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

Introduction: Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.

Methods: We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017.

A computational approach to early sepsis detection.

Objective: To develop high-performance early sepsis prediction technology for the general patient population.

Methods: Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission.

Results: A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode.

Discharge recommendation based on a novel technique of homeostatic analysis.

Objective: We propose a computational framework for integrating diverse patient measurements into an aggregate health score and applying it to patient stability prediction.

Materials And Methods: We mapped retrospective patient data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II clinical database into a discrete multidimensional space, which was searched for measurement combinations and trends relevant to patient outcomes of interest. Patient trajectories through this space were then used to make outcome predictions.

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

Background: Hypertension is common after intracranial hemorrhage (ICH) and may be associated with higher mortality and adverse neurologic outcome. The American Heart Association recommends that blood pressure be maintained at a mean arterial pressure (MAP) less than 130 mm Hg to prevent secondary brain injury.

Objectives: To prospectively evaluate whether a new method of assessing hypertension in ICH more accurately identifies patients at risk for adverse outcomes.

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

Continuous monitoring of physiologic vital signs is routine in neurocritical care. However, this patient information is usually only recorded intermittently (most often hourly) in the medical record. It is unclear whether this is sufficient to represent the occurrence of secondary brain insults (SBIs) or whether more frequent data collection will provide more comprehensive information for patient care.

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

Objectives: Prior studies suggest that the emergency department (ED) occurrence of secondary brain insults (SBIs), such as systemic hypotension and hypoxia, worsens outcome in patients with traumatic brain injury. However, previous methods of assessing SBIs have been relatively crude, generally only determining the incidence and duration of events. The authors hypothesized that a new method that accounts for the cumulative depth and duration of SBIs would provide a more informative measure that better correlates with outcome.