Executive Summary of the Second International Guidelines for the Diagnosis and Management of Pediatric Acute Respiratory Distress Syndrome (PALICC-2)

OBJECTIVES: We sought to update our 2015 work in the Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2) guidelines for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS), considering new evidence and topic areas that were not previously addressed. DESIGN: International consensus conference series involving 52 multidisciplinary international content experts in PARDS and four methodology experts from 15 countries, using consensus conference methodology, and implementation science. SETTING: Not applicable. PATIENTS: Patients with or at risk for PARDS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Eleven subgroups conducted systematic or scoping reviews addressing 11 topic areas: 1) definition, incidence, and epidemiology; 2) pathobiology, severity, and risk stratification; 3) ventilatory support; 4) pulmonary-specific ancillary treatment; 5) nonpulmonary treatment; 6) monitoring; 7) noninvasive respiratory support; 8) extracorporeal support; 9) morbidity and long-term outcomes; 10) clinical informatics and data science; and 11) resource-limited settings. The search included MEDLINE, EMBASE, and CINAHL Complete (EBSCOhost) and was updated in March 2022. Grading of Recommendations, Assessment, Development, and Evaluation methodology was used to summarize evidence and develop the recommendations, which were discussed and voted on by all PALICC-2 experts. There were 146 recommendations and statements, including: 34 recommendations for clinical practice; 112 consensus-based statements with 18 on PARDS definition, 55 on good practice, seven on policy, and 32 on research. All recommendations and statements had agreement greater than 80%. CONCLUSIONS: PALICC-2 recommendations and consensus-based statements should facilitate the implementation and adherence to the best clinical practice in patients with PARDS. These results will also inform the development of future programs of research that are crucially needed to provide stronger evidence to guide the pediatric critical care teams managing these patients.</p

Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study

<div><p>Background</p><p>In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap.</p><p>Objective</p><p>The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (_5min SpO₂) after a ventilator setting change.</p><p>Data sources</p><p>Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%).</p><p>Performance metrics of predictive models</p><p>Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO₂ with area under the curves < 0.75.</p><p>Conclusion</p><p>This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels.</p></div

Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia

Intravenous Immunoglobulin Use In Critically Ill Children

Purpose: The use of intravenous immunoglobulins (IVIG) has increased significantly in the last decade causing challenges for blood suppliers to respond to the demand. Indications for which IVIG infusion should be given to critically ill children remain unclear. The objective of this study is to characterize the epidemiology of IVIG use in this population. Methods: We performed a single-center retrospective cohort study of all patients aged between 3 days and 18 years who received at least one IVIG infusion while hospitalized in the pediatric intensive care unit of the Centre hospitalier universitaire (CHU) Sainte-Justine, Montréal Quebec (Canada) between January 1, 2013 and December 31, 2018. Results: One hundred and seventy-two patients received a total of 342 IVIG infusions over the study period. Most common indications for IVIG infusions were staphylococcal or streptococcal toxic shock syndrome (n=53/342, 15.5%), immunoglobulin replacement in chylothorax (n=37/342, 10.9%), prophylaxis following bone marrow transplantation (n=31/342, 9.1%), myocarditis (n=25/342, 7.3%) and post-solid organ transplant complications (n=21/342, 6.1%). The median dose of IVIG per infusion was 0.95 g/kg (IQR 0.5-1.0) and median number of IVIG infusions per patient was one (IQR: 1-2). Seventy-nine percent of IVIG infusions given were administrated for off-label indications with regards to Health Canada recommendations. Conclusion: This study identified the most common indications for IVIG infusion in critically ill children in a tertiary care pediatric intensive care unit. Given the costs, the known adverse events associated with IVIG and the pressure that blood suppliers are facing to meet the demands, clinical trials are needed to evaluate the efficacy and safety of IVIG in conditions where use is significant

Evaluation of the SIMULRESP: A simulation software of child and teenager cardiorespiratory physiology

International audienceAbstract Background Mathematical models based on the physiology when programmed as a software can be used to teach cardiorespiratory physiology and to forecast the effect of various ventilatory support strategies. We developed a cardiorespiratory simulator for children called “SimulResp.” The purpose of this study was to evaluate the quality of SimulResp. Methods SimulResp quality was evaluated on accuracy, robustness, repeatability, and reproducibility. Blood gas values (pH, PaCO 2 , PaO 2, and SaO 2 ) were simulated for several subjects with different characteristics and in different situations and compared to expected values available as reference. The correlation between reference and simulated data was evaluated by the coefficient of determination and Intraclass correlation coefficient. The agreement was evaluated with the Bland & Altman analysis. Results SimulResp produced healthy child physiological values within normal range (pH 7.40 ± 0.5; PaCO 2 40 ± 5 mmHg; PaO 2 90 ± 10 mmHg; SaO 2 97 ± 3%) starting from a weight of 25−35 kg, regardless of ventilator support. SimulResp failed to simulate accurate values for subjects under 25 kg and/or affected with pulmonary disease and mechanically ventilated. Based on the repeatability was considered as excellent and the reproducibility as mild to good. SimulResp's prediction remains stable within time. Conclusions The cardiorespiratory simulator SimulResp requires further development before future integration into a clinical decision support system

