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.

[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.

[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.

[New 2023 global definition of acute respiratory distress syndrome: progress and limitation].

Acute respiratory distress syndrome (ARDS) presents a challenge in clinical diagnosis as it lacks a definitive gold standard. Over the past 55 years, there have been several revisions to the definition of ARDS. With the progress of clinical practice and scientific research, the limitations of the "Berlin definition" have become increasingly evident. In response to these changes, the 2023 global definition of ARDS aims to address these issues by expanding the diagnostic targets, chest imaging, and methods for assessing hypoxia. Additionally, the new definition increases the diagnostic criteria to accommodate resource-constrained settings. The expansion facilitates early identification and treatment interventions for ARDS, thereby advancing epidemiological and clinically related research. Nevertheless, the broad nature of this revision may include patients who do not actually have ARDS, thus raising the risk of false-positive diagnoses. Therefore, additional verification is crucial to ascertain the validity and accuracy of the 2023 global definition of ARDS.

[Pulse oximetric saturation/fraction of inspired oxygen and partial pressure of oxygen/fraction of inspired oxygen in diagnosing acute respiratory distress syndrome: which is better?]

Acute Respiratory Distress Syndrome (ARDS) is distinguished by hypoxemia, contributing to heightened morbidity, elevated mortality rates, and substantial healthcare expenses, thereby imposing a significant burden on patients and society. Presently, effective treatments for ARDS are lacking, emphasizing the pivotal role of early diagnosis and timely intervention in its successful management. The partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2, P/F) has traditionally served as a crucial metric for assessing patient hypoxemia and disease severity. While relatively accurate, its reliance on advanced technical expertise and specific medical equipment conditions constrains its implementation in areas with underdeveloped medical standards, resulting in missed diagnoses and treatments for ARDS patients. Conversely, the Pulse oximetric saturation/fraction of inspired oxygen (SpO2/FiO2, S/F) has garnered increasing attention owing to its straightforward, non-invasive, and sustainable monitoring attributes. This article seeks to meticulously compare the correlation, accuracy, and clinical feasibility of S/F with P/F in ARDS diagnosis, so as to propose diagnostic indicators for more quickly and accurately assessing the oxygenation status of ARDS patients.

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.

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.

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.

Lung tissue expression of epithelial injury markers is associated with acute lung injury severity but does not discriminate sepsis from ARDS.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure in critically ill patients, and diffuse alveolar damage (DAD) is considered its histological hallmark. Sepsis is one of the most common aetiology of ARDS with the highest case-fatality rate. Identifying ARDS patients and differentiate them from other causes of acute respiratory failure remains a challenge. To address this, many studies have focused on identifying biomarkers that can help assess lung epithelial injury. However, there is scarce information available regarding the tissue expression of these markers. Evaluating the expression of elafin, RAGE, and SP-D in lung tissue offers a potential bridge between serological markers and the underlying histopathological changes. Therefore, we hypothesize that the expression of epithelial injury markers varies between sepsis and ARDS as well as according to its severity. METHODS: We compared the post-mortem lung tissue expression of the epithelial injury markers RAGE, SP-D, and elafin of patients that died of sepsis, ARDS, and controls that died from non-pulmonary causes. Lung tissue was collected during routine autopsy and protein expression was assessed by immunohistochemistry. We also assessed the lung injury by a semi-quantitative analysis. RESULTS: We observed that all features of DAD were milder in septic group compared to ARDS group. Elafin tissue expression was increased and SP-D was decreased in the sepsis and ARDS groups. Severe ARDS expressed higher levels of elafin and RAGE, and they were negatively correlated with PaO2/FiO2 ratio, and positively correlated with bronchopneumonia percentage and hyaline membrane score. RAGE tissue expression was negatively correlated with mechanical ventilation duration in both ARDS and septic groups. In septic patients, elafin was positively correlated with ICU admission length, SP-D was positively correlated with serum lactate and RAGE was correlated with C-reactive protein. CONCLUSIONS: Lung tissue expression of elafin and RAGE, but not SP-D, is associated with ARDS severity, but does not discriminate sepsis patients from ARDS patients.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

BACKGROUND: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS: We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS: The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.

Breath metabolomics for diagnosis of acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation. AIM: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients. METHODS: This 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). RESULTS: A 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. CONCLUSION: An 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.

Differences of respiratory mechanics in mechanical ventilation of acute respiratory distress syndrome between patients with COVID-19 and Influenza A.

BACKGROUND: Whether COVID-19-induced acute respiratory distress syndrome (ARDS) should be approached differently in terms of mechanical ventilation therapy compared to other virus-induced ARDS is debatable. Therefore, we aimed to ascertain whether the respiratory mechanical characteristics of COVID-19-induced ARDS differ from those of influenza A induced ARDS, in order to establish a rationale for mechanical ventilation therapy in COVID-19-induced ARDS. METHODS: This was a retrospective cohort study comparing patients with COVID-19-induced ARDS and influenza A induced ARDS. We included intensive care unit (ICU) patients with COVID-19 or Influenza A aged ≥ 19, who were diagnosed with ARDS according to the Berlin definition between January 2015 and July 2021. Ventilation parameters for respiratory mechanics were collected at specific times on days one, three, and seven after intubation. RESULTS: The median age of the 87 participants was 71.0 (62.0-78.0) years old, and 63.2% were male. The ratio of partial pressure of oxygen in arterial blood to the fractional of inspiratory oxygen concentration in COVID-19-induced ARDS was lower than that in influenza A induced ARDS during the initial stages of mechanical ventilation (influenza A induced ARDS 216.1 vs. COVID-19-induced ARDS 167.9, p = 0.009, day 1). The positive end expiratory pressure remained consistently higher in the COVID-19 group throughout the follow-up period (7.0 vs. 10.0, p < 0.001, day 1). COVID-19 and influenza A initially showed different directions for peak inspiratory pressure and dynamic compliance; however, after day 3, both groups exhibited similar directions. Dynamic driving pressure exhibited opposite trends between the two groups during mechanical ventilation. CONCLUSIONS: Respiratory mechanics show clear differences between COVID-19-induced ARDS and influenza A induced ARDS. Based on these findings, we can consider future treatment strategies for COVID-19-induced ARDS.

Diagnosis and Epidemiology of Acute Respiratory Failure.

Acute respiratory failure is a common clinical finding caused by insufficient oxygenation (hypoxemia) or ventilation (hypocapnia). Understanding the pathophysiology of acute respiratory failure can help to facilitate recognition, diagnosis, and treatment. The cause of acute respiratory failure can be identified through utilization of physical examination findings, laboratory analysis, and chest imaging.