Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78-0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65-0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients' early risk stratification.

[Clinical characteristics and prognosis of acute gastrointestinal injury in patients with sepsis-associated acute respiratory distress syndrome].

OBJECTIVE: To observe the clinical characteristics and prognosis of patients with acute respiratory distress syndrome (ARDS) in sepsis combined with acute gastrointestinal injury (AGI) of different grades, and to further explore the risk factors associated with the poor prognosis of patients. METHODS: The clinical data of patients with septic ARDS admitted to the intensive care unit (ICU) of Tianjin First Central Hospital from March to October 2023 were collected. According to the 2012 European Association of Critical Care Medicine AGI definition and grading criteria, the patients were categorized into AGI grade 0- IV groups. The clinical characteristics and 28-day clinical outcomes of the patients were observed; the risk factors related to the prognosis of patients with septic ARDS combined with AGI were analyzed by using univariate and multivariate Logistic regression; and the receiver operator characteristic curve (ROC curve) and calibration curves were plotted to evaluate the predictive value of each risk factor on the prognosis of patients with septic ARDS combined with AGI. RESULTS: A total of 92 patients with septic ARDS were enrolled, including 7 patients in the AGI 0 group, 20 patients in the AGI I group, 38 patients in the AGI II group, 23 patients in the AGI III group, and 4 patients in the AGI IV group. The incidence of AGI was 92.39%. With the increase of AGI grade, the ARDS grade increased, and acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), intra-abdominal pressure (IAP), white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), lymphocyte percentage (LYM%), and 28-day mortality all showed a significant increasing trend, while the oxygenation index (PaO2/FiO2) showed a significant decreasing trend (all P < 0.05). Pearson correlation analysis showed that APACHE II score, SOFA score, and ARDS classification were positively correlated with patients' AGI grade (Pearson correlation index was 0.386, 0.473, and 0.372, respectively, all P < 0.001), and PaO2/FiO2 was negatively correlated with patients' AGI grade (Pearson correlation index was -0.425, P < 0.001). Among the patients with septic ARDS combined with AGI, there were 68 survivors and 17 deaths at 28 days. The differences in APACHE II score, SOFA score, ARDS grade, AGI grade, PaO2/FiO2, IAP, AGI 7-day worst value, length of ICU stay, and total length of hospital stay between the survival and death groups were statistically significant. Univariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.350, 95% confidence interval (95%CI) was 1.071-1.702, P = 0.011], PaO2/FiO2 (OR = 0.964, 95%CI was 0.933-0.996, P = 0.027) and AGI 7-day worst value (OR = 2.103, 95%CI was 1.194-3.702, P = 0.010) were the risk factors for 28-day mortality in patients with septic ARDS combined with AGI. Multivariate Logistic regression analysis showed that SOFA score (OR = 1.384, 95%CI was 1.153-1.661, P < 0.001), PaO2/FiO2 (OR = 0.983, 95%CI was 0.968-0.999, P = 0.035) and AGI 7-day worst value (OR = 1.992, 95%CI was 1.141-3.478, P = 0.015) were the independent risk factors for 28-day mortality in patients with septic ARDS combined with AGI. ROC curve analysis showed that SOFA score, PaO2/FiO2 and AGI 7-day worst value had predictive value for the 28-day prognosis of patients with septic ARDS combined with AGI. The area under the ROC curve (AUC) was 0.824 (95%CI was 0.697-0.950), 0.760 (95%CI was 0.642-0.877) and 0.721 (95%CI was 0.586-0.857), respectively, all P < 0.01; when the best cut-off values of the above metrics were 5.50 points, 163.45 mmHg (1 mmHg≈0.133 kPa), and 2.50 grade, the sensitivities were 94.1%, 94.1%, 31.9%, respectively, and the specificities were 80.9%, 67.6%, 88.2%, respectively. CONCLUSIONS: The incidence of AGI in patients with septic ARDS is about 90%, and the higher the AGI grade, the worse the prognosis of the patients. SOFA score, PaO2/FiO2 and AGI 7-day worst value have a certain predictive value for the prognosis of patients with septic ARDS combined with AGI, among which, the larger the SOFA score and AGI 7-day worst value, and the smaller the PaO2/FiO2, the higher the patients' mortality.

