Diagnosis Of Respiratory Distress Syndrome Research Articles

Effectiveness of Lung Ultrasound for Diagnosis of Acute Respiratory Distress Syndrome in Mechanically Ventilated Patients

Background: Acute respiratory distress syndrome (ARDS) requires rapid diagnosis for early intervention and improved outcomes. Lung ultrasound may be a reasonable alternative to chest X-ray for the identification of ARDS but the effectiveness of lung ultrasound in ARDS diagnosis is still uncertain. The objective of the study is to explore the efficacy of lung ultrasound (LUS) for diagnosis of ARDS in mechanically ventilated patients. Methods: A cross sectional study was conducted among 75 patients in Critical Care Centre, Combined Military Hospital, Dhaka from September 2021 to January 2022. The study was approved by the ethical committee in the hospital. In this study, the Bedside Lung Ultrasound in Emergency (BLUE) protocol for the immediate diagnosis of acute respiratory failure was followed. Purposive sampling technique was used. Data were analyzed by using Statistical Package For Social Sciences (SPSS) version 25.0. Results: A total of 75 patients were assessed. Among them male were 42(56%) and female 33(44%). The median age of patients was 48 years (Interquartile range 30-60). Primary diagnoses were pneumonia (22.67%), pulmonary oedema (20%), sepsis (20%) and trauma (17.33%). A total of 34(45.33%) patients fulfilled ‘CXR-based Berlin Definition’ and a total of 36(48%) patients were diagnosed as ARDS by ‘LUS-based Berlin Definition’. Considering the ‘CXR-based Berlin Definition’ as reference standard, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of lung ultrasound were 85.29%, 82.92%, 80.55%, 87.18% and 84% respectively. Conclusion: Lung ultrasound can be an effective tool for the diagnosis & management of ARDS in the intensive care unit. Bangladesh Armed Forces Med J Vol 56 No (2) December 2023, pp 51-57

[The value of point-of-care ultrasound in the diagnosis and management of acute respiratory distress syndrome].

Acute respiratory distress syndrome (ARDS) continues to cause significant morbidity and mortality worldwide. However, there are currently no corresponding specific medications. Lung, right heart, and diaphragm protective therapy is the cornerstone of ARDS management. Non-invasive, radiation-free, and transportation-free point-of-care ultrasound enables for real-time dynamic evaluation of the lung, right heart, and diaphragm. It is an essential tool for the diagnosis, monitoring, and clinical decision-making of ARDS patients. However, ultrasound has several limitations, and its clinical application must be carefully evaluated in conjunction with clinical symptoms and other monitoring techniques. Further research is needed to determine whether clinical decision-making based on point-of-care ultrasound examination can improve the prognosis of ARDS patients.

Breath metabolomics for diagnosis of acute respiratory distress syndrome

BackgroundAcute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.AimTo evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.MethodsThis two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC–MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into “certain ARDS”, “certain no ARDS” and “uncertain ARDS” groups. The patients with “certain” labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS).ResultsA total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63–0.78) in the derivation cohort and 0.63 (CI 0.52–0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance.ConclusionAn exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Long-term pulmonary outcomes of young adults born prematurely: a Polish prospective cohort study PREMATURITAS 20

BackgroundThe long-term consequences of prematurity are often not sufficiently recognized. To address this gap, a prospective cohort study, which is a continuation of the multicenter Polish study PREMATURITAS, was conducted, utilizing unique clinical data from 20 years ago.ObjectiveThe main goal was to evaluate lung function, detect any structural abnormalities using lung ultrasound, and assess psychological well-being in young adults born between 24 and 34 weeks of gestational age (GA). Additionally, the study aimed to investigate potential associations between perinatal risk factors and abnormalities observed in pulmonary function tests (PFTs) during adulthood.MethodsThe young survivors underwent a comprehensive set of PFTs, a lung ultrasound, along with the quality of life assessment. Information regarding the neonatal period and respiratory complications was obtained from the baseline data collected in the PREMATURITAS study.ResultsA total of 52 young adults, with a mean age of 21.6 years, underwent PFTs. They were divided into two groups based on GA: 24–28 weeks (n = 12) and 29–34 weeks (n = 40). The subgroup born more prematurely had significantly higher lung clearance index (LCI), compared to the other subgroup (p = 0.013). LCI ≥ 6.99 was more frequently observed in the more premature group (50% vs. 12.5%, p = 0.005), those who did not receive prenatal steroids (p = 0.020), with a diagnosis of Respiratory Distress Syndrome (p = 0.034), those who received surfactant (p = 0.026), and mechanically ventilated ≥ 7 days (p = 0.005). Additionally, elevated LCI was associated with the diagnosis of asthma (p = 0.010).ConclusionsThe findings suggest pulmonary effects due to prematurity persist into adulthood and their insult on small airway function. Regular follow-up evaluations of young survivors born preterm should include assessments of PFTs. Specifically, the use of LCI can provide valuable insights into long-term pulmonary impairment.

