#
Predictive Statistical Diagnosis to Determine the Probability of Survival in Adult Subjects with Traumatic Brain Injury^{ †}

^{1}

^{2}

^{3}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Trauma Scoring Systems

## 3. Approaches to Determine Probability of Survival

_{s}) for adults sustaining traumatic injuries from blunt and penetrating mechanisms [31]. The probability of survival using this system is given by

_{i}is a constant for mechanism i, β

_{AGE,i}is the coefficient related to age and mechanism i, β

_{RTS,i}is the coefficient associated with RTS and mechanism i, β

_{ISS,i}is the coefficient associated with ISS and mechanism i. RTS is obtained by

_{RR}is the coefficient associated with respiration rate (RR), β

_{SBP}is the coefficient associated with systolic blood pressure (SBP), and β

_{GCS}is the coefficient associated with GCS. TRISS however has a number of shortcomings related to calibration of its coefficients, variable inter-relationships or interactions and strong linear assumptions between predictor variable and survival outcome [31,32]. Adjustments to its coefficients result in performance variations by TRISS in predicting probability of survival for trauma patients [33].

_{s}= 53%, then on average 53 out of every 100 patients have survived and 47 patients have not survived.

## 4. Predictive Statistical Diagnosis

**x**) belongs [36,37]. It uses example cases of known types, represented in a training data set to obtain the values of its calibration parameters. Once these parameters are calibrated, it can classify an unknown case into the types represented by t.

**x**and parameter vector

**θ**belongs to the type t

_{1}is given by Bayesian statistics as

_{1}) is the prior probability of type t

_{1}, p(

**x**|t

_{1},

**θ**) is the probability density function of

**x**for a given type t

_{1}. Equation (4) can be rewritten as predictive density function for an observation

**x**on a case of type t assessed on the training data

**Z**as [36,37].

**x**|t

_{1},

**Z**) can be replaced with [37]

_{t}cases of type t with feature vectors

**x**,

_{1}**x**, …

_{2}**x**; v

_{n}_{t}is the degrees of freedom given by n

_{t}− 1,

**m**

_{t}and

**S**

_{t}are the mean and the covariance matrices respectively.

**S**t

_{d}represents a d-dimensional student t density determined as

**b**and

**c**relate to Equation (6) as v = v

_{t}, b =

**m**

_{t}and $\mathit{c}=\left(1+\frac{1}{{n}_{t}}\right){\mathit{S}}_{t}$. $\Gamma $ is the gamma function, T and −1 represent matrix transpose and inversion operations, respectively. Using Equation (5), p(t

_{1}|

**x**,

**θ**) is determined for the cases of known types. Then to compute the probabilities for the unknown cases (i.e., those on the validation data set), Equation (7) uses the observation vector

**x**for cases of known types but retains the mean (

**m**

_{t}) and covariance (

**S**

_{t}) matrices to identify an unknown type. The parameters

**m**

_{t}and

**S**

_{t}are calibration information for the PSD.

## 5. Methodology

## 6. Results and Discussion

_{22153}) is associated with pulse rate = 2 (categorized as normal category), respiration rate = 2 (categorized as normal category), systolic blood pressure = 1 (categorized as abnormal category), AIS = 5 (critical) and GCS = 3 (categorized as mild injury). Only one of the associated cases has been correctly identified by Ps14 however 6 were correctly identified by PSD. There were 24 cases associated with the injury pattern X

_{22151}. For this injury pattern pulse rate = 2 (categorized as normal category), respiration rate = 2 (categorized as normal category), systolic blood pressure = 1 (categorized as abnormal category), AIS = 5 (critical) and GCS = 1 (categorized as severe injury). Ps14 has performed better than PSD by correctly identifying from 21 out of 24 cases while PSD identified 18 cases correctly. For some injury patterns the identification accuracy of the two models (PSD and Ps14) is 0%. An example for this is injury pattern X

_{22143}. This associates with pattern pulse rate = 2 (categorized as normal category), respiration rate = 2 (categorized as normal category), systolic blood pressure = 1 (categorized as abnormal category), AIS = 4, and GCS = 3 (categorized as mild injury). The reason why PSD and Ps14 performance differ or in some injury patterns they fail to identify the outcome correctly requires further investigation.

