Fig 1.
(A-E). Methodological work-flow for integrating diverse clinical TBI data. (A) Hypothetical example of a spatial bi-plot of individual patients (grey points) on 2 functional endpoints after TBI (GOS-E AND PTSD). The same approach can be applied to multiple metrics simultaneously using multivariate pattern detectors (e.g., principal component analysis) to produce a multivariate view of function. (B) In TRACK-TBI Pilot the same individuals (N = 586) were tracked prospectively across multiple domains (function, biomarkers, imaging) providing connections (lines) across domains to improve patient classification using the full syndromic space. (C) Multivariate pattern detection lens can be used to categorize (colors) patients across all domains. (D) Patient grouping by multivariate lens. (E) Topological visualization renders patient groups into individual nodes, colored by the multivariate lens. Edges (black lines) indicate individuals appearing in both groups producing a syndromic map of patient clusters.
Table 1.
Patient clinical characteristics.
Table 2.
Descriptive statistics of CDE variables included in TDA to map TBI patients into a network topology based on TBI severity.
Fig 2.
(A-F). TBI CDE network topology identifies the PARP1 SNP as a candidate predictor of GOS-E deficits in mild TBI. Patients with TBI were mapped into a TDA network, highlighting color schemes for CT (A) and MRI (B) pathology and whether they had a confirmed diagnosis of PTSD (DSM IV) at 6 months post-TBI (C). Patients in the circled regions of the network were identified due to substantial dysfunction measured by the GOS-E both at 3 months (D) and 6 months (E) post-TBI, compared with other patients in the network with no CT pathology and no diagnosis of PTSD. Data-driven exploration of these patients in the network revealed a significant categorical enrichment for the PARP1 SNP (F), particularly the heterozygous allele (A/T). Heat map represents range of numerical values for each measure: Panels A-C yes (1 = red) vs, no (0 = blue); Panels D-E GOS-E range from less than 3 (blue) to 8 (red); Panel F PARP1 allele A/A = 1 = blue, A/T = 2 = yellow/green, T/T = 3 = red.
Fig 3.
(A-B). Hypothesis testing of PARP1 genetic polymorphism influence on GOS-E deficits in mild TBI. GOS-E scores between 3 and 6 months post-TBI were plotted for patients who were CT negative (A) or CT positive (B), based on the SNP allele expressed (A/A = blue, A/T = yellow/green, T/T = red). Hypothesis testing of the interaction between CT pathology and the SNP allele over time revealed a significant 3-way interaction; however, no significance was detected at each time point individually. Only change in GOS-E over time was significant in patients with a negative head CT.*p < .05.
Table 3.
General linear model statistics for PARP1 SNP interaction with CT pathology on GOS-E recovery.
Table 4.
General linear model statistics for PARP1 SNP interaction on GOS-E recovery by presence or absence of CT pathology.
Table 5.
Clinical characteristics of TDA-identified patient subgroup with PARP1 SNP (N = 37) alongside to all patients with PARP1 SNP (N = 298).