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“Clinical prediction model for masked hypertension diagnosed by 24-h ambulatory blood pressure measurements in a sample from specialized hospital.”

Journal of Human Hypertension volume 39, pages 199–204 (2025)Cite this article

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Abstract

The conventional assessment of the relationship between arterial hypertension (AH) and cardiovascular damage has predominantly relied on office measurements. However, the diagnostic significance of ambulatory and home measurements has gained prominence, particularly in identifying distinct AH phenotypes like masked hypertension (MH), characterized by normal office values but elevated readings outside the clinical setting, carrying comparable risks to sustained AH. Current guidelines advocate for Ambulatory Blood Pressure Monitoring (ABPM) in individuals with office values exceeding 130/85 mmHg. This study aims to develop a clinical prediction model to identify masked hypertension in individuals with normal office blood pressure and to create a clinical score.

A cross-sectional study was conducted in a secondary level hospital, including patients aged 18–85 years with average office blood pressure <140/90 mmHg who underwent a valid ABPM on the same day. Pregnant and postpartum women were excluded. A multivariable logistic regression model with calibration, discrimination, and stability parameters was applied to predict masked hypertension. 506 individuals with valid ABPM were analysed. The prevalence of masked hypertension was 30.8%. The selected variables were: diastolic blood pressure, pulse pressure, waist diameter and sex. The model calibrated adequately (Hosmer-Lemeshow test p = 0.35), with an AUC of 0.72 (95% CI, 0.67–0.77). Significant differences existed between the traditional and the new models (p < 0.001). A user-friendly clinical model was developed, with a clinical score achieving 90% specificity using an estimated probability of 0.4 with a 10-point score.

A novel model, performed with easily collectable clinical variables, showed robust calibration, stability, and discrimination. It outperforms sole reliance on office blood pressure, exhibiting high specificity (~90%) for masked hypertension detection. Its internal validity suggests a potential for enhanced masked hypertension identification.

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Additional data are available from the corresponding author on reasonable request.

References

  1. Cesario V, Presta V, Figliuzzi I, Citoni B, Battistoni A, Miceli F, et al. Epidemiological impact and clinical consequences of masked hypertension: a narrative review. High Blood Press Cardiovasc Prev. 2020;27:195–201. https://doi.org/10.1007/s40292-020-00382-1.

    Article  PubMed  Google Scholar 

  2. Mancia G, Bombelli M, Facchetti R, Madotto F, Quarti-Trevano F, Polo Friz H, et al. Long-term risk of sustained hypertension in white-coat or masked hypertension. Hypertension. 2009;54:226–32. https://doi.org/10.1161/HYPERTENSIONAHA.109.129882.

    Article  PubMed  CAS  Google Scholar 

  3. Mancia G, Kreutz R, Brunström M, Burnier M, Grassi G, Januszewicz A, et al. 2023 ESH guidelines for the management of arterial hypertension The Task Force for the management of arterial hypertension of the European Society of Hypertension: endorsed by the International Society of Hypertension (ISH) and the European renal association (ERA). J Hypertens. 2023;41:1874–2071. https://doi.org/10.1097/HJH.0000000000003480.

    Article  PubMed  CAS  Google Scholar 

  4. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension. 2020;75:1334–57. https://doi.org/10.1161/HYPERTENSIONAHA.120.15026.

    Article  PubMed  CAS  Google Scholar 

  5. Pierdomenico SD, Pierdomenico AM, Coccina F, Clement DL, De Buyzere ML, De Bacquer DA, et al. Prognostic value of masked uncontrolled hypertension. Hypertension. 2018;72:862–9. https://doi.org/10.1161/HYPERTENSIONAHA.118.11499.

    Article  PubMed  CAS  Google Scholar 

  6. Salazar MR, Espeche WG, Balbín E, Leiva Sisnieguez CE, Minetto J, Leiva Sisnieguez BC, et al. Prevalence of isolated nocturnal hypertension according to 2018 European Society of Cardiology and European Society of Hypertension office blood pressure categories. J Hypertens. 2020;38:434–40. https://doi.org/10.1097/HJH.0000000000002278.

    Article  PubMed  CAS  Google Scholar 

  7. Sullivan LM, Massaro JM, D’Agostino Sr RB. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med. 2004;23:1631–60. https://doi.org/10.1002/sim.1742.