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

BACKGROUND: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based on the Berlin definition. We also aim to develop a visualization tool that helps clinicians efficiently assess ARDS criteria. METHODS: ARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data in an electronic health record (EHR) system. The study cohort includes 19,534 ICU admissions in the Medical Information Mart for Intensive Care III (MIMIC-III) database. The output is the ARDS diagnosis, onset time, and severity. RESULTS: ARDSFlag includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%±0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%±0.5%) and echocardiogram notes (accuracy 98.4%±0.3%). A test set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that ARDSFlag generates an overall accuracy of 89.0% (specificity = 91.7%, recall = 80.3%, and precision = 75.0%) in detecting ARDS cases. CONCLUSION: To our best knowledge, this is the first study to focus on developing a method to automate the detection of ARDS. Some studies have developed and used other methods to answer other research questions. Expectedly, ARDSFlag generates a significantly higher performance in all accuracy measures compared to those methods.

TM9SF1 offers utility as an efficient predictor of clinical severity and mortality among acute respiratory distress syndrome patients.

Introduction: Acute respiratory distress syndrome (ARDS) is a major cause of death among critically ill patients in intensive care settings, underscoring the need to identify biomarkers capable of predicting ARDS patient clinical status and prognosis at an early time point. This study specifically sought to explore the utility and clinical relevance of TM9SF1 as a biomarker for the early prediction of disease severity and prognostic outcomes in patients with ARDS. Methods: This study enrolled 123 patients with severe ARDS and 116 patients with non-severe ARDS for whom follow-up information was available. The mRNA levels of TM9SF1 and cytokines in peripheral blood mononuclear cells from these patients were evaluated by qPCR. The predictive performance of TM9SF1 and other clinical indicators was evaluated using received operating characteristic (ROC) curves. A predictive nomogram was developed based on TM9SF1 expression and evaluated for its ability in the early prediction of severe disease and mortality in patients with ARDS. Results: TM9SF1 mRNA expression was found to be significantly increased in patients with severe ARDS relative to those with non-severe disease or healthy controls. ARDS severity increased in correspondence with the level of TM9SF1 expression (odds ratio [OR] = 2.43, 95% confidence interval [CI] = 2.15-3.72, P = 0.005), and high TM9SF1 levels were associated with a greater risk of mortality (hazard ratio [HR] = 2.27, 95% CI = 2.20-4.39, P = 0.001). ROC curves demonstrated that relative to other clinical indicators, TM9SF1 offered superior performance in the prediction of ARDS severity and mortality. A novel nomogram incorporating TM9SF1 expression together with age, D-dimer levels, and C-reactive protein (CRP) levels was developed and was used to predict ARDS severity (AUC = 0.887, 95% CI = 0.715-0.943). A separate model incorporating TM9SF1 expression, age, neutrophil-lymphocyte ratio (NLR), and D-dimer levels (C-index = 0.890, 95% CI = 0.627-0.957) was also developed for predicting mortality. Conclusion: Increases in ARDS severity and patient mortality were observed with rising levels of TM9SF1 expression. TM9SF1 may thus offer utility as a novel biomarker for the early prediction of ARDS patient disease status and clinical outcomes.

A deep dive into burn-mediated ARDS severity assessment: a retrospective study on hematological markers.

Acute Respiratory Distress Syndrome (ARDS) is a critical form of Acute Lung Injury (ALI), challenging clinical diagnosis and severity assessment. This study evaluates the potential utility of various hematological markers in burn-mediated ARDS, including Neutrophil-to-Lymphocyte Ratio (NLR), Mean Platelet Volume (MPV), MPV-to-Lymphocyte Ratio (MPVLR), Platelet count, and Platelet Distribution Width (PDW). Employing a retrospective analysis of data collected over 12 years, this study focuses on the relationship between these hematological markers and ARDS diagnosis and severity in hospitalized patients. The study establishes NLR as a reliable systemic inflammation marker associated with ARDS severity. Elevated MPV and MPVLR also emerged as significant markers correlating with adverse outcomes. These findings suggest these economical, routinely measured markers can enhance traditional clinical criteria, offering a more objective approach to ARDS diagnosis and severity assessment. Hematological markers such as NLR, MPV, MPVLR, Platelet count, and PDW could be invaluable in clinical settings for diagnosing and assessing ARDS severity. They offer a cost-effective, accessible means to improve diagnostic accuracy and patient stratification in ARDS. However, further prospective studies are necessary to confirm these findings and investigate their integration with other diagnostic tools in diverse clinical settings.

