Key Takeaways
- Heart failure economics describe only the 2.7 percent of adults with symptomatic Stage C or D disease, because administrative claims begin at diagnosis.
- The 80.6 percent of adults in Stage A or B carry real, active costs that claims record as hypertension, diabetes, or obesity, never as heart failure.
- Prevention economics and value-based contracting both fail for the same reason. The upstream population has no baseline cost, so the denominator for prevention does not exist.
- A single hospitalization near $11,335 costs two to four times a full year of guideline-directed therapy, yet prior authorization and copay design suppress that therapy anyway.
The most cited number in heart failure economics describes a fraction of the disease. Administrative data begins at diagnosis, which means it begins at Stage C. Four in five adults carrying the disease upstream never enter the ledger. The arithmetic holds. What fails is the system's ability to see past the diagnosis.
A number that disagrees with itself
The 2025 HF STATS report from the Heart Failure Society of America puts the direct medical cost of heart failure at roughly 32 billion dollars, then concedes in the same document that other analyses reach as high as 227 billion. That is a sevenfold spread inside one authoritative source. Projected forward, the same report offers 142 billion by 2050 under one method and up to 858 billion under another.
When a field cannot settle its headline figure within a single report, the disagreement is not sloppiness. It is a signal that the number is measuring something narrower than the disease it names. Every one of those estimates draws on administrative claims, and claims share a common origin point. They start when a diagnosis code is recorded.
What the ledger can see
A heart failure code is written when symptoms declare themselves, which places it at Stage C or later. Everything downstream of that code is countable. The hospitalization that runs about 11,335 dollars for a single Medicare index event, the 75 to 80 percent of direct costs that sit in inpatient care, the readmissions, the procedures. This is real spending and it is measured with precision.
The question is how much of the disease that precision actually covers. In the pooled community cohort that Mohebi and colleagues assembled from MESA, the Cardiovascular Health Study, and Framingham, symptomatic heart failure, Stages C and D together, accounts for 2.7 percent of adults. The entire economic literature on heart failure describes that 2.7 percent. It is a sharp, well-lit picture of the smallest room in the house.
What it cannot
Under the 2022 staging, 37.4 percent of adults are Stage A, meaning at risk, and 43.2 percent are Stage B, meaning structural or functional change without symptoms. The 2022 criteria did not invent these people. They renamed them. Stage B alone climbed from 15.9 to 43.2 percent when the definition changed, so more than a quarter of the adult population crossed into pre-heart-failure without a single new echocardiogram being performed.
Their costs are real and they are being spent right now. The spending sits in antihypertensives, in diabetes management, in the downstream care of obesity and structural heart disease. None of it is recorded as heart failure, because no code exists to record it that way. The money is in the system. The attribution is not.
The apparatus can see heart failure only after the heart has already failed, and each step that produces that blind spot is individually defensible.
The lens builds the blind spot
A billing code is the trigger for measurement, and measurement is what management responds to. Because the code for heart failure activates only when symptoms force the diagnosis, the whole apparatus switches on after the disease has declared itself. That is the mechanism, and it is worth naming plainly. The way the system sees heart failure is what manufactures the crisis it then struggles to afford.
The clinical record shows the same pattern from the other side. In Sandhu and colleagues' analysis of incident heart failure, 38 percent of patients were first diagnosed in an acute care setting, and nearly half had potential heart failure symptoms documented in the six months before that diagnosis. Women and Black patients were more likely than men and White patients to be identified in the emergency department rather than the clinic. Among Medicare beneficiaries, roughly 65 percent of first diagnoses occur in the emergency room or inpatient setting. The disease is visible in the chart well before it is visible in a code.
The problem compounds with the disease we now see least well. Heart failure with preserved ejection fraction accounts for more than half of all cases, and roughly a third of HFpEF patients have normal-appearing echocardiograms at rest despite confirmed disease. The version of heart failure that is hardest to detect has become the majority, while the instruments that define the burden were calibrated for the version medicine could already recognize.
