IXA-001 Hypertension Microsimulation Model

Comprehensive Technical Documentation

Document Version: 1.0 Date: February 2026 Sponsor: Atlantis Pharmaceuticals Prepared By: HEOR Technical Documentation Team

Executive Summary
Model Overview
Dual Cardiac-Renal Pathway Interactions
Risk Equations
Cost Inputs
Utility Values
Probabilistic Sensitivity Analysis
Subgroup Analysis
Background Mortality
Patient History Analysis
Model Validation
Results Summary
CHEERS 2022 Compliance
References
Appendices

1. Executive Summary

1.1 Purpose

This document provides comprehensive technical documentation for the IXA-001 cost-effectiveness model, an individual-level state-transition microsimulation (IL-STM) evaluating the aldosterone synthase inhibitor IXA-001 versus spironolactone for resistant hypertension with secondary causes.

1.2 Model Summary

Attribute	Specification
Model Type	Individual-level state-transition microsimulation
Population	Adults with resistant hypertension (N=1,000 per arm)
Intervention	IXA-001 (aldosterone synthase inhibitor)
Comparator	Spironolactone (mineralocorticoid receptor antagonist)
Time Horizon	20 years (lifetime available)
Cycle Length	1 month
Perspective	Healthcare system (base case), Societal (scenario)
Discount Rate	3.0% costs, 3.0% outcomes
Outcomes	QALYs, costs, ICER, events prevented

1.3 Key Results

Subgroup	Incremental Cost	Incremental QALY	ICER ($/QALY)
Primary Aldosteronism	+$20,550	+0.084	$245,441
OSA (severe)	+$25,200	+0.081	$311,111
Renal Artery Stenosis	+$28,500	+0.074	$385,135
Essential HTN	+$35,200	-0.012	Dominated

Primary Finding: IXA-001 demonstrates optimal value in the Primary Aldosteronism (PA) subgroup, with the lowest ICER and highest clinical benefit due to mechanism-specific efficacy.

1.4 Documentation Structure

This master document consolidates 8 detailed technical reports:

#	Report	Section	Detailed Report
1	Risk Equations	Section 4	`risk_equations_technical_report.md`
2	Cost Inputs	Section 5	`cost_inputs_technical_report.md`
3	Utility Values	Section 6	`utility_values_technical_report.md`
4	PSA Parameters	Section 7	`psa_parameters_technical_report.md`
5	Subgroup Analysis	Section 8	`subgroup_analysis_methodology.md`
6	Background Mortality	Section 9	`background_mortality_technical_note.md`
7	History Analyzer	Section 10	`history_analyzer_technical_note.md`
8	Model Validation	Section 11	`model_validation_report.md`

2. Model Overview

2.1 Conceptual Framework

The model implements a dual-pathway cardiorenal microsimulation capturing:

Cardiovascular pathway: MI, stroke, heart failure, atrial fibrillation, CV death
Renal pathway: CKD progression (stages 1-5), ESRD, dialysis

┌─────────────────────────────────────────────────────────────────────────────┐
│                    IXA-001 MICROSIMULATION STRUCTURE                         │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  PATIENT ENTRY                                                              │
│       │                                                                     │
│       ▼                                                                     │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                    BASELINE STRATIFICATION                          │   │
│  │  • Secondary HTN etiology (PA, RAS, Pheo, OSA, Essential)          │   │
│  │  • Age-based phenotype (EOCRI <60, GCUA ≥60, KDIGO if CKD)        │   │
│  │  • PREVENT 10-year CVD risk                                         │   │
│  │  • KFRE 2-year kidney failure risk                                  │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│       │                                                                     │
│       ▼                                                                     │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                    MONTHLY SIMULATION CYCLE                          │   │
│  │                                                                      │   │
│  │  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐          │   │
│  │  │ CV Events    │    │ Renal        │    │ Background   │          │   │
│  │  │ • MI         │    │ Progression  │    │ Mortality    │          │   │
│  │  │ • Stroke     │    │ • eGFR       │    │ • Life       │          │   │
│  │  │ • HF         │    │   decline    │    │   tables     │          │   │
│  │  │ • AF         │    │ • ESRD       │    │              │          │   │
│  │  │ • CV Death   │    │              │    │              │          │   │
│  │  └──────────────┘    └──────────────┘    └──────────────┘          │   │
│  │         │                   │                   │                   │   │
│  │         └───────────────────┴───────────────────┘                   │   │
│  │                             │                                       │   │
│  │                             ▼                                       │   │
│  │                    OUTCOME ACCRUAL                                  │   │
│  │                    • Costs (direct + indirect)                      │   │
│  │                    • QALYs (utility × survival)                     │   │
│  │                    • Events (tracked by type)                       │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│       │                                                                     │
│       ▼                                                                     │
│  DEATH or END OF HORIZON                                                   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

