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

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Abstract

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.

Related collections

Most cited references 39

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Estimating the global incidence of traumatic brain injury

Michael Dewan, Abbas Rattani, Saksham Gupta … (2018)

Traumatic brain injury (TBI)—the “silent epidemic”—contributes to worldwide death and disability more than any other traumatic insult. Yet, TBI incidence and distribution across regions and socioeconomic divides remain unknown. In an effort to promote advocacy, understanding, and targeted intervention, the authors sought to quantify the case burden of TBI across World Health Organization (WHO) regions and World Bank (WB) income groups. Open-source epidemiological data on road traffic injuries (RTIs) were used to model the incidence of TBI using literature-derived ratios. First, a systematic review on the proportion of RTIs resulting in TBI was conducted, and a meta-analysis of study-derived proportions was performed. Next, a separate systematic review identified primary source studies describing mechanisms of injury contributing to TBI, and an additional meta-analysis yielded a proportion of TBI that is secondary to the mechanism of RTI. Then, the incidence of RTI as published by the Global Burden of Disease Study 2015 was applied to these two ratios to generate the incidence and estimated case volume of TBI for each WHO region and WB income group. Relevant articles and registries were identified via systematic review; study quality was higher in the high-income countries (HICs) than in the low- and middle-income countries (LMICs). Sixty-nine million (95% CI 64–74 million) individuals worldwide are estimated to sustain a TBI each year. The proportion of TBIs resulting from road traffic collisions was greatest in Africa and Southeast Asia (both 56%) and lowest in North America (25%). The incidence of RTI was similar in Southeast Asia (1.5% of the population per year) and Europe (1.2%). The overall incidence of TBI per 100,000 people was greatest in North America (1299 cases, 95% CI 650–1947) and Europe (1012 cases, 95% CI 911–1113) and least in Africa (801 cases, 95% CI 732–871) and the Eastern Mediterranean (897 cases, 95% CI 771–1023). The LMICs experience nearly 3 times more cases of TBI proportionally than HICs. Sixty-nine million (95% CI 64–74 million) individuals are estimated to suffer TBI from all causes each year, with the Southeast Asian and Western Pacific regions experiencing the greatest overall burden of disease. Head injury following road traffic collision is more common in LMICs, and the proportion of TBIs secondary to road traffic collision is likewise greatest in these countries. Meanwhile, the estimated incidence of TBI is highest in regions with higher-quality data, specifically in North America and Europe.

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Edema and brain trauma.

A. W. Unterberg, J. Stover, B Kress … (2004)

Brain edema leading to an expansion of brain volume has a crucial impact on morbidity and mortality following traumatic brain injury (TBI) as it increases intracranial pressure, impairs cerebral perfusion and oxygenation, and contributes to additional ischemic injuries. Classically, two major types of traumatic brain edema exist: "vasogenic" due to blood-brain barrier (BBB) disruption resulting in extracellular water accumulation and "cytotoxic/cellular" due to sustained intracellular water collection. A third type, "osmotic" brain edema is caused by osmotic imbalances between blood and tissue. Rarely after TBI do we encounter a "hydrocephalic edema/interstitial" brain edema related to an obstruction of cerebrospinal fluid outflow. Following TBI, various mediators are released which enhance vasogenic and/or cytotoxic brain edema. These include glutamate, lactate, H(+), K(+), Ca(2+), nitric oxide, arachidonic acid and its metabolites, free oxygen radicals, histamine, and kinins. Thus, avoiding cerebral anaerobic metabolism and acidosis is beneficial to control lactate and H(+), but no compound inhibiting mediators/mediator channels showed beneficial results in conducted clinical trials, despite successful experimental studies. Hence, anti-edematous therapy in TBI patients is still symptomatic and rather non-specific (e.g. mannitol infusion, controlled hyperventilation). For many years, vasogenic brain edema was accepted as the prevalent edema type following TBI. The development of mechanical TBI models ("weight drop," "fluid percussion injury," and "controlled cortical impact injury") and the use of magnetic resonance imaging, however, revealed that "cytotoxic" edema is of decisive pathophysiological importance following TBI as it develops early and persists while BBB integrity is gradually restored. These findings suggest that cytotoxic and vasogenic brain edema are two entities which can be targeted simultaneously or according to their temporal prevalence.

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Author and article information

Contributors

Ayman El-Menyar:

ORCID: http://orcid.org/0000-0003-2584-953X

aymanco65@yahoo.com

Journal

Journal ID (nlm-ta): Scand J Trauma Resusc Emerg Med

Journal ID (iso-abbrev): Scand J Trauma Resusc Emerg Med

Title: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1757-7241

Publication date (Electronic): 27 May 2020

Publication date PMC-release: 27 May 2020

Publication date Collection: 2020

Volume: 28

Electronic Location Identifier: 44

Affiliations

[1 ]GRID grid.413548.f, ISNI 0000 0004 0571 546X, Assistant Executive Director of Nursing, , Hamad Medical Corporation, ; Doha, Qatar

[2 ]GRID grid.412603.2, ISNI 0000 0004 0634 1084, College of Business and Economics, Management Information Systems, Qatar University, ; Doha, Qatar

[3 ]GRID grid.170430.1, ISNI 0000 0001 2159 2859, Industrial Engineering, , University of Central Florida, ; Orlando, USA

[4 ]GRID grid.413548.f, ISNI 0000 0004 0571 546X, Department of Surgery, Trauma Surgery, , Hamad Medical Corporation, ; Doha, Qatar

[5 ]GRID grid.413548.f, ISNI 0000 0004 0571 546X, Department of Surgery, Trauma Surgery, Clinical Research, , Hamad Medical Corporation, ; Doha, Qatar

[6 ]GRID grid.416973.e, ISNI 0000 0004 0582 4340, Department of Clinical Medicine, , Weill Cornell Medical College Hamad General Hospital, ; Doha, Qatar

Author information

Ayman El-Menyar http://orcid.org/0000-0003-2584-953X

Article

Publisher ID: 738

DOI: 10.1186/s13049-020-00738-5

PMC ID: 7251921

PubMed ID: 32460867

SO-VID: a443f796-f6a2-4540-a920-f690f9595dbd

License:

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