Two heart failures, one of them missing
| Dimension | Diagnosed HF (in the data) | Upstream HF (Stage A and B) |
|---|---|---|
| Share of adults | 2.7 percent (Stage C and D) | 80.6 percent (Stage A and B) |
| Coding identity | Discrete HF diagnosis code | No HF code; recorded as hypertension, diabetes, obesity, structural disease |
| Cost visibility | Fully attributed to heart failure | Fragmented across disease-specific silos |
| Dominant cost driver | Hospitalization, 75 to 80 percent of direct cost | Ambulatory chronic disease management, unattributed |
| Prevention ROI | Not applicable, disease already established | Structurally unmeasurable, no baseline exists |
What the blind spot breaks
Three consequences follow, and each lands on a different decision maker. Prevention economics become unmeasurable, because there is no baseline cost for Stage A and B against which to score an offset. If an SGLT2 inhibitor, a GLP-1 receptor agonist, or a biomarker-guided screening program slows progression from Stage B to Stage C, the savings cannot be quantified, since the starting cost was never captured. The intervention may work and the ledger will stay silent.
Value-based care inherits the same void. A shared-savings or capitated model for heart failure needs a defined population and a baseline. With the upstream denominator undefined, there is no population to contract around and no number to bend. Every payer betting on prevention is betting on a quantity its own data cannot produce. And the true lifetime cost of heart failure, from the accumulation of risk factors through preclinical disease to symptomatic failure, remains unknown, which means the single most important figure for prioritizing the disease has never been calculated.
The objection, answered
A fair reader will push back here. Upstream management is not free, so perhaps the economics of seeing earlier are weaker than they appear once the cost of therapy is counted. The recent pricing data answer this directly. A full year of four-pillar guideline-directed medical therapy for HFrEF runs approximately 2,900 to 6,200 dollars in annual payer cost, inclusive of an SGLT2 inhibitor and branded ARNI at the high end. A single heart failure hospitalization costs roughly 11,335 dollars.
One admission therefore costs two to four times an entire year of the therapy that helps keep a patient out of the hospital, even after every medication is priced in. The comparison holds with the drugs fully loaded, which is precisely the objection it needs to survive. One honest boundary applies. This is a within-diagnosis comparison, measuring the cost of managing a diagnosed patient against the cost of admitting one. It still does not reach Stage A and B, whose economics remain unmeasured. The cost logic for intervening earlier is directionally clear and quantitatively undefined, and that gap is the whole point.
The therapy does not reach the patient
The cost comparison assumes the therapy reaches the patient, and for the most part it does not. The failure starts before the prescription, since more than 80 percent of eligible heart failure patients are never prescribed these medications at all. Among those who are, the attrition is steep. In a 2026 cohort of 6,111 patients discharged after a heart failure hospitalization, 54 percent of new guideline-directed prescriptions went unfilled within seven days, and by six months only 42 percent of patients remained adherent and 51 percent persistent to their discharge regimen.
That same study found no significant difference in adherence by insurance category, which looks paradoxical until you notice what insurance category cannot capture. It says nothing about formulary tier, copay amount, or prior authorization, each of which varies widely within every insurance type. The signal that did emerge was structural. Fill rates ranged from 38 to 68 percent across hospital sites, which points at pharmacy support, discharge delivery programs, and authorization workflows rather than at the patient's insurance card.
The granular mechanics are where the effect concentrates, and they are payer-controlled. In a linked pharmacy and record analysis of 2,183 heart failure patients newly prescribed an ARNI or SGLT2 inhibitor, a prior authorization requirement made patients 3.03 times slower to fill the ARNI, 6.75 times slower to fill the SGLT2 inhibitor, and 2.23 times more likely to never fill the SGLT2 inhibitor at all. The requirement was not evenly applied. Patients identifying as Black or Hispanic were more likely to face it, the same groups that Sandhu found were more often diagnosed in the emergency department than the clinic.
Cost sharing accounts for much of the rest. In a cohort of 94,610 adults with diabetes or heart failure, a copayment of 50 dollars or more cut the odds of sustained adherence to a GLP-1 receptor agonist by roughly half and to an SGLT2 inhibitor by about a third, compared with a low copayment. Prior authorization and benefit design are both dials the payer sets, operating on the precise therapy the cost math says the system should want patients to take. The two-to-four-times advantage of managing a patient rather than admitting one is real, and it describes a saving the delivery system actively prevents. The case for prevention is undercut twice, once by a measurement apparatus that cannot see the upstream population and again by a payment apparatus that impedes the therapy even after the patient is in view.