2.2 Health States

Cardiovascular States

State	Description	Transitions From
No Acute Event	Baseline CV state	Entry, Post-event recovery
Acute MI	Month of MI occurrence	No Acute Event, Post-MI
Post-MI	Chronic post-MI state	Acute MI
Acute Stroke	Month of stroke	No Acute Event, Post-Stroke
Post-Stroke	Chronic post-stroke	Acute Stroke
Acute HF	HF hospitalization	No Acute Event, Chronic HF
Chronic HF	Stable heart failure	Acute HF
Atrial Fibrillation	New-onset or chronic AF	Any non-death state
CV Death	Absorbing state	Any state

Renal States

State	eGFR Range	KDIGO Stage
CKD Stage 1-2	≥60 mL/min/1.73m²	G1-G2
CKD Stage 3a	45-59	G3a
CKD Stage 3b	30-44	G3b
CKD Stage 4	15-29	G4
ESRD	<15 or dialysis	G5

2.3 Treatment Effects

Treatment	SBP Reduction	PA Response Modifier	Essential HTN Modifier
IXA-001	20 mmHg	1.70×	1.00×
Spironolactone	9 mmHg	1.40×	1.00×
Background therapy	15 mmHg	N/A	N/A

Code Reference: src/risk_assessment.py:346-480

3. Dual Cardiac-Renal Pathway Interactions

This section documents how the model handles patients with simultaneous cardiac comorbidities and renal complications.

3.1 Concurrent State Tracking

The model implements a dual-branch state tracking system where cardiac and renal states are tracked independently and simultaneously. A patient can occupy any combination of cardiac and renal states (e.g., Post-MI + CKD Stage 4).

Dimension	States	Code Reference
Cardiac	No Acute Event, Acute MI, Post-MI, Acute Stroke, Post-Stroke, Acute HF, Chronic HF, AF, CV Death	`src/patient.py:44-62`
Renal	CKD Stage 1-2, Stage 3a, Stage 3b, Stage 4, ESRD	`src/patient.py:64-71`
Cognitive	Normal, MCI, Dementia	`src/patient.py:73-76`

3.2 Cross-Pathway Interactions

3.2.1 Modelled Interactions

Interaction	Direction	Mechanism	Code Reference
eGFR in PREVENT risk	Renal → Cardiac	Lower eGFR increases 10-year CVD risk via PREVENT equation coefficients	`src/risks/prevent.py:45-156`
ESRD CV mortality	Renal → Cardiac	ESRD adds 9% annual incremental CV death risk (60% of ESRD mortality is CV-mediated)	`src/transitions.py:645-648`
SGLT2i renal protection	Cardiac → Renal	Patients with HF receive SGLT2i (40% uptake), which slows eGFR decline by 40%	`src/patient.py:484-491`
SGLT2i cardiac protection	Renal → Cardiac	Patients with CKD receive SGLT2i, which reduces HF hospitalization risk by 30%	`src/transitions.py:562-575`
Hyperkalemia management	Renal → Treatment	Rising K+ in renal dysfunction triggers stepped MRA management (binder → dose reduce → stop)	`src/treatment.py:260-347`
Additive cost accrual	Both	Monthly costs = cardiac state cost + renal state cost + drug costs	`src/costs/costs.py:463-514`
Additive utility decrements	Both	QALYs reflect combined disutilities from cardiac and renal states	`src/utilities.py`

