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Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.

ABSTRACT: Severe physical injury is a leading cause of posttraumatic syndrome (PTS). This is to develop a biomarker-based diagnostic algorithm for posttraumatic syndrome (BioPTS) study. This is a 2-year longitudinal cohort study assessing patients who were hospitalized beginning in 2015 at Chonnam National University Hospital in Gwangju, Korea, after experiencing severe physical injuries. Baseline evaluations were made during the acute phase (within 1 month) of the physical injury and included extensive information on sociodemographic and clinical variables as well as a list of biomarkers. All participants will be followed up for 2 years, and the diagnostic and predictive validities of various biomarkers for PTS will be estimated. The BioPTS study will develop the most accurate models for the diagnosis and prediction of PTS, and will contribute to existing research regarding the complex relationships between severe physical injury and psychological issues.

SUBMITTER: Kim JW

PROVIDER: S-EPMC5561411 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.

Kim Ju-Wan JW Kang Hee-Ju HJ Bae Kyung-Yeol KY Kim Sung-Wan SW Oh Hyun-Kyong HK Kim Min-Gon MG Kim Jae-Min JM

Psychiatry investigation 20170711 4

Severe physical injury is a leading cause of posttraumatic syndrome (PTS). This is to develop a biomarker-based diagnostic algorithm for posttraumatic syndrome (BioPTS) study. This is a 2-year longitudinal cohort study assessing patients who were hospitalized beginning in 2015 at Chonnam National University Hospital in Gwangju, Korea, after experiencing severe physical injuries. Baseline evaluations were made during the acute phase (within 1 month) of the physical injury and included extensive i ...[more]

PMID: 28845180

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Project description:Introduction: Transcutaneous osseointegration following amputation (TOFA) confers better mobility and quality of life for most patients versus socket prosthesis rehabilitation. Peri-TOFA infection remains the most frequent complication and lacks an evidence-based diagnostic algorithm. This study's objective was to investigate preoperative factors associated with positive intraoperative cultures among patients suspected of having peri-TOFA infection in order to create an evidence-based diagnostic algorithm. Methods: We conducted a retrospective study of 83 surgeries (70 patients) performed to manage suspected lower-extremity peri-TOFA infection at a specialty orthopedic practice and tertiary referral hospital in a major urban center. The diagnosis of infection was defined as positive intraoperative cultures. Preoperative patient history (fevers, subjective pain, increased drainage), physician examination findings (local cellulitis, purulent discharge, implant looseness), and laboratory data (white blood cell count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and external swab culture) were evaluated for association with subsequent positive intraoperative cultures using regression and area under receiver-operator curve (AUC) modeling. Results: Peri-implant limb pain (highly correlated with infection), ESR >30 (highly correlated against infection), positive preoperative swab (moderately correlated with infection), gross implant motion (moderately correlated against infection), and erythema or cellulitis of the transcutaneous region (mildly correlated with infection) were variables included in the best AUC model, which achieved an 85 % positive predictive value. Other clinical findings and laboratory values (notably CRP and WBC) were non-predictive of infection. Conclusions: This seminal investigation to develop a preoperative diagnostic algorithm for peri-TOFA infection suggests that the clinical examination remains paramount. Further evaluation of a wider spectrum of clinical, laboratory, and imaging data, consistently and routinely collected with prospective data techniques in larger cohorts of patients, is necessary to create a robust predictive algorithm.

Project description:Posttraumatic epilepsy (PTE) is a significant complication following traumatic brain injury (TBI), yet the role of genetic variation in modulating PTE onset is unclear. We hypothesized that TBI-induced inflammation likely contributes to seizure development. We assessed whether genetic variation in the interleukin-1beta (IL-1?) gene, IL-1? levels in cerebrospinal fluid (CSF) and serum, and CSF/serum IL-1? ratios would predict PTE development post-TBI.We investigated PTE development in 256 Caucasian adults with moderate-to-severe TBI. IL-1? tagging and functional single nucleotide polymorphisms (SNPs) were genotyped. Genetic variance and PTE development were assessed. Serum and CSF IL-1? levels were collected from a subset of subjects (n = 59) during the first week postinjury and evaluated for their associations with IL-1? gene variants, and also PTE. Temporally matched CSF/serum IL-1? ratios were also generated to reflect the relative contribution of serum IL-1? to CSF IL-1?.Multivariate analysis showed that higher CSF/serum IL-1? ratios were associated with increased risk for PTE over time (p = 0.008). Multivariate analysis for rs1143634 revealed an association between the CT genotype and increased PTE risk over time (p = 0.005). The CT genotype group also had lower serum IL-1? levels (p = 0.014) and higher IL-1? CSF/serum ratios (p = 0.093).This is the first report implicating IL-1? gene variability in PTE risk and linking (1) IL-1? gene variation with serum IL-1? levels observed after TBI and (2) IL-1? ratios with PTE risk. Given these findings, we propose that genetic and IL-1? ratio associations with PTE may be attributable to biologic variability with blood-brain barrier integrity during TBI recovery. These results provide a rationale for further studies (1) validating the impact of genetic variability on IL-1? production after TBI, (2) assessing genetically mediated signaling mechanisms that contribute to IL-1? CSF/serum associations with PTE, and (3) evaluating targeted IL-1? therapies that reduce PTE.

Dataset Information

Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.

Publications

Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.

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