Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach.
Ontology highlight
ABSTRACT:
SUBMITTER: Podell J
PROVIDER: S-EPMC9829683 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach.
Podell Jamie J Yang Shiming S Miller Serenity S Felix Ryan R Tripathi Hemantkumar H Parikh Gunjan G Miller Catriona C Chen Hegang H Kuo Yi-Mei YM Lin Chien Yu CY Hu Peter P Badjatia Neeraj N
Scientific reports 20230109 1
Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hos ...[more]
Similar Datasets
Project description:BackgroundIdentifying patients at risk of secondary neurologic deterioration (SND) after moderate traumatic brain injury (moTBI) is a challenge, as such patients will need specific care. No simple scoring system has been evaluated to date. This study aimed to determine clinical and radiological factors associated with SND after moTBI and to propose a triage score.MethodsAll adults admitted in our academic trauma center between January 2016 and January 2019 for moTBI (Glasgow Coma Scale [GCS] score, 9-13) were eligible. SND during the first week was defined either by a decrease in GCS score of >2 points from the admission GCS in the absence of pharmacologic sedation or by a deterioration in neurologic status associated with an intervention, such as mechanical ventilation, sedation, osmotherapy, transfer to the intensive care unit (ICU), or neurosurgical intervention (for intracranial mass lesions or depressed skull fracture). Clinical, biological, and radiological independent predictors of SND were identified by logistic regression (LR). An internal validation was performed using a bootstrap technique. A weighted score was defined based on beta (β) coefficients of the LR.ResultsA total of 142 patients were included. Forty-six patients (32%) showed SND, and 14-day mortality rate was 18.4%. Independent variables associated with SND were age above 60 years (odds ratio [OR], 3.45 [95% confidence interval {CI}, 1.45-8.48]; P = .005), brain frontal contusion (OR, 3.22 [95% CI, 1.31-8.49]; P = .01), prehospital or admission arterial hypotension (OR, 4.86 [95% CI, 2.03-12.60]; P = .006), and a Marshall computed tomography (CT) score of 6 (OR, 3.25 [95% CI, 1.31-8.20]; P = .01). The SND score was defined with a range from 0 to 10. The score included the following variables: age >60 years (3 points), prehospital or admission arterial hypotension (3 points), frontal contusion (2 points), and Marshall CT score of 6 (2 points). The score was able to detect patients at risk of SND, with an area under the receiver operating characteristic curve (AUC) of 0.73 (95% CI, 0.65-0.82). A score of 3 had a sensitivity of 85%, a specificity of 50%, a VPN of 87%, and a VPP of 44 % to predict SND.ConclusionsIn this study, we demonstrate that moTBI patients have a significant risk of SND. A simple weighted score at hospital admission could be able to detect patients at risk of SND. The use of the score may enable optimization of care resources for these patients.
| S-EPMC10699506 | biostudies-literature
Project description:Background: In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently impact outcome. We investigated whether continuous vital sign variability and waveform analysis of the electrocardiogram (ECG) or photoplethysmogram (PPG) within the first hour of resuscitation may enhance the ability to predict ND in the initial 48 hours after traumatic brain injury (TBI). Methods: Retrospective analysis of ND in TBI patients enrolled in the prospective Oximetry and Noninvasive Predictors Of Intervention Need after Trauma (ONPOINT) study. ND was defined as any of the following occurring in the first 48 h: new asymmetric pupillary dilatation (>2 mm), 2 point GCS decline, interval worsening of CT scan as assessed by the Marshall score, or intervention for cerebral edema. Beat-to-beat variation of ECG or PPG, as well as waveform features during the first 15 and 60 min after arrival in the TRU were analyzed to determine physiologic parameters associated with future ND. Physiologic and admission clinical variables were combined in multivariable logistic regression models predicting ND and inpatient mortality. Results: There were 33 (17%) patients with ND among 191 patients (mean age 43 years old, GCS 13, ISS 12, 69% men) who met study criteria. ND was associated with ICU admission (P < 0.001) and inpatient mortality (P < 0.001). Both ECG (AUROC: 0.84, 95% CI: 0.76,0.93) and PPG (AUROC: 0.87, 95% CI: 0.80, 0.93) analyses during the first 15 min of resuscitation demonstrated a greater ability to predict ND then clinical characteristics alone (AUROC: 0.69, 95% CI: 0.59, 0.8). Age (P = 0.02), Marshall score (P = 0.001), penetrating injury (P = 0.02), and predictive probability for ND by PPG analysis at 15 min (P = 0.03) were independently associated with inpatient mortality. Conclusions: Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.
| S-EPMC6167472 | biostudies-literature
Project description:Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate. Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population. Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve. Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902-0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)-over other models across a range of threshold probability values. Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).
