Effect of anesthesia depth on postoperative clinical outcome in patients with supratentorial tumor (DEPTH): study protocol for a randomized controlled trial

Signal processing and machine learning algorithm to classify anaesthesia depth

BACKGROUND

Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures.

METHODS

Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1.

RESULTS

A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states.

CONCLUSION

Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.

Urinary albumin creatinine ratio associated with postoperative delirium in elderly patients undergoing elective non-cardiac surgery: A prospective observational study

Introduction

The blood‐brain barrier (BBB) disruption contributes to postoperative delirium, but cost‐effective and non‐invasive assessment of its permeability is not practicable in the clinical settings. Urine albumin to creatinine ratio (UACR), reflecting systemic vascular endothelial dysfunction, may be a prognostic and predictive factor associated with postoperative delirium. The aim was to analyze the relationship between UACR and postoperative delirium in elderly patients undergoing elective non‐cardiac surgery.

Materials and methods

Through stratified random sampling, a cohort of 408 individuals aged 60 years and older scheduled for elective non‐cardiac surgery were included between February and August 2019 in the single‐center, prospective, observational study. The presence of delirium was assessed using the Confusion Assessment Method (CAM) or Confusion Assessment Method for the ICU (CAM‐ICU) on the day of surgery, at 2 h after the surgery ending time and on the first 3 consecutive days with repeated twice‐daily, with at least 6‐h intervals between assessments. Urine samples were collected on one day before surgery, and 1st day and 3rd day after surgery. The primary outcome was the presence of postoperative delirium, and association of the level of UACR with postoperative delirium was evaluated with unadjusted/adjusted analyses and multivariable logistic regression.

Results

Postoperative delirium was observed in 26.75% (107 of 400) of patients within 3 days post‐surgery. UACR‐Pre (OR, 1.30; 95% CI, 1.14–1.49, p < 0.001), UACR‐POD1 (OR, 1.20; 95% CI, 1.13–1.27, p < 0.001), and UACR‐POD3 (OR, 1.14; 95% CI, 1.08–1.20, p < 0.001) between the delirium and non‐delirium groups show a significant difference, even after adjusting for age, education levels, and other factors.

Conclusion

As the marker of endothelial dysfunction, the high perioperative UACR value may be linked to the postoperative delirium in elderly patients undergoing elective non‐cardiac surgery.

Brain metastases and treatment: multiplying cognitive toxicities

INTRODUCTION

Thirty per cent of cancer patients develop brain metastases, with multiple combination or sequential treatment modalities available, to treat systemic or central nervous system (CNS) disease. Most patients experience toxicities as a result of these treatments, of which cognitive impairment is one of the adverse events most commonly reported, causing major impairment of the patient's quality of life. Areas covered: This article reviews the role of cancer treatments in cognitive decline of patients with brain metastases: surgery, radiotherapy, chemotherapy, targeted therapies, immunotherapies and hormone therapy. Pathological and molecular mechanisms, as well as future directions for limiting cognitive toxicities are also presented. Other causes of cognitive impairment in this population are discussed in order to refine the benefit-risk balance of each treatment modality. Expert opinion: Cumulative cognitive toxicity should be taken into account, and tailored to the patient's cognitive risk in the light of the expected survival benefit. Standardization of cognitive assessment in this context is needed in order to better appreciate each treatment's responsibility in cognitive impairment, keeping in mind disease itself impacts cognition in this context.