Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness. (April 2019)

• Record Type: Journal Article
• Authors: Muller, Emily; Shock, Jonathan P.; Bender, Andreas; Kleeberger, Julian; Högen, Tobias; Rosenfelder, Martin; Bah, Bubacarr; Lopez-Rolon, Alex
• Is Part Of: Computers in biology and medicine. Volume 107(2019)
• Page Start: 145
• Page End: 152
• Publication Date: 2019-04
• Subjects: Machine learning -- Neurological outcome -- Clinical prediction modelling -- Imputation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.02.006
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels. Objectives: To apply predictive models using machine learning methods to examine, one year after onset, the prognostic power of serial measurements of NSE in patients with AI-DOC. To compare the discriminative accuracy of this method to both standard single-day, absolute, and difference-between-days, relative NSE levels. Methods: Classification algorithms were implemented and K-nearest neighbours (KNN) imputation was used to avoid complete case elimination of patients with missing NSE values. Non-imputed measurements from Day 0 to Day 6 were used for single day and difference-between-days. Results: The naive Bayes classifier on imputed serial NSE measurements returned an AUC of ( 0.81 ± 0.07 ) for n = 126 patients (100 poor outcome). This was greater than logistic regression ( 0.73 ± 0.08 ) and all other classifiers. Naive Bayes gave a specificity and sensitivity of 96 % and 49 %, respectively, for an (uncalibrated) probability decision threshold of 90 % . The maximum AUC for a single day was Day 3 (0.75) for a subset of n = 79 (61 poor outcome) patients, and for differences between Day 1 and Day 4 (0.81) for a subset of n = 46 (39 poor outcome) patients. Conclusion: Imputation avoided the elimination of patients with missing data and naive Bayes outperformed all other classifiers. Machine learning algorithms could detect automatically discriminatory features and the overall predictive power increased from standard methods due to the larger data set. Code availability: Data analysis code is available under GNU at:https://github.com/emilymuller1991/outcome_prediction_nse . Highlights: Machine learning algorithms were implemented on serial NSE measurements to predict poor neurological outcome of patients with AI-DOCs. KNN imputation was implemented to avoid complete case elimination of patients and class distribution was maintained. The naive Bayes algorithm returned a higher AUC than single day methods and matched the highest AUC for difference-between-days.