From Valves to Vessels: A Machine Learning Approach to Explore Heart Failure Risk in ICU Patients with Aortic Valve and Aortic Vascular Disorders

Research Article

Austin J Nephrol Hypertens. 2023; 10(2): 1110.

From Valves to Vessels: A Machine Learning Approach to Explore Heart Failure Risk in ICU Patients with Aortic Valve and Aortic Vascular Disorders

Karamo Bah1; Adama Ns Bah2; Amadou Wurry Jallow3; Musa Touray4*

1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan

2Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan

3Department of Medical Laboratory Science and Biotechnology, Taipei Medical University, Taiwan

4School of Medicine and Allied Health Sciences, University of The Gambia, The Gambia

*Corresponding author: Dr. Musa TouraySenior Lecturer School of Medicine and Allied Health Sciences University of the Gambia, West Africa. Email: [email protected]

Received: October 25, 2023 Accepted: November 29, 2023 Published: December 06,2023

Abstract

Background and objectives: Aortic valve and aortic vascular disorders represent a subset of cardiovascular conditions that can lead to heart failure. Aortic valve diseases, such as aortic stenosis or regurgitation, and aortic vascular diseases, including aortic aneurysms and aortic dissection, can contribute to impaired cardiac function and increase the risk of heart failure. This study aims to investigate the risk of heart failure in patients with aortic valve and aortic vascular disorders within 30-days of admission in the Intensive Care Unit (ICU).

Methods: Patients from a US-based critical care database (MIMIC-III) who developed heart failure in the ICU within 30 days of follow-up were included. Two predictive models, XGBoost and logistic regression, were developed and evaluated using ROC, sensitivity, specificity, and F1 measure. The dataset was split into training and testing samples in an 8:2 ratio.

Results: Out of 2,871 patients analyzed, 1,062 (37%) developed heart failure in the ICU during the 30-day follow-up. Key predictors of heart failure included creatinine, phosphate, age, COPD, INR, diabetes, CAD, magnesium, atrial fibrillation, and hyperlipidemia. The logistic regression model outperformed XGBoost with (AU-ROC, 0.78 vs. 0.77, respectively).

Conclusions: This study demonstrates the potential of machine learning techniques to enhance predictive modeling in critical care research. It provides valuable insights into heart failure risk in patients with aortic valve and aortic vascular disorders admitted to the ICU.

Keywords: Aortic disorders; Heart failure; Intensive care unit; Length of stay; Mortality; Machine learning

Introduction

Heart Failure (HF) is a clinical syndrome characterized by the reduced ability of the heart to pump and/or fill with blood. In 2021, a consensus on the universal definition and classification of HF was proposed, defining HF as a clinical syndrome with symptoms and/or signs caused by cardiac abnormality [1]. HF was categorized based on left ventricular Ejection Fraction (EF) into HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved EF (HFpEF). A new entity, HF with improved EF, was also introduced. HF is considered a global pandemic, affecting an estimated 64.3 million people worldwide in 2017, with prevalence expected to rise due to improved survival and longer life expectancy. The burden of HF on healthcare expenditures is significant, with projections indicating a substantial increase in costs by 2030 [2]. The prevalence of Heart Failure (HF) varies significantly between countries and regions, with the highest rates observed in Central Europe, North Africa, and the Middle East (ranging from 1133 to 1196 per 100,000 people) and lower rates in Eastern Europe and Southeast Asia (ranging from 498 to 595 per 100,000 people) [3]. In the next sections, we present a summary of epidemiological data on HF prevalence, focusing on various geographical areas. Heart failure is a significant and complex cardiovascular condition that poses a substantial burden on public health globally. It affects millions of people and is associated with high morbidity and mortality rates. While heart failure is commonly studied in the general population, there is a need for more focused investigations in specific patient cohorts with underlying cardiovascular disorders.

Machine learning has made significant advancements in healthcare. AI is being used to aid in case triage and diagnoses [4], improve image scanning and segmentation [5], assist with decision-making[6], predict disease risk [7,8], and even in neuroimaging [9]. These applications have the potential to revolutionize healthcare and improve patient outcomes. Researchers have developed deep learning models to predict clinical conditions using Electronic Health Records (EHRs). One study utilized LSTM networks and CNNs to predict diseases like heart failure and stroke, achieving improved accuracy by incorporating both structured and unstructured data from progress and diagnosis notes [10]. In another study, a deep neural network model predicted post-stroke pneumonia with high accuracy, reaching 92.8% and 90.5% AUC for 7-day and 14-day predictions, respectively [7]. Additionally, ML-based models, such as SRML-Mortality Predictor, demonstrated the ability to predict mortality in specific conditions, like paralytic ileus, with an 81.30% accuracy rate [11]. These predictive algorithms can provide valuable insights for informed clinical decision-making.

The aim of this study is to investigate the risk of heart failure in patients with aortic valve and aortic vascular disorders within 30-days of admission in the Intensive Care Unit (ICU).The secondary outcomes are to assess the 30-day mortality rate, the Length of Hospital Stay(LoS)and to investigate the clinical implications of the machine learning predictions for improving patient outcomes in this population.

Method

Data Source

In this retrospective investigation, we analyzed data retrieved from the Medical Information Mart for Intensive Care (MIMIC) repositories. MIMIC databases contain extensive and anonymized health-related information of critical care patients admitted to the Beth Israel Deaconess Medical Center, a prominent tertiary medical facility in Boston, USA. The dataset encompasses various variables such as demographics, vital signs, laboratory outcomes, prescriptions, and clinical notes, providing valuable insights into critical patient profiles [12]. In this study, we conducted an analysis of the MIMIC databases, specifically focusing on the most recent version, MIMIC-III v1.4. The MIMIC-III clinical database encompasses data collected between 2001 and 2012, utilizing the MetaVision (iMDSoft, Wakefield, MA, USA) and CareVue (Philips Healthcare, Cambridge, MA, USA) systems. Notably, the original Philips CareVue system, comprising archived data from 2001 to 2008, was subsequently replaced by the advanced MetaVision data management system, which remains in active use today.

Ethics and Data Use Agreement

After successfully completing the mandatory online human research ethics training as mandated by PhysioNet Clinical Databases (Certification Number: 55140935), we obtained data access following the prescribed procedures. The study was conducted in accordance with the Declaration of Helsinki.

Definition of the Outcome of Interest (cases and controls)

In the context of the study conducted in ICU patients to explore the risk of heart failure after having aortic disorders or aortic vascular problems within a 30-day follow-up period, the definitions of cases and controls would be as follows:

Cases in this study refer to ICU patients who were diagnosed with aortic disorders (such as aortic valve disorders or aortic vascular problems) and subsequently developed heart failure within the 30-day follow-up period. These are individuals who experienced heart failure as an outcome of interest during their stay in the ICU. Cases were selected based on the International Classification of Diseases (ICD) procedure codes (ICD-9 CODE) from the MIMIC III data. Controls, in this study, is defined as ICU patients with aortic disorders who did not develop heart failure during the 30-day follow-up period. They are individuals who did not experience the outcome of interest (heart failure) during their ICU stay and within the 30-day time frame (Figure 1).