Christopher Barton, MD

Professor Emeritus of Emergency Medicine

Zuckerberg San Francisco General Hospital

+1 628 206-5762

Education:
BA: UC Irvine
MD: UCSF
Training: UCSF
Board Certifications: Emergency Medicine

Research and Academic Interests:

Dr. Barton's current research interest is Physiologic Informatics, which is the application of signal processing algorithms and methods to clinical data and using these methods to describe associations to clinical outcomes. Currently, he is focusing on acute neurologic emergencies, e.g., stroke, traumatic brain injury, and computing the "dose" of hypertension or hypotension and showing its relationship to neurologic outcome.

In collaboration with Dr. Claude Hemphill, Director of Neuro-Critical Care, he has developed a novel method to evaluate the impact of abnormal vital signs in the prehospital setting and in the ED on secondary brain injury. They have shown that the new method - using the area under the curve - is a more accurate method of assessing secondary brain insults in acute neurologic emergencies. In addition to this work, Dr. Barton joined the UCSF-SFGH Brain and Spinal Cord Injury (BASIC) group, a consortium of Neurosurgery, Neurology and Emergency Medicine Clinical Investigators.

University and National Positions:

Clinical Affairs Committee
Research Committee
Academic Affairs Committee - ACEP
Associate Editor, Academic Emergency Medicine

Publications:

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Computers in biology and medicine

Barton C, Chettipally U, Zhou Y, Jiang Z, Lynn-Palevsky A, Le S, Calvert J, Das R

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data.

Canadian journal of kidney health and disease

Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, Das R

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

BMJ open

Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

BMJ open respiratory research

Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R

Cost and mortality impact of an algorithm-driven sepsis prediction system.

Journal of medical economics

Calvert J, Hoffman J, Barton C, Shimabukuro D, Ries M, Chettipally U, Kerem Y, Jay M, Mataraso S, Das R

The predictive value of an adjusted COPD assessment test score on the risk of respiratory-related hospitalizations in severe COPD patients.

Chronic respiratory disease

Barton CA, Bassett KL, Buckman J, Effing TW, Frith PA, van der Palen J, Sloots JM

Cumulative dose of hypertension predicts outcome in intracranial hemorrhage better than American Heart Association guidelines.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

Barton CW, Hemphill JC

A novel method of evaluating the impact of secondary brain insults on functional outcomes in traumatic brain-injured patients.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

Barton CW, Hemphill JC, Morabito D, Manley G

Christopher Barton, MD

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data.

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

Cost and mortality impact of an algorithm-driven sepsis prediction system.

The predictive value of an adjusted COPD assessment test score on the risk of respiratory-related hospitalizations in severe COPD patients.

A computational approach to mortality prediction of alcohol use disorder inpatients.

A computational approach to early sepsis detection.

High-performance detection and early prediction of septic shock for alcohol-use disorder patients.

Discharge recommendation based on a novel technique of homeostatic analysis.

Cumulative dose of hypertension predicts outcome in intracranial hemorrhage better than American Heart Association guidelines.

Do medications affect vital signs in the prehospital treatment of acute decompensated heart failure?

Interactions between psychosocial problems and management of asthma: who is at risk of dying?

Influence of data resolution and interpolation method on assessment of secondary brain insults in neurocritical care.

A novel method of evaluating the impact of secondary brain insults on functional outcomes in traumatic brain-injured patients.

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Christopher Barton, MD