Is it Prime Time for ProBNP to unlock value? Measuring effectiveness in Heart Failure

Faced with the many new treatments in development for Heart Failure and the prospect of assessing value relative to effective and largely genericised standard of care – should we start to think differently about how we classify and measure effectiveness in heart failure?

Heart failure is currently classified based on left ventricular ejection fraction (LVEF) into three categories: HFrEF (LVEF <40%), HFpEF (LVEF ≥50%), and HFmrEF (LVEF 40-49%) [1,2]. However, this classification has limitations, particularly for HFpEF and HFmrEF, as it fails to fully capture the complexity and heterogeneity of heart failure [6,7].

ProBNP (pro-B-type Natriuretic Peptide) has the potential to improve heart failure classification by providing valuable prognostic information across all subtypes[10,11]. It helps refine HFpEF and HFmrEF diagnosis, differentiate between cardiac and non-cardiac causes of symptoms, and importantly to monitor treatment response[15,21,23].

Incorporating proBNP into heart failure classification could enhance the value proposition in economic assessment for new treatments. It allows for more precise patient stratification [1,2], improved prognostic modelling [3,4], and enhanced treatment response assessment [5,6]. This could potentially support value-based pricing models and even help to justify earlier interventions [7,8].

The combination of LVEF and proBNP enables a more personalized medicine approach[1,2], potentially justifying premium pricing for treatments effective in specific patient phenotypes on the basis of the value they confer. It could also facilitate more sophisticated comparative effectiveness research and better alignment with value-based care models [9,10].

ProBNP-guided management has been associated with reduced hospitalization rates in several studies [7,8]. Given that hospitalizations account for a significant portion of heart failure-related healthcare costs, this could lead to substantial cost savings. The ability to track proBNP changes over time provides a quantifiable measure of treatment efficacy, and could support value-based pricing models where reimbursement is tied to measurable improvements in patient outcomes [9,10].

By identifying patients at risk of progressing to more severe heart failure, proBNP testing could justify earlier, potentially less costly interventions. This might lead to long-term cost savings by preventing disease progression and associated complications [11,12]. The inclusion of proBNP in heart failure management provides an additional outcome measure for health technology assessments, potentially justifying higher prices for treatments that demonstrate significant improvements in proBNP levels and associated outcomes [13,14].

The combination of LVEF and proBNP may allow for a more personalized approach to patient care, aligning with the trend towards precision medicine. This could improve the overall cost-effectiveness of heart failure management by ensuring patients receive the most appropriate treatments[15,16].

ProBNP levels provide an additional metric for comparing the effectiveness of different heart failure treatments. This could lead to more informed decision-making about which treatments to prioritize from a system perspective[17,18].

In conclusion, while the current LVEF-based classification system for heart failure has limitations, incorporating proBNP as a biomarker could refine these classifications. This approach provides additional information on ventricular stress severity and patient prognosis, potentially leading to more accurate diagnosis and creating the prospect of individualized treatment strategies [27,28]. From a payer perspective, while the incorporation of proBNP testing does incur some additional costs, the potential for improved patient outcomes and more efficient resource allocation suggests it could be cost-effective in the long term. However, more real-world studies are needed to fully quantify its economic impact across different healthcare systems and patient populations.

References:

1. Yancy CW, et al. J Am Coll Cardiol. 2017;70(6):776-803.

2. Ponikowski P, et al. Eur Heart J. 2016;37(27):2129-2200.

3. Januzzi JL Jr, et al. Eur Heart J. 2006;27(3):330-337.

4. Gaggin HK, Januzzi JL Jr. Biochim Biophys Acta. 2013;1832(12):2442-2450.

5. Zile MR, et al. J Am Coll Cardiol. 2016;68(22):2425-2436.

6. Januzzi JL Jr, et al. JAMA. 2010;304(22):2494-2502.

7. Felker GM, et al. JAMA. 2017;318(8):713-720.

8. Stienen S, et al. Eur J Heart Fail. 2018;20(10):1468-1478.

9. Anker SD, et al. Eur J Heart Fail. 2016;18(5):482-489.

10. Desai AS, Stevenson LW. Circulation. 2012;125(10):1285-1287.

11. Ledwidge M, et al. JAMA. 2013;310(1):66-74.

12. Hlatky MA, et al. Circulation. 2009;119(12):1671-1675.

13. Neumann PJ, et al. Health Aff (Millwood). 2008;27(6):1620-1631.

14. Morrow DA, de Lemos JA. Circulation. 2007;116(5):e99-e109.

15. Jameson JL, Longo DL. N Engl J Med. 2015;372(23):2229-2234.

16. Ahmad T, et al. JACC Heart Fail. 2018;6(6):489-497.

17. Heidenreich PA, et al. J Am Coll Cardiol. 2013;62(25):e147-e239.

18. Januzzi JL Jr, et al. J Am Coll Cardiol. 2019;74(15):1980-1991.

21. Januzzi JL, et al. J Am Coll Cardiol. 2020;75(22):2885-2896.

23. Packer M, et al. Eur J Heart Fail. 2018;20(3):395-398.

27. Stevenson LW, et al. Heart Fail Clin. 2018;14(1):93-102.

28. Gaggin HK, et al. Heart Fail Clin. 2018;14(1):109-118.

Written by Nic Gwatkin
Decisive Dialogue 27th September 2024

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