A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

Abstract

Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24 hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24 hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach

“…The limitations of existing scoring systems have lead to a rise in researchers exploring machine learning techniques for mortality prediction [13][14][15][16][17][18][19][20] , as well as the related issues of predicting the onset of various intervention methods 21,22 detecting the risk of sepsis [23][24][25][26] and other clinical deterioration events 27,28 . Machine learning approaches have the advantage of being relatively easy to continuously update and recalibrate, with algorithms OPEN 1 College of Science and Engineering, James Cook University, Townsville 4811, Australia.…”

Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation

“…Previously published clinical applications for neural networks include predicting mortality in the critical care setting [ 28 , 29 ], predicting the occurrence of severe sepsis [ 30 , 31 ], and identifying optimal approaches to treat infection [ 32 ]. These studies demonstrate the applicability of neural networks to questions in clinical medicine and utilized similar approaches to examine complex clinical relationships.…”

Artificial Intelligence for Clinical Decision Support in Sepsis

“…The development of decision support systems that relied on advances in machine learning is a field of innovation in healthcare strategies. Predicting the development of septic shock is one of the active areas ( 27 ). Many studies have developed intelligent decision support tools related to septic shock to improve clinical results and promote real-time optimization of medical resources.…”