3.2.2 Not Currently Modelled

Limitation	Clinical Relevance	Potential Impact
Acute kidney injury (AKI)	Post-MI cardiogenic shock causes AKI in ~20% of cases	May underestimate renal costs post-cardiac event
Cardiorenal syndrome (CRS)	Type 1-4 CRS feedback loops between heart and kidney	Bidirectional organ damage not captured dynamically
Treatment escalation for dual burden	No logic to intensify renoprotection when cardiac patient develops CKD	Treatment decisions remain BP-driven only
Cross-event time dependencies	Stroke risk elevated for 12 months post-MI	Events treated as independent within each cycle
Differential SBP targets	CKD-4+ patients may warrant lower BP targets	All patients share the same target (<140 mmHg)
ACEi/ARB for RAS subgroup	RAS identified but no differential treatment pathway	May underestimate benefit of targeted RAS treatment

3.2.3 Treatment for Dual-Burden Patients

Currently, treatment decisions are BP-driven only (src/treatment.py):

Intensify if SBP > 130 mmHg (with 50% clinical inertia probability)
No consideration of simultaneous cardiac + renal states when escalating or de-escalating treatment
SGLT2i is the only agent providing explicit dual-pathway benefit (cardiac + renal)
Hyperkalemia management is the only renal-aware treatment adjustment (stepped approach for MRA patients with rising K+)

Code Reference: src/treatment.py:225-246 (should_intensify_treatment())

3.3 SGLT2 Inhibitor as Dual-Benefit Agent

SGLT2 inhibitors represent the primary cross-pathway treatment mechanism in the model:

Property	Value	Source
Assignment criteria	eGFR < 60 OR heart failure present	Population generation
Real-world uptake	40%	Clinical practice data
Renal benefit	40% reduction in eGFR decline rate	DAPA-CKD (Heerspink 2020)
Cardiac benefit	30% reduction in HF hospitalization	DAPA-HF (McMurray 2019)
Monthly cost (US)	$450	WAC pricing
Monthly cost (UK)	£35	BNF pricing

Code References:

Assignment logic: src/population.py:388-397
Renal protection: src/patient.py:484-491
Cardiac protection: src/transitions.py:562-575

3.4 Cost Structure for Dual-Burden Patients

Costs are calculated additively across pathways:

Total Monthly Cost = Cardiac State Cost + Renal State Cost + Drug Cost + SGLT2i + Monitoring

Example: Post-MI + CKD Stage 4 + PA patient on IXA-001
  Post-MI management:    $458/month ($5,500/year)
  CKD Stage 4:           $667/month ($8,000/year)
  IXA-001:               $500/month
  SGLT2i:                $450/month
  Standard care:          $75/month
  ─────────────────────────────────────────────────
  Total:               $2,150/month ($25,800/year)

Code Reference: src/costs/costs.py:463-514 (get_total_cost())

3.5 Implications for Cost-Effectiveness

Dual cardiac-renal burden patients represent the highest-cost subgroup and therefore the greatest opportunity for cost offsets from effective treatment:

PA patients with concurrent CKD have both elevated cardiac risk (2.05× HF) and accelerated renal decline (1.80× ESRD), making them the primary value driver for IXA-001
SGLT2i co-prescription provides dual benefits that partially offset costs in both pathways
Event cost savings from prevented MI, stroke, HF, and AF are amplified in dual-burden patients due to higher baseline event rates

4. Risk Equations

Detailed report: risk_equations_technical_report.md

4.1 AHA PREVENT Equations

The model uses the 2024 AHA PREVENT equations for 10-year CVD risk:

$$\text{Risk}_{10yr} = 1 - S_0(t)^{\exp(\beta \cdot X - \bar{X})}$$

Coefficients (Female)

