scholarly journals Effect of primary decompressive craniectomy on outcomes in severe traumatic brain injury with mass lesions and the independent predictors for operation decision

2020 ◽  
Author(s):  
Chen Yang ◽  
Jia-Rui Zhang ◽  
Gang Zhu ◽  
Hao Guo ◽  
Fei Gao ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Decompressive Craniectomy ◽  
Pupillary Light Reflex ◽  
Mass Lesion ◽  
Light Reflex ◽  
Pupillary Light ◽  
Mass Lesions ◽  
Basal Cisterns

Abstract Background: Although operative indications for traumatic brain injury (TBI) have been evaluated, neurosurgeons often face a dilemma of whether or not to remove the bone flap after mass lesion evacuation, and a useful predictive scoring model for which patients should be decompressive craniectomy (DC) has yet to be developed. The aim of this study was firstly to compare the outcomes of craniotomy and DC, and secondly to determine independent predictors and develop a multivariate logistic regression equation to determine whom should perform primary DC in TBI patients with mass lesions.Methods: A total of nine different variables were evaluated. All 245 patients with severe TBI in this study were retrospectively evaluated between June 2015 and May 2019 and all underwent decompressive craniectomy (DC) or craniotomy for mass lesion removal. The 6-month mortality and Extended Glasgow Outcome Scale (GOSE) were compared between DC and craniotomy. By using univariate, multiple logistic regression and prognostic regression scoring equations it was possible to draw Receiver Operating Characteristic curves (ROC) to predict the decision for DC.Results: The overall 6-month mortality in the entire cohort was 11.43% (28/245). DC patients had a lower mean preoperative Glasgow Coma Scale (GCS) (p = 0.01); more patients with GCS of 6 (p=0.007);more unresponsive pupillary light reflex (p< 0.001); more closed basal cisterns (p< 0.001); and more patients with diffuse injury (p=0.025) than craniotomy patients. Given the greater severity, patients undergoing primary DC had higher 6-month mortality than the remainder of the cohort. However, in the surviving patients, the favorable GOSE rate was similar in two groups. We found that pupillary light reflex and basal cisterns were independent predictors for DC decision. Using ROC curve to predict the probability of DC, the sensitivity was 81.6% and the specificity was 84.9%.Conclusion: Our preliminary findings showed that the primary DC may benefit subgroups of sTBI with mass lesions, and unresponsive pre-op pupil reaction, and closed basal cistern to predict the DC decision were useful. These sensitive variables can be used as a referential guideline in our daily practice to decide to perform or avoid primary DC.

scholarly journals Contribution of pupillary light reflex assessment to Glasgow Coma Scale for prognostication in patients with traumatic brain injury

2021 ◽  
Author(s):  
Amna A. Butt ◽  
Folefac D. Atem ◽  
Sonja E. Stutzman ◽  
Venkatesh Aiyagari ◽  
Aardhra M. Venkatachalam ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Glasgow Coma Scale ◽  
Pupillary Light Reflex ◽  
Coma Scale ◽  
Light Reflex ◽  
Pupillary Light

scholarly journals Factors associated with shunt-dependent hydrocephalus after decompressive craniectomy for traumatic brain injury

2018 ◽  
Vol 128 (5) ◽  
pp. 1547-1552 ◽  
Author(s):  
Aditya Vedantam ◽  
Jose-Miguel Yamal ◽  
Hyunsoo Hwang ◽  
Claudia S. Robertson ◽  
Shankar P. Gopinath
Keyword(s):  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Decompressive Craniectomy ◽  
Controlled Trial ◽  
Statistical Significance ◽  
Ventriculoperitoneal Shunting ◽  
Younger Age ◽  
Frontal Horn ◽  
Severe Tbi

