---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- healthcare
- cardiology
- hypertension
- htn
- high-blood-pressure
- blood-pressure-monitoring
- abpm
- ambulatory-bp-monitoring
- home-bp-monitoring
- central-aortic-pressure
- pulse-wave-velocity
- pwv
- augmentation-index
- arterial-stiffness
- bp-variability
- white-coat-hypertension
- masked-hypertension
- resistant-hypertension
- nocturnal-dipping
- non-dipper
- reverse-dipper
- ace-inhibitor
- arb
- calcium-channel-blocker
- ccb
- thiazide
- beta-blocker
- mra
- spironolactone
- antihypertensive
- medication-adherence
- bp-response
- side-effects
- pill-burden
- lifestyle-modification
- dash-diet
- sodium-intake
- physical-activity
- mets
- sleep-quality
- osa
- obstructive-sleep-apnea
- ascvd
- ascvd-pooled-cohort
- framingham-risk
- pooled-cohort-equation
- mace
- major-adverse-cardiovascular-event
- mi-prediction
- stroke-prediction
- hf-hospitalization
- atrial-fibrillation
- cv-death
- lvh
- left-ventricular-hypertrophy
- lv-mass-index
- e-e-prime
- diastolic-dysfunction
- carotid-imt
- carotid-plaque
- retinopathy
- microalbuminuria
- macroalbuminuria
- uacr
- ckd
- kdigo
- egfr
- ckd-epi
- ckd-stage
- hs-crp
- bnp
- troponin
- acc-aha-2017
- esh-2018
- abpm-task-force
- carey-resistant-htn
- aha-acc-pce-2013
- longitudinal-ehr
- ehr-synthetic
- clinical-trial-simulation
pretty_name: HCCAR003 — Synthetic Hypertension & Cardiovascular Risk Dataset (Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files: hccar003_dataset.parquet
---

# HCCAR003 — Synthetic Hypertension & Cardiovascular Risk Dataset (Sample Preview)

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

A **longitudinal hypertension cohort dataset** with quarterly visit-level
records spanning **7 clinical modules**: BP monitoring (office, home, ABPM
24hr/day/night with dipping pattern, central aortic, pulse wave velocity,
augmentation index, BP variability), antihypertensive medications (up to
4 drug slots, 8 drug classes — ACEi, ARB, CCB, Thiazide, Beta-blocker,
Alpha-blocker, MRA), lifestyle factors (DASH diet, dietary sodium,
physical activity METs, sleep, smoking, alcohol, stress), biomarkers
(full lipid panel, hs-CRP, creatinine, eGFR with KDIGO CKD stage, UACR
with microalbuminuria/macroalbuminuria, electrolytes, glucose, HbA1c,
BNP, troponin, ASCVD 10y risk, Framingham), end-organ damage (LVH, LV
mass index, LVEF, E/e' ratio, carotid IMT, plaque, retinopathy grade,
white matter lesion volume, lacunar infarcts), and **MACE outcomes**
(MI, ischemic / hemorrhagic stroke, TIA, HF hospitalization, AF new
onset, CV death, all-cause death, study dropout).

Calibrated benchmark-first against **ACC/AHA 2017 Hypertension Guidelines**
(Whelton et al.), **ACC/AHA 2013 Pooled Cohort Equations** (Goff et al.),
**KDIGO 2012 CKD Classification**, **ABPM Task Force / ESH 2013
Recommendations**, **AHA/ACC Resistant Hypertension Scientific Statement
(Carey et al. 2018)**, and **ESH/ESC 2018 Hypertension Guidelines**.

This is the **sample preview** — 150 patients × ~12 quarterly visits over
3 years (~1,800 visit records, ~1.1 MB). The full product covers 10,000+
patients × full 10-year follow-up (40 quarterly visits) with extended
medication titration histories, multi-cuff measurement protocols, and
pre-built scenario configs for SPRINT-style intensive vs standard BP
target trials, salt-sensitive hypertension studies, and resistant
hypertension subgroup analysis.

---

## Dataset summary

| Table | Rows (sample) | What it contains |
|---|---:|---|
| `hccar003_dataset` | 1,747 | One row per patient × visit. 99 features across 7 clinical modules (demographics carried forward + BP monitoring + medications + lifestyle + biomarkers + end-organ damage + MACE outcomes). 150 unique patients with up to 12 quarterly visits each (some patients drop out due to death or withdrawal) |

Provided in both **CSV** and **Parquet**. Aggregate to patient-level via
`groupby('patient_id')` for cross-sectional analysis.

