AI-powered early detection of kidney issues could give surgeons vital insights to intervene proactively and safeguard organ health.
A new partnership between Rice University and Baylor College of Medicine (BCM) utilises AI to alert clinicians promptly about signs of kidney problems, allowing them to take crucial action before irreversible damage occurs.
Supported by nearly $2.5 million in funding from the National Institutes of Health, the project combines Rice’s expertise in statistics and machine learning with BCM’s clinical knowledge and extensive real-world data from thousands of patients undergoing cardiac surgery.
Rice will oversee the advancement of machine learning and statistical modelling, with the AI component co-led by the data science team at BCM.
Meng Li, an associate professor of statistics at Rice, said: ‘Early prediction would enable targeted interventions that can improve outcomes, but prior risk tools are static and have limited value in the dynamic post-operative environment.’
AKI after heart surgery affects about 1 in 5 patients and can increase mortality fivefold and hospital costs threefold, the researchers said.
The challenge is that clinicians typically diagnose AKI using drops in urine output or rises in serum creatinine – signals that often appear after the best window for treatment has passed.
Dr Ravi Ghanta, principal investigator for the project and cardiothoracic surgeon at BCM, said: ‘Presently, AKI is identified via clinical parameters, but these represent late findings often manifesting after the ideal treatment window.’
Catching AKI earlier could prompt adjustments to fluids, blood pressure-supporting medications and avoidance of kidney-stressing drugs – simple steps that can prevent or lessen the injury.
Every heart-surgery patient generates a trove of data – vital signs, lab results, medication doses and fluid intake and output – updated minute by minute in their electronic medical record (EMR).
To make sense of this complex information, the Rice–Baylor team will train ensemble machine-learning models using a uniquely comprehensive BCM dataset of more than 9,000 patients and roughly 68 million data points.
The goal is threefold:
- To detect AKI earlier, potentially up to 24 hours before conventional signs appear
- To recommend personalised, data-driven interventions that can reduce an individual patient’s risk
- To validate the system prospectively inside cardiovascular ICUs, measuring both its accuracy and how closely clinicians’ actions align with the tool’s recommendations.
Meng Li said: ‘Our central hypothesis is that dynamic machine-learning models can accurately predict AKI in real time from routinely collected EMR data and augment clinical decision-making by quantifying risk reduction of therapeutic interventions. A major barrier to clinical AI is trust. This project emphasises interpretability, showing which factors drive each prediction and how much specific actions might lower risk. The researchers will combine robust feature engineering with symbolic regression to create human-readable “digital biomarkers” and a simple bedside scoring system that clinicians can understand and critique.’
By the end of the four-year grant, the team aims to deliver a system capable of earlier and more precise detection of AKI in real time, along with actionable, personalised recommendations proven to reduce patient risk.
They will also carry out prospective, real-world validation of a machine learning-enabled clinical decision support tool and develop a generalisable blueprint for translating trustworthy AI into bedside care – applicable well beyond heart surgery or kidney injury.