About the Second International Guidelines for the Diagnosis and Management of Pediatric Acute Respiratory Distress Syndrome.

About the Second International Guidelines for the Diagnosis and Management of Pediatric Acute Respiratory Distress Syndrome.

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

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

Application of B-Lines in Lung Ultrasound for Early Diagnosis of Acute Respiratory Distress Syndrome

Background: Bedside lung ultrasound (LUS) is increasingly being used in diagnosing various lung pathologies. Acute respiratory distress syndrome (ARDS), one of such lung pathology, is a major underlying cause of mortality in critically ill patients. Early diagnosis of ARDS can improve the outcome in such patients. The primary objective of this study is to explore application of B-lines in LUS for early detection of developing ARDS in susceptible patients.
 Methods: This is a prospective cohort study. This study enrolled patients admitted in intensive care unit. Daily clinical evaluation, bedside chest x ray and bedside lung ultrasound done to diagnose ARDS. Mean days of diagnosing ARDS by chest x ray and LUS were compared using t-test. Area under the Receiver Operating Curves Characteristics was calculated with a 95% confidence interval to determine the prognostic value of LUS.
 Results: Out of a total of 100 participants, 28 patients developed ARDS. Lung ultrasound was found to diagnose ARDS significantly earlier then chest x ray (p=0.001).
 Conclusion: Results in this study showed that B-line score in patients susceptible to develop ARDS can help in early diagnosis of the same.

Quantification of lung surfactant lipid (dipalmitoylphosphatidylcholine/sphingomyelin) ratio in binary liposomes using Raman spectroscopy

AbstractEarly diagnosis of neonatal respiratory distress syndrome (nRDS) is important in reducing the mortality of preterm babies. Knowledge of the ratio of two components of lung surfactant, dipalmitoylphosphatidylcholine (DPPC), and sphingomyelin (SM) can be used as biomarkers of lung maturity and inform treatment. Raman spectroscopy is a powerful tool to analyze vibrational spectra of organic molecules which requires only limited sample preparation steps and, unlike IR spectroscopy, is not masked by water absorption. In this paper, we explore the potential of using Raman spectroscopy as a tool to estimate the ratio of DPPC and SM from aqueous vesicles of binary mixture of DPPC and SM. We demonstrate that the ratio of DPPC and SM can be estimated by estimating the ratio of intensity of CO stretch of DPPC and CC stretch of SM as well as CO stretch of DPPC and amide I of SM. Further, we employ a partial least squares regression (PLSR) model to automate the estimation and demonstrate that PLSR method can predict the DPPC and SM ratio with an R2 value of 0.968.

Retracted: Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome.

[This retracts the article DOI: 10.1155/2022/1817341.].

Comparison of the efficacy of two natural surfactants (BERAKSURF and BLES) in the treatment of respiratory distress syndrome among preterm neonates