---

## Calibration sources

All ten validation metrics target named clinical / regulatory standards:

- **ACC/AHA 2017 Hypertension Guidelines** (Whelton et al. 2018) — HTN
  stage classification (Normal, Elevated, Stage 1, Stage 2, Crisis)
- **ACC/AHA 2013 Pooled Cohort Equations** (Goff et al. 2014) — 10-year
  ASCVD risk (race/sex-specific, baseline column included with known
  calibration issues — see Limitations)
- **KDIGO 2012 CKD Classification** — eGFR-based G1-G5 staging,
  UACR-based A1-A3 albuminuria categories
- **ABPM Task Force / ESH 2013** (O'Brien et al. 2013) — ambulatory
  blood pressure monitoring definitions, dipping pattern classification,
  masked hypertension criteria
- **AHA/ACC Resistant Hypertension Scientific Statement** (Carey et al.
  2018) — resistant HTN = SBP ≥130/80 on ≥3 antihypertensives
- **ESH/ESC 2018 Hypertension Guidelines** (Williams et al. 2018) —
  European HTN management framework
- **CKD-EPI 2009** (Levey et al. 2009) — eGFR calculation (note: the
  generator uses the OLD race-coefficient version; the 2021 NKF-ASN
  refit removed the race coefficient. See Limitations.)
- **Survival analysis monotonicity** — CV death ⊆ all-cause death

---

## 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 | `pulse_pressure_equals_sbp_minus_dbp_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity |
| 2 | `map_equals_dbp_plus_pp_third_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity |
| 3 | `ckd_stage_matches_kdigo_egfr_rate` | 0.999 | 0.99 | ±0.01 | FLOOR | KDIGO 2012 |
| 4 | `albuminuria_flags_match_uacr_bands_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | KDIGO ACR thresholds |
| 5 | `abpm_dipping_pattern_matches_dip_pct_rate` | 0.991 | 0.99 | ±0.01 | FLOOR | ABPM Task Force / ESH 2013 |
| 6 | `resistant_htn_requires_3_drugs_and_sbp_130_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Carey et al. 2018 |
| 7 | `masked_htn_definition_match_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ABPM Task Force |
| 8 | `cv_death_implies_all_cause_death_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Survival monotonicity |
| 9 | `mace_event_flag_matches_event_type_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | MACE composite endpoint |
| 10 | `bp_in_physiologic_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Physiologic plausibility |

---

## Schema highlights (99 cols)

### Identity & demographics (8 cols, carried per visit)
`patient_id` (HC-CAR-XXXXXX), `visit_number` (1-12), `visit_date`,
`years_from_baseline`, `age_at_visit`, `sex` (Male / Female),
`race_ethnicity` (NonHispanic_White / NonHispanic_Black / Hispanic /
Asian / Other), `htn_stage_baseline` (Normal / Elevated / Stage1_HTN /
Stage2_HTN / Crisis).

### BP monitoring (19 cols)
`sbp_office_mmhg`, `dbp_office_mmhg`, `pp_office_mmhg`, `map_office_mmhg`,
`sbp_home_avg_mmhg`, `dbp_home_avg_mmhg`, `abpm_sbp_24hr_mmhg`,
`abpm_dbp_24hr_mmhg`, `abpm_sbp_daytime_mmhg`, `abpm_sbp_nighttime_mmhg`,
`abpm_dipping_pct`, `abpm_dipping_pattern` (Reverse_dipper / Non-dipper /
Dipper / Extreme_dipper), `central_aortic_sbp_mmhg`,
`pulse_wave_velocity_ms`, `augmentation_index_pct`, `bp_variability_sd_sbp`,
`white_coat_effect_mmhg`, `white_coat_flag`, `masked_hypertension_flag`.

### Medications (12 cols)
`drug_class_{1-4}`, `drug_name_{1-3}`, `drug_dose_1`,
`n_antihypertensive_drugs` (0-4), `medication_adherence_pct`,
`bp_response_sbp_mmhg`, `resistant_htn_flag`, `side_effect_code`
(Dry_Cough / Peripheral_Edema / Hypokalemia / Bradycardia /
Orthostatic_Hypotension / Hyperkalemia / None), `pill_burden_score`.

### Lifestyle (8 cols)
`bmi_kg_m2`, `dietary_sodium_mg_day`, `dash_diet_score`,
`physical_activity_mets_hr_wk`, `sleep_hours_night`, `smoking_status`
(Never / Former / Current), `alcohol_drinks_week`, `stress_score`.

