Update of the European Society of Anaesthesiology and Intensive Care Medicine evidence-based and consensus-based guideline on postoperative delirium in adult patients.

Postoperative delirium (POD) remains a common, dangerous and resource-consuming adverse event but is often preventable. The whole peri-operative team can play a key role in its management. This update to the 2017 ESAIC Guideline on the prevention of POD is evidence-based and consensus-based and considers the literature between 01 April 2015, and 28 February 2022. The search terms of the broad literature search were identical to those used in the first version of the guideline published in 2017. POD was defined in accordance with the DSM-5 criteria. POD had to be measured with a validated POD screening tool, at least once per day for at least 3 days starting in the recovery room or postanaesthesia care unit on the day of surgery or, at latest, on postoperative day 1. Recent literature confirmed the pathogenic role of surgery-induced inflammation, and this concept reinforces the positive role of multicomponent strategies aimed to reduce the surgical stress response. Although some putative precipitating risk factors are not modifiable (length of surgery, surgical site), others (such as depth of anaesthesia, appropriate analgesia and haemodynamic stability) are under the control of the anaesthesiologists. Multicomponent preoperative, intra-operative and postoperative preventive measures showed potential to reduce the incidence and duration of POD, confirming the pivotal role of a comprehensive and team-based approach to improve patients' clinical and functional status.

Hereditary angioedema in cardiac surgery: Perioperative management considerations for a rare disease.

INTRODUCTION: Hereditary Angioedema is a rare disease caused by C1 esterase inhibitor deficiency leading to diffuse and potentially life-threatening oedema formation. Preventing attacks is critical, particularly for patients undergoing cardiac surgery. CASE REPORT: We report a case of a 71-years-old woman with a history of Hereditary Angioedema scheduled for open-heart surgery on Cardiopulmonary Bypass. Multidisciplinar teamwork and patient-targeted strategy were crucial to obtain a favorable outcome. DISCUSSION: Cardiac surgery is a major stressor for Angioedema attacks because of Complement cascade and inflammatory response activation leading to potential life-threatening oedema formation. In literature only few cases of complex open heart surgery under Cardiopulmonary Bypass are described. CONCLUSION: Continuous updating and multidisciplinarity are key elements to manage patients with Hereditary Angioedema in cardiac surgery in order to reduce morbidity and mortality.

A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead.

PURPOSE: To date, the assessment of disability in older people is obtained utilizing a Comprehensive Geriatric Assessment (CGA). However, it is often difficult to understand which areas of CGA are most predictive of the disability. The aim of this study is to evaluate the possibility to early predict-1 year ahead-the disability level of a patient using machine leaning models. METHODS: Community-dwelling older people were enrolled in this study. CGA was made at baseline and at 1 year follow-up. After collecting input/independent variables (i.e., age, gender, schooling followed, body mass index, information on smoking, polypharmacy, functional status, cognitive performance, depression, nutritional status), we performed two distinct Support Vector Machine models (SVMs) able to predict functional status 1 year ahead. To validate the choice of the model, the results achieved with the SVMs were compared with the output produced by simple linear regression models. RESULTS: 218 patients (mean age = 78.01; SD = 7.85; male = 39%) were recruited. The combination of the two SVMs is able to achieve a higher prediction accuracy (exceeding 80% instances correctly classified vs 67% instances correctly classified by the combination of the two linear regression models). Furthermore, SVMs are able to classify both the three categories, self sufficiently, disability risk and disability, while linear regression model separates the population only in two groups (self-sufficiency and disability) without identifying the intermediate category (disability risk) which turns out to be the most critical one. CONCLUSIONS: The development of such a model can contribute to the early detection of patients at risk of self-sufficiency loss.