OBJECTIVEPosttraumatic hydrocephalus (PTH) affects 11.9%–36% of patients undergoing decompressive craniectomy (DC) and is an important cause of morbidity after traumatic brain injury (TBI). Early diagnosis and treatment of PTH can prevent further neurological compromise in patients who are recovering from TBI. There is limited data on predictors of shunting for PTH after DC for TBI.METHODSProspectively collected data from the erythropoietin severe TBI randomized controlled trial were studied. Demographic, clinical, and imaging data were analyzed for enrolled patients who underwent a DC. All head CT scans during admission were reviewed and assessed for PTH by the Gudeman criteria or the modified Frontal Horn Index ≥ 33%. The presence of subdural hygromas was categorized as unilateral/bilateral hemispheric or interhemispheric. Using L1-regularized logistic regression to select variables, a multiple logistic regression model was created with ventriculoperitoneal shunting as the binary outcome. Statistical significance was set at p < 0.05.RESULTSA total of 60 patients who underwent DC were studied. Fifteen patients (25%) underwent placement of a ventriculoperitoneal shunt for PTH. The majority of patients underwent unilateral decompressive hemicraniectomy (n = 46, 77%). Seven patients (12%) underwent bifrontal DC. Unilateral and bilateral hemispheric hygromas were noted in 31 (52%) and 7 (11%) patients, respectively. Interhemispheric hygromas were observed in 19 patients (32%). The mean duration from injury to first CT scan showing hemispheric subdural hygroma and interhemispheric hygroma was 7.9 ± 6.5 days and 14.9 ± 11.7 days, respectively. The median duration from injury to shunt placement was 43.7 days. Multivariate analysis showed that the presence of interhemispheric hygroma (OR 63.6, p = 0.001) and younger age (OR 0.78, p = 0.009) were significantly associated with the need for a shunt after DC.CONCLUSIONSThe presence of interhemispheric subdural hygromas and younger age were associated with shunt-dependent hydrocephalus after DC in patients with severe TBI.

scholarly journals Death after discharge: prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wenxing Cui ◽  
Shunnan Ge ◽  
Yingwu Shi ◽  
Xun Wu ◽  
Jianing Luo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Random Forest ◽  
Decompressive Craniectomy ◽  
Prognostic Model ◽  
Mortality Prediction ◽  
Random Tree ◽  
Prognostic Models ◽  
Biochemical Tests

Abstract Background Despite advances in decompressive craniectomy (DC) for the treatment of traumatic brain injury (TBI), these patients are at risk of having a poor long-term prognosis. The aim of this study was to predict 1-year mortality in TBI patients undergoing DC using logistic regression and random tree models. Methods This was a retrospective analysis of TBI patients undergoing DC from January 1, 2015, to April 25, 2019. Patient demographic characteristics, biochemical tests, and intraoperative factors were collected. One-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were used to evaluate model performance. Results Of the 230 patients, 70 (30.4%) died within 1 year. Older age (OR, 1.066; 95% CI, 1.045–1.087; P < 0.001), higher Glasgow Coma Score (GCS) (OR, 0.737; 95% CI, 0.660–0.824; P < 0.001), higher d-dimer (OR, 1.005; 95% CI, 1.001–1.009; P = 0.015), coagulopathy (OR, 2.965; 95% CI, 1.808–4.864; P < 0.001), hypotension (OR, 3.862; 95% CI, 2.176–6.855; P < 0.001), and completely effaced basal cisterns (OR, 3.766; 95% CI, 2.255–6.290; P < 0.001) were independent predictors of 1-year mortality. Random forest demonstrated better performance for 1-year mortality prediction, which achieved an overall accuracy of 0.810, sensitivity of 0.833, specificity of 0.800, and AUC of 0.830 on the testing data compared to the logistic regression model. Conclusions The random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC. Further external tests are required to verify our prognostic model.

scholarly journals Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy

2016 ◽  
Vol 124 (6) ◽  
pp. 1640-1645 ◽  
Author(s):  
Kenji Fujimoto ◽  
Masaki Miura ◽  
Tadahiro Otsuka ◽  
Jun-ichi Kuratsu
Keyword(s):  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Decompressive Craniectomy ◽  
Roc Curve ◽  
Area Under The Curve ◽  
Prognostic Indicator ◽  
Unfavorable Outcome ◽  
Multivariable Logistic Regression Analysis ◽  
The Relationship