Variable	Coefficient (β)	Reference Mean
Age	0.0634	55.0
SBP (treated)	0.0180	130.0
Total Cholesterol	0.0045	200.0
HDL Cholesterol	-0.0267	55.0
eGFR	-0.0089	85.0
log(UACR)	0.1250	2.3
Diabetes	0.4200	0.12
Current Smoker	0.5100	0.15
BMI	0.0156	28.0

Baseline Survival: S₀(10) = 0.9680

Probability Conversion

Annual: p_annual = 1 - (1 - p_10yr)^(1/10)
Monthly: p_monthly = 1 - (1 - p_annual)^(1/12)

Code Reference: src/risks/prevent.py:45-156

4.2 Kidney Failure Risk Equation (KFRE)

4-variable KFRE for 2-year kidney failure risk:

$$\text{Risk}_{2yr} = 1 - 0.9750^{\exp(\text{Linear Predictor} - 7.222)}$$

Variable	Coefficient
Age	-0.2201
Sex (male=1)	0.2467
eGFR	-0.5567
log(UACR)	0.4510

Applicability: eGFR 15-59 mL/min/1.73m²

Code Reference: src/risks/kfre.py:28-95

4.3 Risk Ratio per 10 mmHg SBP Reduction

Outcome	Risk Ratio	95% CI	Source
MI	0.78	0.70-0.86	Ettehad 2016
Stroke	0.64	0.57-0.72	Ettehad 2016
Heart Failure	0.72	0.65-0.80	Ettehad 2016
CV Death	0.75	0.67-0.84	Ettehad 2016
ESRD	0.80	0.68-0.94	Xie 2016
Atrial Fibrillation	0.82	0.72-0.94	Okin 2015

5. Cost Inputs

Detailed report: cost_inputs_technical_report.md

5.1 Drug Costs (US$ 2024)

Drug	Monthly Cost	Annual Cost	Source
IXA-001	$500	$6,000	Assumed launch price
Spironolactone	$15	$180	NADAC 2024
Background therapy	$75	$900	NADAC 2024

5.2 Acute Event Costs

Event	Cost	PSA Distribution	Source
Myocardial Infarction	$25,000	Gamma(25, 1000)	HCUP 2022
Ischemic Stroke	$15,200	Gamma(15.2, 1000)	HCUP 2022
Hemorrhagic Stroke	$22,000	Gamma(22, 1000)	HCUP 2022
Heart Failure	$18,000	Gamma(18, 1000)	HCUP 2022
Atrial Fibrillation	$8,500	Gamma(8.5, 1000)	HCUP 2022
Hyperkalemia	$12,000	Gamma(12, 1000)	HCUP 2022
ESRD Initiation	$35,000	Gamma(35, 1000)	USRDS 2023

5.3 Chronic Management Costs (Annual)

Condition	Annual Cost	Source
Post-MI	$8,000	Medicare claims
Post-Stroke	$12,000	Medicare claims
Chronic HF	$15,000	Medicare claims
CKD Stage 3	$4,500	Medicare claims
CKD Stage 4	$8,000	Medicare claims
ESRD (dialysis)	$90,000	USRDS 2023
Chronic AF	$8,500	Medicare claims

5.4 Indirect Costs (Societal Perspective)

Event	Productivity Loss	Duration	Total
MI	$4,500/month	3 months	$13,500
Stroke	$6,000/month	6 months	$36,000
HF Hospitalization	$3,500/month	2 months	$7,000
ESRD	$2,500/month	Ongoing	$30,000/year

Code Reference: src/costs/costs.py:45-180

6. Utility Values

Detailed report: utility_values_technical_report.md

6.1 Baseline Utilities by Age

Age	Male	Female	Source
40	0.88	0.86	Sullivan 2011
50	0.86	0.84	Sullivan 2011
60	0.83	0.80	Sullivan 2011
70	0.79	0.76	Sullivan 2011
80	0.74	0.70	Sullivan 2011

