README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- healthcare
- cardiology
- heart-failure
- hf
- hfref
- hfmref
- hfpef
- advanced-heart-failure
- end-stage-heart-failure
- nyha
- lvef
- ejection-fraction
- echocardiography
- echocardiogram
- echo
- diastolic-function
- ase-2016
- ea-ratio
- e-e-prime
- gls
- global-longitudinal-strain
- bnp
- nt-probnp
- troponin
- biomarkers
- ckd-epi
- egfr
- cardiorenal-syndrome
- gdmt
- guideline-directed-medical-therapy
- 4-pillar
- arni
- sglt2i
- entresto
- jardiance
- farxiga
- beta-blocker
- mra
- spironolactone
- ace-inhibitor
- arb
- icd
- crt
- lvad
- heart-transplant
- pinnacle-registry
- aha-2022
- acc-aha-guidelines
- cms-hrrp
- 30-day-readmission
- readmission
- mortality
- mace
- kccq
- six-minute-walk
- vo2-max
- cpet
- atrial-fibrillation
- charlson-comorbidity
- cardiometabolic
- longitudinal-ehr
- ehr-synthetic
pretty_name: HCCAR001 — Synthetic Heart Failure Dataset (Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: baseline
  data_files: hccar001_baseline.parquet
- config_name: visits
  data_files: hccar001_visits.parquet
---

# HCCAR001 — Synthetic Heart Failure Dataset (Sample Preview)

**XpertSystems.ai | Synthetic Data Factory | Healthcare / Cardiology Vertical**

A **two-table longitudinal heart failure patient dataset** spanning the
full clinical-research data surface for HF cohorts: baseline patient
records (~114 features per patient covering demographics, full
echocardiographic assessment with diastolic function, 14 biomarkers
including BNP/NT-proBNP/troponin/CKD-EPI eGFR, guideline-directed
medical therapy (GDMT) 4-pillar prescribing, device therapy (ICD/CRT/
LVAD/transplant), hospitalization outcomes, functional status (NYHA,
6MWD, KCCQ, VO₂ max), comorbidities, and vital signs) plus quarterly
follow-up visits over 3 years tracking LVEF/BNP/NYHA trajectories.

Calibrated benchmark-first against **ACC/AHA 2022 Heart Failure
Guidelines**, **ASE 2016 Recommendations for Evaluation of LV Diastolic
Function**, **CKD-EPI 2009**, **PINNACLE Registry** (real-world GDMT
prescribing rates), **CMS HRRP** (30-day readmission benchmarks),
**SCD-HeFT / DINAMIT** (primary-prevention ICD criteria), and **CARE-HF
/ COMPANION** (CRT eligibility criteria).

This is the **sample preview** — 200 patients × 12 quarterly visits over
3 years (200 baseline records + 2,400 visit records, ~500 KB). The full
product covers 10,000+ patients × full 3-year follow-up with extended
echocardiographic / CMR modules, full CPET / device interrogation
detail, complete medication titration trajectories, and pre-built cohort
configs for HFrEF clinical trial simulation, HFpEF treatment-effect
heterogeneity studies, and cardiorenal-cardiometabolic comorbidity
analysis.

---

## Dataset summary

| Table | Rows (sample) | What it contains |
|---|---:|---|
| `baseline` | 200 | One row per patient. 114 features spanning: demographics + enrollment, cardiac function (LVEF, LV dimensions, GLS, RV function, TAPSE), diastolic function (E/A, E/e', LAVI, TRPG, diastolic grade), biomarkers (BNP, NT-proBNP, troponin I/T, creatinine, eGFR, BUN, electrolytes, hemoglobin, CRP, albumin, uric acid, iron studies, CKD stage), GDMT 4-pillar + ARNI/ivabradine/hydralazine-nitrate + device therapy (ICD/CRT/LVAD/transplant), hospitalization (LOS, ICU, 30/90/180-day readmission, ED visits, hospitalization risk score), functional outcomes (6MWD, KCCQ, VO₂ max, EF response, NYHA improvement, MACE, mortality, time-to-event), comorbidities (AF, diabetes, HTN, CAD, valvular, sleep apnea, COPD, anemia, obesity, depression, CCI), and vital signs (HR, SBP/DBP, MAP, weight, SpO₂, JVP, edema grade, rales) |
| `visits` | 2,400 | Quarterly visit-level records over 12 visits × 3 years. LVEF trajectory, BNP trajectory, NYHA class updates, weight changes — useful for longitudinal modeling, treatment-response trajectories, and time-to-event ML |

Both tables provided in **CSV** and **Parquet**. Join on `patient_id`.

