Subgroup Analysis Technical Report

IXA-001 Budget Impact Model

Document Version: 1.0 Date: February 2026 Author: HEOR Technical Documentation Team Status: Final

1. Executive Summary

This technical report documents the subgroup analysis methodology in the IXA-001 Budget Impact Model (BIM). The model stratifies budget impact estimates across clinically meaningful patient subgroups to inform targeted formulary positioning, prior authorization criteria, and value-based contracting negotiations.

2. Subgroup Framework

2.1 Rationale for Subgroup Analysis

Subgroup analysis in budget impact modeling serves to:

Identify high-value patient populations where IXA-001 provides greatest budget efficiency
Support prior authorization criteria development
Enable value-based contracting based on patient characteristics
Inform formulary tier decisions by indication
Support regional budget allocation

2.2 Pre-Specified Subgroups

Dimension	Categories	Clinical Rationale
Secondary HTN Etiology	PA, RAS, Pheo, OSA, Essential	IXA-001 targets aldosterone pathway; PA shows greatest benefit
Age	<65, ≥65 years	Event rates and treatment tolerability vary by age
CKD Status	CKD Stage 3-4, Non-CKD	Renal protection is key outcome; CKD patients at higher risk
Diabetes Status	Diabetic, Non-diabetic	Cardiorenal risk amplification
Treatment History	MRA-naive, MRA-experienced	Prior MRA use affects response expectations

3. Secondary HTN Etiology Subgroups

3.1 Primary Aldosteronism (PA)

Prevalence: 15% of resistant HTN population

Parameter	Value	Rationale
Baseline event multiplier	1.5-3.0×	Higher aldosterone-mediated damage
IXA-001 efficacy multiplier	1.3×	Targeted mechanism
Event cost offset	+85% vs. general	Higher baseline events
Net BI impact	Favorable	Significant cost offsets

Budget Impact Comparison (5-year, per 1000 patients):

Metric	General Population	PA Subgroup	Difference
Drug cost	$30.0M	$30.0M	$0
Event cost offset	-$12.5M	-$23.1M	-$10.6M
Net BI	$17.5M	$6.9M	-$10.6M
Cost per event prevented	$18,500	$7,200	-61%

3.2 Renovascular Stenosis (RAS)

Prevalence: 8% of resistant HTN population

Parameter	Value	Rationale
Baseline event multiplier	1.2×	Moderate risk elevation
IXA-001 efficacy multiplier	1.1×	Partial mechanism overlap
Event cost offset	+35% vs. general	Moderate benefit

3.3 Pheochromocytoma (Pheo)

Prevalence: 2% of resistant HTN population

Parameter	Value	Rationale
Baseline event multiplier	1.4×	Catecholamine-mediated damage
IXA-001 efficacy multiplier	0.9×	Not primary mechanism
Event cost offset	+15% vs. general	Limited benefit

3.4 Obstructive Sleep Apnea (OSA)

Prevalence: 25% of resistant HTN population

Parameter	Value	Rationale
Baseline event multiplier	1.3×	Elevated nocturnal BP
IXA-001 efficacy multiplier	1.05×	Some aldosterone overlap
Event cost offset	+28% vs. general	Moderate benefit

3.5 Essential HTN (No Secondary Cause)

Prevalence: 50% of resistant HTN population

Parameter	Value	Rationale
Baseline event multiplier	1.0×	Reference population
IXA-001 efficacy multiplier	1.0×	Standard response
Event cost offset	Reference	Baseline comparison

4. Age-Based Subgroups

4.1 Age <65 Years

Prevalence: 55% of resistant HTN population

Parameter	Value	Source
CV event rate multiplier	0.7×	SPRINT subgroup
Treatment persistence	85% at Year 5	Higher adherence
Indirect cost impact	High	Working age, productivity

Budget Impact Drivers:

Lower baseline event rates reduce cost offset potential
Higher indirect cost savings (productivity)
Better treatment persistence sustains benefit

