SBIR/STTR Award attributes
More than 5 million patients are admitted annually to United States ICUs with average mortality rate reported ranging from 8-19%, or about 500,000 deaths annually. Sepsis is the leading cause of in-hospital mortality, where one in three inpatient deaths are due to sepsis. Incidence of sepsis has been increasing with 1.7 million sepsis cases and 270,000 deaths per year. Early identification of deterioration has been shown to reduce the need for patient transfer to higher care units, reduce lengths of stay, and improve survival rates. Each hour of delay in ICU admission has been associated with a 1.5% increased risk of ICU death and a 1% increase in risk of hospital death. Many studies support that there is an increase in mortality rate for every hour delay in antibiotics. Pairing patient risk stratification with appropriate levels of hospital intervention is essential to reduce risk of mortality. Patients in intermediate units between the levels of monitoring found in floor units and ICUs are especially difficult to predict possibility of condition deterioration. Automated monitoring, alerts, and trend analysis are essential to identifying and proactively intervening patients under duress. Current methods of monitoring patient health have low specificity and have significant room for improvement.This project will develop Deep-CDS, a cloud-based deep learning system for context-sensitive clinical decision support in monitoring and predicting the deterioration of patient health and progression of sepsis risk factors in real-time to improve outcomes and optimize the management of care across the hospital population. To support the clinical care team, Deep-CDS provides team members with (a) a clinical care knowledgebase, (b) an early warning score for deteriorating health conditions, (c) a model for predicting septic conditions, (d) evidence-based clinical practice guidelines, and (e) visualization of patient health status trends. Deep-CDS addresses NIGMS Priorities for Small Business Development of Sepsis Diagnostics and Therapeutics, NOT-GM-20- 028: 1) Diagnostic tools for emergency department settings; 2) Predictive clinical algorithms and point-of-care diagnostics; 3) Technologies that combine various types of data for diagnosis of sepsis patients; and 4) Clinical decision support, including use of artificial intelligence and machine learning approaches, to develop tools for early recognition of sepsis, assessment of treatment responses and patient deterioration, and long-term prognosis prediction in various care settings.
