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Purpose <p>Heart failure (HF) is a disease that leads to approximately 300,000 fatalities annually in Europe and 250,000 deaths each year in the United States. Type 2 Diabetes Mellitus (T2DM) is a significant risk factor for HF, and testing for N-terminal (NT)-pro hormone BNP (NT-proBNP) can aid in early detection of HF in T2DM patients. We therefore developed and validated the HFriskT2DM-HScore, an algorithm to predict the risk of HF in T2DM patients, so guiding NT-proBNP investigation in a primary care setting.</p> Methods <p>Using a primary care database, we formed a cohort of patients aged ≥18 years diagnosed with T2DM between 2002 and 2022. A multivariate Cox model was adopted to assess the determinants associated with the occurrence of HF to combine them to form an individual score.</p> Results <p>Within a cohort of 167,618 patients (52.3% males; mean age 64.4 (SD: 14.4); HF rate equal to 6.7 cases per 1000 person-years), we developed the HFriskT2DM-HScore. When it was applied to the validation sub-cohort we found an explained variation and discrimination value of 43% (95% CI: 42–44) and 81% (95% CI: 0.80–0.83), respectively. Calibration slope was equal to 0.93 (95% CI: 0.81–1.1; <i>p</i> = 0.3123).</p> Conclusion <p>The HFriskT2DM-HScore might be implemented as a decision support system for primary care to appropriately ease the prescription of NT-proBNP and early identification of HF.</p>

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Assessing the risk of heart failure in type 2 diabetes: a prediction algorithm to sustain the evaluation of NT-proBNP in primary care

  • 期刊论文

  • 发表日期：

    2025年1月12日

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  • Francesco Lapi,
  • Ettore Marconi,
  • Gerardo Medea,
  • Iacopo Cricelli,
  • Damiano Parretti,
  • Alessandro Rossi,
  • Claudio Cricelli

摘要

Purpose

Heart failure (HF) is a disease that leads to approximately 300,000 fatalities annually in Europe and 250,000 deaths each year in the United States. Type 2 Diabetes Mellitus (T2DM) is a significant risk factor for HF, and testing for N-terminal (NT)-pro hormone BNP (NT-proBNP) can aid in early detection of HF in T2DM patients. We therefore developed and validated the HFriskT2DM-HScore, an algorithm to predict the risk of HF in T2DM patients, so guiding NT-proBNP investigation in a primary care setting.

Methods

Using a primary care database, we formed a cohort of patients aged ≥18 years diagnosed with T2DM between 2002 and 2022. A multivariate Cox model was adopted to assess the determinants associated with the occurrence of HF to combine them to form an individual score.

Results

Within a cohort of 167,618 patients (52.3% males; mean age 64.4 (SD: 14.4); HF rate equal to 6.7 cases per 1000 person-years), we developed the HFriskT2DM-HScore. When it was applied to the validation sub-cohort we found an explained variation and discrimination value of 43% (95% CI: 42–44) and 81% (95% CI: 0.80–0.83), respectively. Calibration slope was equal to 0.93 (95% CI: 0.81–1.1; p = 0.3123).

Conclusion

The HFriskT2DM-HScore might be implemented as a decision support system for primary care to appropriately ease the prescription of NT-proBNP and early identification of HF.