1. Predicting Outcome of Acquired Brain Injury by the Evolution of Paroxysmal Sympathetic Hyperactivity Signs.
- Author
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Lucca LF, De Tanti A, Cava F, Romoli A, Formisano R, Scarponi F, Estraneo A, Frattini D, Tonin P, Bertolino C, Salucci P, Hakiki B, D'Ippolito M, Zampolini M, Masotta O, Premoselli S, Interlenghi M, Salvatore C, Polidori A, and Cerasa A
- Subjects
- Adult, Aged, Autonomic Nervous System Diseases physiopathology, Brain Injuries physiopathology, Cohort Studies, Consciousness Disorders physiopathology, Female, Glasgow Outcome Scale, Hospitalization, Humans, Machine Learning, Male, Middle Aged, Neurological Rehabilitation, Outcome Assessment, Health Care, Predictive Value of Tests, Prognosis, Recovery of Function, Autonomic Nervous System Diseases diagnosis, Autonomic Nervous System Diseases etiology, Brain Injuries complications, Brain Injuries rehabilitation, Consciousness Disorders diagnosis, Consciousness Disorders etiology
- Abstract
In this multi-center study, we provide a systematic evaluation of the clinical variability associated with paroxysmal sympathetic hyperactivity (PSH) in patients with acquired brain injury (ABI) to determine how these signs can impact outcomes. A total of 156 ABI patients with a disorder of consciousness (DoC) were admitted to neurorehabilitation subacute units (intensive rehabilitation unit; IRU) and evaluated at baseline (T0), after 4 months from event (T1), and at discharge (T2). The outcome measure was the Glasgow Outcome Scale-Extended, whereas age, sex, etiology, Coma Recovery Scale-Revised (CRS-r), Rancho Los Amigos Scale (RLAS), Early Rehabilitation Barthel Index (ERBI), PSH-Assessment Measure (PSH-AM) scores and other clinical features were considered as predictive factors. A machine learning (ML) approach was used to identify the best predictive model of clinical outcomes. The etiology was predominantly vascular (50.8%), followed by traumatic (36.2%). At admission, prevalence of PSH was 31.3%, which decreased to 16.6% and 4.4% at T1 and T2, respectively. At T2, 2.8% were dead and 61.1% had a full recovery of consciousness, whereas 36.1% remained in VS or MCS. A support vector machine (SVM)-based ML approach provides the best model with 82% accuracy in predicting outcomes. Analysis of variable importance shows that the most important clinical factors influencing the outcome are the PSH-AM scores measured at T0 and T1, together with neurological diagnosis, CRS-r, and RLAS scores measured at T0. This joint multi-center effort provides a comprehensive picture of the clinical impact of PSH signs in ABI patients, demonstrating its predictive value in comparison with other well-known clinical measurements.
- Published
- 2021
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