6.2 Health State Utilities

State	Utility	PSA Distribution	Source
Post-MI	0.88	Beta(70.4, 9.6)	NICE DSU TSD 12
Post-Stroke	0.82	Beta(65.6, 14.4)	NICE DSU TSD 12
Chronic HF	0.85	Beta(68, 12)	NICE DSU TSD 12
CKD Stage 3	0.90	Beta(72, 8)	Gorodetskaya 2005
CKD Stage 4	0.80	Beta(64, 16)	Gorodetskaya 2005
ESRD	0.65	Beta(52, 28)	Gorodetskaya 2005
Atrial Fibrillation	0.90	Beta(72, 8)	NICE AF guidelines

6.3 Disutility Decrements

Event/State	Disutility	Duration	Source
Acute MI	0.15	1 month	Sullivan 2011
Chronic Post-MI	0.12	Permanent	Sullivan 2011
Acute Stroke	0.30	1 month	Sullivan 2011
Chronic Post-Stroke	0.18	Permanent	Sullivan 2011
Chronic HF	0.15	Permanent	Sullivan 2011
ESRD	0.35	Permanent	Gorodetskaya 2005
Hyperkalemia	0.05	1 month	Assumed

6.4 QALY Calculation

$$\text{QALY}_t = \text{Utility}_t \times \text{Survival}_t \times \frac{1}{(1+r)^t}$$

Half-cycle correction applied
Discount rate: 3.0% annually
Additive disutility model for comorbidities

Code Reference: src/utilities.py:25-145

7. Probabilistic Sensitivity Analysis

Detailed report: psa_parameters_technical_report.md

7.1 Parameter Summary

Category	Count	Distribution Types
Treatment Effects	5	Normal
Risk Ratios	6	Lognormal
Phenotype Modifiers	7	Lognormal
PA Risk Modifiers	6	Lognormal
Acute Costs	7	Gamma
Chronic Costs	8	Gamma
Utilities	8	Beta
Disutilities	8	Beta
Total	47	-

7.2 Key Parameter Distributions

Treatment Effects

Parameter	Distribution	Mean	SD
IXA-001 SBP reduction	Normal	20 mmHg	2.0
Spironolactone SBP reduction	Normal	9 mmHg	1.5

Risk Ratios (per 10 mmHg)

Parameter	Distribution	Median	σ
RR MI	Lognormal	0.78	0.05
RR Stroke	Lognormal	0.64	0.06
RR HF	Lognormal	0.72	0.05

PA Risk Modifiers

Parameter	Distribution	Mean	σ
PA HF modifier	Lognormal	2.05	0.12
PA AF modifier	Lognormal	3.0	0.20
PA ESRD modifier	Lognormal	1.80	0.15

7.3 Correlation Structure

Four correlation groups with Cholesky decomposition:

Acute Costs: MI, Stroke, HF, AF, ESRD (ρ = 0.30-0.60)
Utilities: Post-MI, Post-Stroke, CHF, ESRD, AF (ρ = 0.45-0.70)
Risk Ratios: MI, Stroke, HF, Death, AF (ρ = 0.50-0.80)
Disutilities: All event disutilities (ρ = 0.40-0.65)

7.4 Convergence

Metric	Recommendation
Base case iterations	1,000
Subgroup analyses	2,000
Monte Carlo SE target	<2% of mean

Code Reference: src/psa.py:45-698

8. Subgroup Analysis

Detailed report: subgroup_analysis_methodology.md

8.1 Pre-Specified Subgroups

Dimension 1: Secondary HTN Etiology

Subgroup	Prevalence	IXA-001 Response	Baseline Risk Modifier (HF)
Primary Aldosteronism	15-20%	1.70×	2.05×
Renal Artery Stenosis	5-15%	1.05×	1.45×
Pheochromocytoma	0.5-1%	0.40×	1.70×
OSA (severe)	10-15%	1.20×	1.28×
Essential HTN	50-60%	1.00×	1.00×

Dimension 2: Age-Based Phenotype

Phenotype	Age Range	eGFR	Key Risk Profile
EOCRI Type A	18-59	>60	Early Metabolic
EOCRI Type B	18-59	>60	Silent Renal (KEY TARGET)
EOCRI Type C	18-59	>60	Premature Vascular
GCUA Type I	≥60	>60	Accelerated Ager
GCUA Type II	≥60	>60	Silent Renal
GCUA Type III	≥60	>60	Vascular Dominant
GCUA Type IV	≥60	>60	Senescent
KDIGO	Any	≤60	CKD pathway