## 7. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Mullins, R.J. A historical perspective of trauma system development in the United States. J. Trauma Acute Care Surg.
**1999**, 47, S8–S14. [Google Scholar] [CrossRef] - Laytin, A.D.; Dicker, R.A.; Gerdin, M.; Roy, N.; Sarang, B.; Kumar, V.; Juillard, C. Comparing traditional and novel injury scoring systems in a US level-I trauma center: An opportunity for improved injury surveillance in low- and middle-income countries. J. Surg. Res.
**2017**, 215, 60–66. [Google Scholar] [CrossRef] [PubMed] - Dillon, B.; Wang, W.; Mouamra, O. A comparison study of the injury score models. Eur. J. Trauma
**2006**, 6, 538–547. [Google Scholar] [CrossRef] - Wisner, D.H. History and current status of trauma scoring system. Arch. Surg.
**1992**, 127, 111–117. [Google Scholar] [CrossRef] [PubMed] - Kim, Y.J. Injury severity scoring systems: A review of application to practice. Nurs. Crit. Care
**2012**, 17, 138–150. [Google Scholar] [CrossRef] [PubMed] - Fani-Salek, M.H.; Totten, V.Y.; Terezakis, S.A. Trauma scoring systems explained. Emerg. Med. Australas.
**1999**, 11, 155–166. [Google Scholar] [CrossRef] - Meredith, W.; Rutledge, R.; Hansen, A.F. Field triage of trauma patients based upon the ability to follow commands. J. Trauma Inj. Infect. Crit. Care
**1995**, 38, 129–134. [Google Scholar] [CrossRef] - Reith, F.C.M.; Lingsma, H.F.; Gabbe, B.J.; Lecky, F.E.; Roberts, I.; Maas, A.I.R. Differential effects of the Glasgow coma scale score and its components: Analysis of 54,069 patients with traumatic brain injury. Injury
**2017**, 48, 1932–1943. [Google Scholar] [CrossRef] [PubMed] - Moon, J.; Seo, B.; Jang, J.; Lee, J.; Moon, H. Evaluation of probability of survival using trauma and injury severity score method in severe neurotrauma patients. J. Korean Neurosurg. Soc.
**2013**, 54, 42–46. [Google Scholar] [CrossRef] [PubMed] - Chawda, M.N.; Hildebrand, F.; Pape, H.C.; Giannoudis, P.V. Predicting outcome after multiple trauma: Which scoring system? Injury
**2004**, 35, 347–358. [Google Scholar] [CrossRef] - Pike, I.; Khalil, M.; Yanchar, N.L.; Tamim, H.; Nathens, A.B.; Macpherson, A.K. Establishing an injury indicator for severe paediatric injury. Inj. Prev.
**2017**, 23, 118–123. [Google Scholar] [CrossRef] [PubMed] - Kuwabara, K.; Matsuda, S.; Fushimi, K.; Ishikawa, K.B.; Horiguchi, H.; Fujimori, K. Probability of survival, early critical care process, and resource use in trauma patients. Am. J. Emerg. Med.
**2010**, 28, 673–681. [Google Scholar] [CrossRef] [PubMed] - Escobedo, L.V.S.; Habboushe, J.; Kaafarani, H.; Velmahos, G.; Shah, K.; Lee, J. Traumatic brain injury: A case-based review. World J. Emerg. Med.
**2013**, 4, 252–259. [Google Scholar] [CrossRef] [PubMed] - Menon, D.K.; Schwab, K.; Wright, W.D.; Mass, A.I. Position statement: Definition of traumatic brain injury. Arch. Phys. Med. Rehabil.
**2010**, 91, 1637–1640. [Google Scholar] [CrossRef] [PubMed] - Chapman, C.J.; Diaz-Arrastia, R. Military traumatic brain injury: A review. Alzheimers Dement.
**2014**, 10, S97–S104. [Google Scholar] [CrossRef] [PubMed] - Saatchi, R.; Oke, S.; Allen, E.M.; Jervis, B.W.; Hudson, N. Signal processing of the contingent negative variation in schizophrenia using multilayer perceptrons and predictive statistical diagnosis. IEE Proc. Sci. Meas. Technol.
**1995**, 14, 269–277. [Google Scholar] [CrossRef] - Saatchi, R. Single-trial lambda wave identification using a fuzzy inference system and predictive statistical diagnosis. J. Neural Eng.
**2004**, 1, 21–31. [Google Scholar] [CrossRef] [PubMed] - Gennarelli, T.A.; Wodzin, E. AIS 2005: A contemporary injury scale. Injury
**2006**, 37, 1083–1091. [Google Scholar] [CrossRef] [PubMed] - Stevenson, M.; Segui-Gomez, M.; Lescohier, I.; Di Scala, C.; McDonald-Smith, G. An overview of the injury severity score and the new injury severity score. Inj. Prev.