    Article  PubMed  Google Scholar 

  8. Booth JN 3rd, Muntner P, Diaz KM, Viera AJ, Bello NA, et al. Evaluation of criteria to detect masked hypertension. J Clin Hypertens. 2016;18:1086–94. https://doi.org/10.1111/jch.12830.

    Article  Google Scholar 

  9. Hung MH, Shih LC, Wang YC, Leu HB, Huang PH, Wu TC, et al. Prediction of masked hypertension and masked uncontrolled hypertension using machine learning. Front Cardiovasc Med. 2021;8:778306. https://doi.org/10.3389/fcvm.2021.778306.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Coccina F, Borrelli P, Pierdomenico AM, Pizzicannella J, Guagnano MT, Cuccurullo C, et al. Prediction of masked uncontrolled hypertension detected by ambulatory blood pressure monitoring. Diagnostics. 2022;12:3156. https://doi.org/10.3390/diagnostics12123156.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Alves MAM, Feitosa ADM, Mota-Gomes MA, Paiva AMG, Barroso WS, Miranda RD, et al. Accuracy of screening strategies for masked hypertension: a large-scale nationwide study based on home blood pressure monitoring. Hypertens Res. 2023;46:742–50. https://doi.org/10.1038/s41440-022-01103-y.

    Article  PubMed  Google Scholar 

  12. Stergiou GS, Kyriakoulis KG, McManus RJ, Andreadis EA, Jula A, Kollias A, et al. Phenotypes of masked hypertension: isolated ambulatory, isolated home and dual masked hypertension. J Hypertens. 2020;38:218–23.

    Article  PubMed  CAS  Google Scholar 

  13. Ministerio de Salud. 4ta Encuesta Nacional Factores de riesgo cardiovascular 2019. Disponible en: https://bancos.salud.gob.ar/sites/default/files/2020-01/4ta-encuesta-nacional-factores-riesgo_2019_principales-resultados.pdf.

  14. Cuspidi C, Sala C, Tadic M, Rescaldani M, Grassi G, Mancia G. Untreated masked hypertension and subclinical cardiac damage: a systematic review and meta-analysis. Am J Hypertens. 2015;28:806–13.

    Article  PubMed  Google Scholar 

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Acknowledgements

To the nurses of our unit, Lic. Irma Eugenio Acero, Lic. Jorge Osvaldo Belaunzaran, Marina Celeste Rios, and our secretary Pablo Scheyola. Fermin Minetto who is working on an application for using the model. Trinidad Cerri for the revision of the adaptation to English.

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Authors and Affiliations

  1. Cardiometabolic Diseases Unit, San Martin Hospital, La Plata, Argentina

    J. Minetto, W. Espeche, C. E. Leiva Sisnieguez, G. Cerri, J. I. Perez Duhalde, D. Olano & M. R. Salazar

  2. School of Medicine, National University of La Plata, La Plata, Argentina

    J. Minetto, W. Espeche, C. E. Leiva Sisnieguez, G. Cerri, J. I. Perez Duhalde & M. R. Salazar

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  1. J. Minetto
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Contributions

Minetto J: design, execution, writing, statistics, final manuscript review. Espeche Walter: design, execution, writing, statistical review of final manuscript. Cerri G: execution, patient care, manuscript review. Perez Duhalde JI execution, patient care, manuscript review. Leiva Sisnieguez CE: final review and statistics. Olano D: Final review and statistics. Salazar MR : design, final review and statistics.

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Correspondence to J. Minetto.

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The authors declare no competing interests.

Ethics

All methods were conducted in accordance with relevant guidelines and regulations, in adherence to the Declaration of Helsinki. Ethical approval for this study was obtained from the designated ethics committee (Protocol No.: HSMLP2024/0121). Informed consent was obtained from all participants prior to their inclusion in the study.

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Minetto, J., Espeche, W., Leiva Sisnieguez, C.E. et al. “Clinical prediction model for masked hypertension diagnosed by 24-h ambulatory blood pressure measurements in a sample from specialized hospital.”. J Hum Hypertens 39, 199–204 (2025). https://doi.org/10.1038/s41371-024-00980-9

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