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Intensive care survivors often experience post-intensive care sequelae, which are frequently gathered together under the term “post-intensive care syndrome” (PICS). The consequences of PICS on quality of life, health-related costs and hospital readmissions are real public health problems. In the present Viewpoint, we summarize current knowledge and gaps in our understanding of PICS and approaches to management.

Background Clinical research on nutritional and metabolic interventions in critically ill patients is heterogenous regarding time points, outcomes and measurement instruments used, impeding intervention development and data syntheses, and ultimately worsening clinical outcomes. We aimed to identify and develop a set of core outcome domains and associated measurement instruments to include in all research in critically ill patients. Methods An updated systematic review informed a two-stage modified Delphi consensus process (domains followed by instruments). Measurement instruments for domains considered ‘essential’ were taken through the second stage of the Delphi and a subsequent consensus meeting. Results In total, 213 participants (41 patients/caregivers, 50 clinical researchers and 122 healthcare professionals) from 24 countries contributed. Consensus was reached on time points (30 and 90 days post-randomisation). Three domains were considered ‘essential’ at 30 days (survival, physical function and Infection) and five at 90 days (survival, physical function, activities of daily living, nutritional status and muscle/nerve function). Core ‘essential’ measurement instruments reached consensus for survival and activities of daily living, and ‘recommended’ measurement instruments for physical function, nutritional status and muscle/nerve function. No consensus was reached for a measurement instrument for Infection. Four further domains met criteria for ‘recommended,’ but not ‘essential,’ to measure at 30 days post-randomisation (organ dysfunction, muscle/nerve function, nutritional status and wound healing) and three at 90 days (frailty, body composition and organ dysfunction). Conclusion The CONCISE core outcome set is an internationally agreed minimum set of outcomes for use at 30 and 90 days post-randomisation, in nutritional and metabolic clinical research in critically ill adults.

A Patient Data Management System (PDMS) is being developed for use in theIntensive Care Unit (ICU) of the Montreal Children's Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Placing such a computer based system in a critical care environment such as the ICU not only requires extensive verification and validation t o ensure its proper functioning, but also proper training of the medical staff. A patient data simulator was developed for the purpose o f complete testing of the PDMS. The simulator also allows for training the medical staff independantly of hospital facilities and a t a remote location from the ICU. This paper outlines the design and functionality of the simulator and focuses on it's implementation using OS/2's Presentation Manager window environment. Some experimental results and performance evaluations are presented, along with areas of future development and use.

A real-time trend analysis module is currently being developed for the Patient Data Management System (PDMS) at the pediatric intensive care unit of the Montreal Children's Hospital. The PDMS is based on a personal computer acquiring, in real-time, patient data from a bcal area network of fourteen bedside monitors and displaying their trends graphically, among other tasks. The system is based on IBM model 50 running under the OS/2 multitasking operating system and uses the 8514/A high resolution color video display.Although threshold alarms are an efficient way of attracting the user's attention, only an automatic mal-time analysis of the trends can provide a warning about slowly varying trends or short interval disturbances before the underlying condition results in a critical situation. This paper presents the design and implementation of the real-time trend analysis module of the PDMS system based on two different supervised neural networks using the general delta rule karning mechanism. The integration of such a module with a diagnosis expert system is also discussed.