**2001**, 7, 10–13. [Google Scholar] [CrossRef] [PubMed] - Baker, S.P.; O’Neill, B.; Haddon, W.; Long, W. The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. J. Trauma
**1974**, 14, 187–196. [Google Scholar] [CrossRef] [PubMed] - Stoner, H.B.; Barton, R.N.; Little, R.A.; Yates, D.W. Measuring the severity of injury. Br. Med. J.
**1977**, 2, 1247–1249. [Google Scholar] [CrossRef] [PubMed] - McDonald, S. Impairments in social cognition following severe traumatic brain injury. J. Int. Neuropsychol. Soc.
**2013**, 19, 1–16. [Google Scholar] [CrossRef] [PubMed] - Middleton, P.M. Practical use of the Glasgow Coma Scale; a comprehensive narrative review of GCS methodology. J. Australas. Emerg. Nurs.
**2012**, 15, 170–183. [Google Scholar] [CrossRef] [PubMed] - Jennett, B.; Teasdale, G. Aspects of coma after severe head injury. Lancet
**1977**, 1, 878–881. [Google Scholar] [CrossRef] - Chung, P.; Khan, F. Traumatic brain injury (TBI): Overview of diagnosis and treatment. J. Neurol. Neurolphysiol.
**2013**, 5, 182–192. [Google Scholar] - Marshall, M.S.; Riechers, R.G. Diagnosis and management of moderate and severe traumatic brain injury sustained in combat. Mil. Med.
**2012**, 177, 76–85. [Google Scholar] [CrossRef] [PubMed] - Zuercher, M.; Ummenhofer, W.; Baltussen, A.; Walder, B. The use of Glasgow Coma Scale in injury assessment: A critical review. Brain Inj.
**2009**, 23, 371–384. [Google Scholar] [CrossRef] [PubMed] - Geocadin, R. Traumatic brain injury. In Handbook of Neuro Critical Care; Bhardwaj, A., Mirski, M., Ulatowski, J., Totowa, N.J., Eds.; Humana Press: New York, NY, USA, 2004; pp. 73–89. [Google Scholar]
- Prasad, K. The Glasgow Coma Scale: A critical appraisal of its clinimetric properties. J. Clin. Epidemiol.
**1996**, 49, 755–763. [Google Scholar] [CrossRef] - Champion, H.R.; Sacco, W.J.; Copes, W.S.; Gann, D.S.; Gennarelli, T.A.; Flanagan, M.E. A revision of the trauma score. J. Trauma
**1989**, 29, 623–629. [Google Scholar] [CrossRef] [PubMed] - Schluter, P.J. The trauma and injury severity score (TRISS) revised. Injury
**2011**, 42, 90–96. [Google Scholar] [CrossRef] [PubMed] - Siritongtaworn, P.; Opasanon, S. The use of trauma score-injury severity score (TRISS) at Siriraj Hospital: How accurate is it? J. Med. Assoc. Thail.
**2009**, 92, 1016–1021. [Google Scholar] - Domingues, C.A.; Nogueira, L.S.; Settervall, C.H.S.; Sousa, C. Performance of trauma and injury severity score (TRISS) adjustments: An integrative review. Rev. Esc. Enferm. USP
**2015**, 49, 138–146. [Google Scholar] [CrossRef] [PubMed] - Trauma Audit and Research Network. Improvements in the Probability of Survival Model. Available online: https://www.tarn.ac.uk/Content.aspx?ca=4&c=3515 (accessed on 5 April 2018).
- Charlson, M.E.; Charlson, R.E.; Peterson, J.C.; Marinopoulos, S.S.; Briggs, W.M.; Hollenberg, J.P. The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J. Clin. Epidemiol.
**2008**, 6, 1234–1240. [Google Scholar] [CrossRef] [PubMed] - Aitchison, J.; Dunsmore, I.R. Statistical Prediction Analysis; Cambridge University Press: Cambridge, UK, 1975. [Google Scholar]
- Aitchison, J.; Habbema, J.D.E.; Kay, J.W. A critical comparison of two methods of statistical discrimination. Appl. Stat.
**1977**, 26, 15–27. [Google Scholar] [CrossRef] - The Royal Children’s Hospital Melbourne. Normal Ranges for Physiological Variables. Available online: https://www.rch.org.au/clinicalguide/guideline_index/Normal_Ranges_for_Physiological_Variables (accessed on 5 April 2018).
- Moppett, I.K. Traumatic brain injury: Assessment, resuscitation and early management. Br. J. Anaesth.
**2007**, 99, 18–31. [Google Scholar] [CrossRef] [PubMed] - Janich, K.; Nguyen, H.S.; Patel, M.; Shabani, S.; Montoure, A.; Doan, N. Management of adult traumatic brain injury: A review. J. Trauma Treat.
**2016**, 5. [Google Scholar] [CrossRef] - Saleh, M.; Saatchi, R.; Lecky, F.; Burke, D. Analysis of the influence of trauma injury factors on the probability of survival. Int. J. Biol. Biomed. Eng.
**2017**, 11, 88–96. [Google Scholar]