---

## Calibration sources

All ten validation metrics target named clinical / regulatory standards:

- **ACC/AHA 2022 HF Guidelines** — HF phenotype LVEF bands (HFrEF ≤40%,
  HFmrEF 41-49%, HFpEF ≥50%, Advanced <30%)
- **ASE 2016** Recommendations for Evaluation of LV Diastolic Function —
  E/A ratio, E/e', LAVI, TRPG diagnostic criteria
- **Lang et al. (ASE 2015)** — chamber quantification recommendations
  for LVEDV/LVESV/SV identities
- **CKD-EPI 2009** — eGFR formula physiologic bounds [8, 140]
- **PINNACLE Registry** — real-world GDMT 4-pillar prescribing rates
  for outpatient HFrEF cohorts
- **CMS HRRP** (Hospital Readmissions Reduction Program) — 30-day HF
  readmission national benchmark 22-25% + temporal monotonicity
- **SCD-HeFT (Bardy et al. 2005) + DINAMIT (Hohnloser et al. 2004)** —
  primary prevention ICD eligibility (LVEF ≤35%, NYHA II-III)
- **CARE-HF (Cleland et al. 2005) + COMPANION (Bristow et al. 2004)** —
  CRT eligibility (LVEF ≤35%, NYHA III-IV ambulatory, LBBB)

---

## 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 | `lvef_in_phenotype_band_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACC/AHA 2022 |
| 2 | `lvesv_equals_lvedv_times_one_minus_ef_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Lang et al. ASE 2015 |
| 3 | `stroke_volume_equals_lvedv_minus_lvesv_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ASE 2015 |
| 4 | `cardiac_index_equals_co_over_bsa_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity |
| 5 | `egfr_in_physiologic_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CKD-EPI 2009 |
| 6 | `gdmt_4_pillar_requires_lvef_under_40_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACC/AHA 2022 + PINNACLE |
| 7 | `icd_requires_lvef_under_35_and_nyha_ge_2_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | SCD-HeFT / DINAMIT |
| 8 | `crt_requires_lvef_under_35_and_nyha_ge_3_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CARE-HF / COMPANION |
| 9 | `readmission_temporal_ordering_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CMS HRRP |
| 10 | `ea_ratio_equals_e_over_a_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ASE 2016 |

---

## Schema highlights

### `baseline` (200 rows × 114 cols)

**Demographics (11 cols):** `patient_id` (UUID), `site_id`,
`hf_phenotype` (HFrEF / HFmrEF / HFpEF / Advanced), `age_at_baseline`,
`sex_male`, `race_ethnicity` (5 categories), `bmi_kg_m2`, `bsa_m2`,
`nyha_class_baseline` (1-4), `index_hospitalization`, `enrollment_date`.

**Cardiac function (13 cols):** `lvef_pct_baseline`, `lvedd_mm_baseline`,
`lvesd_mm_baseline`, `lvedv_ml_baseline`, `lvesv_ml_baseline`,
`stroke_volume_ml_baseline`, `heart_rate_baseline_bpm`,
`cardiac_output_l_min_baseline`, `cardiac_index_l_min_m2_baseline`,
`gls_pct_baseline`, `rv_function_baseline`, `tapse_mm_baseline`,
`echo_quality_baseline`.

**Diastolic function (9 cols):** `diastolic_grade_baseline` (Grade_1 /
Grade_2 / Grade_3 / Indeterminate), `e_velocity_cm_s_baseline`,
`a_velocity_cm_s_baseline`, `ea_ratio_baseline`, `e_prime_cm_s_baseline`,
`e_e_prime_ratio_baseline`, `lavi_ml_m2_baseline`, `trpg_mmhg_baseline`,
`ivrt_ms_baseline`.

**Biomarkers (15 cols):** `bnp_pg_ml_baseline`, `nt_probnp_pg_ml_baseline`,
`troponin_i_ng_ml_baseline`, `troponin_t_ng_ml_baseline`,
`creatinine_mg_dl_baseline`, `egfr_ml_min_173m2_baseline`,
`bun_mg_dl_baseline`, `sodium_meq_l_baseline`, `potassium_meq_l_baseline`,
`hemoglobin_g_dl_baseline`, `iron_studies_baseline`, `crp_mg_l_baseline`,
`albumin_g_dl_baseline`, `uric_acid_mg_dl_baseline`, `ckd_stage_baseline`.

**Treatment & GDMT (14 cols):** `gdmt_acei_arb_arni`, `gdmt_betablocker`,
`gdmt_mra`, `gdmt_sglt2i`, `gdmt_4_pillar_flag`, `arni_flag`,
`ivabradine_flag`, `hydralazine_nitrate_flag`, `icd_flag`, `crt_flag`,
`lvad_flag`, `transplant_flag`, `diuretic_loop_dose_mg`,
`gdmt_dose_optimization_pct`.