OBJECT Rotterdam CT scoring is a CT classification system for grouping patients with traumatic brain injury (TBI) based on multiple CT characteristics. This retrospective study aimed to determine the relationship between initial or preoperative Rotterdam CT scores and TBI prognosis after decompressive craniectomy (DC). METHODS The authors retrospectively reviewed the medical records of all consecutive patients who underwent DC for nonpenetrating TBI in 2 hospitals from January 2006 through December 2013. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the relationship between initial or preoperative Rotterdam CT scores and mortality at 30 days or Glasgow Outcome Scale (GOS) scores at least 3 months after the time of injury. Unfavorable outcomes were GOS Scores 1–3 and favorable outcomes were GOS Scores 4 and 5. RESULTS A total of 48 cases involving patients who underwent DC for TBI were included in this study. Univariate analyses showed that initial Rotterdam CT scores were significantly associated with mortality and both initial and preoperative Rotterdam CT scores were significantly associated with unfavorable outcomes. Multivariable logistic regression analysis adjusted for established predictors of TBI outcomes showed that initial Rotterdam CT scores were significantly associated with mortality (OR 4.98, 95% CI 1.40–17.78, p = 0.01) and unfavorable outcomes (OR 3.66, 95% CI 1.29–10.39, p = 0.02) and preoperative Rotterdam CT scores were significantly associated with unfavorable outcomes (OR 15.29, 95% CI 2.50–93.53, p = 0.003). ROC curve analyses showed cutoff values for the initial Rotterdam CT score of 5.5 (area under the curve [AUC] 0.74, 95% CI 0.59–0.90, p = 0.009, sensitivity 50.0%, and specificity 88.2%) for mortality and 4.5 (AUC 0.71, 95% CI 0.56–0.86, p = 0.02, sensitivity 62.5%, and specificity 75.0%) for an unfavorable outcome and a cutoff value for the preoperative Rotterdam CT score of 4.5 (AUC 0.81, 95% CI 0.69–0.94, p < 0.001, sensitivity 90.6%, and specificity 56.2%) for an unfavorable outcome. CONCLUSIONS Assessment of changes in Rotterdam CT scores over time may serve as a prognostic indicator in TBI and can help determine which patients require DC.

scholarly journals Death After Discharge: Prognostic Model of 1-year Mortality in Traumatic Brain Injury Patients Undergoing Decompressive Craniectomy

2020 ◽  
Author(s):  
Wenxing Cui ◽  
Shunnan Ge ◽  
Yingwu Shi ◽  
Xun Wu ◽  
Jianing Luo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Random Forest ◽  
Decompressive Craniectomy ◽  
Prognostic Model ◽  
Mortality Prediction ◽  
Random Tree ◽  
Prognostic Models ◽  
Biochemical Tests

Abstract OBJECTIVE: Despite advances in decompressive craniectomy (DC) for the treatment of traumatic brain injury (TBI), a high risk of poor long-term prognosis exists in these patients. The aim of this study is to predict 1-year mortality in TBI patients undergoing DC using the logistic regression and random tree models.METHODS: This was a retrospective analysis of TBI patients undergoing DC from January 1, 2015 to April 25, 2019. Patient demographic characteristics, biochemical tests and intraoperative factors were collected. 1-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms. Overall accuracy, sensitivity, specificity and area under the receiver operating characteristic curves (AUC) were used to evaluate model performance.RESULTS: Of the 230 patients, 70 (30.4%) died within 1 year. Older age (OR, 1.066; 95% CI, 1.045-1.087; P < 0.001), higher Glasgow coma score (GCS) (OR, 0.737; 95% CI, 0.660-0.824; P < 0.001), higher d-dimer (OR, 1.005; 95% CI, 1.001-1.009; P = 0.015), coagulopathy (OR, 2.965; 95% CI, 1.808-4.864; P < 0.001), hypotension (OR, 3.862; 95% CI, 2.176-6.855; P < 0.001) and completely effaced basal cisterns (OR, 3.766; 95% CI, 2.255-6.290; P < 0.001) were independent predictors of 1-year mortality. Random forest demonstrated better performance for 1-year mortality prediction, which achieved overall accuracy of 0.810, sensitivity of 0.833, specificity of 0.800, and AUC of 0.830 on the testing data, compared to logistic regression model.CONCLUSIONS: Random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC. Further external test is required to verify our prognostic model.

Sign in / Sign up

Export Citation Format

Share Document