Version 1
: Received: 26 April 2024 / Approved: 26 April 2024 / Online: 26 April 2024 (20:12:01 CEST)
How to cite:
Jung, J.; Kim, D.; Hwang, I. Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical diagnosis Data from the MIMIC-IV Database. Preprints2024, 2024041771. https://doi.org/10.20944/preprints202404.1771.v1
Jung, J.; Kim, D.; Hwang, I. Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical diagnosis Data from the MIMIC-IV Database. Preprints 2024, 2024041771. https://doi.org/10.20944/preprints202404.1771.v1
Jung, J.; Kim, D.; Hwang, I. Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical diagnosis Data from the MIMIC-IV Database. Preprints2024, 2024041771. https://doi.org/10.20944/preprints202404.1771.v1
APA Style
Jung, J., Kim, D., & Hwang, I. (2024). Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical diagnosis Data from the MIMIC-IV Database. Preprints. https://doi.org/10.20944/preprints202404.1771.v1
Chicago/Turabian Style
Jung, J., Doyoon Kim and Inkyung Hwang. 2024 "Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical diagnosis Data from the MIMIC-IV Database" Preprints. https://doi.org/10.20944/preprints202404.1771.v1
Abstract
Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors utilizing medical history data for hypertension patients. Specifically, we focused solely on utilizing medical data preceding the diagnosis of hypertension to enable patients to anticipate the onset of potential heart failure at the time of hypertension diagnosis. Age-specific and ICD system-specific predictive factors were identified utilizing the MIMIC-IV database through the application of two analytical approaches: chi-square tests and XGBoost modeling. Our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.
Keywords
heart failure; hypertension; predictive factors; MIMIC-IV database; data mining; XGBoost modeling; chi-square test
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