The pattern is bigger than one organ
Heart failure is one instance of a failure that repeats across cardiometabolic medicine. The clearest parallel is cardiovascular-kidney-metabolic syndrome, the organizing framework the American Heart Association introduced in 2023 and codified in the 2026 multi-society guideline. CKM describes the same upstream continuum this argument is concerned with, staged from 0 to 4, and it is even more prevalent than pre-heart-failure. Only 7.9 percent of US adults are CKM Stage 0, which places roughly nine in ten adults in some stage of the syndrome.
CKM has no diagnosis code. The nearest proxy, E88.810 for metabolic syndrome, is narrower and cannot record the staging. A plan working from claims cannot identify the CKM population, because the syndrome itself has no code to carry it. The economics of CKM are invisible in the same way heart failure's Stage A and B economics are invisible, and for the same reason.
The tool meant to bring rigor to this population deepens the gap rather than closing it. PREVENT, the risk equation now embedded across the 2025 and 2026 prevention guidelines, runs on measured physiologic inputs such as eGFR, systolic pressure, and lab values. Payers adjudicating the same decisions see only diagnosis codes and pharmacy fills, so the score a prescriber can calculate is one the payer cannot verify. I have written about that asymmetry separately in What PREVENT Prevents. Heart failure, CKM, and PREVENT are three views of one problem. The clinical framework has moved upstream to the risk continuum, and the measurement and payment infrastructure has stayed downstream at the diagnosis.
Telling the whole story
Two moves would close it. Linking longitudinal cohort data, the same Framingham, Cardiovascular Health Study, and MESA populations used to establish the staging prevalence, with claims data would allow retrospective attribution of Stage A and B spending to the heart failure continuum for the first time. Prospective studies that stage patients at enrollment and track all-cause utilization across the full A to D trajectory would produce the first true lifecycle cost estimate for the disease.
Neither is possible without a coding infrastructure that lets primary care record what it manages upstream. That is the connective tissue between this argument and the diagnostic one, and it is where the reform has to begin. The 2022 guideline told clinicians to find at-risk and pre-heart-failure patients. The billing and quality infrastructure never gave them a way to record having done so.
The 30-billion-dollar figure is an accurate account of diagnosed, symptomatic heart failure. It becomes misleading only when it is read as the cost of the disease rather than the cost of its final act. Under the current framework, the most expensive four-fifths of the heart failure population is economically invisible, and the case for investing upstream stays theoretical for want of a denominator no one has built.
This is the clarity available in the gray space. The obstacle to prevention economics is not a missing therapy or an absent guideline. It is a measurement apparatus that switches on too late to see what it is meant to prevent. For payers the missing denominator is a pricing problem they own. For CMS it is a coding and measurement problem only they can authorize. For health systems it is the operational reality behind every late diagnosis. And for anyone building screening, biomarkers, or AI-guided detection, the uncharacterized 80 percent is less a data gap than an addressable market.
Frequently asked questions
Why do heart failure cost estimates vary so widely?
They measure different slices of the same disease. HF STATS 2025 cites roughly 32 billion dollars in direct medical cost while noting other analyses reach 227 billion. The spread reflects which patients and cost categories each study captures. All of them draw on administrative data that begins at diagnosis, so all describe symptomatic disease and none capture the upstream Stage A and B population.
What share of adults are in Stage A or Stage B heart failure?
Under the 2022 ACC/AHA/HFSA criteria, the Mohebi pooled community cohort found 37.4 percent of adults in Stage A and 43.2 percent in Stage B. Combined, 80.6 percent carry heart failure risk or preclinical disease, while symptomatic Stage C and D together account for 2.7 percent.
Why are Stage A and B costs invisible in claims data?
No ICD code records Stage A or Stage B as a distinct entity. These patients are coded under hypertension, diabetes, obesity, or structural heart disease, and those costs are attributed to the individual conditions rather than to heart failure. Claims also cannot identify subclinical dysfunction such as elevated natriuretic peptides or diastolic abnormalities unless a clinician screens for them, which is not routine.
Does outpatient management cost less than hospitalization once drugs are counted?
Yes. A full year of four-pillar guideline-directed therapy for HFrEF runs roughly 2,900 to 6,200 dollars in annual payer cost, inclusive of an SGLT2 inhibitor and branded ARNI. A single hospitalization costs about 11,335 dollars, so one admission costs two to four times an entire year of the therapy that helps prevent it.
Why does this matter for value-based care?