**Hospitalization (13 cols):** `hospitalization_count_3yr`,
`index_los_days`, `index_icu_flag`, `readmission_30d_flag`,
`readmission_90d_flag`, `readmission_180d_flag`, `readmission_cause`,
`iv_diuretic_flag`, `diuretic_response_ml_mg`,
`discharge_congestion_score`, `ed_visit_count_1yr`,
`days_to_readmission`, `hospitalization_risk_score`.

**Functional outcomes (15 cols):** `six_mwd_meters_baseline`,
`kccq_overall_score_baseline`, `vo2_max_ml_kg_min_baseline`,
`lvef_change_12m_pct`, `ef_response_flag`, `nyha_improvement_flag`,
`six_mwd_trend_12m_meters`, `kccq_change_12m`, `mace_flag_3yr`,
`mortality_flag_3yr`, `cv_death_flag_3yr`, `time_to_death_days`,
`borg_dyspnea_scale_baseline`, `patient_global_impression_12m`,
`cpet_reason`.

**Comorbidities & vitals (25 cols):** `atrial_fibrillation_flag`,
`af_type`, `diabetes_flag`, `diabetes_type`, `hypertension_flag`,
`cad_flag`, `prior_mi_flag`, `valvular_disease`, `sleep_apnea_flag`,
`copd_flag`, `anemia_flag`, `obesity_flag`, `depression_flag`,
`charlson_comorbidity_index`, `heart_rate_bpm_baseline`,
`systolic_bp_mmhg_baseline`, `diastolic_bp_mmhg_baseline`,
`pulse_pressure_mmhg_baseline`, `map_mmhg_baseline`,
`weight_kg_baseline`, `oxygen_saturation_pct_baseline`,
`jvp_cmh2o_baseline`, `peripheral_edema_grade_baseline`,
`rales_flag_baseline`.

### `visits` (2,400 rows × 11 cols)
`patient_id`, `visit_number` (1-12), `visit_date`,
`months_from_baseline` (0-33), `hf_phenotype`, `nyha_class_visit`,
`lvef_pct_visit`, `lvef_trend_visit`, `bnp_pg_ml_visit`,
`bnp_trend_pct_visit`, `weight_change_kg_visit`.

---

## Suggested use cases

- **HF phenotype classification ML** — train classifiers for
  HFrEF / HFmrEF / HFpEF / Advanced from echo + biomarker features
- **30-day readmission prediction** — classifier on
  `readmission_30d_flag` from demographics, biomarkers, GDMT status,
  hospitalization risk features (CMS HRRP-relevant)
- **GDMT optimization ML** — predict which patients are eligible for
  4-pillar therapy, which are likely to achieve target doses
- **Mortality / MACE risk stratification** — Cox models or survival
  forests on `mortality_flag_3yr` + `time_to_death_days` with right-
  censoring
- **EF response prediction** — regressor for `lvef_change_12m_pct` from
  baseline echo + GDMT + biomarkers; identifies likely responders
- **Diastolic function classification** — train multi-class on
  `diastolic_grade_baseline` (Grade 1/2/3/Indeterminate) from E/A, E/e',
  LAVI features per ASE 2016
- **Cardiorenal risk modeling** — joint modeling of `egfr_ml_min_173m2`
  trajectory and `bnp_pg_ml` trajectory across visits; useful for
  cardiorenal syndrome detection
- **NLP-augmented EHR research** — table provides structured ground
  truth for NLP extraction from clinical notes; pair with synthetic
  clinical text generators
- **Clinical trial cohort simulation** — filter to specific eligibility
  criteria (e.g., HFrEF + NYHA II-III + LVEF ≤35% + eGFR ≥30) and
  benchmark expected event rates / power calculations
- **Treatment effect heterogeneity** — train uplift / CATE models for
  SGLT2i, ARNI, MRA effects across phenotype subgroups
- **Longitudinal LVEF / BNP trajectory clustering** — unsupervised
  clustering on visit-level trajectories to discover responder
  phenotypes
- **NYHA class transitions** — Markov / state-space models on
  `nyha_class_visit` transitions
- **Device therapy appropriateness ML** — train models that flag
  potential under-utilization of ICD/CRT in eligible patients
- **Hospitalization cost / utilization modeling** — combine
  `index_los_days`, `index_icu_flag`, `ed_visit_count_1yr`,
  `hospitalization_count_3yr` for utilization predictive analytics

