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The Wardi Research Lab

About this Lab

The Wardi lab focuses primarily on novel strategies to improve care of patients with sepsis. This includes development, validation and importantly implementation of predictive models into clinical care. Dr. Wardi oversaw the first successful implementation of a deep-learning model to predict sepsis in the Emergency Department and was able to show a 17% relative decrease in sepsis-related mortality after going live with this model using evidence-based implantation science strategies. The lab emphasizes the use of multi-modal data to augment predictive abilities, through either wearable devices in high-risk patients, use of large-language models, or additive value of novel biomarkers and assessments of host-response in suspected infection. Dr. Wardi has various industry partners exploring these strategies through funded agreements. The lab has also focused on the epidemiology of sepsis and analyses of national sepsis policy with prominent publications in journals such as Critical Care Medicine, Annals of Internal Medicine and Nature digital medicine. Given the overlap between sepsis and physiologic deterioration, the lab has worked to assess novel predictive models to identify patients at high-risk of deterioration. 

The Principal Investigator

wardi-new.jpgDr. Gabriel Wardi completed his undergraduate and graduate education in Atlanta. He moved to San Diego for Emergency Medicine residency where he served as the education chief resident during his final year. He is the first graduate of the joint Critical Care Medicine fellowship offered by the Division of Pulmonary and Critical Care Medicine and Department of Emergency Medicine. He is an associate professor at UCSD and the Chief of the Division of Emergency Critical Care in the Department of Emergency Medicine. Dr. Wardi attends in both the emergency department and the ICUs as an Associate Professor of Emergency Medicine and Medicine. He is or has been funded by various grants from the NIH, the Gordon and Betty Moore Foundation, the National Foundation of Emergency Medicine and various industry sponsored trials. He is the Medical Director for Hospital Sepsis at UC San Diego and previously created and directed the Residency Transition Course for the School of Medicine. His work in sepsis care and machine-learning has been highlight by various regional, national and international media outlets.

 

Research Focus

Sepsis

Cardiac Arrest Management

Machine-learning Applications in Acute Care

Implementation of Novel Predictive Models

Lab Members

  • Allison Donahue, M.D., UC San Diego Emergency Medicine Resident
  • Michael Self, M.D., UC San Diego EM - Anesthesia Critical Care Faculty
  • Julia Weston, M.D., UC San Diego Internal Medicine Resident
  • Romir Maheshwary, M.D., UC San Diego Internal Medicine Resident
  • Eduardo Sanchez, UC San Diego Undergraduate Student
  • May Kwak, UC San Diego Internal Medicine Resident
  • James Ford, UC San Diego Emergency Medicine Faculty

Interdisciplinary Collaborators

  • Shamim Nemati, Ph.D., UC San Diego Biomedical Informatics Faculty
  • Atul Malhotra, M.D., UC San Diego Pulmonary Critical Care Faculty
  • Angela Meier, M.D., Ph.D., UC San Diego Anesthesia Critical Care Faculty
  • Aaron Boussina, M.S., UC San Diego Biomedical Informatics PhD Student

 

Dr. Wardi is available to mentor motivated undergraduates, Masters students, residents and fellows for a variety of topics ranging from clinical applications of data science, sepsis, and cardiac arrest in critical care and emergency medicine. He is particularly interested in assessing how the application of machine-learning techniques into clinical care can improve patient-centered outcomes through more precise care. 

 

Selected Publications

  1. Boussina, A., Shashikumar, S.P., Malhotra, A…, Wardi G. Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digit. Med. 7, 14 (2024).
  2. Ford JS, Morrison JC, Kyaw M, Hewlett M, Tahir P, Jain S, Nemati S, Malhotra A, Wardi G. The Effect of Severe Sepsis and Septic Shock Management Bundle (SEP-1) Compliance and Implementation on Mortality Among Patients With Sepsis : A Systematic Review. Ann Intern Med. 2025
  3. Wardi G, Owens R, Josef C, Malhotra A, Longhurst C, Nemati S. Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us. Crit Care Med. 2023 Aug 1;51(8):985-991.
  4. Boussina A, Wardi G, Shashikumar SP, Malhotra A, Zheng K, Nemati S. Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study. J Med Internet Res. 2023 Jun 23
  5. Sobel J, Hayden SR, Wardi G. The Knowledge Gap: Mentorship in Emergency Medicine Residency. Ann Emerg Med. 2023
  6. Shashikumar SP, Wardi G, Malhotra A, Nemati S. Artificial intelligence sepsis prediction algorithm learns to say "I don't know". NPJ Digit Med. 2021 Sep 09; 4(1):134.
  7. Wardi G, Carlile M, Holder A, Shashikumar S, Hayden SR, Nemati S. Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm. Ann Emerg Med. 2021 04; 77(4):395-406.
  8. Wardi G, Tainter CR, Ramnath VR, Brennan JJ, Tolia V, Castillo EM, Hsia RY, Malhotra A, Schmidt U, Meier A. Age-related incidence and outcomes of sepsis in California, 2008-2015. J Crit Care. 2021 04; 62:212-217.
  9. Wardi G, Brice J, Correia M, Liu D, Self M, Tainter C. Demystifying Lactate in the Emergency Department. Ann Emerg Med. 2020 02; 75(2):287-298.
  10. Shashikumar SP, Wardi G, Paul P, Carlile M, Brenner LN, Hibbert KA, North CM, Mukerji SS, Robbins GK, Shao YP, Westover MB, Nemati S, Malhotra A. Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation. Chest. 2021 06; 159(6):2264-2273.