8.2 Subgroup Results Summary

Subgroup	N	Δ Cost	Δ QALY	ICER	Events Prevented (per 1000)
Primary Aldosteronism	180	+$20,550	+0.084	$245,441	MI:12, Stroke:15, HF:28, AF:33
OSA (severe)	120	+$25,200	+0.081	$311,111	MI:8, Stroke:10, HF:15, AF:12
RAS	85	+$28,500	+0.074	$385,135	MI:6, Stroke:8, HF:12, ESRD:10
Essential HTN	500	+$35,200	-0.012	Dominated	MI:2, Stroke:3, HF:4, AF:3

8.3 Value-Based Prescribing Recommendation

┌─────────────────────────────────────────────────────────────────┐
│                TREATMENT SELECTION ALGORITHM                     │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  IF Primary Aldosteronism CONFIRMED:                           │
│     → IXA-001 RECOMMENDED (ICER $245K, best value)             │
│                                                                 │
│  IF OSA with Severe AHI + CPAP-intolerant:                     │
│     → IXA-001 CONSIDER (ICER $311K, moderate value)            │
│                                                                 │
│  IF RAS or Pheochromocytoma:                                   │
│     → Address primary etiology first                           │
│     → IXA-001 LIMITED VALUE                                    │
│                                                                 │
│  IF Essential/Unexplained Resistant HTN:                       │
│     → Spironolactone PREFERRED (IXA-001 dominated)             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Code Reference: src/risk_assessment.py:146-480

9. Background Mortality

Detailed report: background_mortality_technical_note.md

9.1 Life Table Sources

Country	Source	Year	Age Resolution
United States	SSA Actuarial Life Tables	2021	Single-year
United Kingdom	ONS National Life Tables	2020-2022	5-year intervals

9.2 Selected Mortality Rates (US)

Age	Male qx	Female qx	Male:Female Ratio
50	0.00485	0.00349	1.39
60	0.01152	0.00829	1.39
65	0.01743	0.01272	1.37
70	0.02679	0.02000	1.34
80	0.06653	0.05386	1.24

9.3 Probability Conversions

Annual to Monthly: $$p_{month} = 1 - (1 - p_{year})^{1/12}$$

Example (65-year-old male):

Annual qx = 0.01743
Monthly = 1 - (1 - 0.01743)^(1/12) = 0.001463

9.4 Competing Risks Framework

Total Mortality = Background + CV Deaths + Renal Deaths

Adjusted Background = Life Table × (1 - CV_Fraction - Renal_Fraction)
                    = Life Table × (1 - 0.28 - 0.03)
                    = Life Table × 0.69

Code Reference: src/risks/life_tables.py:94-297

10. Patient History Analysis

Detailed report: history_analyzer_technical_note.md

10.1 Dynamic Risk Modifiers

The PatientHistoryAnalyzer leverages full patient trajectories to modify risk:

Modifier Type	Range	Key Drivers
CVD Risk	0.5× - 5.0×	Prior events, clustering, comorbidities
Renal Progression	0.6× - 2.0×	eGFR trajectory, albuminuria
Mortality	1.0× - 4.0×	Charlson score, COPD, SUD
Adherence	0.3× - 1.0×	Mental health, substance use

10.2 Trajectory Classification

eGFR Trajectories

Type	Annual Decline	Modifier	Prevalence
Rapid Decliner	>3 mL/min/yr	1.5×	17%
Normal Decliner	1-3 mL/min/yr	1.0×	45%
Slow Decliner	0.5-1 mL/min/yr	0.8×	23%
Stable	<0.5 mL/min/yr	0.6×	15%

BP Control Quality

Grade	Average SBP	CVD Modifier
Excellent	<130 mmHg	0.85×
Good	130-139 mmHg	1.00×
Fair	140-149 mmHg	1.20×
Poor	≥150 mmHg	1.50×

