Nomograms in cardiology: from evidence to clinical practice

Authors

Keywords:

cardiac rehabilitation; nomograms; precision medicine; primary health care

Abstract

Introduction: Nomograms are graphical tools that integrate multiple variables to estimate individualized probabilities of clinical outcomes, and they have demonstrated predictive capacity superior to traditional scales.

Objective: To characterize the clinical value of nomograms as precision medicine tools in cardiology, with an emphasis on their application in primary care and cardiac rehabilitation.

Development: Evidence shows that nomograms have as much value as scales such as TIMI or Framingham, and they allow estimating not only major adverse events but also functional outcomes, therapeutic adherence, and response to interventions. Their potential is relevant in primary care, for early stratification and chronic disease management; and in cardiac rehabilitation, to predict therapeutic compliance, individualize exercise prescription, and optimize resources. However, barriers persist such as insufficient external validation, the risk of methodological bias and the lack of integration into health information systems.

Conclusions: To incorporate nomograms into routine cardiological practice, a coordinated effort is required that includes external validation of existing models in local populations, methodological standardization, and their technological integration at the point of clinical care.

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References

1. Ramírez Domínguez LG. La historia y proyección de la cardiología: de los latidos del pasado a los desafíos del futuro [Internet]. Rev Multimed. 2025 [acceso: 05/03/2026]; 29:e3239. Disponible en: https://revmultimed.sld.cu/index.php/mtm/article/view/3239

2. López Ferrero LH, González Veliz A, Llerena Rojas LD, Aroche Aportela R, Calderín Pino R, Amat Santos I. Experiencia inicial en Cuba del implante valvular aórtico transcatéter [Internet]. Rev Cuba Cardiol Cir Cardiovasc. 2025 [acceso: 03/03/2026]; 31:e2343. Disponible en: https://revcardiologia.sld.cu/index.php/revcardiologia/article/view/2343

3. Jiménez López M, Hidalgo Mesa CJ, Barreto Fiu EE, Sánchez Pérez Y, Jiménez Rodríguez Y, Guedes Díaz R, et al. Prevención de la hipertensión arterial desde un modelo predictivo y nomograma de riesgo [Internet]. Medicentro Electrónica. 2025 [acceso: 05/03/2026];29:e4314. Disponible en: http://scielo.sld.cu/pdf/mdc/v29/1029-3043-mdc-29-e4314.pdf

4. Rodríguez Perón JM. Medicina de precisión y medicina basada en la evidencia [Internet]. Rev Cuba Med Mil. 2019 [acceso: 04/03/2026];48(4):918-26. Disponible en: https://revmedmilitar.sld.cu/index.php/mil/article/view/321

5. Fang C, Chen Z, Zhang J, Jin X, Yang M. Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction [Internet]. Front Cardiovasc Med. 2022 [acceso: 05/03/2026]; 9:1050785. Disponible en: https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1050785/full

6. Zhao JY, Dou JQ, Chen MW. Construction of a risk prediction model for hypertension in type 2 diabetes: Independent risk factors and nomogram [Internet].World J Diabetes. 2025 [acceso: 05/03/2026];16(5):102141. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC12142180/

7. Hou S, Jiang Y, Zhao F, Wei Z, Wang T, Chen J, et al. Construction and validation of a risk prediction model for extensive abdominal aortic calcification in hypertensive patients aged 40 and above: a cross-sectional study based on the 2013-2014 NHANES database [Internet]. BMC Cardiovasc Disord. 2025 [acceso: 05/03/2026];25(1):462. Disponible en: https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-025-04912-4

8. Zhou B, Yan J, Wang Q, He Q, Ao W, Yang Y, et al. Factors influencing cardiac rehabilitation compliance in elderly myocardial infarction patients and the development of a nomogram prediction model [Internet]. Patient Prefer Adherence. 2025 [acceso: 05/03/2026];19:2015-25. Disponible en: https://www.dovepress.com/factors-influencing-cardiac-rehabilitation-compliance-in-elderly-myoca-peer-reviewed-fulltext-article-PPA

9. Shen T, Ren C, Zhao W, Tao L, Xu S, Zhang C, et al. Development and validation of a prediction model for cardiovascular events in exercise assessment of coronary heart disease patients after percutaneous coronary intervention [Internet]. Front Cardiovasc Med. 2022 [acceso: 05/03/2026];9:798446. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/pmid/35557530/

10. Zhang LX, Wang TT, Jiang Y, Hou SB, Zhao FF, Zhou XJ, et al. Construction and verification of the prediction model for risk of sleep disturbance in elderly patients with hypertension: a cross-sectional survey based on NHANES database from 2005 to 2018 [Internet]. BMC Geriatr. 2024 [acceso: 05/03/2026];24(1):848. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC11490173/

11. Kong S, Chen C, Zheng G, Yao H, Li J, Ye H, et al. A prognostic nomogram for long-term major adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention [Internet]. BMC Cardiovasc Disord. 2021 [acceso: 05/03/2026];21(1):253. Disponible en: https://link.springer.com/article/10.1186/s12872-021-02051-0

12. Xie J, Song L, Yang Z, Bai H, Feng C. Development of a risk factor nomogram prediction model for patients with acute coronary syndrome complicated by hypertension using LASSO regression analysis [Internet]. BMC Cardiovasc Disord. 2025 [acceso: 05/03/2026];25(1):866. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC12699854/

13. Larrinaga Sandrino V, Benitez Ledesma J, Dufurneax Kindelan R, Hernández García S, Hernández Meléndez DE, Rivas Estany E. Charlas educativas para la salud como parte de un programa integral de rehabilitación cardiovascular [Internet]. Rev Cuba Med Fis Rehabil. 2025 [acceso: 05/03/2026];17:e1003. Disponible en: https://revrehabilitacion.sld.cu/index.php/reh/article/view/1003

14. Park S, Battumur B, Yoon SY, Lee Y, Park SH, Lee K, et al. Western exercise capacity nomograms for Korean patients with cardiovascular disease [Internet]. J Korean Med Sci. 2023 [acceso: 05/03/2026];38(23):e179. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC10261710/

15. Garib Hernández R, Nápoles Riaño DA, Velázquez González VA, Pérez Pérez SM, Hechavarría Del Río Z. Validación de un modelo predictivo para la insuficiencia cardíaca en hipertensos [Internet]. Rev Cuba Med Mil. 2026 [acceso: 04/03/2026];55(1):e026076819. Disponible en: https://revmedmilitar.sld.cu/index.php/mil/article/view/76819

16. Liu J, Zhang N, Liu T. Predicting hypertension in type 2 diabetes mellitus: Insights from a nomogram model [Internet]. World J Diabetes. 2025;16(7):107501. DOI: https://dx.doi.org/10.4239/wjd.v16.i7.107501

17. Zhang Z, Ma X, Li Z, Wei H, Jia K, Xu C, et al. Nomogram for predicting postoperative blood pressure reduction in hypertensive patients: a single-center retrospective study [Internet]. Int J Med Sci. 2025 [acceso: 05/03/2026];22(14):3581-90. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC12434825/

Published

2026-09-03

How to Cite

1.
Pérez García TJ, Bermúdez Yera G de J. Nomograms in cardiology: from evidence to clinical practice. Rev. cuba. med. mil [Internet]. 2026 Sep. 3 [cited 2026 Oct. 19];55(3):e026077445. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/77445