Evaluation of the classifications of severity in acute respiratory distress syndrome in childhood by the Berlin Consensus and the Pediatric Acute Lung Injury Consensus Conference.

OBJECTIVE: To compare two methods for defining and classifying the severity of pediatric acute respiratory distress syndrome: the Berlin classification, which uses the relationship between the partial pressure of oxygen and the fraction of inspired oxygen, and the classification of the Pediatric Acute Lung Injury Consensus Conference, which uses the oxygenation index. METHODS: This was a prospective study of patients aged 0 - 18 years with a diagnosis of acute respiratory distress syndrome who were invasively mechanically ventilated and provided one to three arterial blood gas samples, totaling 140 valid measurements. These measures were evaluated for correlation using the Spearman test and agreement using the kappa coefficient between the two classifications, initially using the general population of the study and then subdividing it into patients with and without bronchospasm and those with and without the use of neuromuscular blockers. The effect of these two factors (bronchospasm and neuromuscular blocking agent) separately and together on both classifications was also assessed using two-way analysis of variance. RESULTS: In the general population, who were 54 patients aged 0 - 18 years a strong negative correlation was found by Spearman's test (ρ -0.91; p < 0.001), and strong agreement was found by the kappa coefficient (0.62; p < 0.001) in the comparison between Berlin and Pediatric Acute Lung Injury Consensus Conference. In the populations with and without bronchospasm and who did and did not use neuromuscular blockers, the correlation coefficients were similar to those of the general population, though among patients not using neuromuscular blockers, there was greater agreement between the classifications than for patients using neuromuscular blockers (kappa 0.67 versus 0.56, p < 0.001 for both). Neuromuscular blockers had a significant effect on the relationship between the partial pressure of oxygen and the fraction of inspired oxygen (analysis of variance; F: 12.9; p < 0.001) and the oxygenation index (analysis of variance; F: 8.3; p = 0.004). CONCLUSION: There was a strong correlation and agreement between the two classifications in the general population and in the subgroups studied. Use of neuromuscular blockers had a significant effect on the severity of acute respiratory distress syndrome.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top.

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.

A systematic review of machine learning models for management, prediction and classification of ARDS.

AIM: Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD: In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS: Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION: For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.

[Analysis of clinical treatment of acute respiratory distress syndrome assisted by artificial intelligence].

OBJECTIVE: To evaluate the clinical practice of intensive care unit (ICU) physicians at Hebei General Hospital in identifying patients meeting the diagnostic criteria for acute respiratory distress syndrome (ARDS) and the current status of invasive mechanical ventilation management and adjunctive therapy in these patients, and to analyze the incidence and clinical outcomes of ARDS. METHODS: A retrospective cohort study was conducted. The patients who were hospitalized in the ICU of Hebei General Hospital from April 10, 2017 to June 30, 2022 and met the Berlin definition diagnostic criteria for ARDS were enrolled as study subjects. Artificial intelligence (AI) technology was applied to search the basic information (age, gender, height, body weight, etc.), auxiliary examination, electronic medical record, non-drug doctor's advice, drug doctor's advice, critical report, scoring system, monitoring master table and other data of the above medical records in the electronic medical record system of the hospital. The first set of laboratory indicators sequentially retrieved from the system daily from 05:00 to 10:00 and vital signs and mechanical ventilation-related parameters recorded in the "critical care report" at 06:00 daily were extracted, and outcome indicators of the patients were collected. RESULTS: After screening and analysis, a total of 255 patients who met the ARDS diagnostic criteria were finally enrolled. The overall incidence of ARDS in the ICU accounted for 3.4% (255/7 434) of the total number of ICU patients, of which mild, moderate and severe ARDS accounted for 22.4% (57/255), 49.0% (125/255), and 28.6% (73/255), respectively, while the recognition rates of clinical doctors were 71.9% (41/57), 58.4% (73/125) and 71.2% (52/73), respectively. During the ICU stay, 250 patients (98.0%) received only invasive mechanical ventilation, while 5 patients (2.0%) received both non-invasive and invasive mechanical ventilation. The tidal volume/ideal body weight of ARDS patients was 7.64 (6.49, 9.01) mL/kg, and the positive end-expiratory pressure (PEEP) was 8.0 (5.0, 10.0) cmH2O (1 cmH2O ≈ 0.098 kPa). In addition, during the diagnosis and detection of ARDS, only 7 patients were recorded the platform pressure and 6 patients were recorded the drive pressure. Regarding adjunctive therapies, 137 patients (53.7%) received deep sedation, 26 patients (10.2%) underwent lung recruitment, 55 patients (21.6%) received prone ventilation, 42 patients (16.5%) were treated with high-dose steroids, 19 patients (7.5%) were treated with neuromuscular blockade, and 8 patients (3.1%) were treated with extracorporeal membrane oxygenation (ECMO). Finally, 70 patients (27.5%) were discharged automatically, while 50 patients (19.6%) died in the ICU, of which the ICU mortality of mild, moderate, and severe ARDS patients were 15.8% (9/57), 22.4% (28/125), and 17.8% (13/73), respectively. After follow-up, it was found that all 70 patients discharged automatically died within 28 days after discharge, and the overall ICU mortality adjusted accordingly was 47.1% (120/255). CONCLUSIONS: The overall incidence of ARDS in ICU patients at Hebei General Hospital is relatively low, with a high recognition rate by clinical physicians. Despite the high level of compliance and implementation of lung protective ventilation strategies and auxiliary treatment measures, it is still necessary to further improve the level of standardization in the implementation of small tidal volume and respiratory mechanics monitoring. For the implementation of auxiliary measures such as prone ventilation, it is necessary to further improve the enthusiasm of medical staff. The mortality in ICU is relatively low in ARDS patients, while the rate of spontaneous discharge is relatively high.

