Proximal femoral fractures (often denoted as hip fractures) are amongst the most prevalent fractures in older patients and associated with significant mortality and morbidity.Failure to recover to... Show moreProximal femoral fractures (often denoted as hip fractures) are amongst the most prevalent fractures in older patients and associated with significant mortality and morbidity.Failure to recover to prefracture levels of function has important social and economic implications, as these patient’s risk losing their independence and self-reliance. The primary aim of this thesis is to provide a better understanding of the factors relevant for the functional prognosis of patients with a proximal femoral fracture.This thesis covers two parts, focusing on the effects of surgical aspects and patient demographics.Outcomes of previously performed studies on prognostic factors of recovery proved hard to compare. This can be attributed to the high level of heterogeneity and methodology of these studies, for instance in the method to objectify recovery. For the studies in this thesis, we have opted to compare outcomes with the patients’ individual prefracture level of function. Surgical aspects, such as different approaches to place a prosthesis, seemed to have a reserved effect on recovery. Factors which seemed of conclusive relevance were health scores based on the comorbidity and prefracture level of function. This emphasizes the importance of a holistic and geriatric approach for patients with proximal hip fractures. Show less
Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These... Show moreDespite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (+/- 7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (+/- 2) compared to 26 (+/- 1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients. Show less
