Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression.

This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies

Prediction of survival probabilities with Bayesian Decision Trees

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application

Causes of brain dysfunction in acute coma: a cohort study of 1027 patients in the emergency department

BACKGROUND: Coma of unknown etiology (CUE) is a major challenge in emergency medicine. CUE is caused by a wide variety of pathologies that require immediate and targeted treatment. However, there is little empirical data guiding rational and efficient management of CUE. We present a detailed investigation on the causes of CUE in patients presenting to the ED of a university hospital. METHODS: One thousand twenty-seven consecutive ED patients with CUE were enrolled. Applying a retrospective observational study design, we analyzed all clinical, laboratory and imaging findings resulting from a standardized emergency work-up of each patient. Following a predefined protocol, we identified main and accessory coma-explaining pathologies and related these with (i.a.) GCS and in-hospital mortality. RESULTS: On admission, 854 of the 1027 patients presented with persistent CUE. Their main diagnoses were classified into acute primary brain lesions (39%), primary brain pathologies without acute lesions (25%) and pathologies that affected the brain secondarily (36%). In-hospital mortality associated with persistent CUE amounted to 25%. 33% of patients with persistent CUE presented with more than one coma-explaining pathology. In 173 of the 1027 patients, CUE had already resolved on admission. However, these patients showed a spectrum of main diagnoses similar to persistent CUE and a significant in-hospital mortality of 5%. CONCLUSION: The data from our cohort show that the spectrum of conditions underlying CUE is broad and may include a surprisingly high number of coincidences of multiple coma-explaining pathologies. This finding has not been reported so far. Thus, significant pathologies may be masked by initial findings and only appear at the end of the diagnostic work-up. Furthermore, even transient CUE showed a significant mortality, thus rendering GCS cutoffs for selection of high- and low-risk patients questionable. Taken together, our data advocate for a standardized diagnostic work-up that should be triggered by the emergency symptom CUE and not by any suspected diagnosis. This standardized routine should always be completed - even when initial coma-explaining diagnoses may seem evident

Bayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Bank

Trauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian in ference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Intraoperative Quantification of Bone Perfusion in Lower Extremity Injury Surgery

Orthopaedic surgery is one of the most common surgical categories. In particular, lower extremity injuries sustained from trauma can be complex and life-threatening injuries that are addressed through orthopaedic trauma surgery. Timely evaluation and surgical debridement following lower extremity injury is essential, because devitalized bones and tissues will result in high surgical site infection rates. However, the current clinical judgment of what constitutes “devitalized tissue” is subjective and dependent on surgeon experience, so it is necessary to develop imaging techniques for guiding surgical debridement, in order to control infection rates and to improve patient outcome. In this thesis work, computational models of fluorescence-guided debridement in lower extremity injury surgery will be developed, by quantifying bone perfusion intraoperatively using Dynamic contrast-enhanced fluorescence imaging (DCE-FI) system. Perfusion is an important factor of tissue viability, and therefore quantifying perfusion is essential for fluorescence-guided debridement. In Chapters 3-7 of this thesis, we explore the performance of DCE-FI in quantifying perfusion from benchtop to translation: We proposed a modified fluorescent microsphere quantification technique using cryomacrotome in animal model. This technique can measure bone perfusion in periosteal and endosteal separately, and therefore to validate bone perfusion measurements obtained by DCE-FI; We developed pre-clinical rodent contaminated fracture model to correlate DCE-FI with infection risk, and compare with multi-modality scanning; Furthermore in clinical studies, we investigated first-pass kinetic parameters of DCE-FI and arterial input functions for characterization of perfusion changes during lower limb amputation surgery; We conducted the first in-human use of dynamic contrast-enhanced texture analysis for orthopaedic trauma classification, suggesting that spatiotemporal features from DCE-FI can classify bone perfusion intraoperatively with high accuracy and sensitivity; We established clinical machine learning infection risk predictive model on open fracture surgery, where pixel-scaled prediction on infection risk will be accomplished. In conclusion, pharmacokinetic and spatiotemporal patterns of dynamic contrast-enhanced imaging show great potential for quantifying bone perfusion and prognosing bone infection. The thesis work will decrease surgical site infection risk and improve successful rates of lower extremity injury surgery

Computer Aided Traumatic Pelvic Injury Decision-making

Traumatic pelvic injury is one of the most dangerous injuries because it is often associated with severe hemorrhage as well as serious complications. It is therefore vital to provide immediate medical treatment to increase the survival rate of pelvic injury patients. However, it is often difficult to make treatment decisions, as cases are complex and display similar patterns. It has been suggested that the use of computer aided decision-making in a trauma support system is the most efficient way to reduce the cost of trauma care. In our previous work, we found that creating rules using all available variables results in lower accuracy than when using only significant variables. This is because less relevant attributes and/or less reliable attributes with regards to the means of measurement can result in random correlation that is clinically meaningless. Based on this knowledge, we designed an efficient computer assisted trauma decision making system for traumatic pelvic injuries using a machine learning algor thm. More specifically, a rule-based system was designed to create a reliable method of making predictions/recommendations on the status and exact outcome – i.e. home or rehabilitation - of pelvic trauma patients using a nonlinear regression and classification (CART) method. The resulting computer aided system can aid physicians in making rapid and accurate decisions. Three machine learning algorithms were compared to evaluate the proposed method