[An early warning model for sepsis complicated with acute respiratory distress syndrome based on synthetic minority oversampling technique algorithm].

OBJECTIVE: To explore the independent risk factors of acute respiratory distress syndrome (ARDS) in patients with sepsis, establish an early warning model, and verify the predictive value of the model based on synthetic minority oversampling technique (SMOTE) algorithm. METHODS: A retrospective case-control study was conducted. 566 patients with sepsis who were admitted to Jinan People's Hospital from October 2016 to October 2022 were enrolled. General information, underlying diseases, infection sites, initial cause, severity scores, blood and arterial blood gas analysis indicators at admission, treatment measures, complications, and prognosis indicators of patients were collected. The patients were grouped according to whether ARDS occurred during hospitalization, and the clinical data between the two groups were observed and compared. Univariate and binary multivariate Logistic regression analysis were used to select the independent risk factors of ARDS during hospitalization in septic patients, and regression equation was established to construct the early warning model. Simultaneously, the dataset was improved using the SMOTE algorithm to build an enhanced warning model. Receiver operator characteristic curve (ROC curve) was drawn to validate the prediction efficiency of the model. RESULTS: 566 patients with sepsis were included in the final analysis, of which 163 developed ARDS during hospitalization and 403 did not. Univariate analysis showed that there were statistically significant differences in age, body mass index (BMI), malignant tumor, blood transfusion history, pancreas and peripancreatic infection, gastrointestinal tract infection, pulmonary infection as the initial etiology, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, albumin (Alb), blood urea nitrogen (BUN), mechanical ventilation therapy, septic shock and length of intensive care unit (ICU) stay between the two groups. Binary multivariate Logistic regression analysis showed that age [odds ratio (OR) = 3.449, 95% confidence interval (95%CI) was 2.197-5.414, P = 0.000], pulmonary infection as the initial etiology (OR = 2.309, 95%CI was 1.427-3.737, P = 0.001), pancreas and peripancreatic infection (OR = 1.937, 95%CI was 1.236-3.035, P = 0.004), septic shock (OR = 3.381, 95%CI was 1.890-6.047, P = 0.000), SOFA score (OR = 9.311, 95%CI was 5.831-14.867, P = 0.000) were independent influencing factors of ARDS during hospitalization in septic patients. An early warning model was established based on the above risk factors: P1 = -4.558+1.238×age+0.837×pulmonary infection as the initial etiology+0.661×pancreas and peripancreatic infection+1.218×septic shock+2.231×SOFA score. ROC curve analysis showed that the area under the ROC curve (AUC) of the model for ARDS during hospitalization in septic patients was 0.882 (95%CI was 0.851-0.914) with sensitivity of 79.8% and specificity of 83.4%. The dataset was improved based on the SMOTE algorithm, and the early warning model was rebuilt: P2 = -3.279+1.288×age+0.763×pulmonary infection as the initial etiology+0.635×pancreas and peripancreatic infection+1.068×septic shock+2.201×SOFA score. ROC curve analysis showed that the AUC of the model for ARDS during hospitalization in septic patients was 0.890 (95%CI was 0.867-0.913) with sensitivity of 85.3% and specificity of 79.1%. This result further confirmed that the early warning model constructed by the independent risk factors mentioned above had high predictive performance. CONCLUSIONS: Risk factors for the occurrence of ARDS during hospitalization in patients with sepsis include age, pulmonary infection as the initial etiology, pancreatic and peripancreatic infection, septic shock, and SOFA score. Clinically, the probability of ARDS in patients with sepsis can be assessed based on the warning model established using these risk factors, allowing for early intervention and improvement of prognosis.

