Coagulopathy as prognostic marker in acute traumatic brain injury

Chhabra, Gaurav; Sharma, Swati; Subramanian, Arulselvi; Agrawal, Deepak; Sinha, Sumit; Mukhopadhyay, Ayan

doi:10.4103/0974-2700.115332

Cited by 40 publications

(10 citation statements)

References 24 publications

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“…utilized similar criteria and observed the incidence to be 46%. [ 30 ] In our study, development of coagulopathy was associated with longer ICU stay; it correlates with the literature. [ 31 ] The presence of TBI coagulopathy in our study was associated with 3.13-fold increased risk of mortality which is contrary to the results by Affonseca et al .…”

Section: Discussionsupporting

confidence: 90%

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Non- Neurological Complications after Traumatic Brain Injury: A Prospective Observational Study

Goyal

Hazarika

Khandelwal

et al. 2018

Indian Journal of Critical Care Medicine

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Introduction and Aims:Recognizing and treating nonneurological complications occurring in traumatic brain injury (TBI) patients during intensive care unit (ICU) stay are challenging. The aim is to estimate various nonneurological complications in TBI patients. The secondary aim is to see the effect of these complications on ICU stay, disability, and mortality.Materials and Methods:This was a prospective observational study at the neuro-ICU of a Level-I trauma center. A total of 154 TBI patients were enrolled. The period of the study was from admission to discharge from ICU or demise. Inclusion criteria were patients aged >16 years and patients with severe TBI (Glasgow coma score [GCS] ≤8). Nonneurological complications were frequent in TBI patients.Results:We observed respiratory complications to be the most common (61%). Other complications, in the decreasing order, included dyselectrolytemia (46.1%), cardiovascular (34.4%), coagulopathy (33.1%), sepsis (26%), abdominal complications (17.5%), and acute kidney injury (AKI, 3.9%). The presence of systemic complications except AKI was found to be significantly associated with increased ICU stay. Most of the patients of AKI died early in ICU. Respiratory dysfunction was found to be independently associated with 3.05 times higher risk of worsening clinical condition (disability) (P < 0.018). The presence of cardiovascular complications during ICU stay (4.2 times, P < 0.005), AKI (24.7 times, P < 0.02), coagulopathy (3.13 times, P < 0.047), and GCS <6 (4.2 times, P < 0.006) of TBI was independently associated with significantly increased risk of ICU mortality.Conclusion:TBI patients tend to have poor outcome due to concomitant nonneurological complications. These have significant bearing on ICU stay, disability, and mortality.

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Section: Discussionsupporting

confidence: 90%

“…Reported incidence of coagulopathy in TBI patients ranges from 10% to 97%. [ 24 25 26 27 28 29 30 ] Our study suggests 33%. The wide variation is due to different criteria used by different authors.…”

Section: Discussionmentioning

confidence: 68%

Non- Neurological Complications after Traumatic Brain Injury: A Prospective Observational Study

Goyal

Hazarika

Khandelwal

et al. 2018

Indian Journal of Critical Care Medicine

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“…reported GCS <8, effaced cistern, Hb <10 g/dl, and D-dimer >1 mg/dl strongly predict the development of coagulopathy. [ 19 ] Similarly, we also found Hb <10 g/dl is a significant predictor of coagulopathy among the adult TBI group but not in the pediatric TBI group. In our study, we also observed that leukocytosis (total lung capacity >11,000) is strongly correlated with coagulopathy among the adult population, but same does not hold true for the pediatric group.…”

Section: Discussionsupporting

confidence: 65%

Comparative study of derangement of coagulation profile between adult and pediatric population in moderate to severe traumatic brain injury: A prospective study in a tertiary care trauma center

2018

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Object:Coagulopathy is a common occurrence following traumatic brain injury (TBI). There are various studies showing incidence and risk factors of coagulopathy and their correlation with poor outcome in adult as well as paediatric age groups. Exact incidence, associated risk factors, treatment guideline for coagulopathy and its impact on outcome are still lacking. In our study we compared the adults and paediatric age groups TBI patients for incidence and risk factors of coagulopathy and its impact on outcome.Methods:Prospective study of 200 patients including 152 adult patients (age > 18 years) and 48 paediatric (Age < 18 years) patients of TBI admitted in intensive care unit of trauma centre of a tertiary care centre was performed from august 2015 to march 2016. Both population were further subdivided into moderate TBI and severe TBI as per Glasgow coma score (GCS). Patient with long bone injury, chest injury and abdominal injuries, coagulation disorder, liver disease, medical disease like diabetes mellitus and hypertension were excluded from study. Coagulation profile were compared in the both groups (Adult and paediatric) and correlated with the outcome. Chi- Square test, student t test and Odds ratios were used for statistical analysis.Results:Mean age among the adult and paediatric population were 37.89 ± 11.88 years and 11.41 ± 5.90, respectively. Among the patient with moderate TBI, coagulopathy was seen in 30% patients of adult TBI whereas it was 12.5% among the paediatric TBI (P = 0.185). Among the severe TBI group coagulopathy was observed in 68.03% and 37.5% of adult and paediatric age group respectively (P = 0.0016). There was significant correlation found between midline shift and coagulopathy in the paediatric age group (P = 0.022; OR - 4.58). E. There was significant association of coagulopathy and contusion on CT scan among the adult population (P = 0.007; OR - 3.487) found whereas no such correlation were observed in paediatric population.Conclusion:Coagulopathy was significantly higher among the adult patient with severe TBI as compare to paediatric patient with severe TBI. There was no statistically significant difference in mortality among patients of both the age groups with coagulopathy.

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“…Other researches have already focused on the relation between partial thromboplastin time test and TBI prognosis. In 2013, Chhabra et al reinforced the high incidence of coagulopathy following TBI and demonstrated that the presence of coagulopathy is a strong predictor of in-hospital mortality (29). In agreement with these results, a recent article by Yuan et al revealed that coagulation tests could improve the predictive power of the standard models for in-hospital mortality after TBI (30).…”

Section: Discussionmentioning

confidence: 90%

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population

et al. 2020

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Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene.Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members.Level of evidence: This observational study provides a level IV evidence on prognosis after TBI.

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Coagulopathy as prognostic marker in acute traumatic brain injury

Cited by 40 publications

References 24 publications

Non- Neurological Complications after Traumatic Brain Injury: A Prospective Observational Study

Non- Neurological Complications after Traumatic Brain Injury: A Prospective Observational Study

Comparative study of derangement of coagulation profile between adult and pediatric population in moderate to severe traumatic brain injury: A prospective study in a tertiary care trauma center

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population

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