Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks

Xiaobin Shen, Jonathan Elmer, George H. Chen
Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR 219:667-690, 2023.

Abstract

Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (i.e., the task of neurological prognostication) could help with treatment decisions: which patients are likely to awaken from their coma and should be kept on life-sustaining therapies, and which are so ill that they would unlikely benefit from treatment? In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients’ available EEG time series could vary in length. Predictions themselves are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient’s probability of awakening or of dying across multiple time horizons (e.g., within the next 24, 48, or 72 hours). Our framework is based on using any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). For some patients, we do not know which of these happened first since they were still in a coma when data collection stopped (i.e., their outcome is censored). Competing risks models readily accommodate such patients. We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 post-cardiac-arrest coma patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient’s static features and summary statistics from the patient’s latest hour’s worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient’s EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks).

Cite this Paper

BibTeX
@InProceedings{pmlr-v219-shen23a, title = {Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks}, author = {Shen, Xiaobin and Elmer, Jonathan and Chen, George H.}, booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference}, pages = {667--690}, year = {2023}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo and Yeung, Serene}, volume = {219}, series = {Proceedings of Machine Learning Research}, month = {11--12 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v219/shen23a/shen23a.pdf}, url = {https://proceedings.mlr.press/v219/shen23a.html}, abstract = {Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (i.e., the task of neurological prognostication) could help with treatment decisions: which patients are likely to awaken from their coma and should be kept on life-sustaining therapies, and which are so ill that they would unlikely benefit from treatment? In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients’ available EEG time series could vary in length. Predictions themselves are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient’s probability of awakening or of dying across multiple time horizons (e.g., within the next 24, 48, or 72 hours). Our framework is based on using any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). For some patients, we do not know which of these happened first since they were still in a coma when data collection stopped (i.e., their outcome is censored). Competing risks models readily accommodate such patients. We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 post-cardiac-arrest coma patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient’s static features and summary statistics from the patient’s latest hour’s worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient’s EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks).} }
Endnote
%0 Conference Paper %T Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks %A Xiaobin Shen %A Jonathan Elmer %A George H. Chen %B Proceedings of the 8th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2023 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %E Serene Yeung %F pmlr-v219-shen23a %I PMLR %P 667--690 %U https://proceedings.mlr.press/v219/shen23a.html %V 219 %X Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (i.e., the task of neurological prognostication) could help with treatment decisions: which patients are likely to awaken from their coma and should be kept on life-sustaining therapies, and which are so ill that they would unlikely benefit from treatment? In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients’ available EEG time series could vary in length. Predictions themselves are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient’s probability of awakening or of dying across multiple time horizons (e.g., within the next 24, 48, or 72 hours). Our framework is based on using any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). For some patients, we do not know which of these happened first since they were still in a coma when data collection stopped (i.e., their outcome is censored). Competing risks models readily accommodate such patients. We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 post-cardiac-arrest coma patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient’s static features and summary statistics from the patient’s latest hour’s worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient’s EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks).
APA
Shen, X., Elmer, J. & Chen, G.H.. (2023). Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks. Proceedings of the 8th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 219:667-690 Available from https://proceedings.mlr.press/v219/shen23a.html.

Related Material