Focal Spot, Winter 2008/2009

https://digitalcommons.wustl.edu/focal_spot_archives/1110/thumbnail.jp

Risk Scores for Predicting Mortality in Flail Chest

The objective of this thesis was to develop two risk scores which could predict the individual risk of in-hospital mortality for patients with flail chest using data from the Ontario Trauma Registry. The first study describes the univariate analyses conducted to identify mortality predictors. The second study details the logistic regression analysis that generated a risk score. Finally, the third study describes the decision tree analysis that produced the second risk score. The two risk scores were then compared. In summary, these three studies show that a minority of flail chest patients are currently undergoing operative repair and that a risk score may be a useful adjunct for surgeons to determine the individual risk of in-hospital mortality in patients requiring operative repair for flail chest

Multilayer network methodologies for brain data analysis and modelling

The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: brain data coming from Traumatic Brain Injury (TBI) patients and data collected for the study of neurocognitive healthy ageing. In both cases there was the need of applying computational techniques able to integrate different features. To do so we used multilayer networks. For two groups of TBI patients (adults and paediatrics), time series data were collected from the observations of IntraCranial Pressure (ICP) and Heart Rate (HR). We first detected events of simultaneous increase of HR and ICP, which we called brain-heart crosstalks. Subsequently time series were translated into graphs, and network measures, during brain-heart crosstalks, were obtained. These were then included as predictors in a mortality outcome model, with crosstalks. Causality measures were also investigated, using a Granger causality approach, to understand the dynamics of signals during these events. We further applied multilayer networks to study neurocognitive ageing. To do so, we implemented a pipeline for community detection, which we called NetRank, applying it to the Cam-CAN, a large cross-sectional cohort for the study of healthy neurocognitive ageing. Using multilayer networks modelling, we identified subgroups of individuals, with similar lifestyles, and we related them to structural and functional brain features. We believe that multilayer networks and their extensions represent a powerful tool to be used in integrative and cross modal neuroscience datasets. New insights on cognitive neuroscience and time series analysis, can in fact be gained trough multilayer network, possibly improving patients managements and allowing to develop new predictive tools.EPSR

A Decision Tree Approach To The Assessment Of Posttraumatic Stress Disorder

Structured diagnostic interviews are widely considered to be the optimal method of assessing symptoms of posttraumatic stress; however few clinicians report using structured assessments to guide clinical practice. One key impediment to the use of structured assessments in clinical practice is the amount of time required for test administration and interpretation. Thus, the present research conducted an initial feasibility study using a normative sample of college-aged adults (n = 88) to develop an assessment protocol based on the clinician administered PTSD scale (caps). Decision tree analysis was utilized to identify a subset of predictor variables within the 17 caps symptom criteria variables that were most predictive of a diagnosis of posttraumatic stress disorder (PTSD). The algorithm-driven sequence of questions reduced the number of items administered by more than 75% and classified the validation sample at 100.0% accuracy for those without a diagnosis of PTSD and 85.7% accuracy for those with a diagnosis of PTSD. The present study also demonstrated the feasibility of computer administration of the algorithm-based sequence in a normative sample of college-aged adults (n = 197). The algorithm-based, computer-administered sequence had high sensitivity and specificity and excellent diagnostic agreement with the computer-administered full caps sequence. These results demonstrated the feasibility of developing a protocol to assess PTSD in a way that imposes little assessment burden while still providing a reliable diagnosis

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

Towards the Development of an Integrative, Evidence-based Suite of Indicators for the Prediction of Outcome Following Mild Traumatic Brain Injury

This thesis focuses on identifying factors that could be used to predict recovery following concussion. The first study is a pilot assessment of blood-based biomarkers, neuropsychological tests and MRI outcomes, followed by a protocol paper for a large scale clinical study designed to identify predictive indicators. The thesis features three journal publications, one of which is a seminal review article on a novel neuroimaging analysis technique called Quantitative Susceptibility Mapping

Prediction of Medical Outcomes with Modern Modelling Techniques

Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry

Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients\ua0were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population