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.
Authors
- Stefania Auciello
- Carmine Morisco (ORCID: https://orcid.org/0000-0002-2158-6317)
- O Santucci
- Filomena Liccardi
- Raffaele Izzo (ORCID: https://orcid.org/0000-0002-0308-2836)
- Maria Virginia Manzi (ORCID: https://orcid.org/0000-0002-1979-7131)
- Christian Basile (ORCID: https://orcid.org/0000-0002-4680-3058)
- Nicola De Luca (ORCID: https://orcid.org/0000-0002-9606-6586)
- Pasquale Ambrosino (ORCID: https://orcid.org/0000-0002-9398-0428)
- Fiorella Paladino
- Maria Lembo (ORCID: https://orcid.org/0000-0001-6454-2560)
- Giorgio Alfredo Spedicato (ORCID: https://orcid.org/0000-0002-0315-8888)
- Costantino Mancusi (ORCID: https://orcid.org/0000-0001-6690-1408)
- Giovanni Esposito (ORCID: https://orcid.org/0000-0003-0565-7127)
- Ilaria Fucile (ORCID: https://orcid.org/0000-0002-8507-269X)
Institutions
- 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)
Publication Details
- Journal
- Diagnostics
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3390/diagnostics15162062
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute