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

• Published in: PloS one Vol. 14; no. 4; p. e0213836
• Main Authors: Christie, S. Ariane, Conroy, Amanda S., Callcut, Rachael A., Hubbard, Alan E., Cohen, Mitchell J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.04.2019; Public Library of Science (PLoS)
• Subjects: Adult; Adult respiratory distress syndrome; Algorithms; Blood Transfusion - statistics & numerical data; Clinical Decision-Making - methods; Computer and Information Sciences; Data mining; Death; Decision making; Decision Support Techniques; Discrimination; Emergency medicine; Female; Health aspects; Humans; Information management; Injuries; Machine Learning; Male; Medical research; Medicine and Health Sciences; Methods; Middle Aged; Models, Biological; Multiple Organ Failure - diagnosis; Multiple Organ Failure - etiology; Physical Sciences; Precision medicine; Prognosis; Prospective Studies; Research and Analysis Methods; Respiratory distress syndrome; Respiratory Distress Syndrome - diagnosis; Respiratory Distress Syndrome - etiology; Risk Assessment - methods; ROC Curve; Technology application; Thromboembolism; Time Factors; Venous Thromboembolism - diagnosis; Venous Thromboembolism - etiology; Wounds and Injuries - complications; Wounds and Injuries - mortality; Wounds and Injuries - therapy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0213836

Machine 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. SuperLearner, 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. SuperLearner 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. Machine 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.