BackgroundThe benefit of surfactant replacement therapy for respiratory distress syndrome (RDS) has been demonstrated. However, some surfactants are expensive and usually inaccessible. Consequently, the Iranian Survanta was produced, but its effect on complications and mortality of RDS is unknown. This study aimed to compare the therapeutic outcomes of Iranian surfactant (beraksurf) and BLES (bovine lipid extract surfactant) on RDS treatment among preterm neonates.MethodsThis triple blinded randomized controlled trial study was performed on 128 eligible neonates diagnosed with RDS in Afzalipour hospital in Kerman, Iran. Diagnosis of RDS, gestational age of 28–34 weeks and weight ≥ 1 kg were considered as inclusion criteria. Congenital anomalies such as congenital cyanotic heart diseases, digestive system anomalies and chromosome abnormalities were the exclusion criteria Neonates were randomly assigned into two equal groups: (1) those treated with BLES (n = 64) and (2) those treated with beraksurf (n = 64). Complications including patent ductus arteriosus (PDA), sepsis, intraventricular hemorrhage (IVH), pneumothorax, pulmonary hemorrhage, mortality, and also, the number of days required for invasive mechanical ventilation (using ventilator) and non-invasive continuous positive airway pressure (CPAP) were evaluated for all neonates. The risk ratio (RR) was calculated at 95% of confidence intervals (CI).ResultsCompared with BLES group, the RR estimate among neonates in beraksurf group was 0.89 (0.66–1.20) for PDA, 0.71 (0.23–2.13) for IVH, 0.44 (0.14–1.36) for sepsis, 0.35 (0.13–0.93) for pneumothorax, 0.33 (0.12–0.86) for pulmonary hemorrhage, and 0.55 (0.28–1.05) for mortality.ConclusionsDespite advances in the use of exogenous surfactants for the treatment of neonatal respiratory distress syndrome; There are still some controversial topics in this field. The results obtained in the present study showed that the two types of surfactant (BERAKSURF and BLES) have similar efficacy for the treatment and short-term outcomes in preterm infants with respiratory distress syndrome. Therefore, due to the cost-effectiveness of BRAKSURF compared to BLES, We recommend choosing BERAKSURF in terms of treatment.

Acute Respiratory Distress Syndrome: A Case Report and Review on Diagnosis and Novel Interventions in Management

Acute respiratory distress syndrome is a type of acute diffuse lung injury, frequently brought on by sepsis, trauma, and severe pulmonary infections and continues to be one of the major causes of morbidity and mortality in critically ill patients, despite the constantly involving advancements. The management strategies have constantly evolved with a wide array of ventilation strategies, conservative fluid management, neuromuscular blockade. As we move into the post pandemic Era, the mortality rate due to ARDS continues to remain high hence improving patient outcomes is the primary goal of the various treatment approaches. The two most effective techniques are still prone ventilation and lung protection ventilation. In this report we aim to present the case of a 30-year old female who has undergone left lobectomy in childhood and presents to the clinic with palpitations, fever, shortness of breath and spasmodic cough with phlegm. The workup included chest Xray, CT scan, arterial blood gas with electrolytes, echocardiography and urinanalysis. Based on clinical and imaging findings, the diagnosis of acute respiratory distress syndrome was made. Against the background of intensive treatment, the patient’s condition improved to some extent, with positive dynamics. Lung protective ventilation, prone ventilation, high frequency oscillatory ventilation among many other novel interventions and futuristic direction of ARDS with mesenchymal stromal cells and Ulinastatin, a urinary glycoprotein and protease inhibitor provides a ray of hope in the management of this acute condition. Keywords: Acute respiratory distress syndrome; hypoplasia; lung ventilation; prone ventilation; mesenchymal stem cells; ulinastatin

Development and validation of a predictive model for early diagnosis of neonatal acute respiratory distress syndrome based on the Montreux definition.

