CoMET Visit the CoMET Site


Overview
CoMET® (Continuous Monitoring of Event Trajectories) is a predictive analytical tool that provides early warning of a patient’s risk for critical events, often up to 12 hours before symptoms are evident, allowing proactive diagnosis and treatment. The application displays continuously updated trajectories of a patient’s risk for developing adverse conditions such as sepsis, hemorrhage, respiratory failure, mortality, cardiovascular collapse, and others.

Benefits
Example Screen

CoMET image

System Overview
HK-HiQ Enterprise Gateway System diagram

References

i Ruminski, Caroline M., Matthew T. Clark, Douglas E. Lake, Rebecca R. Kitzmiller, Jessica Keim-Malpass, Matthew P. Robertson, Theresa R. Simons, J. Randall Moorman, and J. Forrest Calland. "Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit." Journal of clinical monitoring and computing 33, no. 4 (2019): 703-711.

Blackwell, J. N., Keim-Malpass, J., Clark, M. T., Kowalski, R. L., Najjar, S. N., Bourque, J. M., Lake, D. E., & Moorman, J. R. (2020). Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All. Critical care explorations, 2(5), e0116.

Glass, G., Hartka, T. R., Keim-Malpass, J., Enfield, K. B., & Clark, M. T. (2021). Dynamic data in the ED predict requirement for ICU transfer following acute care admission. Journal of clinical monitoring and computing, 35(3), 515-523.

Moss, T. J., Clark, M. T., Calland, J. F., Enfield, K. B., Voss, J. D., Lake, D. E., & Moorman, J. R. (2017). Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study. PloS one, 12(8), e0181448.

Ruminski, C. M., Clark, M. T., Lake, D. E., Kitzmiller, R. R., Keim-Malpass, J., Robertson, M. P., ... & Calland, J. F. (2019). Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of clinical monitoring and computing, 33(4), 703-711.

Spaeder, M. C., Moorman, J. R., Tran, C. A., Keim-Malpass, J., Zschaebitz, J. V., Lake, D. E., & Clark, M. T. (2019). Predictive analytics in the pediatric intensive care unit for early identification of sepsis: capturing the context of age. Pediatric research, 86(5), 655-661.