Predictive statistical diagnosis to determine the probability of survival in adult subjects with traumatic brain injury

SALEH, Mohammed, SAATCHI, Reza, LECKY, Fiona and BURKE, Derek (2018). Predictive statistical diagnosis to determine the probability of survival in adult subjects with traumatic brain injury. Technologies, 6 (41), 1-16.

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Official URL: http://www.mdpi.com/2227-7080/6/2/41
Link to published version:: 10.3390/technologies6020041

Abstract

Determining the probability of survival after injury is important as it can inform triage, clinical research and audit. A number of methods have been reported for determining the probability of survival after injury. However, these have shortcomings and thus further developments are needed to improve their reliability and accuracy. In this study, a Bayesian method called Predictive Statistical Diagnosis (PSD) was developed to determine probability of survival in 4124 adults (age: mean = 67.9 years, standard deviation = 21.6 years) with traumatic brain injuries (TBI). In total, 86.2% of cases had survived and 13.8% of cases had not survived their injuries. The parameters considered as inputs to PSD were age, abbreviated injury score (AIS), Glasgow coma score (GCS), pulse rate (PR), systolic blood pressure (SBP) and respiration rate (RR). PSD statistically modeled the TBI cases and their associated injury outcomes, i.e., survived or not survived. The model was calibrated on randomly selected, roughly 2/3 (number 2676), of the cases and its performance was validated on the remaining cases (number 1448, i.e. validation dataset). The effectiveness of PSD in determining the probability of survival was compared with a method called Ps14 that uses regression modeling. With all parameters (i.e., age, AIS, GCS, SBP, RR and PR) included as inputs to PSD, it correctly identified 90.8% of survivors and 50.0% of non-survivors in the validation dataset while Ps14 identified 97.4% of survivors and 40.2% of non-survivors in the validation dataset. When age, AIS and GCS were used on their own as inputs to PSD, it correctly identified 82.4% of the survivors and 65.0% of non-survivors in the validation dataset. Age affected the performance of PSD in determining the survival outcomes. The number of non-surviving cases included in this study may have not been sufficiently high to indicate the full potential of PSD and a further study with a larger number of cases would be beneficial.

Item Type: Article
Additional Information: This article belongs to the Special Issue Selected Papers from AAATE2017 Congress From: kelly.tan@mdpi.com To: Reza Saatchi Subject: [Technologies] Manuscript ID: technolgies-280777- accepted for publication Date: 04 04 2018 Dear Prof. Saatchi, We are pleased to inform you that the following paper has been officially accepted for publication: Manuscript ID: technologies-280777 Type of manuscript: Article Title: Predictive Statistical Diagnosis to Determine Probability of Survival in Adult Subjects with Traumatic Injury Authors: Mohammed Saleh, Reza Saatchi *, Fiona Lecky, Derek Burke Received: 24 February 2018 E-mails: b3046810@my.shu.ac.uk, r.saatchi@shu.ac.uk, f.e.lecky@sheffield.ac.uk, Derek.Burke@sch.nhs.uk Submitted to section: Assistive Technologies, http://www.mdpi.com/journal/technologies/sections/assistive Selected Papers from AAATE2017 Congress http://www.mdpi.com/journal/technologies/special_issues/aaate2017 http://susy.mdpi.com/user/manuscripts/review_info/70bed77c338532c40f0a64182fde1c48 We will now make the final preparations for publication, then return the manuscript to you for your approval. Kind regards, Ms. Kelly Tan Assistant Editor E-Mail: kelly.tan@mdpi.com MDPI Technologies Editorial Office MDPI Branch Office, Wuhan Tel. +027 878 086 58 Fax +027 876 125 88 E-Mail: technologies@mdpi.com http://www.mdpi.com/journal/technologies/ MDPI Postfach, CH-4020 Basel, Switzerland Office: St. Alban-Anlage 66, 4052 Basel Tel. +41 61 683 77 34; Fax: +41 61 302 89 18 Website: www.mdpi.com
Uncontrolled Keywords: Traumatic brain injury, probability of survival, predictive statistical diagnosis, Bayesian modelling
Research Institute, Centre or Group: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Mobile Machine and Vision Laboratory
Departments: Arts, Computing, Engineering and Sciences > Engineering and Mathematics
Identification Number: 10.3390/technologies6020041
Related URLs:
Depositing User: Reza Saatchi
Date Deposited: 11 Apr 2018 13:06
Last Modified: 11 Apr 2018 13:13
URI: http://shura.shu.ac.uk/id/eprint/19149

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