OverviewOur goal is to use cutting-edge data analytics to directly improve the care of patients treated within the healthcare system. To that end, we use advanced data processing and machine-learning techniques to predict which patients are at highest risk of certain outcomes, and who will benefit most from certain interventions. Our key philosophy it to not only develop new methods and evaluate existing methods, but to apply those methods in real world scenarios to improve patient outcomes. We seek to advance and rigorously evaluate these systems in a clinical context to determine if patient care can truly benefit from these novel tools.
Acute Kidney Alerts
Acute kidney injury (AKI) affects up to 20% of hospitalized patients and increases the risk of dying in the hospital by a factor of 10. However, AKI is asymptomatic and can go unrecognized by even well-trained medical providers. Automated detection of AKI coupled with provider alerting has the potential to improve outcomes. We are conducting three randomized trials of AKI alerts to evaluate the ability of alerts to slow the progression of AKI, avoid dialysis, and save lives.
Electronic Alerts for Acute Kidney Injury Amelioration, ELAIA-1: Active. This trial will randomize roughly 6,000 patients with AKI to AKI alerts or usual care with a goal of determining whether alerts reduce the rate of worsening of acute kidney injury, dialysis, or death.
ELAIA-2: Anticipated Start of January 2019. This trial will randomize patients with AKI who are receiving a kidney-toxic medication to alerts (highlighting the particular medication) versus usual care.
ELAIA-3: Anticipated Start of January 2020. This trial will use an advanced machine-learning technique known as uplift modeling to target AKI alerts to a subset of of patients who are most likely to be benefited, by reducing alert fatigue and improving overall effectiveness.
Predicting Imminent AKI
While AKI carries substantial risk, there remains no therapeutic intervention that can alter the course of AKI once it develops beyond optimizing usual care. Our AKI Tomorrow studies use a real-time predictive model to identify hospitalized patients who are at risk of developing AKI within the next 24 hours.
AKI Tomorrow Pilot Phase: Currently Active. This study will enroll 30 patients we predict may develop AKI in the next 24 hours. After informed consent, we obtain blood, urine, and access to their electronic medical record to determine the accuracy of our algorithm and measure biomarkers to understand the underlying pathophysiology in order to guide the additional steps required to improve their care.
AKI Tomorrow Full Study: Anticipated Start of January 2019. By exporting data to an "artificial intelligence" server, we can create models that predict AKI with much greater fidelity and feed those predictions back into the clinical system.
Future Directions. We will integrate this predictive engine with clinical laboratory medicine to facilitate reflex testing of residual blood samples to augment the information already available in the electronic health record. Additionally, we will evaluate the efficacy of our AKI Tomorrow model. Does it meaningfully improve care for hospitalized patients by randomizing to usual care vs. prediction-based intervention?
Research Assistant, Yale University
Harold Feldman, MD, MSCE
George S. Pepper Professor of Public Health and Preventative Medicine, Yale University
Amit Garg, MD, PhD
Professor of Medicine (Nephrology), Western University, London, Ontario, Canada
Stephen Latham, JD, PhD
Director, Interdisciplinary Center for Bioethics, Yale University
Research Associate, Yale University
Paul M. Palevsky, MD
Professor of Medicine, Renal-Electrolyte Division, University of Pittsburgh
- Oh J, Bia JR, Ubaid-Ullah M, Testani JM, Wilson FP: Provider acceptance of an automated electronic alert for acute kidney injury. Clin Kidney J. 2016 Aug; 2016 Jun 10. PMID: 27478598
- Wilson FP, Shashaty M, Testani J, Aqeel I, Borovskiy Y, Ellenberg SS, Feldman HI, Fernandez H, Gitelman Y, Lin J, Negoianu D, Parikh CR, Reese PP, Urbani R, Fuchs B: Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet. 2015 May 16; 2015 Feb 26. PMID: 25726515
- Wilson FP, Reese PP, Shashaty MG, Ellenberg SS, Gitelman Y, Bansal AD, Urbani R, Feldman HI, Fuchs B: A trial of in-hospital, electronic alerts for acute kidney injury: design and rationale. Clin Trials. 2014 Oct; 2014 Jul 14. PMID: 25023200