Feasibility of Hidden Markov Models for the Description of Time-Varying Physiologic State After Severe Traumatic Brain Injury. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Asgari, Shadnaz; Adams, Hadie; Kasprowicz, Magdalena; Czosnyka, Marek; Smielewski, Peter; Ercole, Ari
• Is Part Of: Critical care medicine. Volume 47:Issue 11(2019)
• Publication Date: 2019-11
• Subjects: cerebrovascular circulation -- critical care -- detection algorithms -- intracranial pressure -- Markov chains -- traumatic brain injury; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000003966
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : Objectives: Continuous assessment of physiology after traumatic brain injury is essential to prevent secondary brain insults. The present work aims at the development of a method for detecting physiologic states associated with the outcome from time-series physiologic measurements using a hidden Markov model. Design: Unsupervised clustering of hourly values of intracranial pressure/cerebral perfusion pressure, the compensatory reserve index, and autoregulation status was attempted using a hidden Markov model. A ternary state variable was learned to classify the patient's physiologic state at any point in time into three categories ("good, " "intermediate, " or "poor") and determined the physiologic parameters associated with each state. Setting: The proposed hidden Markov model was trained and applied on a large dataset (28, 939 hr of data) using a stratified 20-fold cross-validation. Patients: The data were collected from 379 traumatic brain injury patients admitted to Addenbrooke's Hospital, Cambridge between 2002 and 2016. Interventions: Retrospective observational analysis. Measurements and Main Results: Unsupervised training of the hidden Markov model yielded states characterized by intracranial pressure, cerebral perfusion pressure, compensatory reserve index, and autoregulation status that were physiologically plausible. The resulting classifier retained a dose-dependent prognostic ability. Dynamic analysis suggested that the hidden Markov model was stable over short periods of time consistent with typical timescales for traumatic brain injury pathogenesis. Conclusions: To our knowledge, this is the first application of unsupervised learning to multidimensional time-series traumatic brain injury physiology. We demonstrated that clustering using a hidden Markov model can reduce a complex set of physiologic variables to a simple sequence of clinically plausible time-sensitive physiologic states while retaining prognostic information in a dose-dependent manner. Such states may provide a more natural and parsimonious basis for triggering intervention decisions. Abstract : Supplemental Digital Content is available in the text.