Value-based models need a defined population and a baseline cost. Because the Stage A and B population and its spending are undefined in the data, the denominator for upstream prevention does not exist, and prevention offsets cannot be scored against a baseline that was never measured.
What would it take to measure the full burden?
Link longitudinal cohorts such as Framingham, the Cardiovascular Health Study, and MESA to claims for retrospective attribution of Stage A and B costs, and run prospective studies that stage patients at enrollment and track utilization across the full A to D trajectory. Both depend on coding infrastructure that lets primary care record what it manages upstream.
How does this relate to cardiovascular-kidney-metabolic (CKM) syndrome?
CKM syndrome is the same blind spot one level up. The American Heart Association framework, staged 0 to 4, describes the upstream cardiometabolic continuum, and only 7.9 percent of US adults are CKM Stage 0. Like Stage A and B heart failure, CKM has no diagnosis code, so its population and economics stay invisible in claims. The PREVENT risk equation compounds the gap, because it runs on physiologic inputs that payers adjudicating from claims cannot see or verify.
Does prior authorization affect access to heart failure medications?
Yes. In a 2026 analysis of 2,183 heart failure patients, a prior authorization requirement made patients about 6.75 times slower to fill an SGLT2 inhibitor and 2.23 times more likely to never fill it, and it fell disproportionately on Black and Hispanic patients. High copayments compound the effect, cutting the odds of sustained adherence substantially. Even when a patient is diagnosed and prescribed guideline-directed therapy, payer-controlled mechanics suppress the cost-effective treatment the evidence supports.
References
- Bozkurt B, et al. HF STATS 2025: Heart Failure Epidemiology and Outcomes Statistics, an Updated 2025 Report from the Heart Failure Society of America. Journal of Cardiac Failure. 2025.
- Mohebi R, et al. Effect of 2022 ACC/AHA/HFSA Criteria on Stages of Heart Failure in a Pooled Community Cohort. Journal of the American College of Cardiology. 2023. PubMed 37286252.
- Greene SJ, Kaltenbach LA, Fonarow GC, et al. Clinical and Financial Implications of Inpatient and Outpatient Management of Worsening Heart Failure. European Journal of Heart Failure. 2025. PubMed 40419411.
- Heidenreich PA, Fonarow GC, Opsha Y, et al. Economic Issues in Heart Failure in the United States. Journal of Cardiac Failure. 2022.
- Sandhu AT, et al. Disparity in the Setting of Incident Heart Failure Diagnosis. Circulation: Heart Failure. 2021.
- Bhatnagar R, Fonarow GC, Heidenreich PA, Ziaeian B. Expenditure on Heart Failure in the United States: The Medical Expenditure Panel Survey 2009-2018. JACC: Heart Failure. 2022.
- GDMT payer cost estimates derived from Pennsylvania State Maximum Allowable Cost schedules, March 2026, and CMS Maximum Fair Price fact sheet, August 2024. OneAnother Health analysis.
- Bessette LG, Magnani JW, Brooks MM, et al. Initiation, Adherence, and Persistence to Guideline-Directed Medical Therapy After Heart Failure Hospitalization. JAMA Internal Medicine. 2026.
- Mukhopadhyay A, Adhikari S, Li X, et al. Prior Authorization Requirements and Prescription Fill Patterns Among Patients With Heart Failure. JACC: Advances. 2026. doi:10.1016/j.jacadv.2025.102583.
- Essien UR, Singh B, Swabe G, et al. Association of Prescription Co-payment With Adherence to GLP-1 Receptor Agonist and SGLT2 Inhibitor Therapies in Patients With Heart Failure and Diabetes. JAMA Network Open. 2023.
- Aggarwal R, et al. Prevalence of the Cardiovascular-Kidney-Metabolic Syndrome in the United States, NHANES 1999-2020. Journal of the American College of Cardiology / medRxiv. 2024.
- Ndumele CE, et al. 2026 AHA/ACC/ADA/ASN Guideline for the Prevention, Detection, Evaluation, and Management of Cardiovascular-Kidney-Metabolic Syndrome. Journal of the American College of Cardiology. 2026.
All views, analyses, and frameworks presented here reflect independent professional judgment informed by more than two decades of experience across payer strategy, clinical transformation, and health system operations. They do not represent the views or positions of any current or former employer or affiliated organization.