Generalized periodic discharges: Pathophysiology and clinical considerations

Generalized periodic discharges (GPDs) are commonly encountered in metabolic encephalopathy and cerebral hypoxia/ischemia. The clinical significance of this EEG pattern is indistinct, and it is unclear whether treatment with antiepileptic drugs is beneficial. In this study, we discuss potential pathophysiological mechanisms. Based on the literature, supplemented with simulations in a minimal computational model, we conclude that selective synaptic failure or neuronal damage of inhibitory interneurons, leading to disinhibition of excitatory pyramidal cells, presumably plays a critical role. Reversibility probably depends on the potential for functional recovery of these interneurons. Whether antiepileptic drugs are helpful for regaining function is unclea

Mathematical Models for Optimisation of Drug Administration in Intensive Care Units

Clinical status of critically ill patients is often extreme and rapidly evolving. Hence, pharmacological therapies must be tailored to patients' characteristics and adapted according to the evolution of their clinical pictures. To identify optimal personalized treatments, possible scenarios produced by different therapeutic choices must be predicted and compared. This process requires complex analyses involving the development of appropriate mathematical models. In this Thesis, I focused on two important aspects of the pharmacological treatment of critically ill patients: the administration of antimicrobial drugs and the control of their glycaemic level. Although these problems are clinically very different, the modelling of their pathophysiological mechanisms can be addressed with similar tools. I performed analyses based on retrospective clinical data collected with MargheritaTre, an electronic health record developed by GiViTI. The software to synchronize databases from hospitals to our laboratory and to preprocess data for analyses was written for the purpose of this Thesis. Starting from the study of the physiological mechanisms at the basis of vancomycin pharmacokinetics I constructed a model to describe the evolution of the plasma concentration of this drug in critically ill patients. Compartment models were fitted on a sample of 141 patients, testing about 30 patient covariates and several functional dependencies for each variable. Glucose dynamics were described through a system of delay differential equations reproducing intake, uptake and endogenous production of glucose, and organ-organ interactions mediated by hormones. Existing models, describing only the dynamics of glucose and insulin, fail to reproduce the correct evolution when glucose concentrations vary too rapidly. I improved these models, by introducing an equation describing glucagon dynamics and taking into account its effect on glucose metabolism. I investigated the dynamical properties of my model with analytical analyses, numerical simulations and fitting it to observed data

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

Studies on the effects of movement on critically ill patients

No abstract available

The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust

Dynamic pituitary-adrenal interactions in the critically ill after cardiac surgery

Context: Patients with critical illness are thought to be at risk of adrenal insufficiency. There are no models of dynamic hypothalamic-pituitary-adrenal (HPA) axis function in this group of patients and thus current methods of diagnosis are based on aggregated, static models.Objective: To characterize the secretory dynamics of the HPA axis in the critically ill (CI) after cardiac surgery.Design: Mathematical modeling of cohorts.Setting: Cardiac critical care unit.Patients: 20 male patients CI at least 48 hours after cardiac surgery and 19 healthy (H) male volunteers. Interventions: None.Main Outcome Measures: Measures of hormone secretory dynamics were generated from serum adrenocorticotrophic hormone (ACTH) sampled every hour and total cortisol every 10 min for 24 h.Results: All CI patients had pulsatile ACTH and cortisol profiles. CI patients had similar ACTH secretion (1036.4 [737.6] pg/mL/24 h) compared to the H volunteers (1502.3 [1152.2] pg/mL/24 h; P=.20), but increased cortisol secretion (CI: 14 447.0 [5709.3] vs H: 5915.5 [1686.7)] nmol/L/24 h; P<.0001). This increase in cortisol was due to nonpulsatile (CI: 9253.4 [3348.8] vs H: 960 [589.0] nmol/L/24 h, P<.0001), rather than pulsatile cortisol secretion (CI: 5193.1 [3018.5] vs H: 4955.1 [1753.6] nmol/L/24 h; P=.43). Seven (35%) of the 20 CI patients had cortisol pulse nadirs below the current international guideline threshold for critical illness-related corticosteroid insufficiency, but an overall secretion that would not be considered deficient.Conclusions: This study supports the premise that current tests of HPA axis function are unhelpful in the diagnosis of adrenal insufficiency in the CI. The reduced ACTH and increase in nonpulsatile cortisol secretion imply that the secretion of cortisol is driven by factors outside the HPA axis in critical illness.Diabetes mellitus: pathophysiological changes and therap

Right ventricular performance in the cardiac surgical patient

In recent years there has been growing acknowledgement that the performance of the right ventricle (RV), which is an essential part of the cardiovascular system, plays a role in clinically relevant endpoints. In this thesis we aimed to improve the understanding of RV performance in the cardiosurgical patient. We demonstrated that postoperative right ventricular ejection fraction (RVEF), as assessed by contemporary pulmonary artery catheter, was a strong independent risk factor for long-term mortality. Impaired RVEF was not only associated with mortality directly after the surgical procedure, but its association continued to increase in the two years following the intervention. RVEF was additionally associated with markers of morbidity, such as length of stay in the intensive care unit (ICU), duration of mechanical ventilation, and increased use of inotropic drugs. The observed association between RVEF and outcome does not allow for conclusions on cause-effect relationship. We combined both invasive haemodynamic pulmonary artery catheter-monitoring with transoesophageal echocardiography. We observed that a significant reduction regional (longitudinal) echocardiographic parameters was not accompanied by a reduction in global measures of RV function. These data highlight that regional RV measurements in the postoperative cardiac surgical setting do not reflect global RV function, and should therefore be interpreted with caution. Managing patients with an impaired RV function is challenging. Although thought to be one of the cornerstones in the treatment of postoperative RV dysfunction, the increase in systemic blood pressure in patients with a moderate or poor RVEF and hypotension was shown to be ineffective in the clinical postoperative setting