### Biomarkers (22 cols)
`total_cholesterol_mg_dl`, `ldl_cholesterol_mg_dl`, `hdl_cholesterol_mg_dl`,
`triglycerides_mg_dl`, `non_hdl_cholesterol_mg_dl`, `hs_crp_mg_l`,
`creatinine_mg_dl`, `egfr_ml_min_1_73m2`, `uacr_mg_g`, `ckd_stage`
(G1 / G2 / G3a / G3b / G4 / G5), `bun_mg_dl`, `potassium_meq_l`,
`sodium_meq_l`, `glucose_fasting_mg_dl`, `hba1c_pct`, `uric_acid_mg_dl`,
`bnp_pg_ml`, `troponin_i_ng_l`, `ascvd_10yr_risk_pct`,
`framingham_risk_score_pct`, `microalbuminuria_flag`,
`macroalbuminuria_flag`.

### End-organ damage (9 cols)
`lvh_flag`, `lv_mass_index_g_m2`, `lvef_pct`, `e_e_prime_ratio`,
`carotid_imt_mm`, `carotid_plaque_flag`, `retinopathy_grade` (0-4),
`wml_volume_ml`, `lacunar_infarct_flag`.

### MACE outcomes (11 cols)
`mace_event_flag`, `mace_event_type` (MI / Stroke_Ischemic /
Stroke_Hemorrhagic / TIA / HF_Hospitalization / AF_New_Onset / CV_Death /
Non_CV_Death / None), `mi_flag`, `stroke_flag`, `stroke_type`,
`hf_hospitalization_flag`, `af_new_onset_flag`, `cardiovascular_death_flag`,
`all_cause_death_flag`, `study_dropout_flag`, `dropout_reason` (Death /
Withdrawal / None).

### Comorbidities (7 cols, carried forward)
`diabetes_flag`, `dyslipidemia_flag`, `osa_flag`, `statin_use_flag`,
`aspirin_use_flag`, `family_history_htn_flag`,
`charlson_comorbidity_index`.

---

## Suggested use cases

- **BP control prediction** — train classifiers/regressors on
  `sbp_office_mmhg` and `resistant_htn_flag` from baseline + medication
  + lifestyle features
- **ABPM interpretation ML** — predict dipping pattern, masked HTN from
  office BP, home BP, and patient features; useful for ABPM-replacement
  algorithms
- **Medication response prediction** — model `bp_response_sbp_mmhg`
  given drug class combinations and patient characteristics (uplift
  modeling for personalized antihypertensive selection)
- **Resistant HTN cohort identification** — classifier for
  `resistant_htn_flag` for utilization analytics
- **Antihypertensive adherence ML** — predict
  `medication_adherence_pct` from pill burden, side effects, SES, age
- **ASCVD risk recalibration** — train improved 10-year ASCVD models
  to compare against ACC/AHA PCE (note: the included
  `ascvd_10yr_risk_pct` has known calibration issues — useful for
  derivation studies that explicitly correct PCE bugs)
- **MACE survival ML** — Cox / DeepSurv / random survival forests on
  the MACE outcomes (aggregate per-visit flags to patient level for
  TTE analysis)
- **End-organ damage progression** — model `lvh_flag`, `carotid_imt_mm`,
  `wml_volume_ml` trajectories given longitudinal BP control
- **CKD progression in HTN** — model `egfr_ml_min_1_73m2` decline
  trajectories; useful for predicting CKD-G3 → G4 transitions
- **White coat / masked HTN detection** — classifier for office vs
  ambulatory discrepancy; useful for diagnostic workflow automation
- **Salt sensitivity studies** — use `dietary_sodium_mg_day` and
  individual BP response to identify salt-sensitive phenotypes
- **Dipping pattern ML** — predict nocturnal dipping pattern
  (Reverse / Non / Normal / Extreme) from office BP, age, OSA,
  comorbidities; useful for ABPM-free phenotyping
- **Treatment intensification timing ML** — predict when next drug
  should be added from BP trajectory + current regimen
- **Quality improvement / HEDIS analytics** — BP control rate
  measurement, medication intensification audit (HEDIS Controlling
  High Blood Pressure metric)