4.2 Age ≥65 Years

Prevalence: 45% of resistant HTN population

Parameter	Value	Source
CV event rate multiplier	1.5×	SPRINT subgroup
Treatment persistence	72% at Year 5	Age-related decline
Indirect cost impact	Low	Retired population

Budget Impact Drivers:

Higher baseline event rates increase cost offsets
Lower persistence reduces sustained benefit
Greater absolute risk reduction per patient

5. CKD Status Subgroups

5.1 CKD Stage 3-4

Prevalence: 25% of resistant HTN population

Event	General Rate	CKD Multiplier	CKD Rate
MI	1.5%	1.5×	2.25%
Stroke	1.0%	1.3×	1.30%
HF	1.8%	1.8×	3.24%
ESRD	0.5%	3.5×	1.75%

IXA-001 Efficacy in CKD:

ESRD RRR: 60% (vs. 55% general)
Renal protection is primary value driver
Cost offset: +125% vs. general population

5.2 Non-CKD (eGFR ≥60)

Prevalence: 75% of resistant HTN population

Parameter	Value	Notes
ESRD risk	Very low	<0.1% annual
CV event rates	Standard	Reference
Renal cost offset	Minimal	Low ESRD risk

6. Diabetes Subgroups

6.1 Diabetic Patients

Prevalence: 35% of resistant HTN population

Parameter	Value	Rationale
CV event multiplier	1.4×	Cardiometabolic risk
CKD progression multiplier	1.6×	Diabetic nephropathy
IXA-001 efficacy	1.15×	Synergistic benefit

Budget Impact:

Higher baseline events increase cost offset
Diabetic nephropathy prevention is high-value
Supports tier 2 placement for T2DM + resistant HTN

6.2 Non-Diabetic Patients

Prevalence: 65% of resistant HTN population

Parameter	Value	Notes
CV event rate	Standard	Reference
CKD progression	Standard	Reference
IXA-001 efficacy	Standard	Reference

7. Subgroup-Specific Budget Impact Results

7.1 Summary Table (5-Year, Moderate Scenario, US)

Subgroup	Population	Drug BI ($M)	Event Offset ($M)	Net BI ($M)	BI/Patient
All Patients	3,659,667	$19,826	-$1,496	$18,330	$5,010
PA	548,950	$2,974	-$551	$2,423	$4,414
Non-PA	3,110,717	$16,852	-$945	$15,907	$5,115
Age <65	2,012,817	$10,904	-$688	$10,216	$5,075
Age ≥65	1,646,850	$8,922	-$808	$8,114	$4,927
CKD 3-4	914,917	$4,957	-$524	$4,433	$4,845
Non-CKD	2,744,750	$14,870	-$972	$13,898	$5,065
Diabetic	1,280,883	$6,939	-$592	$6,347	$4,956
Non-Diabetic	2,378,783	$12,887	-$904	$11,983	$5,038

7.2 Budget Impact per Patient by Subgroup

Budget Impact per Patient (5-Year)

PA               ████████████████████ $4,414
Age ≥65          ████████████████████████ $4,927
CKD 3-4          ████████████████████████ $4,845
Diabetic         ████████████████████████ $4,956
Non-Diabetic     █████████████████████████ $5,038
Age <65          █████████████████████████ $5,075
Non-CKD          █████████████████████████ $5,065
Non-PA           █████████████████████████ $5,115
All Patients     █████████████████████████ $5,010
                 $4,000    $4,500    $5,000    $5,500

7.3 Key Finding: PA Subgroup Value

The PA subgroup shows 12% lower budget impact per patient than the general population due to:

Higher baseline event rates (3× AF, 2× HF)
Greater IXA-001 efficacy (1.3× response multiplier)
Substantial cost offsets from event prevention

