Development and implementation of explicit computerized protocols for mechanical ventilation in children

Mechanical ventilation can be perceived as a treatment with a very narrow therapeutic window, i.e., highly efficient but with considerable side effects if not used properly and in a timely manner. Protocols and guidelines have been designed to make mechanical ventilation safer and protective for the lung. However, variable effects and low compliance with use of written protocols have been reported repeatedly. Use of explicit computerized protocols for mechanical ventilation might very soon become a "must." Several closed loop systems are already on the market, and preliminary studies are showing promising results in providing patients with good quality ventilation and eventually weaning them faster from the ventilator. The present paper defines explicit computerized protocols for mechanical ventilation, describes how these protocols are designed, and reports the ones that are available on the market for children

Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals

Photoplethysmogram (PPG) signals are widely used in healthcare for monitoring vital signs, but they are susceptible to motion artifacts that can lead to inaccurate interpretations. In this study, the use of label propagation techniques to propagate labels among PPG samples is explored, particularly in imbalanced class scenarios where clean PPG samples are significantly outnumbered by artifact-contaminated samples. With a precision of 91%, a recall of 90% and an F1 score of 90% for the class without artifacts, the results demonstrate its effectiveness in labeling a medical dataset, even when clean samples are rare. For the classification of artifacts our study compares supervised classifiers such as conventional classifiers and neural networks (MLP, Transformers, FCN) with the semi-supervised label propagation algorithm. With a precision of 89%, a recall of 95% and an F1 score of 92%, the KNN supervised model gives good results, but the semi-supervised algorithm performs better in detecting artifacts. The findings suggest that the semi-supervised algorithm label propagation hold promise for artifact detection in PPG signals, which can enhance the reliability of PPG-based health monitoring systems in real-world applications.Comment: Under preparation to submit to IEEE for possible publication

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36 % of total) and 3503 negative cases classified by two independent physicians using a standardized approach. The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89 % accuracy, 88 % recall, and 89 % precision. Furthermore, a generative autoencoder learning algorithm was proposed to leverage the sparsity reduction that achieved 91% accuracy, 91% recall, and 91% precision. This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution. Further work is needed to use the same methodology in other institutions and other languages.Comment: Submitting to IEEE Transactions on Biomedical Engineering. arXiv admin note: text overlap with arXiv:2104.0393

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

Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children: a cochrane systematic review and meta-analysis

Automated weaning systems may improve adaptation of mechanical support for a patient's ventilatory needs and facilitate systematic and early recognition of their ability to breathe spontaneously and the potential for discontinuation of ventilation. Our objective was to compare mechanical ventilator weaning duration for critically ill adults and children when managed with automated systems versus non-automated strategies. Secondary objectives were to determine differences in duration of ventilation, intensive care unit (ICU) and hospital length of stay (LOS), mortality, and adverse events. Electronic databases were searched to 30 September 2013 without language restrictions. We also searched conference proceedings; trial registration websites; and article reference lists. Two authors independently extracted data and assessed risk of bias. We combined data using random-effects modelling. We identified 21 eligible trials totalling 1,676 participants. Pooled data from 16 trials indicated that automated systems reduced the geometric mean weaning duration by 30% (95% confidence interval (CI) 13% to 45%), with substantial heterogeneity (I(2) = 87%, P <0.00001). Reduced weaning duration was found with mixed or medical ICU populations (42%, 95% CI 10% to 63%) and Smartcare/PS (28%, 95% CI 7% to 49%) but not with surgical populations or using other systems. Automated systems reduced ventilation duration with no heterogeneity (10%, 95% CI 3% to 16%) and ICU LOS (8%, 95% CI 0% to 15%). There was no strong evidence of effect on mortality, hospital LOS, reintubation, self-extubation and non-invasive ventilation following extubation. Automated systems reduced prolonged mechanical ventilation and tracheostomy. Overall quality of evidence was high. Automated systems may reduce weaning and ventilation duration and ICU stay. Due to substantial trial heterogeneity an adequately powered, high quality, multi-centre randomized controlled trial is neede

L'application de la loi du 31 décembre 1989 par les tribunaux d'instance: l'exemple de cinq tribunaux de la région Rhône-Alpes

Etude consacrée au second volet contenu dans le titre premier de la loi n° 89-1010 du 31 décembre 1989 intitulée "loi relative à la prévention et au règlement des difficultés liées au surendettement des particuliers et des familles", qui institue deux procédures visant à permettre le redressement de la situation de particuliers "surendettés". La loi de 1989 a été complétée par un décret n° 90-175 du 21 février 1990. Elle a été ultérieurement modifiée par la loi n° 91-650 du 9 juillet 1991qui a transféré les compétences du tribunal d'instance au juge de l'exécution. Le présent rapport retrace les résultats d'une enquête qui a été menée sur l'application de ces textes par cinq tribunaux d'instance de la région Rhône-Alpes (Grenoble, Lyon, Saint-Etienne, Trévoux, Valence), pendant les deux premières années qui ont suivi l'entrée en vigueur de la loi, soit entre le 1er mars 1990 et le 28 février 1992

Remote Design of a Pediatric Intensive Care Unit Dashboard in Time of Pandemics

To support the pediatric intensive care unit with the COVID-19 pandemic, we followed a user-centered design process to create a dashboard in a context where direct access to users was impossible. To this end, we applied contextual inquiry, user interview, requirement definition, iterative design with user validation and usability testing in a remote fashion. Being unable to be physically present at the hospital limited our understanding of the context of use, extended the duration of the study and limited the number of interviews and testing sessions. However, we were able to benefit from the experience of our team members, adopt an efficient decision-making method to select appropriate requirements and use remote moderated usability testing to conform our design process to an aggressive timeline

Situation awareness-oriented dashboard in ICUs in support of resource management in time of pandemics

ABSTRACT: In a pediatric intensive care unit (PICU) of 32 beds, clinicians manage resources 24 hours a day, 7 days a week, from a large-screen dashboard implemented in 2017. This resource management dashboard efficiently replaces the handwriting information displayed on a whiteboard, offering a synthetic view of the bed’s layout and specific information on staff and equipment at bedside. However, in 2020 when COVID-19 hit, the resource management dashboard showed several limitations. Mainly, its visualization offered to the clinicians limited situation awareness (SA) to perceive, understand and predict the impacts on resource management and decision-making of an unusual flow of patients affected by the most severe form of coronavirus. To identify the SA requirements during a pandemic, we conducted goal-oriented interviews with 11 clinicians working in ICUs. The result is the design of an SA-oriented dashboard with 22 key indicators (KIs): 1 on the admission capacity, 15 at bedside and 6 displayed as statistics in the central area. We conducted a usability evaluation of the SA-oriented dashboard compared to the resource management dashboard with 6 clinicians. The results showed five usability improvements of the SA-oriented dashboard and five limitations. Our work contributes to new knowledge on the clinicians’ SA requirements to support resource management and decision-making in ICUs in times of pandemics

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