Inductively coupled remote plasma-enhanced chemical vapor deposition (rPE-CVD) as a versatile route for the deposition of graphene micro- and nanostructures

Multiple layers of graphene thin films with micro-crystalline orientation and vertical graphene nano-sheets were grown on different substrates (i.e., polycrystalline nickel foil, Ni(111), highly oriented pyrolytic graphite) using a single-step process based on low-pressure remote Plasma-Enhanced Chemical Vapor Deposition (rPE-CVD). In contrast to previous studies, a novel basic approach to this technique including a new remote inductively coupled RF plasma source has been used to (i) minimize the orientational effect of the plasma electrical fields during the catalyst-free growth of graphene nano-sheets, (ii) warrant for a low graphene defect density via low plasma kinetics, (iii) decouple the dissociation process of the gas from the growth process of graphene on the substrate, (iv) tune the feedstock gas chemistry in view of improving the graphene growth, and (v) reduce the growth temperature as compared to conventional chemical vapor deposition (CVD). In order to study the various aspects of the rPE-CVD graphene growth modes and to assess the characteristics of the resulting graphene layers, Raman spectroscopy, XPS, SEM, and STM were used. The results give evidence for the successful performance of this new rPE-CVD plasma deposition source, that can be combined with in situ UHV-based processess for the production of, e. g., hybrid metal ferromagnet/graphene systems.A. R. G. thanks the Spanish Ministry of Economy and Competitiveness (MINECO) for its support through Grant No. CSD2010-00044 (Consolider NANOTHERM). M. P. thanks MINECO for its financial support through the Spanish PTA2014-09788-I fellowship. The work at ICMAB (Raman measurements) was carried out under the auspices of the Spanish Severo Ochoa Centre of Excellence program (grant SEV-2015-0496). SEM, STM, and XPS measurements were performed at ICN2 that acknowledges support from the Severo Ochoa Program (MINECO, Grant SEV-2013-0295).Peer Reviewe

Inductively coupled remote plasma-enhanced chemical vapor deposition (rPE-CVD) as a versatile route for the deposition of graphene micro- and nanostructures

Multiple layers of graphene thin films with micro-crystalline orientation and vertical graphene nano-sheets were grown on different substrates (i.e., polycrystalline nickel foil, Ni(111), highly oriented pyrolytic graphite) using a single-step process based on low-pressure remote Plasma-Enhanced Chemical Vapor Deposition (rPE-CVD). In contrast to previous studies, a novel basic approach to this technique including a new remote inductively coupled RF plasma source has been used to (i) minimize the orientational effect of the plasma electrical fields during the catalyst-free growth of graphene nano-sheets, (ii) warrant for a low graphene defect density via low plasma kinetics, (iii) decouple the dissociation process of the gas from the growth process of graphene on the substrate, (iv) tune the feedstock gas chemistry in view of improving the graphene growth, and (v) reduce the growth temperature as compared to conventional chemical vapor deposition (CVD). In order to study the various aspects of the rPE-CVD graphene growth modes and to assess the characteristics of the resulting graphene layers, Raman spectroscopy, XPS, SEM, and STM were used. The results give evidence for the successful performance of this new rPE-CVD plasma deposition source, that can be combined with in situ UHV-based processess for the production of, e. g., hybrid metal ferromagnet/graphene systems

A specific immune and lymphatic profile characterizes the pre-metastatic state of the sentinel lymph node in patients with early cervical cancer

peer reviewedThe lymph node (LN) pre-metastatic niche is faintly characterized in lymphophilic human neoplasia, although LN metastasis is considered as the strongest prognostic marker of patient survival. Due to its specific dissemination through a complex bilateral pelvic lymphatic system, early cervical cancer is a relevant candidate for investigating the early nodal metastatic process. In the present study, we analyzed in-depth both the lymphatic vasculature and the immune climate of pre-metastatic sentinel LN (SLN), in 48 cases of FIGO stage IB1 cervical neoplasms. An original digital image analysis methodology was used to objectively determine whole slide densities and spatial distributions of immunostained structures. We observed a marked increase in lymphatic vessel density (LVD) and a specific capsular and subcapsular distribution in pre-metastatic SLN when compared with non-sentinel counterparts. Such features persisted in the presence of nodal metastatic colonization. The inflammatory profile attested by CD8+, Foxp3, CD20 and PD-1expression was also significantly increased in pre-metastatic SLN. Remarkably, the densities of CD20+ B cells and PD-1 expressing germinal centers were positively correlated with LVD. All together, these data strongly support the existence of a pre-metastatic dialog between the primary tumor and the first nodal relay. Both lymphatic and immune responses contribute to the elaboration of a specific pre-metastatic microenvironment in human SLN. Moreover, this work provides evidence that, in the context of early cervical cancer, a pre-metastatic lymphangiogenesis occurs within the SLN (pre-metastatic niche) and is associated with a specific humoral immune response