Big data and predictive analytics in neurocritical care

Cortical hemodynamics of electrographic status epilepticus in the critically ill

OBJECTIVES

The pathophysiological mechanisms of status epilepticus (SE) underlying potential brain injury remain largely unclear. This study aims to employ functional near-infrared spectroscopy (fNIRS) combined with video-electroencephalography (vEEG) to monitor brain hemodynamics continuously and non-invasively in critically ill adult patients experiencing electrographic SE. Our primary focus is to investigate neurovascular coupling and cerebrovascular changes associated with seizures, particularly during recurring and/or prolonged episodes.

METHODS

Eleven critically ill adult patients underwent simultaneous vEEG-fNIRS with large cortical coverage. Data from seven patients with identified electrographic SE were analyzed. The timing of recorded seizures was marked using standardized critical care EEG terminology. A general linear model was employed to extract the hemodynamic response to seizures from the fNIRS recordings. Linear mixed-effects models were utilized to correlate hemodynamic responses with seizure characteristics.

RESULTS

A total of >200 h of monitoring and >1000 seizures were recorded. In most patients, an increase in oxyhemoglobin (HbO) and a decrease in deoxyhemoglobin (HbR) were observed during shorter-duration seizures. Although a similar response could also be seen initially for longer-duration seizures, this hemodynamic change was often followed by a progressive decline in HbO concentration and an increase in HbR. At the systemic level, no significant difference in peripheral oxygenation occurred during seizures, and only small changes in mean arterial blood pressure and heart rate occurred in four and two patients, respectively.

SIGNIFICANCE

We demonstrate the feasibility of using multichannel vEEG-fNIRS to measure the hemodynamic changes associated with electrographic seizures in critically ill adult patients. Our findings suggest that disrupted neurovascular coupling is more prevalent during prolonged seizures compared to recurrent short-duration seizures. This research provides valuable insights into the dynamic interplay between neuronal activity and hemodynamics during critical care seizures.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database

Background

Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data.

Methods

This study, based on the MIMIC-III database, extracted demographic characteristics, vital signs, laboratory test values, and comorbidity information of heart failure patients using structured query language. LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. An ensemble learning model based on a soft voting mechanism was constructed. Model performance was evaluated using accuracy, recall, precision, F1 score, and AUC values through cross-validation and on an independent test set.

Results

In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. Additionally, RF and GB models also performed well, with RF achieving an accuracy of 0.79 and an AUC of 0.79 on the independent test set, while the GB model achieved an accuracy of 0.77 and an AUC of 0.79. In contrast, other models such as LR, SVM, and KNN exhibited poorer performance in terms of accuracy and AUC values, indicating the significant advantage of ensemble methods in handling complex clinical prediction tasks.

Conclusion

This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. The overall performance of the ensemble learning model confirms its effectiveness as an adjunct clinical decision-making tool. Future research should further optimize the models and validate them in a broader patient population to enhance their practical utility and accuracy in real clinical settings.

Neurocritical Care in 2024: Where are We Headed?

Providing specialized care to critically ill neurology patients has improved outcomes for patients with neurological emergencies; however, there are still some gaps in neurocritical care (NCC) that offer opportunities for improvement. Among these gaps, improving education of the multidisciplinary NCC team, targeting individualized treatments for neurologically critically ill patients, and reducing disparities for undeserved patients as well as disadvantaged areas are priorities to advance the field. This review focuses on the current challenges neurointensivists face, including difficulties in neuroprognostication, ethical challenges in end-of-life care, and neuropalliative care. Challenges also involve providing specific NCC education for the multidisciplinary NCC team, as well as advancing research to provide treatments for critically ill neurological patients. Finally, the authors describe future directions that can take NCC to the next level.

From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments

Introduction

Numerous complex physiological models derived from intracranial pressure (ICP) monitoring data have been developed. More recently, techniques such as machine learning are being used to develop increasingly sophisticated models to aid in clinical decision-making tasks such as diagnosis and prediction. Whilst their potential clinical impact may be significant, few models based on ICP data are routinely available at a patient's bedside. Further, the ability to refine models using ongoing patient data collection is rare. In this paper we identify and discuss the challenges faced when converting insight from ICP data analysis into deployable tools at the patient bedside.

Research question

To provide an overview of challenges facing implementation of sophisticated ICP models and analyses at the patient bedside.

Material and methods

A narrative review of the barriers facing implementation of sophisticated ICP models and analyses at the patient bedside in a neurocritical care unit combined with a descriptive case study (the CHART-ADAPT project) on the topic.

Results

Key barriers found were technical, analytical, and integrity related. Examples included: lack of interoperability of medical devices for data collection and/or model deployment; inadequate infrastructure, hindering analysis of large volumes of high frequency patient data; a lack of clinical confidence in a model; and ethical, trust, security and patient confidentiality considerations governing the secondary use of patient data.

Discussion and conclusion

To realise the benefits of ICP data analysis, the results need to be promptly delivered and meaningfully communicated. Multiple barriers to implementation remain and solutions which address real-world challenges are required.

Investigation of Cerebral Autoregulation Using Time-Frequency Transformations

The authors carried out the study of the state of systemic and cerebral hemodynamics in normal conditions and in various neurosurgical pathologies using modern signal processing methods. The results characterize the condition for the mechanisms of cerebral circulation Institute of Computer Science and Control, Higher School of Cyber-Physical Systems and Control regulation, which allows for finding a solution to fundamental and specific clinical problems for the effective treatment of patients with various pathologies. The proposed method is based on the continuous wavelet transform of systemic arterial pressure and blood flow velocity signals in the middle cerebral artery recorded by non-invasive methods of photoplethysmography and transcranial doppler ultrasonography. The study of these signals in real-time in the frequency range of Mayer waves makes it possible to determine the cerebral autoregulation state in certain diseases before and after surgical interventions. The proposed method uses a cross-wavelet spectrum, which helps obtain wavelet coherence and a phase shift between the wavelet coefficients of systemic arterial pressure signals and blood flow velocity in the Mayer wave range. The obtained results enable comparing the proposed method with that based on the short-time Fourier transform. The comparison showed that the proposed method has higher sensitivity to changes in cerebral autoregulation and better localization of changes in time and frequency.