| S-EPMC8703138 | biostudies-literature
Project description:(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.
| S-EPMC9320220 | biostudies-literature
Project description:In adult rats, erythropoietin improved outcomes early and late after traumatic brain injury, associated with increased levels of Brain Derived Neurotrophic Factor. Using our model of pediatric traumatic brain injury, controlled cortical impact in 17-day old rats, we previously showed that erythropoietin increased hippocampal neuronal fraction in the first two days after injury. Erythropoietin also decreased activation of caspase3, an apoptotic enzyme modulated by Brain Derived Neurotrophic Factor, and improved Novel Object Recognition testing 14 days after injury. Data on long-term effects of erythropoietin on Brain Derived Neurotrophic Factor expression, histology and cognitive function after developmental traumatic brain injury are lacking. We hypothesized that erythropoietin would increase Brain Derived Neurotrophic Factor and improve long-term object recognition in rat pups after controlled cortical impact, associated with increased neuronal fraction in the hippocampus.MethodsRats pups received erythropoietin or vehicle at 1, 24, and 48 h and 7 days after injury or sham surgery followed by histology at 35 days, Novel Object Recognition testing at adulthood, and Brain Derived Neurotrophic Factor measurements early and late after injury.ResultsErythropoietin improved Novel Object Recognition performance and preserved hippocampal volume, but not neuronal fraction, late after injury.ConclusionsImproved object recognition in erythropoietin treated rats was associated with preserved hippocampal volume late after traumatic brain injury. Erythropoietin is approved to treat various pediatric conditions. Coupled with exciting experimental and clinical studies suggesting it is beneficial after neonatal hypoxic ischemic brain injury, our preliminary findings support further study of erythropoietin use after developmental traumatic brain injury.
| S-EPMC5878733 | biostudies-literature
Project description:Background/objectiveMultimodality neurologic monitoring (MMM) is an emerging technique for management of traumatic brain injury (TBI). An increasing array of MMM-derived biomarkers now exist that are associated with injury severity and functional outcomes after TBI. A standardized MMM reporting process has not been well described, and a paucity of evidence exists relating MMM reporting in TBI management with functional outcomes or adverse events.MethodsProspective implementation of standardized MMM reporting at a single pediatric intensive care unit (PICU) is described that included monitoring of intracranial pressure (ICP), cerebral oxygenation and electroencephalography (EEG). The incidence of clinical decisions made using MMM reporting is described, including timing of neuroimaging, ICP monitoring discontinuation, use of paralytic, hyperosmolar and pentobarbital therapies, neurosurgical interventions, ventilator and CPP adjustments and neurologic prognostication discussions. Retrospective analysis was performed on the association of MMM reporting with initial Glasgow Coma Scale (GCS) and Pediatric Risk of Mortality III (PRISM III) scores, duration of total hospitalization and PICU hospitalization, duration of mechanical ventilation and invasive ICP monitoring, inpatient complications, time with ICP > 20 mmHg, time with cerebral perfusion pressure (CPP) < 40 mmHg and 12-month Glasgow Outcome Scale-Extended Pediatrics (GOSE-Peds) scores. Association of outcomes with MMM reporting was investigated using the Wilcoxon rank-sum test or Fisher's exact test, as appropriate.ResultsEighty-five children with TBI underwent MMM over 6 years, among which 18 underwent daily MMM reporting over a 21-month period. Clinical decision-making influenced by MMM reporting included timing of neuroimaging (100.0%), ICP monitoring discontinuation (100.0%), timing of extubation trials of surviving patients (100.0%), body repositioning (11.1%), paralytic therapy (16.7%), hyperosmolar therapy (22.2%), pentobarbital therapy (33.3%), provocative cerebral autoregulation testing (16.7%), adjustments in CPP thresholds (16.7%), adjustments in PaCO2 thresholds (11.1%), neurosurgical interventions (16.7%) and neurologic prognostication discussions (11.1%). The implementation of MMM reporting was associated with a reduction in ICP monitoring duration (p = 0.0017) and mechanical ventilator duration (p = 0.0018). No significant differences were observed in initial GCS or PRISM III scores, total hospitalization length, PICU hospitalization length, total complications, time with ICP > 20 mmHg, time with CPP < 40 mmHg, use of tier 2 therapy, or 12-month GOS-E Peds scores.ConclusionImplementation of MMM reporting in pediatric TBI management is feasible and can be impactful in tailoring clinical decisions. Prospective work is needed to understand the impact of MMM and MMM reporting systems on functional outcomes and clinical care efficacy.
