Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Dec 14;20(1):336.
doi: 10.1186/s12911-020-01363-z.

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Ahmad Abujaber  1 , Adam Fadlalla  2 , Diala Gammoh  3 , Husham Abdelrahman  4 , Monira Mollazehi  4 , Ayman El-Menyar  5   6
Affiliations

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Ahmad Abujaber et al. BMC Med Inform Decis Mak. .

Abstract

Background: The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).

Methods: A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance.

Results: Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV.

Conclusions: Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

Keywords: Machine learning predictive model; Mechanical ventilation; Mortality; Traumatic brain injury.

PubMed Disclaimer

Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Summarizes the research methodology
Fig. 2
Fig. 2
Predictors importance in logistic regression
Fig. 3
Fig. 3
Predictors importance in artificial neural networks
See this image and copyright information in PMC

References

    1. Dewan M, Rattani A, Gupta S, Baticulon R, Hung YC, Punchak M, et al. Estimating the global incidence of traumatic brain injury. J Neurosurg. 2018;130(4):1–18. - PubMed
    1. Rached M, Gaudet J, Delhumeau C, Walder B. comparison of two simple models for prediction of short term mortality in patients after severe traumatic brain injury. Injury. 2019;50:65–72. doi: 10.1016/j.injury.2018.08.022. - DOI - PubMed
    1. Andelic N, Anke A, Skandsen T, Sigurdardottir S, Sandhaug M, Ader T, et al. Incidence of hospital-admitted severe traumatic brain injury and in-hospital fatality in norway: a national cohort study. Neuroepidemiology. 2012;38:259–267. doi: 10.1159/000338032. - DOI - PubMed
    1. Walder B, Haller G, Rebetez M, Delhumeau C, Bottequin E, Schoettker P, et al. Severe traumatic brain injury in a high-income country: an epidemiological study. J Neurotrauma. 2013;30(23):1934–1942. doi: 10.1089/neu.2013.2955. - DOI - PubMed
    1. Esteban A, Anzueto A, Frutos F, Alía I, Brochard L, Stewart T, et al. Characteristics and outcomes in adult patients receiving mechanical ventilation A 28-day international study. J Am Med Assoc. 2002;287(3):345–355. doi: 10.1001/jama.287.3.345. - DOI - PubMed