Validation and Management of Data Quality Metrics on ICU Patients

Real-time monitoring of lung function is one of the most promising applications of electrical impedance tomography (EIT). There are however some technical challenges that require validating diagnostic information extracted from EIT images. Two new data quality metrics are proposed and are applied on EIT and ventilator data acquired in an intensive care unit (ICU) setting. Their interpretation and usefulness in a clinical context is discussed

Data representation structure to support clinical decision-making in the pediatric intensive care unit: Interview study and preliminary decision support interface design

ABSTRACT: Background: Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients’ status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician’s cognitive processes and clinical decision-making skills. Objective: In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow. Methods: First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs. Results: We created a structure with 3 levels of abstraction—unit level, patient level, and system level—to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure. Conclusions: The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction

Simulations for Mechanical Ventilation in Children: Review and Future Prospects

ABSTRACT: Mechanical ventilation is a very effective therapy, but with many complications. Simulators are used in many fields, including medicine, to enhance safety issues. In the intensive care unit, they are used for teaching cardiorespiratory physiology and ventilation, for testing ventilator performance, for forecasting the effect of ventilatory support, and to determine optimal ventilatory management. They are also used in research and development of clinical decision support systems (CDSSs) and explicit computerized protocols in closed loop. For all those reasons, cardiorespiratory simulators are one of the tools that help to decrease mechanical ventilation duration and complications. This paper describes the different types of simulators described in the literature for physiologic simulation and modeling of the respiratory system, including a new simulator (SimulResp), and proposes a validation process for these simulators

Consensus guidelines for management of hyperammonaemia in paediatric patients receiving continuous kidney replacement therapy.

Hyperammonaemia in children can lead to grave consequences in the form of cerebral oedema, severe neurological impairment and even death. In infants and children, common causes of hyperammonaemia include urea cycle disorders or organic acidaemias. Few studies have assessed the role of extracorporeal therapies in the management of hyperammonaemia in neonates and children. Moreover, consensus guidelines are lacking for the use of non-kidney replacement therapy (NKRT) and kidney replacement therapies (KRTs, including peritoneal dialysis, continuous KRT, haemodialysis and hybrid therapy) to manage hyperammonaemia in neonates and children. Prompt treatment with KRT and/or NKRT, the choice of which depends on the ammonia concentrations and presenting symptoms of the patient, is crucial. This expert Consensus Statement presents recommendations for the management of hyperammonaemia requiring KRT in paediatric populations. Additional studies are required to strengthen these recommendations

Operational Definitions related to Pediatric Ventilator Liberation

BACKGROUND: Common, operational definitions are crucial to assess interventions and outcomes related to pediatric mechanical ventilation. These definitions can reduce unnecessary variability amongst research and quality improvement efforts, to ensure findings are generalizable and can be pooled to establish best practices. RESEARCH QUESTION: Can we establish operational definitions for key elements related to pediatric ventilator liberation using a combination of detailed literature review and consensus-based approaches? STUDY DESIGN AND METHODS: A panel of 26 international experts in pediatric ventilator liberation, two methodologists and two librarians conducted systematic reviews on eight topic areas related to pediatric ventilator liberation. Through a series of virtual meetings, we established draft definitions which were voted upon using an anonymous web-based process. Definitions were revised by incorporating extracted data gathered during the systematic review and discussed in another consensus meeting. A second round of voting was conducted to confirm the final definitions. RESULTS: In eight topic areas identified by the experts, 16 preliminary definitions were established. Based on initial discussion and the first round of voting, modifications were suggested for 11 of the 16 definitions. There was significant variability in how these items were defined in the literature reviewed. The final round of voting achieved ≥80% agreement for all 16 definitions in the following areas: what constitutes respiratory support (invasive mechanical ventilation and non-invasive respiratory support), liberation and failed attempts to liberate from invasive mechanical ventilation, liberation from respiratory support, duration of non-invasive respiratory support, total duration of invasive mechanical ventilation, spontaneous breathing trials, extubation readiness testing, 28-ventilator free days, and planned vs rescue use of post-extubation non-invasive respiratory support. INTERPRETATION: We propose these consensus-based definitions for elements of pediatric ventilator liberation, informed by evidence, be used for future quality improvement initiatives and research studies to improve generalizability, and facilitate comparison.The project was funded by Eunice Kennedy Shriver National Institute of Child Health (NICHD) and Human Development National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH) (R13HD102137), in addition to funds from the Department of Pediatrics at Indiana University School of Medicine, Indianapolis, Indiana