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

ABSTRACT: 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.

SUBMITTER: Panunzi S

PROVIDER: S-EPMC10113248 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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

Panunzi Simona S Lucca Lucia Francesca LF De Tanti Antonio A Cava Francesca F Romoli Annamaria A Formisano Rita R Scarponi Federico F Estraneo Anna A Frattini Diana D Tonin Paolo P Piergentilli Ilaria I Pioggia Giovanni G De Gaetano Andrea A Cerasa Antonio A

Scientific reports 20230418 1

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 ( ...[more]

PMID: 37072538

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Project description:OBJECTIVES:Intracranial pressure in traumatic brain injury is dynamic and influenced by factors like injury patterns, treatments, and genetics. Existing studies use time invariant summary intracranial pressure measures thus potentially losing critical information about temporal trends. We identified longitudinal intracranial pressure trajectories in severe traumatic brain injury and evaluated whether they predicted outcome. We further interrogated the model to explore whether ABCC8 polymorphisms (a known cerebraledema regulator) differed across trajectory groups. DESIGN:Prospective observational cohort. SETTING:Single-center academic medical center. PATIENTS:Four-hundred four severe traumatic brain injury patients. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We used group-based trajectory modeling to identify hourly intracranial pressure trajectories in days 0-5 post traumatic brain injury incorporating risk factor adjustment (age, sex, Glasgow Coma Scale 6score, craniectomy, primary hemorrhage pattern). We compared 6-month outcomes (Glasgow Outcome Scale, Disability Rating Scale, mortality) and ABCC8 tag-single-nucleotide polymorphisms associated with cerebral edema (rs2237982, rs7105832) across groups. Regression models determined whether trajectory groups predicted outcome. A six trajectory group model best fit the data, identifying cohorts differing in initial intracranial pressure, evolution, and number/proportion of spikes greater than 20?mm Hg. There were pattern differences in age, hemorrhage type, and craniectomy rates. ABCC8 polymorphisms differed across groups. GOS (p = 0.006), Disability Rating Scale (p = 0.001), mortality (p < 0.0001), and rs2237982 (p = 0.035) differed across groups. Unfavorable outcomes were surprisingly predicted by both low intracranial pressure trajectories and sustained intracranial hypertension. Intracranial pressure variability differed across groups (p < 0.001) and may reflect preserved/impaired intracranial elastance/compliance. CONCLUSIONS:We employed a novel approach investigating longitudinal/dynamic intracranial pressure patterns in traumatic brain injury. In a risk adjusted model, six groups were identified and predicted outcomes. If validated, trajectory modeling may be a first step toward developing a new, granular approach for intracranial pressure phenotyping in conjunction with other phenotyping tools like biomarkers and neuroimaging. This may be particularly relevant in light of changing traumatic brain injury demographics toward the elderly.

Project description:BACKGROUND:The assumption is that executive dysfunctions (EF), associated with frontal lobe injury, are responsible for behavioral disturbances. Some studies do not find a relationship between EF and behavior following frontal lobe lesions. Our main goal of this study was to use a novel statistical method, graph theory, to analyze this relationship in different brain injury groups; frontal lobe damage, non-frontal lobe damage, and controls. Within the frontal group, we expect to find a pattern of executive nodes that are highly interconnected. METHODS:For each group, we modeled the relationship between executive functions and behavior as a network of interdependent variables. The cognitive tests and the behavioral questionnaire are the "nodes" in the network, while the relationships between the nodes were modeled as the correlations between two nodes corrected for the correlation with all other nodes in the network. Sparse networks were estimated within each group using graphical LASSO. We analyzed the relative importance of the nodes within a network (centrality) and the clustering (modularity) of the different nodes. RESULTS:Network analysis showed distinct patterns of relationships between EF and behavior in the three subgroups. The performance on the verbal learning test is the most central node in all the networks. In the frontal group, verbal memory forms a community with working memory and fluency. The behavioral nodes do not differentiate between groups or form clusters with cognitive nodes. No other communities were found for cognitive and behavioral nodes. CONCLUSION:The cognitive phenotype of the frontal lobe damaged group, with its stability and proportion, might be theoretically interpreted as a potential "buffer" for possible cognitive executive deficits. This might explain some of the ambiguity found in the literature. This alternative approach on cognitive test scores provides a different and possibly complimentary perspective of the neuropsychology of brain-injured patients.

Dataset Information

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

Publications

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

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