Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept

Abstract

Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury. To address this challenge, we developed the Machine intelligence Learning optimizer (MiLo), an automated machine learning (ML) platform, to automatically produce ML models for predicting burn sepsis. We conducted a retrospective analysis of 211 adult patients (age ≥ 18 years) with severe burn injury (≥ 20% total body surface area) to generate training and test datasets for ML applications. the MiLo approach was compared against an exhaustive "non-automated" ML approach as well as standard statistical methods. For this study, traditional multivariate logistic regression (LR) identified seven predictors of burn sepsis when controlled for age and burn size (OR 2.8, 95% CI 1.99-4.04, P = 0.032). The area under the ROC (ROC-AUC) when using these seven predictors was 0.88. Next, the non-automated ML approach produced an optimal model based on LR using 16 out of the 23 features from the study dataset. Model accuracy was 86% with ROC-AUC of 0.96. In contrast, MILO identified a k-nearest neighbor-based model using only five features to be the best performer with an accuracy of 90% and a ROC-AUC of 0.96. Machine learning augments burn sepsis prediction. MILO identified models more quickly, with less required features, and found to be analytically superior to traditional ML approaches. Future studies are needed to clinically validate the performance of MILO-derived ML models for sepsis prediction. Burn patients are at high risk for infections, with sepsis being the most common cause of morbidity and mortality 1. Traditional indicators of sepsis defined previously by the Surviving Sepsis Campaign 2 and other organizations exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response to burn injury. For example, the systemic inflammatory response syndrome 2,3 lacks clinical sensitivity and specificity when applied to severely burned patients 1 , while the newer 2016 "Sepsis-3" criteria remain controversial in both burned and non-burned patients 4-7. To this end, early and accurate recognition of sepsis represents a significant clinical knowledge gap in burn critical care. The American Burn Association (ABA) Consensus Guidelines published in 2007 was intended to better differentiate burn sepsis from the natural host-response to injury (Table 1) 1. These guidelines recognized deficiencies of traditional indications of sepsis and removed less specific parameters such as white blood cell count (WBC). Fever was redefined as temperatures > 39 °C to improve specificity and at the cost of sensitivity. Glycemic variability and thrombocytopenia were also included in the ABA Consensus Guidelines; however, measurement of glycemic variability is challenging without continuous glucose monitoring technology and platelet count aids ...

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“…Optimizer (MILO) platform (Regents of the University of California, Oakland) 14. This automated ML platform has been used for sepsis prediction previously in severely burned patients.…”

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