**Figure 1.**(

**a**) Age boxplots for the subjects that survived and those that did not survive; (

**b**) age distribution for all subjects; (

**c**) age subjects of subjects in calibration set; (

**d**) age distribution of subjects in validation set.

**Figure 2.**(

**a**) Age distributions of the subjects in the validation set for the cases that (

**a**) survived and (

**b**) had not survived.

**Figure 3.**Relationships between age, sex, abbreviated injury score (AIS) and Glasgow coma score (GCS) for the subjects included in the validation dataset that survived.

**Figure 4.**Relationships between age, sex, AIS and GCS for the subjects included in the validation dataset that had not survived.

**Figure 5.**The relationship between the prior probability of not surviving and the associated percentage correct identification for the survived (blue plot) and not survived (red plot) cases. The subjects were from the calibration set. Only the section centered on the peak (i.e., prior probability = 0.27) is shown in this figure.

**Figure 6.**The interrelationships between injury parameters for non-surviving cases. The values next to the circles indicate the number of associated cases. Larger values are highlighted by darker circles. Subjects are from the validation dataset.

**Figure 7.**Identification results for Ps14 for non-surviving cases in the validation dataset: (

**a**) correctly identified cases (

**b**) misidentified cases. The values next to the circles indicate the number of associated cases.

**Figure 8.**Identification results for PSD for non-surviving cases included in the validation dataset. (

**a**) Correctly identified cases (

**b**) misidentified cases. The values next to the circles indicate the number of associated cases.

**Figure 9.**The number of cases in the validation set correctly identified by Ps14 and PSD (

**a**) non-surviving cases; (

**b**) surviving cases. The middle bar indicates the overlap in correct identification of cases by both Ps14 and PSD.

**Figure 10.**Relationship between (

**a**) AIS and systolic blood pressure; (

**b**) GCS and systolic blood pressure for cases that were included in the validation set and had not survived. Blue = abnormal category, Green = normal category.

Best Motor Response (M Score) | Best Verbal Response (V Score) | Eye Opening (E Score) |
---|---|---|

Moves limb to command (6) | Oriented (5) | Spontaneous (4) |

Localizes to painful stimulus (5) | Confused (4) | Open to speech (3) |

Withdraws from painful stimulus (4) | Inappropriate words (3) | Open to pain (2) |

Abnormal flexion response (3) | Incomprehensible words (2) | None (1) |

Abnormal extension response (2) | No verbal (1) | - |

No motor response (1) | - |

**Table 2.**Revised Trauma Score [30].

Code | Glasgow Coma Scale | Systolic Blood Pressure (mmHg) | Respiratory Rate (Breaths Per Minute) |
---|---|---|---|

4 | 13–15 | >89 | 10–29 |

3 | 9–12 | 76–89 | >29 |

2 | 6–8 | 50–75 | 6–9 |

1 | 4–5 | 1–49 | 1–5 |

0 | 3 | - | - |

Gender | Age (Years) | Injury Outcomes | |||
---|---|---|---|---|---|

Male | Female | Mean | Standard Deviation | Survived | Did Not Survive |

2488 (60.3%) | 1636 (39.7%) | 67.9 | 21.6 | 3553 (86.2%) | 571 (13.8%) |

Parameter | All Subjects | Survived | Did Not Survive |
---|---|---|---|

1448 | 1224 | 224 | |

Mean | 68.2 | 66.0 | 80.3 |

Median | 75.1 | 71.6 | 83.7 |

Mode | 87.5 | 87.5 | 85.7 |

Standard deviation | 21.2 | 21.7 | 13.6 |

Variance | 450.7 | 469.5 | 176.1 |

Range | 86.2 | 86.2 | 77.2 |

Minimum | 17.0 | 17.0 | 21.8 |

Maximum | 103.2 | 103.2 | 99.0 |

**Table 5.**Categorization of Glasgow coma score (GCS), pulse rate (PR, beats per minute, bpm), respiratory rate (RR, breaths per minute, bpm) and systolic blood pressure.