Endotyping in ARDS: one step forward in precision medicine.

BACKGROUND: The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort. METHODS: A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality. RESULTS: The LCM suggested three subgroups (n1 = 64, n2 = 86, and n3 = 30), and 23 out of 69 features made these subgroups distinct. The top five discriminating features were IL-8, IL-6, IL-10, TNF-a, and serum lactate. Mortality distinctively varied between subgroups. Individual clinical characteristics within the subgroup associated with mortality included mean PaO2/FiO2 ratio, pneumonia, platelet count, and bicarbonate negatively associated with mortality, while lactate, creatinine, shock, chronic kidney disease, vasopressor/ionotropic use, low GCS at admission, and sepsis were positively associated. IL-8 and Apache II were individual markers strongly associated with mortality (Area Under the Curve = 0.84). PERSPECTIVE: ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.

Accuracy of the Verbal Autopsy questionnaire in the diagnosis of COVID-19 deaths in a Brazilian capital.

The Verbal Autopsy (VA) is a questionnaire about the circumstances surrounding a death. It was widely used in Brazil to assist in postmortem diagnoses and investigate excess mortality during the Coronavirus Disease 2019 (COVID-19) pandemic. This study aimed to determine the accuracy of investigating acute respiratory distress syndrome (ARDS) using VA. This is a cross-sectional study with prospective data collected from January 2020 to August 2021 at the Death Verification Service of Sao Luis city, Brazil. VA was performed for suspected COVID-19 deaths, and one day of the week was randomly chosen to collect samples from patients without suspected COVID-19. Two swabs were collected after death and subjected to reverse transcription-polymerase chain reaction (RT-PCR) for SARS-CoV-2 detection. Of the 250 cases included, the VA questionnaire identified COVID-19-related ARDS in 67.2% (52.98% were positive for COVID-19). The sensitivity of the VA questionnaire was 0.53 (0.45-0.61), the specificity was 0.75 (0.64-0.84), the positive predictive value was 0.81 (0.72-0.88), and the negative predictive value was 0.44 (0.36-0.53). The VA had a lower-than-expected accuracy for detecting COVID-19 deaths; however, because it is an easily accessible and cost-effective tool, it can be combined with more accurate methods to improve its performance.

Plasmapheresis as therapeutic option in a 16-year-old with EVALI: A case report.

Since 2019 when a cluster of cases with acute respiratory distress syndrome (ARDS) associated with e-cigarettes in the United States was reported, there have been increasing numbers of reports. Electronic-cigarette or Vaping Use-associated Lung Injury (EVALI) represents a recent entity of respiratory clinical syndromes, primarily in young adults. We report a previously healthy 16-year-old boy who developed severe ARDS following a brief nonspecific prodromal phase after excessive consumption of e-cigarettes. Despite maximum intensive care therapy, including several weeks of venovenous extracorporeal membrane oxygenation, plasmapheresis and repeated administration of immunoglobulins seemed the only way to achieve therapeutic success. Although many case reports have been published, to our knowledge, there are none to date on the therapeutic use of plasmaphoresis in severe EVALI. This case highlights the clinical features of EVALI and the diagnostic dilemma that can arise with EVALI occurring against the background of an expired SARS-CoV-2 infection, with a paediatric inflammatory syndrome (PIMS) as differential diagnosis. EVALI is a diagnosis of exclusion, and the medical history of vaping and e-cigarette use can provide valuable clues. Ethical approval for this case report (protocol number 23-145 RS) was provided by the Ethical Committee of the Department of Medicine, Philipps-Universität Marburg, Germany on 13 th of June 2023. Written informed consent to publish this case and the associated images was obtained from the patient and his mother.

