Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved

Acute inflammation and psychomotor slowing: Experimental assessment using lipopolysaccharide administration in healthy humans

Data from clinical and cross-sectional studies suggest that inflammation contributes to psychomotor slowing and attentional deficits found in depressive disorder. However, experimental evidence is still lacking. The aim of this study was to clarify the effect of inflammation on psychomotor slowing using an experimental and acute model of inflammation, in which twenty-two healthy volunteers received an intravenous injection of lipopolysaccharide (LPS, dose: 0.8 ​ng/kg body weight) and of placebo, in a randomized order following a double-blind within-subject crossover design. A reaction time test and a go/no-go test were conducted 3 ​h after the LPS/placebo injection and interleukin (IL)-6 and tumor necrosis factor (TNF)-α concentrations were assessed. No effect of experimental inflammation on reaction times or errors for either test was found. However, inflammation was related to worse self-rated performance and lower effort put in the tasks. Exploratory analyses indicated that reaction time fluctuated more over time during acute inflammation. These data indicate that acute inflammation has only modest effects on psychomotor speed and attention in healthy subjects objectively, but alters the subjective evaluation of test performance. Increased variability in reaction time might be the first objective sign of altered psychomotor ability and would merit further investigation

Resilience in Lower Grade Glioma Patients

Current data show that resilience is an important factor in cancer patients’ well-being. We aim to explore the resilience of patients with lower grade glioma (LGG) and the potentially influencing factors. We performed a cross-sectional assessment of adult patients with LGG who were enrolled in the LoG-Glio registry. By phone interview, we administered the following measures: Resilience Scale (RS-13), distress thermometer, Montreal Cognitive Assessment Test for visually impaired patients (MoCA-Blind), internalized stigmatization by brain tumor (ISBI), Eastern Cooperative Oncological Group performance status (ECOG), patients’ perspective questionnaire (PPQ) and typical clinical parameters. We calculated correlations and multivariate regression models. Of 74 patients who were assessed, 38% of those showed a low level of resilience. Our results revealed significant correlations of resilience with distress (p < 0.001, −0.49), MOCA (p = 0.003, 0.342), ECOG (p < 0.001, −0.602), stigmatization (p < 0.001, −0.558), pain (p < 0.001, −0.524), and occupation (p = 0.007, 0.329). In multivariate analyses, resilience was negatively associated with elevated ECOG (p = 0.020, β = −0.383) and stigmatization levels (p = 0.008, β = −0.350). Occupation showed a tendency towards a significant association with resilience (p = 0.088, β = −0.254). Overall, low resilience affected more than one third of our cohort. Low functional status is a specific risk factor for low resilience. The relevant influence of stigmatization on resilience is a novel finding for patients suffering from a glioma and should be routinely identified and targeted in clinical routine