Emily Muller1, Jonathan P Shock2, Andreas Bender3, Julian Kleeberger4, Tobias Högen4, Martin Rosenfelder5, Bubacarr Bah6, Alex Lopez-Rolon7. 1. Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa; African Institute Or Mathematical Sciences, Cape Town, South Africa. Electronic address: emily@aims.ac.za. 2. Department of Mathematics and Applied Mathematics, University of Cape Town, Cape Town, South Africa. 3. Department of Neurology, University of Munich, Munich, Germany; Department of Neurology, Therapiezentrum Burgau, Burgau, Germany. 4. Department of Neurology, University of Munich, Munich, Germany. 5. Department of Neurology, Therapiezentrum Burgau, Burgau, Germany; Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany. 6. Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa; African Institute Or Mathematical Sciences, Cape Town, South Africa. 7. Department of Neurology, Therapiezentrum Burgau, Burgau, Germany.
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.
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.
Authors: Piergiuseppe Liuzzi; Alfonso Magliacano; Francesco De Bellis; Andrea Mannini; Anna Estraneo Journal: Sci Rep Date: 2022-08-05 Impact factor: 4.996