Wean Earlier and Automatically with New technology (the WEAN study): a protocol of a multicentre, pilot randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Weaning is the process during which mechanical ventilation is withdrawn and the work of breathing is transferred from the ventilator back to the patient. Prolonged weaning is associated with development of ventilator-related complications and longer stays in the Intensive Care Unit (ICU). Computerized or Automated Weaning is a novel weaning strategy that continuously measures and adapts ventilator support (by frequently measuring and averaging three breathing parameters) and automatically conducts Spontaneous Breathing Trials to ascertain whether patients can resume autonomous breathing. Automated Weaning holds promise as a strategy to reduce the time spent on the ventilator, decrease ICU length of stay, and improve clinically important outcomes.</p> <p>Methods/Design</p> <p>A pilot weaning randomized controlled trial (RCT) is underway in the ICUs of 8 Canadian hospitals. We will randomize 90 critically ill adults requiring invasive ventilation for at least 24 hours and identified at an early stage of the weaning process to either Automated Weaning (SmartCare™) or Protocolized Weaning. The results of a National Weaning Survey informed the design of the Protocolized Weaning arm. Both weaning protocols are operationalized in Pressure Support mode, include opportunities for Spontaneous Breathing Trials, and share a common sedation protocol, oxygen titration parameters, and extubation and reintubation criteria. The primary outcome of the WEAN study is to evaluate compliance with the proposed weaning and sedation protocols. A key secondary outcome of the pilot RCT is to evaluate clinician acceptance of the weaning and sedation protocols. Prior to initiating the WEAN Study, we conducted a run-in phase, involving two patients per centre (randomizing the first participant to either weaning strategy and assigning the second patient to the alternate strategy) to ensure that participating centres could implement the weaning and sedation protocols and complete the detailed case report forms.</p> <p>Discussion</p> <p>Mechanical ventilation studies are difficult to implement; requiring protocols to be operationalized continuously and entailing detailed daily data collection. As the first multicentre weaning RCT in Canada, the WEAN Study seeks to determine the feasibility of conducting a large scale future weaning trial and to establish a collaborative network of ICU clinicians dedicated to advancing the science of weaning.</p> <p>Trial Registration Number</p> <p>ISRCTN43760151</p

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

A Real-Time Agent Model in an Asynchronous-Object Environment

To build intelligent control systems for real-life applications, we need to design software agents which combine cognitive abilities to reason about complex situations, and reactive abilities to meet hard deadlines. We propose an operational agent model which mixes AI techniques and real-time performances. Our model is based on an ATN (Augmented Transition Network) to dynamically adapt the agent&apos;s behaviour to changes in the environment. Each agent uses a production system and is provided with a synchronization mechanism to avoid the possible inconsistencies of the asynchronous execution of several rule-bases. Our agents communicate by message-passing and are implemented in an asynchronous-object environment. We report on the use of our agent model in intensive care patient monitoring

A realistic model for temporal reasoning in real-time patient monitoring

Time is a central factor in patient monitoring. Introduction of domain-dependent knowledge is essential to ensure efficiency of time managers especially when embedded into systems that interact with real world. We present a realistic temporal reasoning model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgettin