Replacement of assumed preoperative deficits, in addition to generous substitution of an unsubstantiated increased insensible perspiration and third space loss, plays an important role in current perioperative fluid regimens. The consequence is a positive fluid balance and weight gain of up to 10 kg, which may be related to severe complications. Because the intravascular blood volume remains unchanged and insensible perspiration is negligible, the fluid must accumulate inside the body. This concept brings into question common liberal infusion regimens. Blood volume after fasting is normal, and a fluid-consuming third space has never been reliably shown. Crystalloids physiologically load the interstitial space, whereas colloidal volume loading deteriorates a vital part of the vascular barrier. The endothelial glycocalyx plays a key role and is destroyed not only by ischemia and surgery, but also by acute hypervolemia. Therefore, undifferentiated fluid handling may increase the shift toward the interstitial space. Using the right kind of fluid in appropriate amounts at the right time might improve patient outcome.

/pdf/a-rational-approach-to-perioperative-fluid-management-3pdfku4jx3.pdf

A rational approach to perioperative fluid management.

Enhanced recovery after surgery is well established in specialties such as colorectal surgery. It is achieved through the introduction of multiple evidence-based perioperative measures that aim to diminish postoperative organ dysfunction while facilitating recovery. This review aims to present consensus recommendations for the optimal perioperative management of patients undergoing thoracic surgery (principally lung resection). A systematic review of meta-analyses, randomized controlled trials, large non-randomized studies and reviews was conducted for each protocol element. Smaller prospective and retrospective cohort studies were considered only when higher-level evidence was unavailable. The quality of the evidence base was graded by the authors and used to form consensus recommendations for each topic. Development of these recommendations was endorsed by the Enhanced Recovery after Surgery Society and the European Society for Thoracic Surgery. Recommendations were developed for a total of 45 enhanced recovery items covering topics related to preadmission, admission, intraoperative care and postoperative care. Most are based on good-quality studies. In some instances, good-quality data were not available, and subsequent recommendations are generic or based on data extrapolated from other specialties. In other cases, no recommendation can currently be made because either equipoise exists or there is a lack of available evidence. Recommendations are based not only on the quality of the evidence but also on the balance between desirable and undesirable effects. Key recommendations include preoperative counselling, nutritional screening, smoking cessation, prehabilitation for high-risk patients, avoidance of fasting, carbohydrate loading, avoidance of preoperative sedatives, venous thromboembolism prophylaxis, prevention of hypothermia, short-acting anaesthetics to facilitate early emergence, regional anaesthesia, nausea and vomiting control, opioid-sparing analgesia, euvolemic fluid management, minimally invasive surgery, early chest drain removal, avoidance of urinary catheters and early mobilization after surgery. These guidelines outline recommendations for the perioperative management of patients undergoing lung surgery based on the best available evidence. As the recommendation grade for most of the elements is strong, the use of a systematic perioperative care pathway has the potential to improve outcomes after surgery.

/pdf/guidelines-for-enhanced-recovery-after-lung-surgery-560yyu9fh8.pdf

Guidelines for enhanced recovery after lung surgery: recommendations of the Enhanced Recovery After Surgery (ERAS®) Society and the European Society of Thoracic Surgeons (ESTS)

The practice of perioperative fluid therapy is variable, ranging from ‘high volume’ to ‘dry’ regimen. A review of the data on the effect of ‘high volume’ perioperative fluid therapy suggests that the resulting overhydration may have deleterious effects on cardiac and pulmonary function, and on recovery of gastrointestinal motility (postoperative ileus), tissue oxygenation, wound healing and coagulation. These observations call for randomized studies of the effects of ‘high’ vs ‘low’ volume replacement therapy on postoperative morbidity, in order to establish evidence-based guidelines for perioperative fluid management. Perioperative fluid replacement has been, and still is, the focus of much debate. This debate has primarily focused on the various types of fluid components available for replacement therapy, and not on the actual amount of fluid administered.

Pathophysiology and clinical implications of perioperative fluid excess

https://iris.unipa.it/bitstream/10447/55534/2/General-anaesthesia-versus-local-anaesthesia-for-carotid-surgery-%28GALA%29-a-multicentre%2c-randomised-controlled-trial_2008-2009_The-Lancet.pdf

General anaesthesia versus local anaesthesia for carotid surgery (GALA): a multicentre, randomised controlled trial.

https://www.gynecologiconcology-online.net/article/S0090-8258(15)30222-5/pdf

Guidelines for pre- and intra-operative care in gynecologic/oncology surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations — Part I

The stress response to trauma and surgery.

Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms.Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease. 

/pdf/the-role-of-machine-learning-in-developing-non-magnetic-23xoomrj.pdf

J.P. Desborough

Papers

The stress response to trauma and surgery.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review