Alternative Approaches Can Reduce the Use of Test Animals under REACH.

Abstract not availableJRC.I-Institute for Health and Consumer Protection (Ispra

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Burgerkracht bij krimp! : wat kunnen en willen bewoners doen in het beheer van de openbare ruimte?

Nu veel gemeenten moeten bezuinigen, worden burgerparticipatie en burgerinitiatief soms gezien als mogelijkheid om kosten te besparen op groenbeheer. Waar in het verleden burgers soms tegen de klippen op hun inzet in het groen moesten bevechten, worden ze nu uitgenodigd om een grotere rol te nemen. Vooral in krimpgemeenten zien we dat gebeuren. Voor deze gemeenten is de nood ten aanzien van het groenbeheer het hoogst. Ze hebben minder inkomsten door het dalend aantal bewoners en doordat er niet meer wordt gebouw

Remarkable changes in the choice of timing to discuss organ donation with the relatives of a patient: a study in 228 organ donations in 20 years

Introduction: We studied whether the choice of timing of discussing organ donation for the first time with the relatives of a patient with catastrophic brain injury in The Netherlands has changed over time and explored its possible consequences. Second, we investigated how thorough the process of brain death determination was over time by studying the number of medical specialists involved. And we studied the possible influence of the Donor Register on the consent rate.Methods: We performed a retrospective chart review of all effectuated brain dead organ donors between 1987 and 2009 in one Dutch university hospital with a large neurosurgical serving area.Results: A total of 271 medical charts were collected, of which 228 brain dead patients were included. In the first period, organ donation was discussed for the first time after brain death determination (87%). In 13% of the cases, the issue of organ donation was raised before the first EEG. After 1998, we observed a shift in this practice. Discussing organ donation for the first time after brain death determination occurred in only 18% of the cases. In 58% of the cases, the issue of organ donation was discussed before the first EEG but after confirming the absence of all brain stem reflexes, and in 24% of the cases, the issue of organ donation was discussed after the prognosis was deemed catastrophic but before a neurologist or neurosurgeon assessed and determined the absence of all brain stem reflexes as required by the Dutch brain death determination protocol.Conclusions: The phases in the process of brain death determination and the time at which organ donation is first discussed with relatives have changed over time. Possible causes of this chang

Slow recruitment in the HIMALAIA study:lessons for future clinical trials in patients with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on feasibility data

Background : Our randomized clinical trial on induced hypertension in patients with delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) was halted prematurely due to unexpected slow recruitment rates. This raised new questions regarding recruitment feasibility. As our trial can therefore be seen as a feasibility trial, we assessed the reasons for the slow recruitment, aiming to facilitate the design of future randomized trials in aSAH patients with DCI or other critically ill patient categories. Methods : Efficiency of recruitment and factors influencing recruitment were evaluated, based on the patient flow in the two centers that admitted most patients during the study period. We collected numbers of patients who were screened for eligibility, provided informed consent, and developed DCI and who eventually were randomized. Results : Of the 862 aSAH patients admitted in the two centers during the course of the trial, 479 (56%) were eligible for trial participation of whom 404 (84%) were asked for informed consent. Of these, 188 (47%) provided informed consent, of whom 50 (27%) developed DCI. Of these 50 patients, 12 (24%) could not be randomized due to a logistic problem or a contraindication for induced hypertension emerging at the time of randomization, and four (8%) were missed for randomization. Eventually, 34 patients were randomized and received intervention or control treatment. Conclusions : Enrolling patients in a randomized trial on a treatment strategy for DCI proved unfeasible: only 1 out of 25 admitted and 1 out of 14 eligible patients could eventually be randomized. These rates, caused by a large proportion of ineligible patients, a small proportion of patients providing informed consent, and a large proportion of patients with contraindications for treatment, can be used to make sample size calculations for future randomized trials in DCI or otherwise critically ill patients. Facilitating informed consent through improved provision of information on risks, possible benefits, and study procedures may result in improved enrolment