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

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.Technologies 2018, 6, 41 2 of 16 scoring systems can be beneficial for [4,5]: (i) triage: a procedure to assess severity of medical condition for the purpose of setting treatment priority; (ii) prognostic evaluation: a procedure to support predication and management of injury outcomes and (iii) research: studies to compare patient groups on the basis of injury outcomes and assessing medical care and treatments. Trauma scoring systems can be classed as anatomical, physiological and combined. Anatomical scoring systems quantify the extent of individual anatomical injuries, taking into account the injury site by appropriate weightings (coefficients) however these weightings are often not known when the patient presents to hospital after a civilian trauma where most injury mechanisms are blunt (e.g., falls and road traffic collisions) [6]. Physiological scoring systems are based on cardiovascular, neurological and respiratory abnormalities. They provide mechanisms to determine the likelihood of mortality and inform triage; but can lack precision [6]. Combined...

Traumatic brain injury probability of survival assessment in adults using iterative random comparison classification

“…Table 6 provides the results for determining %Ps for cases included in the validation set. The results obtained using IRCC were compared to those from Ps14 (which were already available from the provided TARN database) and PSD (which were determined in an earlier study [26]). Ps14 uses a range of 0–100 to represent %Ps, with a value higher than 50 considered as a survivor, otherwise a not survivor.…”

“…In an earlier study, we reported a method of determining Ps based on predictive statistical diagnosis (PSD) [26]. PSD's principle is based on Bayesian statistic which is a well‐establish method of data classification.…”

“…The data were fully anonymised prior to processing to ensure ethics confirmation, and the study had Sheffield Hallam University Ethics approval. A description of the data was provided in an earlier study [26], however, they are summarised here for completeness. TARN provided a subset of their very large database that contained TBI cases.…”

Artificial intelligence and machine learning for hemorrhagic trauma care

“…Given the capability of ML in extracting important features from large multidimensional data sets predicting real-life outcomes, it is often seen as having significant potential in the field of trauma when it comes to improving access and quality of care, across different regional trauma systems and within a local trauma environment [ 16 ]. Trauma incorporates numerous factors in many forms affecting different organs, and their consequence could be related to the individual’s physiological attributes (e.g., age, fragility, premedical conditions) [ 17 ]. These factors translate into substantial quantity of data features, leading to high dimensional data.…”

Artificial intelligence and statistical techniques to predict probability of injury survival

“…Inclusion of the age with AIS and GCS significantly improved the PSD performance resulting in 82.4% and 65.2% correct identification for the survivors and not survivors respectively. The inclusion of systolic blood pressure with age, AIS and GCS resulted in 83.3% and 64.3% correct identification of survivors and not survivors respectively (Saleh et al, 2018). The study evaluated the performance of PSD in determining the probability of survival in adult subjects with TBI.…”