High-flow nasal oxygen versus conventional oxygen therapy and noninvasive ventilation in COVID-19 respiratory failure: a systematic review and network meta-analysis of randomised controlled trials.

BACKGROUND: Noninvasive methods of respiratory support, including noninvasive ventilation (NIV), continuous positive airway pressure (CPAP), and high-flow nasal oxygen (HFNO), are potential strategies to prevent progression to requirement for invasive mechanical ventilation in acute hypoxaemic respiratory failure. The COVID-19 pandemic provided an opportunity to understand the utility of noninvasive respiratory support among a homogeneous cohort of patients with contemporary management of acute respiratory distress syndrome. We performed a network meta-analysis of studies evaluating the efficacy of NIV (including CPAP) and HFNO, compared with conventional oxygen therapy (COT), in patients with COVID-19. METHODS: PubMed, Embase, and the Cochrane library were searched in May 2023. Standard random-effects meta-analysis was used first to estimate all direct pairwise associations and the results from all studies were combined using frequentist network meta-analysis. Primary outcome was treatment failure, defined as discontinuation of HFNO, NIV, or COT despite progressive disease. Secondary outcome was mortality. RESULTS: We included data from eight RCTs with 2302 patients, (756 [33%] assigned to COT, 371 [16%] to NIV, and 1175 [51%] to HFNO). The odds of treatment failure were similar for NIV (P=0.33) and HFNO (P=0.25), and both were similar to that for COT (reference category). The odds of mortality were similar for all three treatments (odds ratio for NIV vs COT: 1.06 [0.46-2.44] and HFNO vs COT: 0.97 [0.57-1.65]). CONCLUSIONS: Noninvasive ventilation, high-flow nasal oxygen, and conventional oxygen therapy are comparable with regards to treatment failure and mortality in COVID-19-associated acute respiratory failure. PROSPERO REGISTRATION: CRD42023426495.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.

BACKGROUND: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS: A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS: A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS: The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.

The Extreme Lateral Approach to the Craniovertebral Junction: An Anatomical Study.

BACKGROUND: The extreme lateral approach is a direct lateral approach which allows a good control of the entire length of the vertebral artery (VA), the jugular foramen, the lowest cranial nerves, and the jugular-sigmoid complex. Herein we try to exploit the variants of the approach and we identify indications, advantages, and disadvantages. METHODS: All phases of the study were conducted at the Institute of Public Health Section of Legal Medicine and Insurance of the University. We performed the extreme lateral approach in four subjects, who died between 24 and 48 h before in non-traumatic circumstances (three men and one woman). RESULTS: The great auricular nerve, the spinal accessory, the branches of the first ventral spinal nerves, the jugular vein, and the vertebral artery were identified in all the cadavers. In all cases the right VA exited from the transverse foramen of C1. The site of SCM piercing the accessory nerve was at a distance from the tip of the mastoid between 3 and 4 cm (3.3 in one case, 3.4 in 2 cases, and 3.7 in one case). No vessels and nerves have been damaged after being identified and isolated. CONCLUSIONS: Extradural lesions at the ventro-lateral aspect of the CVJ may require an extreme lateral approach, a procedure more aggressive comparing with far lateral approach, which represents a reasonable option for large anterior and anterolateral lesions when greater exposure is required.