Biomarker-Based Risk Stratification Tool in Pediatric Acute Respiratory Distress Syndrome: Single-Center, Longitudinal Validation in a 2014-2019 Cohort.

OBJECTIVES: The Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model (PARDSEVERE) used age and three plasma biomarkers measured within 24 hours of pediatric acute respiratory distress syndrome (ARDS) onset to predict mortality in a pilot cohort of 152 patients. However, longitudinal performance of PARDSEVERE has not been evaluated, and it is unclear whether the risk model can be used to prognosticate after day 0. We, therefore, sought to determine the test characteristics of PARDSEVERE model and population over the first 7 days after ARDS onset. DESIGN: Secondary unplanned post hoc analysis of data from a prospective observational cohort study carried out 2014-2019. SETTING: University-affiliated PICU. PATIENTS: Mechanically ventilated children with ARDS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Between July 2014 and December 2019, 279 patients with ARDS had plasma collected at day 0, 266 at day 3 (11 nonsurvivors, two discharged between days 0 and 3), and 207 at day 7 (27 nonsurvivors, 45 discharged between days 3 and 7). The actual prevalence of mortality on days 0, 3, and 7, was 23% (64/279), 14% (38/266), and 13% (27/207), respectively. The PARDSEVERE risk model for mortality on days 0, 3, and 7 had area under the receiver operating characteristic curve (AUROC [95% CI]) of 0.76 (0.69-0.82), 0.68 (0.60-0.76), and 0.74 (0.65-0.83), respectively. The AUROC data translate into prevalence thresholds for the PARDSEVERE model for mortality (i.e., using the sensitivity and specificity values) of 37%, 27%, and 24% on days 0, 3, and 7, respectively. Negative predictive value (NPV) was high throughout (0.87-0.90 for all three-time points). CONCLUSIONS: In this exploratory analysis of the PARDSEVERE model of mortality risk prediction in a population longitudinal series of data from days 0, 3, and 7 after ARDS diagnosis, the diagnostic performance is in the "acceptable" category. NPV was good. A major limitation is that actual mortality is far below the prevalence threshold for such testing. The model may, therefore, be more useful in cohorts with higher mortality rates (e.g., immunocompromised, other countries), and future enhancements to the model should be explored.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU.

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Consensus statements on the utility of defining ARDS and the utility of past and current definitions of ARDS-protocol for a Delphi study.

INTRODUCTION: Acute respiratory distress syndrome (ARDS), marked by acute hypoxemia and bilateral pulmonary infiltrates, has been defined in multiple ways since its first description. This Delphi study aims to collect global opinions on the conceptual framework of ARDS, assess the usefulness of components within current and past definitions and investigate the role of subphenotyping. The varied expertise of the panel will provide valuable insights for refining future ARDS definitions and improving clinical management. METHODS: A diverse panel of 35-40 experts will be selected based on predefined criteria. Multiple choice questions (MCQs) or 7-point Likert-scale statements will be used in the iterative Delphi rounds to achieve consensus on key aspects related to the utility of definitions and subphenotyping. The Delphi rounds will be continued until a stable agreement or disagreement is achieved for all statements. ANALYSIS: Consensus will be considered as reached when a choice in MCQs or Likert-scale statement achieved ≥80% of votes for agreement or disagreement. The stability will be checked by non-parametric χ2 tests or Kruskal Wallis test starting from the second round of Delphi process. A p-value ≥0.05 will be used to define stability. ETHICS AND DISSEMINATION: The study will be conducted in full concordance with the principles of the Declaration of Helsinki and will be reported according to CREDES guidance. This study has been granted an ethical approval waiver by the NMC Healthcare Regional Research Ethics Committee, Dubai (NMCHC/CR/DXB/REC/APP/002), owing to the nature of the research. Informed consent will be obtained from all panellists before the start of the Delphi process. The study will be published in a peer-review journal with the authorship agreed as per ICMJE requirements. TRIAL REGISTRATION NUMBER: NCT06159465.

A case of severe Plasmodium ovale malaria with acute respiratory distress syndrome and splenic infarction in a male traveller presenting in Italy.

BACKGROUND: Plasmodium ovale malaria is usually considered a tropical infectious disease associated with low morbidity and mortality. However, severe disease and death have previously been reported. CASE PRESENTATION: A case of severe P. ovale malaria in a healthy Caucasian man with a triangle splenic infarction and clinical progression towards Acute Respiratory Distress Syndrome was reported despite a rapid response to oral chloroquine treatment with 24-h parasitaemia clearance. CONCLUSION: Plasmodium ovale malaria is generally considered as a benign disease, with low parasitaemia. However, severe disease and death have occasionally been reported. It is important to be aware that occasionally it can progress to serious illness and death even in immunocompetent individuals.

