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

: 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. : 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). : 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 < 0.05). In multivariable logistic regression, troponin I levels independently predicted the occurrence of HEs (OR: 2.82; 95%CI: 1.65-4.82; < 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: 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: 0.66; 95%CI: 0.60-0.72). : 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.