardioembolic 29%, Small_Vessel 16%,
# Other_Determined 3%, Undetermined 36%

90-day mRS outcome by acute severity

import seaborn as sns
stroke = df[df["stroke_event_flag"] == 1]
sns.boxplot(data=stroke, x="mrs_90_day", y="nihss_score")

AFib appropriateness audit

afib = df[df["afib_flag"] == 1]
appropriateness = afib.groupby(
    pd.cut(afib["chads2vasc_score"], bins=[-1, 1, 2, 9])
)["anticoagulant_therapy"].apply(lambda s: (s != "None").mean())
print(appropriateness)

Honest limitations & generator quirks

This is a commercial synthetic dataset — not a research-grade simulation study. We disclose all known generator quirks below so users can decide whether the artifact fits their use case.

Module 8 (Neuroimaging) uses legacy np.random.poisson global state instead of the modular rng. Two columns are affected: cerebral_microbleed_count and silent_lacunar_infarct_count. Per-row reproducibility for these two columns is not guaranteed even with the same seed (column means are stable; per-patient counts can vary by ±1–2 across runs). This cascades to ~1.7% of stroke_etiology_toast labels (because TOAST gates on silent_lacunar_infarct_count > 2). Mitigation: the sample wrapper calls np.random.seed(seed) before generation, which makes the first call in a process deterministic; distributions are stable across all canonical seeds. The full commercial product migrates these draws to the modular RNG.
AFib prevalence is HF-enriched. The generator's stated Rotterdam Study benchmarks (3.8% age 60–70, 9.5% age 70–80) are upgraded for HF-positive patients via _hf_flag_pre, producing observed prevalence of ~10–18%. This is appropriate for a CV high-risk cohort but not appropriate for population-level epidemiology.
CAC=0 prevalence is ~2% vs MESA ~50%. The generator uses lognormal sampling for coronary_artery_calcium_score starting at exp(2.0)-1 ≈ 6 minimum, producing very few true-zero CAC scores. MESA in a similar age range has 40–55% CAC=0. The CAC distribution is calibrated for an atherosclerosis-enriched cohort, not screening-population CAC distributions. If you're modeling primary prevention screening, treat CAC as relatively rather than absolutely valued.
Stroke events are time-to-event over a ~10-year window but the dataset is cross-sectional. Each patient has one row. time_to_stroke_days is the days-from-index_date to the stroke event (or -1 for no event). This is a survival-style cohort suitable for Cox PH, not a longitudinal panel with repeated visits.
thrombolysis_tpa_flag is gated on tte_days < 270 (~9 months). This represents acute-window eligibility but is broader than the real 4.5-hour clinical window — for clinical realism, treat thrombolysis_tpa_flag as "this patient was administered tPA at some point during their acute event", not "received within the 4.5h window".
Thrombolysis rate in ischemic strokes is low (~0–5%). Real-world IV-tPA rates are 5–15% in modern stroke registries. The synthetic generator under-models acute treatment uptake; if you're modeling acute care pipelines, augment with external thrombolysis rate assumptions.
stroke_etiology_toast rule for Cardioembolic is "any AFib" — real TOAST classification requires both a high-risk cardiac source (e.g., AFib) AND absence of competing etiology. The synthetic rule over-assigns Cardioembolic to AFib patients with carotid stenosis <50% even if they may not have had an actual cardioembolic mechanism. For TOAST classifier training, this remains useful (it's a coherent rule); for epidemiology, it under-counts mixed-mechanism strokes.
mortality_1yr_flag for non-stroke patients is 2% background rate — the generator assigns a small mortality rate to the entire cohort, not just stroke patients. In a real follow-up cohort, you would want time_to_death_days separately tracked.
CHA₂DS₂-VASc and HAS-BLED are zero for non-AFib patients. This is correct AFib-only scoring behavior, but filter on afib_flag == 1 when analyzing these scores or you'll dilute with zeros.
Race/ethnicity is not coupled to outcomes. Real-world stroke epidemiology shows substantial racial disparities (Black patients have ~2× higher stroke incidence and worse outcomes — REGARDS study). The synthetic cohort is intentionally race-blinded to avoid encoding real-world disparity bias into trainees' models. If you're studying disparities, use real REGARDS or Get With The Guidelines data.

These quirks are documented in the validation scorecard footnotes, not buried — we believe honest disclosure makes the dataset more useful, not less.

What you get in the full commercial product

	Sample (this dataset)	Full product
Patients	500	50,000+ (configurable)
Module 8 RNG	Legacy `np.random` (disclosed)	Migrated to modular `rng`
CAC distribution	Atherosclerosis-enriched	Configurable (screening vs enriched)
Cohort type	Cross-sectional survival	Optional longitudinal panel
Thrombolysis modeling	~5% (low)	Configurable, modern era (10–15%)
TOAST rule sophistication	Single-rule cascade	Multi-etiology probabilistic
Validation report	Yes (30 metrics)	Yes + custom scorecard
Format	CSV	CSV, Parquet, JSON
License	CC-BY-NC-4.0 (non-commercial)	Commercial use license
Race-outcome coupling	None (race-blinded)	Configurable disparity profiles
Schema export	—	GWTG-Stroke / REGARDS / SITS mapping
Support	Community	Email / SLA

Citation

@dataset{xpertsystems_hccar010_2026,
  title  = {HC-CAR-010: Stroke Risk Prediction Synthetic Cohort},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hccar010-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against Framingham Heart Study, GBD 2022, MESA, IST-3, SITS-MOST Registry, Rotterdam Study, NHANES 2019-2020, AHA/ASA 2021 Stroke Prevention Guidelines, AHA/ACC/HRS 2023 AFib Guidelines, TOAST classification (Adams 1993).}
}

Contact

Email: pradeep@xpertsystems.ai
Web: https://xpertsystems.ai
Vertical: Healthcare / Cardiology
SKU catalog: 10 SKUs shipped in Cardiology (vertical complete), ~75 SKUs across 8 verticals

XpertSystems.ai — synthetic data, calibrated to real-world registries.