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- Picetti, Edoardo, et al.
(author)
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Early management of isolated severe traumatic brain injury patients in a hospital without neurosurgical capabilities : a consensus and clinical recommendations of the World Society of Emergency Surgery (WSES)
- 2023
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In: World Journal of Emergency Surgery. - : BioMed Central (BMC). - 1749-7922. ; 18:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: Severe traumatic brain-injured (TBI) patients should be primarily admitted to a hub trauma center (hospital with neurosurgical capabilities) to allow immediate delivery of appropriate care in a specialized environment. Sometimes, severe TBI patients are admitted to a spoke hospital (hospital without neurosurgical capabilities), and scarce data are available regarding the optimal management of severe isolated TBI patients who do not have immediate access to neurosurgical care.METHODS: A multidisciplinary consensus panel composed of 41 physicians selected for their established clinical and scientific expertise in the acute management of TBI patients with different specializations (anesthesia/intensive care, neurocritical care, acute care surgery, neurosurgery and neuroradiology) was established. The consensus was endorsed by the World Society of Emergency Surgery, and a modified Delphi approach was adopted.RESULTS: A total of 28 statements were proposed and discussed. Consensus was reached on 22 strong recommendations and 3 weak recommendations. In three cases, where consensus was not reached, no recommendation was provided.CONCLUSIONS: This consensus provides practical recommendations to support clinician's decision making in the management of isolated severe TBI patients in centers without neurosurgical capabilities and during transfer to a hub center.
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- Hutchinson, Peter J, et al.
(author)
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Consensus statement from the 2014 International Microdialysis Forum
- 2015
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In: Intensive Care Medicine. - : Springer Science and Business Media LLC. - 0342-4642 .- 1432-1238. ; 41:9, s. 1517-1528
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Journal article (peer-reviewed)abstract
- Microdialysis enables the chemistry of the extracellular interstitial space to be monitored. Use of this technique in patients with acute brain injury has increased our understanding of the pathophysiology of several acute neurological disorders. In 2004, a consensus document on the clinical application of cerebral microdialysis was published. Since then, there have been significant advances in the clinical use of microdialysis in neurocritical care. The objective of this review is to report on the International Microdialysis Forum held in Cambridge, UK, in April 2014 and to produce a revised and updated consensus statement about its clinical use including technique, data interpretation, relationship with outcome, role in guiding therapy in neurocritical care and research applications.
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- Donald, Rob, et al.
(author)
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Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care
- 2019
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In: Journal of clinical monitoring and computing. - : Springer Science and Business Media LLC. - 1387-1307 .- 1573-2614. ; 33:1, s. 39-51
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Journal article (peer-reviewed)abstract
- Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient's stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9-46.1) and a specificity of 91.5% (95% CI 89.0-93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1-53.2) and specificity of 85.6% (95% CI 82.3-88.8). With a decision threshold of 0.3, > 15min warning of patient instability can be achieved. We have shown, using advanced machine learning techniques running in a live neuro-critical care environment, that it would be possible to give neurointensive teams early warning of potential hypotensive events before they emerge, allowing closer monitoring and earlier clinical assessment in an attempt to prevent the onset of hypotension. The multi-centre clinical infrastructure developed to support the clinical studies provides a solid base for further collaborative research on data quality, false positive correction and the display of early warning data in a clinical setting.
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