Predictive Modeling of Acute Hypertensive Disorders in a Real-World Cohort: Integrating Clinical Predictors and Data-Driven Methods

<b>Background</b>: Acute hypertensive disorders, including hypertensive emergencies (HEs) and urgencies (HUs), are a frequent cause of emergency department (ED) visits. Early differentiation between HEs and HUs is essential, as their clinical management and prognostic implications differ substantially. <b>Methods</b>: We retrospectively analyzed patients admitted to an Italian second-level ED between January and June 2022 with systolic blood pressure (SBP) ≥ 180 mmHg and/or diastolic blood pressure(DBP) ≥ 110 mmHg. Patients were categorized based on the presence of acute hypertension-mediated organ damage (A-HMOD). To identify the main predictors of HEs, we applied both conventional logistic regression and machine learning approaches (Elastic Net and Random Forest). <b>Results</b>: Among 23,678 ED admissions, 261 patients (1.1%) had acute hypertensive disorders, of whom 115 (44%) were diagnosed with HEs and 146 (56%) with HUs. Compared with HU patients, HE patients were older and showed higher SBPand DBP at presentation, along with a greater prevalence of comorbidities such as diabetes, coronary artery disease, and chronic kidney disease (all <i>p</i> < 0.05). In multivariable logistic regression, troponin I levels independently predicted the occurrence of HEs (OR: 2.82; 95%CI: 1.65-4.82; <i>p</i> < 0.001), even after adjusting for confounders. Machine learning analyses confirmed troponin I as the most influential predictor, followed by age and SBP, with the Random Forest model achieving a high predictive performance (AUC<sub>ROC</sub>: 0.93; 95%CI: 0.90-0.96). Elastic Net regression further highlighted troponin I as the most influential variable with the highest standardized coefficient (β = 4.13). As determined by the Youden index, the optimal diagnostic threshold for troponin I was 0.12 ng/mL (AUC<sub>ROC</sub>: 0.66; 95%CI: 0.60-0.72). <b>Conclusions</b>: In patients presenting to the ED, withacute hypertensive disorders, elevated troponin I levels, older age, and higher SBP at admission may serve as early indicators of emergencies.

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Journal
Diagnostics
Published
2025-08-18
DOI
https://doi.org/10.3390/diagnostics15162062
Primary Topic
Machine Learning in Healthcare
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article
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article

Predictive Modeling of Acute Hypertensive Disorders in a Real-World Cohort: Integrating Clinical Predictors and Data-Driven Methods

Stefania Auciello, Carmine Morisco, O Santucci, Filomena Liccardi et al.
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Diagnostics
Machine Learning in Healthcare
article

Predictive Modeling of Acute Hypertensive Disorders in a Real-World Cohort: Integrating Clinical Predictors and Data-Driven Methods

Stefania Auciello, Carmine Morisco, O Santucci, Filomena Liccardi, Raffaele Izzo, Maria Virginia Manzi, Christian Basile, Nicola De Luca, Pasquale Ambrosino, Fiorella Paladino, Maria Lembo, Giorgio Alfredo Spedicato, Costantino Mancusi, Giovanni Esposito, Ilaria Fucile
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Abstract

<b>Background</b>: Acute hypertensive disorders, including hypertensive emergencies (HEs) and urgencies (HUs), are a frequent cause of emergency department (ED) visits. Early differentiation between HEs and HUs is essential, as their clinical management and prognostic implications differ substantially. <b>Methods</b>: We retrospectively analyzed patients admitted to an Italian second-level ED between January and June 2022 with systolic blood pressure (SBP) ≥ 180 mmHg and/or diastolic blood pressure(DBP) ≥ 110 mmHg. Patients were categorized based on the presence of acute hypertension-mediated organ damage (A-HMOD). To identify the main predictors of HEs, we applied both conventional logistic regression and machine learning approaches (Elastic Net and Random Forest). <b>Results</b>: Among 23,678 ED admissions, 261 patients (1.1%) had acute hypertensive disorders, of whom 115 (44%) were diagnosed with HEs and 146 (56%) with HUs. Compared with HU patients, HE patients were older and showed higher SBPand DBP at presentation, along with a greater prevalence of comorbidities such as diabetes, coronary artery disease, and chronic kidney disease (all <i>p</i> < 0.05). In multivariable logistic regression, troponin I levels independently predicted the occurrence of HEs (OR: 2.82; 95%CI: 1.65-4.82; <i>p</i> < 0.001), even after adjusting for confounders. Machine learning analyses confirmed troponin I as the most influential predictor, followed by age and SBP, with the Random Forest model achieving a high predictive performance (AUC<sub>ROC</sub>: 0.93; 95%CI: 0.90-0.96). Elastic Net regression further highlighted troponin I as the most influential variable with the highest standardized coefficient (β = 4.13). As determined by the Youden index, the optimal diagnostic threshold for troponin I was 0.12 ng/mL (AUC<sub>ROC</sub>: 0.66; 95%CI: 0.60-0.72). <b>Conclusions</b>: In patients presenting to the ED, withacute hypertensive disorders, elevated troponin I levels, older age, and higher SBP at admission may serve as early indicators of emergencies.

DiagnosticsVol. 15(16)
Karolinska Institutet (SE), Ospedale Antonio Cardarelli (IT), Istituti Clinici Scientifici Maugeri (IT), Associazione Nazionale Medici Cardiologi Ospedalieri (IT), Federico II University Hospital (IT), University of Milano-Bicocca (IT)
Ministero della Salute
Openalex Percentile: Top 89%
Machine Learning in Healthcare
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