Measures | Range | Category | |
---|---|---|---|

GCS | Score 13–15 | 3 (Mild) | |

Score 9–12 | 2 (Moderate) | ||

Score 3–8 | 1 (Severe) | ||

Pulse rate | 60–100 bpm | Normal = 2 | Abnormal = 1 |

Respiratory rate | 14–20 bpm | Normal = 2 | Abnormal = 1 |

Systolic blood pressure | 90–140 mmHg | Normal = 2 | Abnormal = 1 |

**Table 6.**Analysis of injury patterns for non-surviving cases included in the validation dataset (the patterns with relatively small number of cases are not shown). An x in the trauma parameter columns indicates abnormal or severe categorization for the related parameter.

Injury Scenarios | Number of Cases That Did Not Survive (Figure 6) | Number of Cases Correctly Identified by Ps14 (Figure 7a) | Number of Cases Correctly Identified by PSD (Figure 8a) | Trauma Parameter | ||||
---|---|---|---|---|---|---|---|---|

AIS | GCS | SBP | RR | PR | ||||

X_{22153} | 31 | 1 (3.2%) | 6 (19.4%) | x | x | |||

X_{22151} | 24 | 21 (87.5%) | 18 (75.0%) | x | x | x | ||

X_{21151} | 19 | 17 (89.5%) | 18 (94.7%) | x | x | x | ||

X_{22152} | 15 | 9 (60.0%) | 7 (46.7%) | x | x | x | ||

X_{22143} | 14 | 0 (0.0%) | 0 (0.0%) | x | x | |||

X_{22253} | 12 | 2 (16.7%) | 0 (0.0%) | x | ||||

X_{11151} | 11 | 8 (72.7%) | 10 (90.9%) | x | x | x | x | x |

X_{22251} | 9 | 8 (88.9%) | 4 (44.4%) | x | x | |||

X_{12151} | 8 | 8 (100.0%) | 7 (87.5%) | x | x | x | ||

X_{21243} | 7 | 0 (0.0%) | 0 (0.0%) | x | x | |||

X_{12153} | 7 | 1 (14.3%) | 4 (57.1%) | x | x | x | ||

X_{12143} | 6 | 0 (0.0%) | 0 (0.0%) | x | x | x | ||

X_{12243} | 5 | 0 (0.0%) | 1 (20.0%) | x | x | |||

X_{21153} | 5 | 0 (0.0%) | 4 (80.0%) | x | x | x | ||

X_{11153} | 4 | 1 (25.0%) | 3 (75.0%) | x | x | x | x | |

X_{12251} | 4 | 3 (75.0%) | 3 (75.0%) | x | x | x | ||

X_{12252} | 4 | 2 (50.0%) | 3 (75.0%) | x | x | x | ||

X_{12253} | 4 | 0 (0.0%) | 3 (75.0%) | x | x |

_{abcde}: The subscript “a” represents pulse rate (categorized as 1 abnormal, 2 normal), “b” represents respiration rate (categorized as 1 abnormal, 2 normal), “c” represents systolic blood pressure (categorized as 1 abnormal, 2 normal), “d” represents AIS and “e” represents GCS (1: severe, 2: moderate and 3: mild).

**Table 7.**Comparison of PSD and Ps14 to predict probability of survival for cases in the validation dataset (when probability value was greater than or equal to 0.5, the subject was classed as surviving and when probability value was less than 0.5, the subject was classed as not surviving).

Number of Cases | Ps14 | PSD | |||
---|---|---|---|---|---|

Survived | Did Not Survive | Survived | Did Not Survive | Survived | Did Not Survive |

1224 | 224 | 1192 (97.4%) | 90 (40.2%) | 1112 (90.8%) | 112 (50.0%) |

**Table 8.**Performance comparison of PSD and Ps14 based on age groups for cases in the validation dataset that had not survived.

