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BACKGROUND Post-operative delirium (POD) is a frequent and severe complication after neurosurgical operations. Good prediction of POD after craniotomy in neuro-oncologic patients is important to install prophylactic measures, increase recognition and apply early treatment. Hence, we compared logistic regression with machine learning to build an accurate predictive model in a large dataset. MATERIAL AND METHODS POD was defined in case of a Delirium Observation Scale (DOS) ≥ 3 or start of antipsychotic treatment for delirium within 10 days after surgery. Adult patients undergoing a craniotomy for a neuro-oncologic disease in the Erasmus Medical Centre in Rotterdam were retrospectively included. The cohort was split into a training (75%), after three-fold cross validation, and test set (25%). Logistic regression and Lasso Elastic-Net Regularized Generalized Linear Models (GLMNet) were trained based on 19 pre- and intra-operative features and risk factors were identified based on the superior model. RESULTS We included 1025 neuro-oncologic craniotomies between June 2017 and September 2020. Overall incidence of POD was 18.6% (95%CI 17.4–19.8). Compared to logistic regression, Lasso GLMNet performed superior (AUC 0.73 vs. 0.76) based on the optimal tuning parameters (α=1, λ=0.014). Several non-modifiable risk factors such as age (OR1.01), prior delirium (OR1.04), memory problems (OR1.12), surgery duration (OR1.01) and modifiable risk factors, such as low potassium (OR0.97) levels and opioid administration (OR1.03), were identified. CONCLUSION POD is a frequent complication after craniotomy in neuro-oncologic patients. Lasso GLMNet was useful in predicting POD in this cohort. Validation in a prospective cohort of this model should be applied to further evaluate its value in diminishing POD.

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.

A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and management are combined into a dynamic development cycle that allows stepwise update and refinement. The development of the knowledge component is based on knowledge engineering. In the starting phases, rapid prototyping is convenient to determine and evaluate the specification. The content of the knowledge base is frequently updated during its life time. For this purpose, a knowledge modelling protocol is supplied.

In the preceding Comment Drees discusses the issues of (i) whether my method sums the leading logs, (ii) the use or abuse of the infrared scale ${Q}_{0}$, (iii) a possible ambiguity in my definition of the renormalized scalar masses, and (iv) the threshold effects at the ultraviolet scale. I agree with his critique on point (i). On the other three points, a discussion is presented which might clarify the point of view I presented in my previous paper.