A Mathematical Model for Outcome Prediction in Severe Head Injury

Srinivasan, U. S.; Muthukumar, Natarajan; Gajendran, R; Kumar, Mritunjay

doi:10.1007/978-4-431-68231-8_90

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Patients fulfilling the following inclusion criteria were enrolled: (1) adult age (≥ 18 years; (2) clinical diagnosis of vegetative state/unresponsive wakefulness syndrome (VS/UWS) 30 or in a minimally conscious state (MCS, i.e. patients exhibiting minimal but definitive behavioral evidence of self-awareness or environmental awareness), and patients emerged from MCS (reliable demonstration of either interactive communication or functional object use), based on standardized clinical criteria of Coma Recovery Scale-revised (CRS-r =) [8][9][10]; (3) etiology: traumatic, vascular (i.e., ischemic or hemorrhagic), anoxic and other (i.e., infective or benign tumor); (4) time post-injury ≤ 3 months calculated at admission, (5) first admission in intensive rehabilitation unit (IRU). Patients with mixed etiology (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, in order to provide a clear quantitative depiction of pathophysiological pathways, models have been developed to indirectly calculate physiologically or clinically relevant parameters from experimental data. A first attempt to apply mathematical modeling to predict clinical course of acquired brain injury was made by Srinivasan et al 10 . They created an index combining the predictive values of the Glasgow coma scale (GCS) with the presence of Fronto-Orbicular Reflex and Vertical Oculocephalic Response.…”

Section: Introductionmentioning

confidence: 99%

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Modeling outcome trajectories in patients with acquired brain injury using a non-linear dynamic evolution approach

Panunzi

Lucca

Tanti

et al. 2023

Sci Rep

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This study describes a dynamic non-linear mathematical approach for modeling the course of disease in acquired brain injury (ABI) patients. Data from a multicentric study were used to evaluate the reliability of the Michaelis–Menten (MM) model applied to well-known clinical variables that assess the outcome of ABI patients. The sample consisted of 156 ABI patients admitted to eight neurorehabilitation subacute units and evaluated at baseline (T0), 4 months after the event (T1) and at discharge (T2). The MM model was used to characterize the trend of the first Principal Component Analysis (PCA) dimension (represented by the variables: feeding modality, RLAS, ERBI-A, Tracheostomy, CRS-r and ERBI-B) in order to predict the most plausible outcome, in terms of positive or negative Glasgow outcome score (GOS) at discharge. Exploring the evolution of the PCA dimension 1 over time, after day 86 the MM model better differentiated between the time course for individuals with a positive and negative GOS (accuracy: 85%; sensitivity: 90.6%; specificity: 62.5%). The non-linear dynamic mathematical model can be used to provide more comprehensive trajectories of the clinical evolution of ABI patients during the rehabilitation period. Our model can be used to address patients for interventions designed for a specific outcome trajectory.

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