Predicting Mortality in Elderly Patients with Hypertensive Crises: Development and Validation of a Risk Model

Introduction: Hypertensive crises, which are marked by severe increases in blood pressure with or without acute target organ damage, represent a critical condition associated with substantial morbidity and mortality, particularly among older adults. As global life expectancy increases, the prevalence of hypertension and its complications also rises, creating an urgent need for effective strategies for early risk assessment and intervention. This study aims to develop and validate a risk model for predicting mortality in elderly patients experiencing hypertensive crises. Given the high morbidity and mortality rates associated with hypertensive crises in this population, an accurate risk assessment tool could enhance clinical decision-making and improve patient outcomes. Materials and Methods: A retrospective cohort study was conducted involving 1,500 patients aged 65 and older who presented to the emergency department with hypertensive crises between January and August 2024. Patients with a confirmed diagnosis and complete medical records were included. Data on demographics, clinical findings, laboratory results, and comorbidities were extracted from electronic health records. Univariable and multivariable logistic regression analyses were performed using SPSS version 25 to identify key predictors of mortality. Results: The dataset was complete, with no missing values. The mean patient age was 82.5 years (±10.3), and the overall mortality rate was 15%. A risk model was developed that categorized patients into low-risk (0.14% mortality), medium-risk (75% mortality), and high-risk (99.87% mortality) groups. Receiver Operating Characteristic (ROC) analysis indicated excellent model performance, with an Area Under the Curve (AUC) of 0.999. Conclusion: The proposed risk model demonstrates impressive predictive accuracy for mortality in elderly patients experiencing hypertensive crises. However, its retrospective design may limit external validity. This model can help emergency physicians optimize treatment strategies and improve patient outcomes.