Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

S Ariane Christie,Amanda S Conroy,Alan E Hubbard,Rachael A Callcut,Mitchell J Cohen

doi:10.1371/journal.pone.0213836

Abstract

ObjectiveMachine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.MethodsSuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point.ResultsSuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94–0.97), multi-organ failure (overall AUC 0.84–0.90), and transfusion (overall AUC 0.87–0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84–0.89) and venous thromboembolism (overall AUC 0.73–0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48–0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time.ConclusionsMachine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.

Highlights

Modern trauma and critical care medicine is characterized by voluminous data [1]
SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support
We demonstrate that SuperLearner is capable of generating dynamic prediction for many outcomes, and that even with advanced machine learning approaches, prediction capacity depends on data quality

Summary

Introduction

Modern trauma and critical care medicine is characterized by voluminous data [1]. Advanced monitoring systems reflect the physiologic state of critically injured patients in real time making it possible to access unprecedented amounts of patient-level data [2]. Advanced analytics, including new types of machine learning, can be utilized to extract value from this voluminous data for real time prediction and could lead to bedside precision-medicine-based decision making [3]. In an era when advanced analytics are ubiquitous on our smartphones, many providers still rely on clinical gestalt and/or scoring algorithms to guide decision making. Clinical acumen will always be an essential component of critical care, but even seasoned clinicians cannot systematically integrate all the information available on critically injured patients throughout their hospital course, nor is it possible to anticipate patterns only apparent at the aggregate level [11]

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Conclusion