Comparison of two simple models for prediction of short term mortality in patients after severe traumatic brain injury

Rached, Mohamed A.K.B.; Gaudet, John G.; Delhumeau, Cécile; Walder, Bernhard

doi:10.1016/j.injury.2018.08.022

Cited by 13 publications

(13 citation statements)

References 39 publications

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“…The early prediction of in-hospital mortality in patients with traumatic brain injury is of utmost importance. Early and powerful prediction of mortality helps clinicians and healthcare managers to optimize the management of medical resources, initiate appropriate diagnostics and interventions in a timely manner, conduct comparative audits and ensure that the patients' families and others receive appropriate guidance [3,6]. However, prediction of disease prognosis and outcomes requires developing good prognostic models that include large samples and enjoy high external and internal validity in order to be generalizable beyond a specific research setting [31].…”

Section: Discussionmentioning

confidence: 99%

“…Many prognostic models were published over the years but only few of them achieved sample validity requirements [32]. Usually, clinicians use certain prognostic factors such as GCS to guide their therapeutic decisions and to estimate prognostic outcomes [3,32]. Nonetheless, such predictors may be affected by several factors such as alcohol use which negatively affects the prediction success and the discrimination power of the model [11,12].…”

Section: Discussionmentioning

confidence: 99%

“…TBI causes death and disabilities more than any other trauma [2]. The life expectancy reduces significantly with TBI as the mortality rate increases between 30 and 70% [1,3] compared to other types of injuries. TBI affects millions of people around the world yearly causing a major global burden [3].…”

Section: Introductionmentioning

confidence: 99%

“…The life expectancy reduces significantly with TBI as the mortality rate increases between 30 and 70% [1,3] compared to other types of injuries. TBI affects millions of people around the world yearly causing a major global burden [3]. Globally, 64-74 million individuals around the world are estimated to sustain TBI every year with the greatest burden of the disease is in Southeast Asian and Western Pacific regions [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Abujaber

Fadlalla

Gammoh

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background: The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI). Methods: Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score. Results: A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%. Conclusions: for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Abujaber

Fadlalla

Gammoh

et al. 2020

Scand J Trauma Resusc Emerg Med

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“…The HAIS is principally based on initial CT imaging findings. A prediction model based on: HAIS, pupil reaction, and age, was not inferior when compared with a prediction based on the IMPACT core model [53]. This new model based on HAIS may be used in the case of an invalid or absent GCS.…”

Section: Prehospital and Emergency Carementioning

confidence: 99%

Prehospital and Emergency Care in Adult Patients with Acute Traumatic Brain Injury

2019

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Traumatic brain injury (TBI) is a major healthcare problem and a major burden to society. The identification of a TBI can be challenging in the prehospital setting, particularly in elderly patients with unobserved falls. Errors in triage on scene cannot be ruled out based on limited clinical diagnostics. Potential new mobile diagnostics may decrease these errors. Prehospital care includes decision-making in clinical pathways, means of transport, and the degree of prehospital treatment. Emergency care at hospital admission includes the definitive diagnosis of TBI with, or without extracranial lesions, and triage to the appropriate receiving structure for definitive care. Early risk factors for an unfavorable outcome includes the severity of TBI, pupil reaction and age. These three variables are core variables, included in most predictive models for TBI, to predict short-term mortality. Additional early risk factors of mortality after severe TBI are hypotension and hypothermia. The extent and duration of these two risk factors may be decreased with optimal prehospital and emergency care. Potential new avenues of treatment are the early use of drugs with the capacity to decrease bleeding, and brain edema after TBI. There are still many uncertainties in prehospital and emergency care for TBI patients related to the complexity of TBI patterns.

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Mechanical power of ventilation is associated with mortality in neurocritical patients: a cohort study

Jiang

Zhu

Zhen

et al. 2022

J Clin Monit Comput

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This study aimed to determine the predictive relevance of mechanical power in the clinical outcomes (such as ICU mortality, hospital mortality, 90-day mortality, length of ICU stay, and number of ventilator-free days at day 28) of neurocritical patients. This is a retrospective cohort analysis of an open-access clinical database known as MIMIC–III. The study included patients who had sustained an acute brain injury and required invasive ventilation for at least 24 h. Demographic parameters, disease severity scores (Glasgow coma scale), comorbidities, vital signs, laboratory parameters and ventilator parameters were collected within the first 24 h of ICU admission. The main outcome was the relationship between MP and ICU mortality. A total of 529 patients were selected for the study. The critical value of MP was 12.16 J/min, with the area under the curve (AUC) of the MP was 0.678 (95% CI 0.637–0.718), and compared to the GCS scores, the MP performed significantly better in discrimination (DeLong’s test: p < 0.001). Among these patients elevated MP was associated to higher ICU mortality (OR 1.11; 95% CI 1.06–1.17; p < 0.001), enhanced the risk of hospital mortality, prolonged ICU stay, and decreased the number of ventilator-free days. In the subgroup analysis, high MP was associated with ICU mortality regardless of ARDS (OR 1.01, 95% CI 1.00–1.02, p = 0.009; OR 1.01, 95% CI 1.00–1.02, p = 0.018, respectively) or obesity (OR 1.01, 95% CI 1.00–1.02, p = 0.012; OR 1.01, 95% CI 1.01–1.02, p < 0.001, respectively). In neurocritical care patients undergoing invasive ventilation, elevated MP is linked to higher ICU mortality and a variety of other clinical outcomes.

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Comparison of two simple models for prediction of short term mortality in patients after severe traumatic brain injury

Cited by 13 publications

References 39 publications

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Prehospital and Emergency Care in Adult Patients with Acute Traumatic Brain Injury

Mechanical power of ventilation is associated with mortality in neurocritical patients: a cohort study

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