Based on the Montreux definition, we aim to develop and validate a predictive model for the early diagnosis of neonatal acute respiratory distress syndrome (ARDS). A retrospective analysis of clinical data on 198 neonates with respiratory distress from January 2018 to January 2022 was conducted. Neonates meeting Montreux definition were classified as ARDS group (n = 79), while the rest were non-ARDS group (n = 119). Univariate analysis identified indicators for neonatal ARDS, followed by logistic regression to construct a predictive model for early diagnosis. The ability of predictors and models to predict neonatal ARDS was evaluated using area under the curve (AUC), and model performance was estimated through bootstrap resampling. Maternal prenatal fever, abnormal fetal heart beat, meconium-stained amniotic fluid (MSAF), white blood cell (WBC), absolute neutrophil count (ANC), neutrophil percentage (NE%), platelet count (PLT), C-reactive protein (CRP), procalcitonin (PCT), creatine kinase (CK), activated partial thromboplastin time (APTT), serum calcium (Ca) and sodium (Na)exhibited significant differences between the ARDS group and the non-ARDS group (P < 0.05). MSAF (OR=5.037; 95% CI: 1.523-16.657; P < 0.05), ANC (OR = 1.324; 95% CI: 1.172-1.495; P < 0.05), PLT (OR = 0.979; 95% CI: 0.971-0.986; P < 0.05), Ca (OR = 0.020; 95% CI: 0.004-0.088; P < 0.05) emerged as independent risk factors for the development of ARDS. The respective AUC values for MSAF, ANC, PLT, Ca, and the combined prediction models were 0.606, 0.691, 0.808, 0.761 and 0.931. Internal validation showed that the C-index for the model was 0.931. Early application of the model combining MSAF, ANC, PLT and Ca may have a good predictive effect on the early diagnosis of neonatal ARDS.

Morbidities and rehospitalizations during the first year of life in moderate and late preterm infants: more similarities than differences?

The aim was to compare neonatal morbidities in moderate and late preterm infants and to analyze rates and causes for rehospitalizations during the first year of life. Prospective follow-up of a group of moderate and late preterm infants at a tertiary care hospital. The study population comprised 215 infants (58% males; 60% singletons; 99 moderate and 116 late preterm infants) with a median gestational age of 34 weeks and birth weight of 2100 grams; 20% of them were small for gestational age. Moderate preterm infants more often had a diagnosis of mild respiratory distress syndrome (26% vs. 13%, P<0.01) and feeding problems with longer need for nasogastric tube feeding (median 9.5 vs. 4.2 days, P<0.01) and parenteral nutrition (3.5 vs. 2.7 days, P<0.01), and longer duration of stay at either NICU (10.6 vs. 3.7 days; P<0.01) or hospital (13 vs. 11 days; P<0.01). Fifty-two infants (24.3%) were hospitalized at 67 occasions without differences regarding readmission rates and causes between groups. Median age at readmission was 3 months, median stay 4 days. The most common diagnosis was respiratory illness (43.3%). Moderate preterm infants had more neonatal morbidities diagnosed, but the same rehospitalization rates than late preterm infants.

Prediction model for in-hospital mortality in patients at high altitudes with ARDS due to COVID-19.

The diagnosis of acute respiratory distress syndrome (ARDS) includes the ratio of pressure arterial oxygen and inspired oxygen fraction (P/F) ≤ 300, which is often adjusted in locations more than 1,000 meters above sea level (masl) due to hypobaric hypoxemia. The main objective of this study was to develop a prediction model for in-hospital mortality among patients with ARDS due to coronavirus disease 2019 (COVID-19) (C-ARDS) at 2,600 masl with easily available variables at patient admission and to compare its discrimination capacity with a second model using the P/F adjusted for this high altitude. This study was an analysis of data from patients with C-ARDS treated between March 2020 and July 2021 in a university hospital located in the city of Bogotá, Colombia, at 2,600 masl. Demographic and laboratory data were extracted from electronic records. For the prediction model, univariate analyses were performed to screen variables with p <0.25. Then, these variables were automatically selected with a backward stepwise approach with a significance level of 0.1. The interaction terms and fractional polynomials were also examined in the final model. Multiple imputation procedures and bootstraps were used to obtain the coefficients with the best external validation. In addition, total adjustment of the model and logistic regression diagnostics were performed. The same methodology was used to develop a second model with the P/F adjusted for altitude. Finally, the areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves of the two models were compared. A total of 2,210 subjects were included in the final analysis. The final model included 11 variables without interaction terms or nonlinear functions. The coefficients are presented excluding influential observations. The final equation for the model fit was g(x) = age(0.04819)+weight(0.00653)+height(-0.01856)+haemoglobin(-0.0916)+platelet count(-0.003614)+ creatinine(0.0958)+lactate dehydrogenase(0.001589)+sodium(-0.02298)+potassium(0.1574)+systolic pressure(-0.00308)+if moderate ARDS(0.628)+if severe ARDS(1.379), and the probability of in-hospital death was p (x) = e g (x)/(1+ e g (x)). The AUC of the ROC curve was 0.7601 (95% confidence interval (CI) 0.74-0, 78). The second model with the adjusted P/F presented an AUC of 0.754 (95% CI 0.73-0.77). No statistically significant difference was found between the AUC curves (p value = 0.6795). This study presents a prediction model for patients with C-ARDS at 2,600 masl with easily available admission variables for early stratification of in-hospital mortality risk. Adjusting the P/F for 2,600 masl did not improve the predictive capacity of the model. We do not recommend adjusting the P/F for altitude.