10.3 Time-Decay Function

Prior event risk decays exponentially:

$$\text{Modifier} = 1.0 + (\text{Excess Risk}) \times e^{-0.05 \times \text{months}}$$

Time Since MI	Residual Risk
0 months	1.50×
12 months	1.27×
24 months	1.15×
60 months	1.02×

Code Reference: src/history_analyzer.py:42-502

11. Model Validation

Detailed report: model_validation_report.md

11.1 Validation Framework

Validation Type	Status	Method
Face Validity	✓ Pass	Expert review, conceptual model
Verification	✓ Pass	76 unit tests, 100% pass rate
Internal Validity	✓ Pass	Extreme value testing, mass balance
External Validity	✓ Pass	Calibration to published data
Cross-Validation	✓ Pass	ICER/NICE model comparison
Predictive Validity	Pending	Phase III data comparison

11.2 Unit Test Coverage

Module	Tests	Pass Rate
PREVENT Equations	12	100%
KFRE Calculator	8	100%
Life Tables	6	100%
Cost Module	10	100%
Utilities	8	100%
PSA Sampling	12	100%
Transitions	14	100%
Population	6	100%
Total	76	100%

11.3 External Calibration

Outcome	Model Prediction	Published Data	Source
10-yr MI risk (high risk)	8.2%	7.5-9.0%	Framingham
10-yr Stroke risk	4.1%	3.8-4.5%	ARIC
5-yr ESRD (CKD G4)	18.5%	17-21%	CKD-PC
Life expectancy (65M)	17.1 yrs	17.4 yrs	SSA 2021

11.4 Cross-Validation

Comparator Model	Agreement	Notes
ICER CVD Model	High	Similar PREVENT implementation
NICE HTN Model	Moderate	Different risk equations
Published CEAs	High	ICERs within expected range

Code Reference: tests/*.py

12. Results Summary

12.1 Base Case Results (20-Year, PA Subgroup)

Outcome	IXA-001	Spironolactone	Difference
Total Costs	$142,550	$122,000	+$20,550
Total QALYs	11.284	11.200	+0.084
Life Years	14.52	14.38	+0.14
ICER	-	-	$245,441/QALY

12.2 Events Prevented (PA Subgroup, per 1,000)

Event	IXA-001	Spironolactone	Prevented
MI	45	57	12
Stroke	38	53	15
Heart Failure	82	110	28
ESRD	65	83	18
Atrial Fibrillation	120	153	33
CV Death	28	36	8

12.3 Cost-Effectiveness Acceptability

WTP Threshold	P(Cost-Effective) - PA	P(Cost-Effective) - Overall
$100,000/QALY	8%	2%
$150,000/QALY	15%	5%
$200,000/QALY	35%	12%
$300,000/QALY	65%	28%

12.4 Threshold Pricing

Subgroup	Current Price	Price at $150K WTP	Reduction Needed
Primary Aldosteronism	$500/mo	$467/mo	6.7%
OSA (severe)	$500/mo	$385/mo	23%
RAS	$500/mo	$290/mo	42%
Essential HTN	$500/mo	N/A	Not cost-effective

13. CHEERS 2022 Compliance

Item	Requirement	Status	Documentation
1	Title	✓	This document
2	Abstract	✓	Executive Summary
3	Background/Objectives	✓	Section 1
4	Target Population	✓	Section 2
5	Setting/Location	✓	Section 2
6	Comparators	✓	Section 2
7	Time Horizon	✓	Section 2
8	Discount Rate	✓	Section 6
9	Health Outcomes	✓	Section 6
10	Costs	✓	Section 5
11	Analytic Methods	✓	Section 2-4
12	Measurement of Outcomes	✓	Section 6
13	Valuation of Outcomes	✓	Section 6
14	Valuation Methods	✓	Section 6
15	Resource Estimation	✓	Section 5
16	Unit Costs	✓	Section 5
17	Productivity Costs	✓	Section 5
18	Effect Estimation	✓	Section 4
19	Uncertainty Methods	✓	Section 7
20	Uncertainty Parameters	✓	Section 7
21	Heterogeneity	✓	Section 8
22	Model Validation	✓	Section 11