---

## Loading examples

```python
from datasets import load_dataset

ds = load_dataset("xpertsystems/hccar003-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/hccar003-sample", "hccar003_dataset.parquet",
    repo_type="dataset",
))

# HTN stage distribution
print(df.groupby("patient_id")["htn_stage_baseline"].first()
      .value_counts(normalize=True).round(3))
```

```python
# BP trajectory by HTN stage
import pandas as pd
from huggingface_hub import hf_hub_download

df = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar003-sample", "hccar003_dataset.parquet",
    repo_type="dataset",
))

trajectory = (
    df.groupby(["htn_stage_baseline", "visit_number"])["sbp_office_mmhg"]
    .mean().unstack(level=0).round(1)
)
print(trajectory.head(12))
```

```python
# Resistant HTN identification + drug regimens
import pandas as pd
from huggingface_hub import hf_hub_download

df = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar003-sample", "hccar003_dataset.parquet",
    repo_type="dataset",
))

# Patients with resistant HTN at ANY visit
resistant_pts = df.loc[df["resistant_htn_flag"] == 1, "patient_id"].unique()
print(f"Resistant HTN patients: {len(resistant_pts)} / {df['patient_id'].nunique()}")

# Their typical drug regimens (drug_class_1 distribution)
resist_df = df[df["patient_id"].isin(resistant_pts)]
print(resist_df["drug_class_1"].value_counts().head())
```

```python
# Aggregate MACE outcomes to patient level
import pandas as pd
from huggingface_hub import hf_hub_download

df = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar003-sample", "hccar003_dataset.parquet",
    repo_type="dataset",
))

# Per-patient MACE summary (per-visit flags need aggregation)
patient_mace = df.groupby("patient_id").agg(
    any_mi=("mi_flag", "max"),
    any_stroke=("stroke_flag", "max"),
    any_hf=("hf_hospitalization_flag", "max"),
    any_af=("af_new_onset_flag", "max"),
    cv_death=("cardiovascular_death_flag", "max"),
    all_cause_death=("all_cause_death_flag", "max"),
    follow_up_yrs=("years_from_baseline", "max"),
).round(3)

print("Patient-level event rates:")
print(patient_mace.mean().round(3))
```

```python
# ABPM dipping pattern distribution
import pandas as pd
from huggingface_hub import hf_hub_download

df = pd.read_parquet(hf_hub_download(
    "xpertsystems/hccar003-sample", "hccar003_dataset.parquet",
    repo_type="dataset",
))

print("Dipping pattern distribution by HTN stage:")
print(pd.crosstab(df["htn_stage_baseline"], df["abpm_dipping_pattern"],
                  normalize="index").round(3))
```

