Predictive Statistical Diagnosis to Determine the Probability of Survival in Adult Subjects with Traumatic Brain Injury

• Published in: Technologies (Basel) 2018-06, Vol.6 (2), p.41
• Main Authors: Saleh, Mohammed, Saatchi, Reza, Lecky, Fiona, Burke, Derek
• Format: Article
• Language: English
• Subjects: Abdomen; Adults; Age; Bayesian analysis; Bayesian modelling; Blood pressure; Brain; Brain research; Coma; Consciousness; Contusions; Diagnosis; Head injuries; Heart rate; Hospitals; Mathematical models; Medical imaging; Parameters; Patients; Physiology; predictive statistical diagnosis; probability of survival; Statistical analysis; Statistical prediction; Survival; Trauma; Traumatic brain injury
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies6020041

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.