ARDS and aging: TYMS emerges as a promising biomarker and therapeutic target.

Background: Acute Respiratory Distress Syndrome (ARDS) is a common condition in the intensive care unit (ICU) with a high mortality rate, yet the diagnosis rate remains low. Recent studies have increasingly highlighted the role of aging in the occurrence and progression of ARDS. This study is committed to investigating the pathogenic mechanisms of cellular and genetic changes in elderly ARDS patients, providing theoretical support for the precise treatment of ARDS. Methods: Gene expression profiles for control and ARDS samples were obtained from the Gene Expression Omnibus (GEO) database, while aging-related genes (ARGs) were sourced from the Human Aging Genomic Resources (HAGR) database. Differentially expressed genes (DEGs) were subjected to functional enrichment analysis to understand their roles in ARDS and aging. The Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning pinpointed key modules and marker genes, with ROC curves illustrating their significance. The expression of four ARDS-ARDEGs was validated in lung samples from aged mice with ARDS using qRT-PCR. Gene set enrichment analysis (GSEA) investigated the signaling pathways and immune cell infiltration associated with TYMS expression. Single-nucleus RNA sequencing (snRNA-Seq) explored gene-level differences among cells to investigate intercellular communication during ARDS onset and progression. Results: ARDEGs are involved in cellular responses to DNA damage stimuli, inflammatory reactions, and cellular senescence pathways. The MEmagenta module exhibited a significant correlation with elderly ARDS patients. The LASSO, RRF, and XGBoost algorithms were employed to screen for signature genes, including CKAP2, P2RY14, RBP2, and TYMS. Further validation emphasized the potential role of TYMS in the onset and progression of ARDS. Immune cell infiltration indicated differential proportion and correlations with TYMS expression. SnRNA-Seq and cell-cell communication analysis revealed that TYMS is highly expressed in endothelial cells, and the SEMA3 signaling pathway primarily mediates cell communication between endothelial cells and other cells. Conclusion: Endothelial cell damage associated with aging could contribute to ARDS progression by triggering inflammation. TYMS emerges as a promising diagnostic biomarker and potential therapeutic target for ARDS.

The safety and potential efficacy of exosomes overexpressing CD24 (EXO-CD24) in mild-moderate COVID-19 related ARDS.

INTRODUCTION: EXO-CD24 are exosomes genetically manipulated to over-express Cluster of Differentiation (CD) 24. It consists of two breakthrough technologies: CD24, the drug, as a novel immunomodulator that is smarter than steroids without any side effects, and exosomes as the ideal natural drug carrier. METHODS: A randomized, single blind, dose-finding phase IIb trial in hospitalized patients with mild to moderate Coronavirus disease 2019 (COVID-19) related Acute Respiratory Distress Syndrome (ARDS) was carried out in two medical centers in Athens. Patients received either 109 or 1010 exosome particles of EXO-CD24, daily, for five consecutive days and monitored for 28 days. Efficacy was assessed at day 7 among 91 patients who underwent randomization. The outcome was also compared in a post-hoc analysis with an income control group (n = 202) that fit the inclusion and exclusion criteria. RESULTS: The mean age was 49.4 (± 13.2) years and 74.4% were male. By day 7, 83.7% showed improved respiratory signs and 64% had better oxygen saturation (SpO2) (p < 0.05). There were significant reductions in all inflammatory markers, most notably in C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, fibrinogen and an array of cytokines. Conversely, levels of the anti-inflammatory cytokine Interleukin-10 (IL-10) were increased (p < 0.05). Of all the documented adverse events, none were considered treatment related. No drug-drug interactions were noted. Two patients succumbed to COVID-19. Post-hoc analysis revealed that EXO-CD24 patients exhibited greater improvements in clinical and laboratory outcomes compared to an observational income control group. CONCLUSIONS: EXO-CD24 presents a promising therapeutic approach for hyper-inflammatory state and in particular ARDS. Its unique combination of exosomes, as a drug carrier, and CD24, as an immunomodulator, coupled with inhalation administration, warrants further investigation in a larger, international, randomized, quadri-blind trial against a placebo.