Compliance: 22/22 items (100%)

14. References

Khan SS, et al. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024;149:430-449.
Tangri N, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553-1559.
Ettehad D, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2016;387:957-67.
Monticone S, et al. Cardiovascular events and target organ damage in primary aldosteronism compared with essential hypertension. Lancet Diabetes Endocrinol. 2018;6:41-50.
Sullivan PW, Ghushchyan V. Preference-Based EQ-5D Index Scores for Chronic Conditions in the United States. Med Decis Making. 2006;26:410-20.
Gorodetskaya I, et al. Health-related quality of life and estimates of utility in chronic kidney disease. Kidney Int. 2005;68:2801-2808.
Arias E, Xu J. United States Life Tables, 2021. National Vital Statistics Reports. 2023;72(12):1-64.
HCUP National Inpatient Sample. Healthcare Cost and Utilization Project. Agency for Healthcare Research and Quality. 2022.
United States Renal Data System. 2023 USRDS Annual Data Report. National Institutes of Health, NIDDK. 2023.
Briggs A, Sculpher M, Claxton K. Decision Modelling for Health Economic Evaluation. Oxford University Press. 2006.

15. Appendices

Appendix A: Detailed Technical Reports

Report	File	Pages
Risk Equations	`risk_equations_technical_report.md`	~35
Cost Inputs	`cost_inputs_technical_report.md`	~40
Utility Values	`utility_values_technical_report.md`	~35
Model Validation	`model_validation_report.md`	~50
PSA Parameters	`psa_parameters_technical_report.md`	~25
Subgroup Analysis	`subgroup_analysis_methodology.md`	~30
Background Mortality	`background_mortality_technical_note.md`	~15
History Analyzer	`history_analyzer_technical_note.md`	~20

Appendix B: Code Reference Map

Component	Primary File	Key Functions
Simulation Engine	`src/simulation.py`	`run_simulation()`, `CEAResults`
PREVENT Equations	`src/risks/prevent.py`	`calculate_10yr_risk()`
KFRE Calculator	`src/risks/kfre.py`	`calculate_2yr_risk()`
Life Tables	`src/risks/life_tables.py`	`LifeTableCalculator`
Costs	`src/costs/costs.py`	`CostInputs`, `calculate_costs()`
Utilities	`src/utilities.py`	`calculate_qaly()`
PSA	`src/psa.py`	`PSAIteration`, `CholeskySampler`
Risk Assessment	`src/risk_assessment.py`	`BaselineRiskProfile`
History Analyzer	`src/history_analyzer.py`	`PatientHistoryAnalyzer`
Population	`src/population.py`	`PopulationGenerator`
Transitions	`src/transitions.py`	`*Transition` classes

Appendix C: Glossary

Term	Definition
ASI	Aldosterone Synthase Inhibitor (IXA-001)
CKD	Chronic Kidney Disease
EOCRI	Early-Onset Cardiorenal Risk Indicator (age 18-59)
ESRD	End-Stage Renal Disease
GCUA	Geriatric Cardiorenal-Metabolic Unified Algorithm (age ≥60)
ICER	Incremental Cost-Effectiveness Ratio
IL-STM	Individual-Level State-Transition Microsimulation
KFRE	Kidney Failure Risk Equation
MRA	Mineralocorticoid Receptor Antagonist (spironolactone)
PA	Primary Aldosteronism
PREVENT	AHA Predicting Risk of CVD Events equations
PSA	Probabilistic Sensitivity Analysis
QALY	Quality-Adjusted Life Year
RAS	Renal Artery Stenosis
WTP	Willingness-to-Pay threshold

Document Control

Field	Value
Document ID	IXA-001-CEA-TechDoc-v1.0
Version	1.0
Status	Final
Author	HEOR Technical Documentation Team
Reviewer	[Pending]
Approval	[Pending]
Date	February 2026

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