Total Number of TBI Cases Based on Age Range | Ps14 Prediction Accuracy | PSD Prediction Accuracy | |||||||
---|---|---|---|---|---|---|---|---|---|

Age (Years) | Age (Years) | Identified Correctly | Misidentified | Identified Correctly | Misidentified | ||||

17–65 | ≥66 | 17–65 | ≥66 | 17–65 | ≥66 | 17–65 | ≥66 | 17–65 | ≥66 |

26 | 198 | 6 | 83 | 20 | 115 | 21 | 89 | 5 | 109 |

(26.0%) | (41.9%) | (76.2%) | (58.0%) | (80.7%) | (44.9%) | (19.3%) | (55.0%) |

**Table 9.**Analysis of injury parameters in relation to cases that survived and those that had not survived.

Parameters Injury Grade | All Subjects | Survived | Did Not Survive | |
---|---|---|---|---|

1448 | 1224 | 224 | ||

AIS | 2 | 12 (0.8%) | 12 (1.0%) | 0 (0.0%) |

3 | 159 (11.0%) | 154 (12.6%) | 5 (2.2%) | |

4 | 597 (41.2%) | 551 (45.0%) | 46 (20.5%) | |

5 | 680 (47.0%) | 507 (41.4%) | 173 (77.2%) | |

GCS (categorized) | 1 (Severe) | 147 (10.2%) | 64 (5.2%) | 83 (37.1%) |

2 (Moderate) | 133 (9.2%) | 98 (8.0%) | 35 (15.6%) | |

3 (Mild) | 1168 (80.7%) | 1062 (86.8%) | 106 (47.3%) | |

PR (categorized) | 1 (Abnormal) | 338 (23.3%) | 269 (22.0%) | 69 (30.8%) |

2 (Normal) | 1110 (76.7%) | 955 (78.0%) | 155 (69.2%) | |

RR (categorized) | 1 (Abnormal) | 236 (16.3%) | 176 (14.4%) | 60 (26.8%) |

2 (Normal) | 1212 (83.7%) | 1048 (85.6%) | 164 (73.2%) | |

SBP (categorized) | 1 (Abnormal) | 762 (52.6%) | 602 (49.2%) | 160 (71.4%) |

2 (Normal) | 686 (47.4%) | 622 (50.8%) | 64 (28.6%) |

**Table 10.**Provides the mean and standard deviation of AIS and categorized Glasgow comas score (GCS), pulse rate (PR), respiratory rate (RR) and systolic blood pressure (SBP).

Parameters | Mean | Standard Deviation |
---|---|---|

AIS | 4.75 | 0.48 |

GCS (categorized) | 2.10 | 0.92 |

PR (categorized) | 1.69 | 0.46 |

RR (categorized) | 1.73 | 0.44 |

SBP (categorized) | 1.29 | 0.45 |

**Table 11.**Illustration of the effect of age, pulse rate (PR), systolic blood pressure (SBP) and respiratory rate (RR) on PSD performance in identifying surviving and not-surviving cases included in the validation set.

Number of Cases in the Validation Set | Correct PSD Identification Using AIS and GCS Only | Correct PSD Identification Using AIS and GCS with Age Only | Correct PSD Identification Using AIS, GCS, Age and SBP | Correct PSD Identification Using AIS, GCS, PR, SBP, RR and Age | |||||
---|---|---|---|---|---|---|---|---|---|

Survived | Did Not Survive | Survived | Did Not Survive | Survived | Did Not Survive | Survived | Did Not Survive | Survived | Did Not Survive |

1224 | 224 | 675 (55.1%) | 70 (31.3%) | 1008 (82.4%) | 146 (65.2%) | 1019 (83.3%) | 144 (64.3%) | 1112 (90.8%) | 112 (50.0%) |

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**MDPI and ACS Style**

Saleh, M.; Saatchi, R.; Lecky, F.; Burke, D.
Predictive Statistical Diagnosis to Determine the Probability of Survival in Adult Subjects with Traumatic Brain Injury. *Technologies* **2018**, *6*, 41.
https://doi.org/10.3390/technologies6020041

**AMA Style**

Saleh M, Saatchi R, Lecky F, Burke D.
Predictive Statistical Diagnosis to Determine the Probability of Survival in Adult Subjects with Traumatic Brain Injury. *Technologies*. 2018; 6(2):41.
https://doi.org/10.3390/technologies6020041

**Chicago/Turabian Style**

Saleh, Mohammed, Reza Saatchi, Fiona Lecky, and Derek Burke.
2018. "Predictive Statistical Diagnosis to Determine the Probability of Survival in Adult Subjects with Traumatic Brain Injury" *Technologies* 6, no. 2: 41.
https://doi.org/10.3390/technologies6020041