8. Implementation Details

8.1 SubgroupDefinitions Class

@dataclass
class SubgroupDefinitions:
    """Subgroup category definitions for BIM analysis."""
    # Etiology subgroups
    etiology_categories: Dict[str, float] = field(default_factory=lambda: {
        "primary_aldosteronism": 0.15,
        "renovascular": 0.08,
        "pheochromocytoma": 0.02,
        "sleep_apnea": 0.25,
        "essential": 0.50
    })

    # Age subgroups
    age_categories: Dict[str, float] = field(default_factory=lambda: {
        "under_65": 0.55,
        "65_and_over": 0.45
    })

    # CKD subgroups
    ckd_categories: Dict[str, float] = field(default_factory=lambda: {
        "ckd_stage_3_4": 0.25,
        "non_ckd": 0.75
    })

    # Diabetes subgroups
    diabetes_categories: Dict[str, float] = field(default_factory=lambda: {
        "diabetic": 0.35,
        "non_diabetic": 0.65
    })

8.2 SubgroupParameters Class

@dataclass
class SubgroupParameters:
    """Subgroup-specific parameter modifiers."""
    # Event rate multipliers by subgroup
    event_multipliers: Dict[str, Dict[str, float]] = field(default_factory=lambda: {
        "primary_aldosteronism": {
            "mi": 1.40, "stroke": 1.50, "hf": 2.05,
            "esrd": 1.80, "af": 3.00
        },
        "ckd_stage_3_4": {
            "mi": 1.50, "stroke": 1.30, "hf": 1.80,
            "esrd": 3.50, "af": 1.20
        },
        "65_and_over": {
            "mi": 1.50, "stroke": 1.70, "hf": 2.00,
            "esrd": 1.20, "af": 2.20
        }
    })

    # Efficacy multipliers
    efficacy_multipliers: Dict[str, float] = field(default_factory=lambda: {
        "primary_aldosteronism": 1.30,
        "ckd_stage_3_4": 1.10,
        "diabetic": 1.15,
        "65_and_over": 0.95
    })

8.3 Code References

Definitions: src/bim/inputs.py:SubgroupDefinitions
Parameters: src/bim/inputs.py:SubgroupParameters
Calculation: src/bim/calculator.py:BIMCalculator._calculate_subgroups

9. Formulary Decision Support

9.1 Prior Authorization Criteria Recommendations

Based on subgroup analysis, recommended PA criteria:

Criterion	Requirement	Rationale
Diagnosis	Confirmed resistant HTN	Target population
Prior therapy	Failed ≥2 MRAs or intolerant	Step therapy
Testing	Aldosterone-to-renin ratio	Identifies PA
Monitoring	K+ at baseline, 4 weeks	Safety

9.2 Tier Placement by Subgroup

Subgroup	Recommended Tier	Rationale
PA + CKD	Tier 2 (Preferred)	Highest value, greatest offset
PA only	Tier 2 (Preferred)	Strong value proposition
CKD only	Tier 2 (Preferred)	Renal protection value
Diabetic + CKD	Tier 2 (Preferred)	Cardiorenal value
General	Tier 3 (Non-preferred)	Lower cost offset

10. Validation

10.1 Subgroup Prevalence Validation

Subgroup	Model	Literature	Source
PA	15%	11-21%	Douma 2008
CKD 3-4	25%	20-30%	CRIC
Diabetes	35%	30-40%	NHANES
Age ≥65	45%	40-50%	Census

10.2 Event Multiplier Validation

Subgroup × Event	Model	Literature	Source
PA × AF	3.0×	2.8-3.5×	Monticone 2018
PA × HF	2.05×	1.8-2.3×	Monticone 2018
CKD × ESRD	3.5×	3.0-4.0×	USRDS

11. Limitations

Subgroup independence: Analysis assumes subgroup effects are multiplicative; interactions may differ
Selection bias: Subgroup definitions based on claims data may miss undiagnosed conditions
Small sample sizes: Rare subgroups (Pheo) have higher uncertainty
Treatment effect heterogeneity: RRRs may vary more within subgroups than modeled
Dynamic composition: Subgroup proportions may change over time horizon

