OBJECTIVE: Traumatic subarachnoid hemorrhage (tSAH) is a frequent finding after closed-head injuries, and its presence is a powerful factor associated with poor outcome. The exact mechanism linking tSAH and an adverse outcome is poorly understood. The aim of this study was to identify the factors that may predict outcomes and changes in the computed tomographic (CT) scans of lesions in a selected population of tSAH patients. METHODS: We evaluated 141 patients admitted consecutively from January 1, 1997, to January 31, 1999, with a CT diagnosis of tSAH. The admission and “worst” CT scans were recorded. CT scan changes were reported as “significant CT progression” (changes in the Marshall classification) or “any CT progression.” The amount of subarachnoid blood was recorded using a modified Fisher classification. Outcome was assessed at 6 months after injury with the Glasgow Outcome Scale. RESULTS: Twenty-eight patients (19.9%) had an unfavorable Glasgow Outcome Scale outcome. In the univariate analysis, prognosis was significantly related to age, admission Glasgow Coma Scale score, Marshall CT classification score at admission and on the worst CT scan, amount of tSAH, and volume of the associated brain contusions. From multivariate analysis, the only factors independently related to outcome were the Glasgow Coma Scale score (P < 0.01) and size of the tSAH at admission (P < 0.001). Thirty-four patients (24.1%) had significant CT lesion progression, and 66 patients (46.8%) had some lesion progression. Patients having significant progression of the lesion had a higher risk of an unfavorable outcome (32 versus 10%; P = 0.004). Unadjusted factors predicting CT progression were the Glasgow Coma Scale score at admission, the Marshall classification at admission, the amount of subarachnoid blood, and the presence or volume of associated brain contusions at admission. Independent factors associated with significant CT progression were the amount of tSAH (P < 0.001) and the presence or volume of brain contusions at admission (P < 0.001). CONCLUSION: The outcome of patients with tSAH at admission is related in a logistic regression analysis to the admission Glasgow Coma Scale score and to the amount of subarachnoid blood. These patients also have a significant risk of CT progression. The amount of subarachnoid blood and the presence of associated parenchymal damage are powerful independent factors associated with CT progression, thus linking poor outcomes and CT changes.

Chronic low back pain (CLBP) is a chronic pain syndrome in the lower back region, lasting for at least 3 months. CLBP represents the second leading cause of disability worldwide being a major welfare and economic problem. The prevalence of CLBP in adults has increased more than 100% in the last decade and continues to increase dramatically in the aging population, affecting both men and women in all ethnic groups, with a significant impact on functional capacity and occupational activities. It can also be influenced by psychological factors, such as stress, depression and/or anxiety. Given this complexity, the diagnostic evaluation of patients with CLBP can be very challenging and requires complex clinical decision-making. Answering the question “what is the pain generator” among the several structures potentially involved in CLBP is a key factor in the management of these patients, since a mis-diagnosis can generate therapeutical mistakes. Traditionally, the notion that the etiology of 80% to 90% of LBP cases is unknown has been mistaken perpetuated across decades. In most cases, low back pain can be attributed to specific pain generator, with its own characteristics and with different therapeutical opportunity. Here we discuss about radicular pain, facet Joint pain, sacro-iliac pain, pain related to lumbar stenosis, discogenic pain. Our article aims to offer to the clinicians a simple guidance to identify pain generators in a safer and faster way, relying a correct diagnosis and further therapeutical approach.

OBJECTIVE: Controversy exists about the indications and timing for surgery in head injured patients with an intradural mass lesion. The aim of this study was to survey contemporary approaches to the treatment of head injured patients with an intradural lesion, placing a particular focus on the utilization of decompressive craniectomy. METHODS: A prospective international survey was conducted over a 3-month period in 67 centers from 24 countries on the neurosurgical management of head injured patients with an intradural mass lesion and/or radiological signs of raised intracranial pressure. Information was obtained about demographic, clinical, and radiological features; surgical management, and mortality at discharge. RESULTS: Over the period of the study, data were collected about 729 patients consecutively admitted to one of the participating centers. The survey included 397 patients with a severe head injury (Glasgow Coma Scale [GCS] 3–8), 155 with a moderate head injury (GCS 9–12) and 143 patients with a mild head injury (GCS 13–15). An operation was performed on 502 patients (69%). Emergency surgery (<24 h) was most frequently performed for patients with an extracerebral mass lesions (subdural hematomas) whereas delayed surgery was most frequently performed for an intracerebral hematoma or contusion. Decompressive craniectomy was performed in a substantial number of patients, either during an emergency procedure (n = 134, 33%) or a delayed procedure (n = 47, 31%). The decompressive procedure was nearly always combined with evacuation of a mass lesion. The size of the decompression was however considered too small in 25% of cases. CONCLUSION: The results provide a contemporary picture of neurosurgical surgical approaches to the management of head injured patients with an intradural mass lesion and/or signs of raised intracranial pressure in some Neurosurgical Units across the world. The relative benefits of early versus delayed surgery in patients with intraparenchymal lesions and on the indications, technique and benefits of decompressive craniectomy could be topics for future head injury research.

Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician's decisions. This scenario can include diagnosis, prognosis, and treatment in anesthesiology, intensive care medicine, and pain medicine. Machine Learning is a subcategory of AI. It is based on algorithms trained for decisions making that automatically learn and recognize patterns from data. This article aims to offer an overview of the potential application of AI in anesthesiology and analyzes the operating principles of machine learning Every Machine Learning pathway starts from task definition and ends in model application. High-performance characteristics and strict quality controls are needed during its progress. During this process, different measures can be identified (pre-processing, exploratory data analysis, model selection, model processing and evaluation). For inexperienced operators, the process can be facilitated by ad hoc tools for data engineering, machine learning, and analytics.