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Prevalence and prediction of masked uncontrolled hypertension in patients recently hospitalized for myocardial infarction

Hellqvist, Henrik ; Erlinge, David LU orcid ; Lindahl, Bertil ; Jernberg, Tomas ; Oldgren, Jonas ; James, Stefan ; Al-Khalili, Faris ; Kahan, Thomas and Spaak, Jonas (2025) In European Heart Journal Open 5(6).
Abstract

Aims To study the prevalence of masked uncontrolled hypertension (MUCH) in patients recently hospitalized for myocardial infarction, and to develop machine learning-based prediction models identifying MUCH. Methods and results Ambulatory blood pressure measurement (ABPM) was performed in 99 patients following hospitalization for a myocardial infarction. Sixty-two clinical variables were eligible for machine learning. Variable importance for the prediction of MUCH (office blood pressure <140/90 mm Hg at ABPM start but mean 24-h blood pressure ≥130/80 mm Hg) was assessed using the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithms. Logistic regression, LASSO, and random forest models based on the top... (More)

Aims To study the prevalence of masked uncontrolled hypertension (MUCH) in patients recently hospitalized for myocardial infarction, and to develop machine learning-based prediction models identifying MUCH. Methods and results Ambulatory blood pressure measurement (ABPM) was performed in 99 patients following hospitalization for a myocardial infarction. Sixty-two clinical variables were eligible for machine learning. Variable importance for the prediction of MUCH (office blood pressure <140/90 mm Hg at ABPM start but mean 24-h blood pressure ≥130/80 mm Hg) was assessed using the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithms. Logistic regression, LASSO, and random forest models based on the top variables were evaluated using receiver operating characteristic area under the curve (AUC) in repeated cross-validation. Mean age was 62.1 ± 8.2 years, 73 (74%) were males. The ABPM was performed at a median of 11 weeks after discharge. Among 96 patients with valid 24-h ABPM recordings, 32 (33%) had 24-h mean blood pressure ≥130/80 mm Hg and 17 (18%) were identified with MUCH. Machine learning identified discharge diagnoses of diabetes and hypertension, and kidney dysfunction as most important predictors of MUCH. The best random forest, logistic regression, and LASSO models showed mean AUC 0.82, 0.80, and 0.80, respectively, for prediction of MUCH. Conclusion One in five patients had MUCH at follow-up after a myocardial infarction. The readily available variables diabetes, hypertension, and kidney dysfunction were identified as the most important predictors of MUCH, which may be implemented in a prediction model for identifying this clinically challenging blood pressure phenotype. Previous presentation Preliminary results were presented at the European Society of Cardiology Congress in London 2024 as an oral abstract presentation. Hellqvist H, Erlinge D, Lindahl B, et al. Prevalence and prediction of masked uncontrolled hypertension in patients recently hospitalised for an acute coronary syndrome. European Heart Journal 2024;45 (Suppl 1). doi: 10.1093/eurheartj/ehae666.2566

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Machine learning, Masked hypertension, Myocardial infarction
in
European Heart Journal Open
volume
5
issue
6
article number
oeaf138
publisher
Oxford University Press
external identifiers
  • scopus:105021498854
  • pmid:41230395
ISSN
2752-4191
DOI
10.1093/ehjopen/oeaf138
language
English
LU publication?
yes
id
ae4ce324-3ed2-4eff-b734-55a4f166bced
date added to LUP
2026-01-12 15:37:22
date last changed
2026-01-26 17:01:29
@article{ae4ce324-3ed2-4eff-b734-55a4f166bced,
  abstract     = {{<p>Aims To study the prevalence of masked uncontrolled hypertension (MUCH) in patients recently hospitalized for myocardial infarction, and to develop machine learning-based prediction models identifying MUCH. Methods and results Ambulatory blood pressure measurement (ABPM) was performed in 99 patients following hospitalization for a myocardial infarction. Sixty-two clinical variables were eligible for machine learning. Variable importance for the prediction of MUCH (office blood pressure &lt;140/90 mm Hg at ABPM start but mean 24-h blood pressure ≥130/80 mm Hg) was assessed using the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithms. Logistic regression, LASSO, and random forest models based on the top variables were evaluated using receiver operating characteristic area under the curve (AUC) in repeated cross-validation. Mean age was 62.1 ± 8.2 years, 73 (74%) were males. The ABPM was performed at a median of 11 weeks after discharge. Among 96 patients with valid 24-h ABPM recordings, 32 (33%) had 24-h mean blood pressure ≥130/80 mm Hg and 17 (18%) were identified with MUCH. Machine learning identified discharge diagnoses of diabetes and hypertension, and kidney dysfunction as most important predictors of MUCH. The best random forest, logistic regression, and LASSO models showed mean AUC 0.82, 0.80, and 0.80, respectively, for prediction of MUCH. Conclusion One in five patients had MUCH at follow-up after a myocardial infarction. The readily available variables diabetes, hypertension, and kidney dysfunction were identified as the most important predictors of MUCH, which may be implemented in a prediction model for identifying this clinically challenging blood pressure phenotype. Previous presentation Preliminary results were presented at the European Society of Cardiology Congress in London 2024 as an oral abstract presentation. Hellqvist H, Erlinge D, Lindahl B, et al. Prevalence and prediction of masked uncontrolled hypertension in patients recently hospitalised for an acute coronary syndrome. European Heart Journal 2024;45 (Suppl 1). doi: 10.1093/eurheartj/ehae666.2566</p>}},
  author       = {{Hellqvist, Henrik and Erlinge, David and Lindahl, Bertil and Jernberg, Tomas and Oldgren, Jonas and James, Stefan and Al-Khalili, Faris and Kahan, Thomas and Spaak, Jonas}},
  issn         = {{2752-4191}},
  keywords     = {{Machine learning; Masked hypertension; Myocardial infarction}},
  language     = {{eng}},
  number       = {{6}},
  publisher    = {{Oxford University Press}},
  series       = {{European Heart Journal Open}},
  title        = {{Prevalence and prediction of masked uncontrolled hypertension in patients recently hospitalized for myocardial infarction}},
  url          = {{http://dx.doi.org/10.1093/ehjopen/oeaf138}},
  doi          = {{10.1093/ehjopen/oeaf138}},
  volume       = {{5}},
  year         = {{2025}},
}