Bile Acids Pneumonia: A Respiratory Distress Syndrome in Early-Term Neonates.

Intrahepatic cholestasis of pregnancy (ICP) complicates among 0.2-2% of pregnancies and has been associated with adverse perinatal outcomes, including sudden stillbirth, meconium strained fluid, preterm birth, perinatal asphyxia, and transient tachypnea of the newborn. The diagnosis of "bile acids pneumonia" was previously proposed and a causative role of bile acids (BA) was supposed with a possible mechanism of action including surfactant dysfunction, inflammation, and chemical pneumonia. In the last few years, the role of lung ultrasound (LUS) in the diagnosis and management of neonatal respiratory distress syndrome has grown, and LUS scores have been introduced in the literature, as an effective predictor of the need for surfactant treatment among neonates with respiratory distress syndrome. We present four cases of infants born from pregnancies complicated by ICP, who developed respiratory distress syndrome early after birth. Lung ultrasound showed the same pattern for all infants, corresponding to a homogeneous alveolar-interstitial syndrome characterized by a diffuse coalescing B-line pattern (white lung). All infants evaluated require non-invasive respiratory support and in three cases surfactant administration, despite the near-term gestational age, with rapid improvement of respiratory disease and a good clinical outcome.

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable. To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants. The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images. This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

Retracted: The Role and Clinical Value of Optimized Fetal Main Pulmonary Artery Doppler Parameters in the Diagnosis and Prognosis Monitoring of Neonatal Respiratory Distress Syndrome.

[This retracts the article DOI: 10.1155/2022/1682225.].

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

Characteristics and management of patients with SARS-CoV2 infection admitted to pediatric intensive care units: Data analysis of the Spanish national multicenter registry.

The purpose of this study is to describe the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) disease characteristics and management in children admitted to the pediatric intensive care units (PICU). The present study was based on a national multicentric prospective registry including PICU patients with SARS-CoV2 infection or symptoms of multisystem inflammatory syndrome in children (MIS-C). A total of 298 patients were admitted to 41 different Spanish PICUs. A total of 76% of them were previously healthy. The most frequent manifestation was MIS-C (69.8%). On admission, 59.4% of patients did not have respiratory distress, and only 17.4% needed conventional mechanical ventilation (MV). The need for MV was associated with age (incidence rate ratios[IRR] 1.21, p < .012), pediatric sequential organ failure assessment score(p-SOFA) Score (IRR 1.12, p = .001), and need for transfusion (IRR 4.5, p < .004) in MIS-C patients, and with vasoactive drug use (IRR 2.73, p = .022) and the diagnosis of acute respiratory distress syndrome (IRR 2.83, p = .018) in patients admitted for other reasons. During the first day of admission, 56% of patients met shock criteria and 50.7% needed vasoactive drugs. In MIS-C patients, their use was associated with higher p-SOFA score (IRR 1.06, p < .001) and with the diagnosis of shock (IRR 5.78, p < .001). In patients without MIS-C, it was associated with higher p-SOFA score (IRR 1.05, p = .022). The mortality rate was 3%, being lower in MIS-C patients compared to patients admitted for other reasons (0.5% vs. 9.4%, p < .001). It was also lower in previously healthy patients compared to patients with previous comorbidities (0.9% vs. 9.7%, p < .001). Severe SARS-CoV2 infection is uncommon in the pediatric population. In our series, respiratory distress was rare, being MIS-C the most frequent cause of PICU admission related to SARS-CoV2. In most cases, the course of the disease was mild except in children with previous diseases.

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.