---

## Loading examples

```python
from datasets import load_dataset

baseline = load_dataset("xpertsystems/hccar001-sample", "baseline", split="train")
visits = load_dataset("xpertsystems/hccar001-sample", "visits", split="train")
print(baseline.shape, visits.shape)
```

```python
import pandas as pd
from huggingface_hub import hf_hub_download

baseline = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
    repo_type="dataset",
))
visits = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_visits.parquet",
    repo_type="dataset",
))

# HF phenotype distribution (ACC/AHA 2022)
print(baseline["hf_phenotype"].value_counts(normalize=True).round(3))
```

```python
# GDMT 4-pillar prescribing in HFrEF
import pandas as pd
from huggingface_hub import hf_hub_download

baseline = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
    repo_type="dataset",
))

hfref = baseline[baseline["hf_phenotype"] == "HFrEF"]
gdmt_rates = hfref[[
    "gdmt_acei_arb_arni", "gdmt_betablocker",
    "gdmt_mra", "gdmt_sglt2i", "gdmt_4_pillar_flag"
]].mean().round(3) * 100
print("HFrEF GDMT prescribing rates (%):")
print(gdmt_rates)
```

```python
# Longitudinal LVEF trajectory by GDMT status
import pandas as pd
from huggingface_hub import hf_hub_download

baseline = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
    repo_type="dataset",
))
visits = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_visits.parquet",
    repo_type="dataset",
))

joined = visits.merge(
    baseline[["patient_id", "gdmt_4_pillar_flag", "gdmt_sglt2i"]],
    on="patient_id"
)
joined["responder_group"] = joined["gdmt_4_pillar_flag"] | joined["gdmt_sglt2i"]
trajectory = (
    joined.groupby(["responder_group", "visit_number"])
    ["lvef_pct_visit"].mean().unstack(level=0).round(2)
)
print(trajectory)  # responder group LVEF trajectory recovers; control doesn't
```

```python
# 30-day readmission risk by HF phenotype
import pandas as pd
from huggingface_hub import hf_hub_download

baseline = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
    repo_type="dataset",
))

risk = baseline.groupby("hf_phenotype").agg(
    n=("patient_id", "count"),
    readmit_30d_pct=("readmission_30d_flag", lambda x: x.mean() * 100),
    readmit_90d_pct=("readmission_90d_flag", lambda x: x.mean() * 100),
    mean_nyha=("nyha_class_baseline", "mean"),
    mean_bnp=("bnp_pg_ml_baseline", "mean"),
).round(2)
print(risk)
```

---

## Limitations and honest disclosures

This sample is calibrated for **structural fidelity, not bit-exact reproduction
of any specific HF registry or institutional cohort.** Specifically:

- **30-day readmission rate observed ~30-35%** vs CMS HRRP national
  benchmark 22-25%. The generator's `p_30d = 0.23 × (1 + risk × 0.8)`
  risk-multiplier amplifies above the base rate. Use the TEMPORAL
  ORDERING (30d ≤ 90d ≤ 180d) as the structural validation, not the
  absolute rate. **For absolute-rate calibration, scale to the full
  product or re-fit the risk multiplier.**
- **3-year mortality observed ~10%** vs real-world HFrEF literature
  25-35% (PARADIGM-HF, EMPEROR-Reduced, DAPA-HF placebo arms). The
  generator computes `mort_p = mace_p × 0.45` with `mace_p` clipped
  to [0.03, 0.65], producing a mort_p ceiling of ~0.29. **The
  generator under-predicts mortality for advanced HF.**
- **`bsa_m2` has a dead-code bug in the generator** (line 120 computes
  BSA via Mosteller formula, line 121 immediately overwrites with
  `N(1.9, 0.25)`). The overwritten N(1.9, 0.25) is what's in the
  output. Don't use `bsa` for BMI/height back-calculation.
- **LBBB is not modeled** for CRT eligibility — CARE-HF / COMPANION
  criteria include LVEF ≤35% + NYHA III-IV ambulatory + LBBB. Our
  validation covers the first two (LVEF + NYHA); LBBB status is
  absent. Add this if you need full CRT-D appropriateness classification.
- **Visit-level LVEF can drift OUT of baseline phenotype band** over
  12 quarterly visits as patients respond to GDMT — this is realistic
  clinical behavior (recovered EF). Our validation checks BASELINE LVEF
  in phenotype band, not visit-level. Don't filter visits by
  `lvef_pct_visit` matching baseline phenotype band — you'll lose
  GDMT responders.
- **`site_id` is randomly chosen from 30 generated UUIDs** —
  patients within a site share no clustering structure (no
  hierarchical / random-effects), no provider effects, no
  geographic correlations. Treat as a nominal site label, not a
  hierarchical clustering variable.
- **HFpEF treatment effect modeling is simplified.** GDMT
  prescribing rates from PINNACLE are anchored to outpatient
  HFrEF cohorts; HFpEF GDMT rates (`acei_arb_arni: 0.55,
  betablocker: 0.60, mra: 0.25, sglt2i: 0.45`) are reasonable
  but the EF response to SGLT2i in HFpEF is muted (EMPEROR-
  Preserved-style effects, not EMPEROR-Reduced). Our generator
  applies a uniform `+1.5 ± 1` EF delta for SGLT2i regardless
  of phenotype.
- **Race / ethnicity distribution** (`60% Non-Hispanic White, 20%
  Black/AA, 12% Hispanic, 5% Asian/Pacific Islander, 3% Other`)
  matches general US prevalence but Black/AA HF prevalence is
  ~2x higher than this in real registries. Adjust sampling if
  modeling race-specific GDMT response (e.g., V-HeFT for
  hydralazine-nitrate).
- **Hydralazine-nitrate flag fires at 20% of Black/AA patients**
  (line 389); the generator has no separate eligibility model
  (ISDN should be ACEi-intolerant or sub-target dose). Treat
  as an inclusive eligibility signal.
- **Comorbidities are sampled independently** (lines 621-639) —
  no realistic co-occurrence structure beyond per-phenotype
  base rates. Real diabetes + obesity + sleep apnea + CKD
  exhibit strong co-occurrence (cardiometabolic phenotype);
  this generator treats them as independent.
- **`scipy.stats`, `faker`, `tqdm` are mentioned in the generator's
  docstring** but not used in active code. No external dependencies
  beyond NumPy + Pandas.
- **NYHA class distribution by phenotype** matches the design dict
  (HFrEF [0.10, 0.35, 0.40, 0.15]) within ~5% sampling noise at
  n=200. For tight matching, use the full product (10K patients).
- **Visit count is fixed at 12 quarterly visits** — no realistic
  censoring (loss to follow-up, death before visit, etc). All
  patients have all 12 visits regardless of `mortality_flag_3yr`
  or `time_to_death_days`. For survival ML, use baseline TTE
  variables and don't naively use visit count as risk signal.

The full HCCAR001 product addresses these by calibrated mortality and
readmission rates against real-world HFrEF/HFpEF registries, hierarchical
site / provider structure, dependent comorbidity sampling, full LBBB +
QRS duration + bundle-branch block modeling for CRT eligibility, race-
specific GDMT response heterogeneity, realistic censoring of visit
records by death/LTFU, and pre-built scenario configs (HFrEF clinical
trial simulation, HFpEF treatment-effect heterogeneity, cardiometabolic
phenotype). Contact us for the licensed commercial release.

---

## Companion datasets

This is the first SKU in our **Healthcare / Cardiology** vertical. Related
datasets from elsewhere in the catalog:

- [**Healthcare / Neurology**](https://huggingface.co/xpertsystems) (10 SKUs)
  — synthetic neurological patient datasets covering stroke, MS, epilepsy,
  Parkinson's, ALS, traumatic brain injury, dementia spectrum
- [**Insurance & Risk**](https://huggingface.co/xpertsystems) (10 SKUs) —
  health insurance claims, prior authorization, risk adjustment, MLR
  modeling
- [**Energy & Climate**](https://huggingface.co/xpertsystems) (8 SKUs) —
  power grid, renewables, demand, O&G, smart grid, energy trading, climate
  impact, consumer electricity
- [**Manufacturing**](https://huggingface.co/xpertsystems) (10 SKUs) —
  reliability, quality, operations, supply chain, IIoT
- [**Oil & Gas**](https://huggingface.co/xpertsystems) (17 SKUs) — upstream,
  midstream, downstream, geological / seismic, reservoir simulation

For the broader catalog, see https://huggingface.co/xpertsystems

---

## Citation

```bibtex
@dataset{xpertsystems_hccar001_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HCCAR001 Synthetic Heart Failure Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hccar001-sample}
}
```

---

## Contact

- **Web:** https://xpertsystems.ai
- **Email:** pradeep@xpertsystems.ai
- **Full product catalog:** Cardiology, Neurology, Insurance & Risk, Energy
  & Climate, Manufacturing, Oil & Gas, Cybersecurity, 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
(GDMT eligibility, ICD/CRT criteria, biomarker ranges) are sourced from
published guidelines; users are responsible for verifying against current
ACC/AHA/HFSA guidelines for clinical applications.