| S-EPMC8012079 | biostudies-literature
Project description:PurposeThis study evaluates the correlation between injuries to deep gray matter nuclei, as quantitated by lesions in these nuclei on MR T2 Fast Spin Echo (T2 FSE) images, with 6-month neurological outcome after severe traumatic brain injury (TBI).Materials and methodsNinety-five patients (80 males, mean age = 36.7y) with severe TBI were prospectively enrolled. All patients underwent a MR scan within the 45 days after the trauma that included a T2 FSE acquisition. A 3D deformable atlas of the deep gray matter was registered to this sequence; deep gray matter lesions (DGML) were evaluated using a semi-quantitative classification scheme. The 6-month outcome was dichotomized into unfavorable (death, vegetative or minimally conscious state) or favorable (minimal or no neurologic deficit) outcome.ResultsSixty-six percent of the patients (63/95) had both satisfactory registration of the 3D atlas on T2 FSE and available clinical follow-up. Patients without DGML had an 89% chance (P = 0.0016) of favorable outcome while those with bilateral DGML had an 80% risk of unfavorable outcome (P = 0.00008). Multivariate analysis based on DGML accurately classified patients with unfavorable neurological outcome in 90.5% of the cases.ConclusionLesions in deep gray matter nuclei may predict long-term outcome after severe TBI with high sensitivity and specificity.
| S-EPMC5667824 | biostudies-literature
Project description:Traumatic brain injury (TBI), which can lead to disability, dysfunction, and even death, is a prominent health problem worldwide. Effective therapy for this serious and debilitating condition is needed. Human umbilical cord matrix, known as Wharton's jelly (WJ), provides a natural, interface scaffold that is enriched in mesenchymal stem cells. In this study, we tested the efficacy of WJ tissue transplantation in a weight-drop model of TBI in rats. WJ tissue was cultured and transplanted into the injury site 24 h after TBI. The modified neurologic severity score, body weight, brain edema, and lesion volume were evaluated at various time points after TBI. Cognitive behavior was assessed by the novel object recognition test and the Morris water maze test. Expression of brain-derived neurotrophic factor (BDNF) in the perilesional brain area was measured at day 14 after TBI. We found that WJ tissue transplantation lessened TBI-induced brain edema (day 3), reduced lesion volume (day 28), improved neurologic function (days 21-28), and promoted memory and cognitive recovery. Additionally, expression of BDNF mRNA and protein was higher in WJ tissue-treated rats than in sham-operated or vehicle-treated rats. These data suggest that WJ tissue transplantation can reduce TBI-induced brain injury and may have therapeutic potential for the treatment of TBI.
| S-EPMC4481175 | biostudies-literature
Project description:ObjectiveThe long-term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals' differences in chronological and predicted structural "brain age," and test whether TBI produces progressive atrophy and how this relates to cognitive function.MethodsA predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging-derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age.ResultsThe initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be "older," with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase.InterpretationTBI patients' brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age-associated conditions, motivating further research into the age-like effects of brain injury and other neurological diseases.
| S-EPMC4403966 | biostudies-literature
Project description:BackgroundCopeptin has been reported as a predictive biomarker for the prognosis after traumatic brain injury (TBI). However, most of them were in patients with severe TBI and limited value in predicting outcomes in patients with moderate TBI defined as Glasgow Coma Scale (GCS) score from 9 to 12. We aimed to investigate the predictive value of copeptin in assessing the neurologic outcome following moderate TBI.MethodsPatients were prospectively enrolled between May 2017 and November 2020. We consecutively measured plasma copeptin within 24 h after trauma, days 3, 5, and 7 using ELISA. The primary outcome was to correlate plasma copeptin levels with poor neurologic outcome at 6 months after moderate TBI. The secondary outcome was to compare the prognostic accuracy of copeptin and C-reactive protein (CRP) in assessing the outcome of patient.ResultsA total of 70 patients were included for the final analysis. The results showed that 29 patients (41.4%) experienced a poor neurologic outcome at 6 months. Multivariable logistic regression analysis revealed that increased copeptin (odds ration [OR] = 1.020, 95% CI: 1.005-1.036), GCS score of 9 or 10 (OR = 4.507, 95% CI: 1.266-16.047), and significant abnormal findings on CT (OR = 4.770; 95% CI: 1.133-20.076) were independent risk factors for poor outcomes. Consecutive plasma copeptin levels were significantly different according to outcomes (p < 0.001). Copeptin on day 7 exhibited better prognostic performance than CRP with an area under receiver operating characteristic curve (AUROC) difference of 0.179 (95% CI: 0.032-0.325) in predicting 6-month poor outcomes.ConclusionPlasma copeptin level can be a useful marker in predicting 6-month outcomes in patients with moderate TBI.
| S-EPMC9081440 | biostudies-literature