---

## Limitations and honest disclosures

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

- **The included Pooled Cohort Equation (`ascvd_10yr_risk_pct`) is BUGGY.**
  The generator's PCE implementation (lines 122-153) has multiple errors:
  (a) White Male branch uses `1.764` for both bp_treated and untreated,
  removing treatment effect; (b) White Female has a misplaced
  `-29.799*1` term; (c) Black Male is missing the published intercept;
  (d) Black Female has implausible coefficients on ln_sbp (29.2907 vs
  published 0.295). **Result: ASCVD risk values saturate at clip ceiling
  (75%) for ~93% of patients and at clip floor (1%) for Black Males.**
  Use the column only as a relative-ordering signal, not for absolute
  10-year ASCVD estimation. For accurate ASCVD risk, recompute from
  age, sex, race, total cholesterol, HDL, SBP, BP treatment flag,
  diabetes, smoking using the published formula
  (Goff et al. 2014).
- **eGFR uses the OLD 2009 CKD-EPI formula with race coefficient**
  (line 216-217: `egfr *= 1.159 if Black`). The 2021 NKF-ASN refit
  REMOVED the race coefficient. If you need the modern formula,
  recompute from `creatinine_mg_dl`, `age_at_visit`, `sex` without
  the race multiplier.
- **`carotid_plaque_flag` formula bug**: `int(RNG.random() < (0.05 + 0.01*age - 0.5))`
  effectively gives ZERO carotid plaque to patients under age 45 (the
  probability term goes negative). For full plaque modeling, use the
  full product or augment with separate carotid imaging modules.
- **MACE per-visit flags fire on only ONE visit per patient** (the
  visit within 0.13 years of `event_time`). They are NOT cumulative —
  the flag does not carry forward after the event. **For patient-level
  MACE prediction, aggregate via `groupby('patient_id').max()` on the
  flag columns** (see "Loading examples" above). Time-to-event must be
  derived from the visit_number × 0.25 (quarterly) offset.
- **`af_new_onset_flag` can fire from MACE event AND from a separate
  stochastic check** (line 480): `int(mace_type == 'AF_New_Onset' or
  (yrs > 5 and sbp_off > 160 and random < 0.005))`. So AF can occur
  outside of the formal MACE event window — by design, reflecting that
  AF is sometimes diagnosed incidentally.
- **`_dropout_at` reference in main loop** (line 646) checks a dictionary
  key that's never set by `generate_patient_baseline`. The branch is
  dead code; dropout actually fires via `study_dropout_flag` only.
  Cosmetic side-effect.
- **Mean SBP ~144 mmHg, mean DBP ~97 mmHg** in this sample — higher
  than typical real-world HTN cohorts (~135/85) because the HTN stage
  distribution skews toward Stage 1/2 (~55% of patients). The
  generator's stage probabilities `[Normal: 15%, Elevated: 20%,
  Stage1: 30%, Stage2: 25%, Crisis: 10%]` produce a hypertension-
  enriched cohort by design (suitable for HTN clinical trials, not
  general population epidemiology).
- **Race/ethnicity SBP offsets** (line 106-109): Black patients have
  +6 mmHg SBP offset, Asian -2 mmHg. These match published trial
  observations (e.g., AASK, ALLHAT) but are NOT a complete model of
  hypertension disparities — they encode only the magnitude offset, not
  the underlying mechanisms (RAAS responsiveness, salt sensitivity,
  vascular dysfunction).
- **Visit dropout is independent of clinical state** (line 485:
  `dropout_flag = int(dead_v or (random < 0.003))`). Real HTN cohort
  dropout correlates with poor BP control, adverse drug effects, and
  SES. Treat the sample as informatively-censored data only if you
  augment with realistic dropout mechanisms.
- **Comorbidities are independent draws** (lines 188-194: dm, dys, ckd,
  osa) — no realistic co-occurrence beyond per-flag base rates. Real
  cardiometabolic clustering (diabetes + dyslipidemia + obesity + CKD)
  is much tighter than the generator produces.
- **`scipy.stats` is imported but unused** in active generator code.
  No external compute dependencies beyond numpy + pandas + tqdm. The
  scipy distributions (norm, beta, lognorm, weibull_min) appear in the
  import block but never get called.
- **Masked HTN observed at 0.3-0.5%** in sample — much lower than the
  10-15% prevalence reported in clinical literature. Generator's
  `wc_effect ~ N(8, 6)` and white_coat→office subtraction produces
  predominantly white-coat phenotype (office > home) rather than
  masked (home > office). For masked HTN ML research, augment with
  inverted white-coat scenarios from the full product.
- **Visit count varies by patient** — some patients have 12 visits,
  some have fewer due to dropout. Use `groupby('patient_id').size()`
  to check follow-up duration per patient. Treat as unbalanced panel
  data.
- **Drug-drug interactions and titration are simplified.** The drug
  regimen is fixed at baseline (4 slots, randomly chosen from 7
  classes); no realistic titration logic, no switching due to side
  effects, no addition due to inadequate BP response. For
  pharmacotherapy intensification ML, use the full product.

The full HCCAR003 product addresses these by corrected ACC/AHA PCE
implementation, full 2021 CKD-EPI refit, complete carotid plaque
modeling, MACE flag carry-forward for survival analysis, realistic
medication titration trajectories, dependent comorbidity sampling,
and pre-built scenario configs (SPRINT-style intensive vs standard,
salt-sensitive HTN, resistant HTN subgroup). Contact us for the
licensed commercial release.

---

## Companion datasets

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

- [**HCCAR001**](https://huggingface.co/datasets/xpertsystems/hccar001-sample)
  Heart Failure Dataset — chronic HF longitudinal records with GDMT,
  device therapy, hospitalization, 12 quarterly visits
- [**HCCAR002**](https://huggingface.co/datasets/xpertsystems/hccar002-sample)
  Acute Myocardial Infarction Dataset — STEMI/NSTEMI/UA with serial
  troponin kinetics, intervention timing, in-hospital outcomes
- [**HCCAR003**](https://huggingface.co/datasets/xpertsystems/hccar003-sample)
  Hypertension Dataset (you are here) — longitudinal HTN cohort with
  ABPM, GDMT, MACE outcomes
- [**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)

**Cardiology pairing**: HCCAR001 + HCCAR002 + HCCAR003 covers the full
HTN→AMI→HF clinical trajectory. Hypertension is the leading modifiable
risk factor for AMI (HCCAR002) and HFpEF (HCCAR001 phenotype).

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

---

## Citation

```bibtex
@dataset{xpertsystems_hccar003_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HCCAR003 Synthetic Hypertension \& Cardiovascular Risk Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hccar003-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
(HTN stage, resistant HTN, ABPM dipping pattern, KDIGO CKD stages) are
sourced from published guidelines; users are responsible for verifying
against current ACC/AHA/ESC/KDIGO guidelines for clinical applications.
The included Pooled Cohort Equation implementation has known
calibration issues — see Limitations.