Award Title: Metabolics and clinical based predictive models for acute kidney injury after coronary artery bypass grafting
Funder: The Academy of Medical Sciences

Abstract: Aims
We aim to develop a machine learning-based model based on clinical and metabolomics data to accurately predict postoperative acute kidney injury (AKI) after coronary artery bypass grafting.
 
Background

Acute kidney injury after cardiac surgery is associated with significant mortality, morbidity, and excess healthcare costs. Using a machine learning approach, we have developed a novel prediction model that integrates metabolomic preoperative urine profiling of samples from a study biobank and clinical data from a national audit dataset. In 130 patients undergoing various heart surgery procedures, we have shown that the predictive performance of combined metabolomics and clinical model (c-statistic of 0.87) exceeded that of either the clinical model (c-statistic of 0.86) or metabolomics model alone (c-statistic of 0.82).To take this work further, we now want to focus on (1) isolated coronary artery bypass graft (CABG) cohort with balanced outcomes and control patients, (2) assess prediction by AKI stage (1, 2, 3) and add postoperative metabolomic data to the model.  

Hypothesis
A combined metabolomics-clinical prediction model outperforms models based on either metabolomics or clinical features alone. 

Methods

Using the clinical and metabolomics prediction models developed in our pilot study, we aim to assess model performance for AKI prediction based on 200 urine samples taken from 100 isolated CABG patients pre-operatively and post-operatively (half with AKI outcome and half without). The modelling process will take into account urine metabolomics and clinical predictors. 

Outcomes and impact

To develop a prediction tool to enable clinicians to identify and prevent acute kidney in the NHS.

Lay Abstract: After heart surgery, the kidneys can suffer damage and can stop functioning. This complication is called a renal failure or acute kidney injury. This is a serious complication because it increases the risk of dying after heart surgery and the risk of other major complications. As a result, patients that develop renal failure usually stay longer in hospital. This results in a significant financial burden for the NHS and the use of resources that impact other patients that await treatment. If we could identify which patients are at risk of developing this serious complication early on, it could give us time to prevent it. The current tools for prediction are not good enough. Our preliminary data shows that we could develop an accurate tool to anticipate this complication based on a urine sample analysis and routine clinical patient information we collect before the operation. As a cardiac surgery trainee, this subject area is important to me because I could make a difference in the postoperative management of my patients. I also find exciting the project because I can get involved in two rapidly developing areas of medical research. One area is metabolomics - the large scientific study of chemical processes involving metabolites. This enables us a unique understanding of an individual's biological processes that are altered during disease. The second area is machine learning, a branch of artificial intelligence that enables the use of computer algorithms that can automatically predict a complication through experience and the use of big data.

Research Activity Codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health Categories: RENAL AND UROGENITAL;CARDIOVASCULAR