12. References

Douma S, Petidis K, Doumas M, et al. Prevalence of primary hyperaldosteronism in resistant hypertension. Lancet. 2008;371(9628):1921-1926.
Monticone S, D'Ascenzo F, Moretti C, et al. Cardiovascular events and target organ damage in primary aldosteronism. Eur Heart J. 2018;39(30):2727-2737.
SPRINT Research Group. Intensive Blood-Pressure Control. N Engl J Med. 2015;373(22):2103-2116.
Chronic Renal Insufficiency Cohort Study. Design and Methods. J Am Soc Nephrol. 2003;14(7 Suppl 2):S148-S153.
United States Renal Data System. 2023 Annual Data Report. NIDDK.
Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. 2020.

Document Control:

Version 1.0 - Initial release (February 2026)
Reviewed by: HEOR Technical Lead
Approved for HTA submission

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Subgroup Analysis Technical Report

IXA-001 Budget Impact Model

1. Executive Summary

2. Subgroup Framework

2.1 Rationale for Subgroup Analysis

2.2 Pre-Specified Subgroups

3. Secondary HTN Etiology Subgroups

3.1 Primary Aldosteronism (PA)

3.2 Renovascular Stenosis (RAS)

3.3 Pheochromocytoma (Pheo)

3.4 Obstructive Sleep Apnea (OSA)

3.5 Essential HTN (No Secondary Cause)

4. Age-Based Subgroups

4.1 Age <65 Years

4.2 Age ≥65 Years

5. CKD Status Subgroups

5.1 CKD Stage 3-4

5.2 Non-CKD (eGFR ≥60)

6. Diabetes Subgroups

6.1 Diabetic Patients

6.2 Non-Diabetic Patients

7. Subgroup-Specific Budget Impact Results

7.1 Summary Table (5-Year, Moderate Scenario, US)

7.2 Budget Impact per Patient by Subgroup

7.3 Key Finding: PA Subgroup Value

8. Implementation Details

8.1 SubgroupDefinitions Class

8.2 SubgroupParameters Class

8.3 Code References

9. Formulary Decision Support

9.1 Prior Authorization Criteria Recommendations

9.2 Tier Placement by Subgroup

10. Validation

10.1 Subgroup Prevalence Validation

10.2 Event Multiplier Validation

11. Limitations

12. References

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Subgroup Analysis Technical Report

IXA-001 Budget Impact Model

1. Executive Summary

2. Subgroup Framework

2.1 Rationale for Subgroup Analysis

2.2 Pre-Specified Subgroups

3. Secondary HTN Etiology Subgroups

3.1 Primary Aldosteronism (PA)

3.2 Renovascular Stenosis (RAS)

3.3 Pheochromocytoma (Pheo)

3.4 Obstructive Sleep Apnea (OSA)

3.5 Essential HTN (No Secondary Cause)

4. Age-Based Subgroups

4.1 Age <65 Years

4.2 Age ≥65 Years

5. CKD Status Subgroups

5.1 CKD Stage 3-4

5.2 Non-CKD (eGFR ≥60)

6. Diabetes Subgroups

6.1 Diabetic Patients

6.2 Non-Diabetic Patients

7. Subgroup-Specific Budget Impact Results

7.1 Summary Table (5-Year, Moderate Scenario, US)

7.2 Budget Impact per Patient by Subgroup

7.3 Key Finding: PA Subgroup Value

8. Implementation Details

8.1 SubgroupDefinitions Class

8.2 SubgroupParameters Class

8.3 Code References

9. Formulary Decision Support

9.1 Prior Authorization Criteria Recommendations

9.2 Tier Placement by Subgroup

10. Validation

10.1 Subgroup Prevalence Validation

10.2 Event Multiplier Validation

11. Limitations

12. References