Risk factors for outcomes of acute respiratory distress syndrome patients: a retrospective study

Hospital Outcomes in Patients Who Developed Acute Respiratory Distress Syndrome After Community-Acquired Pneumonia

Purpose: To identify risk factors for and outcomes in acute respiratory distress syndrome (ARDS) in patients hospitalized with community-acquired pneumonia (CAP). Methods: This is a retrospective study using the Premier Healthcare Database between 2016 and 2020. Patients diagnosed with pneumonia, requiring mechanical ventilation (MV), antimicrobial therapy, and hospital admission ≥2 days were included. Multivariable regression models were used for outcomes including in-hospital mortality, hospital length of stay (LOS), intensive care unit (ICU) LOS, and days on MV. Results: 1924 (2.7%) of 72 107 patients with CAP developed ARDS. ARDS was associated with higher mortality (33.7% vs 18.9%; adjusted odds ratio 2.4; 95% confidence interval [CI] 2.16-2.66), longer hospital LOS (13 vs 9 days; adjusted incidence risk ratio (aIRR) 1.24; 95% CI 1.20-1.27), ICU LOS (9 vs 5 days; aIRR 1.51; 95% CI 1.46-1.56), more MV days (8 vs 5; aIRR 1.54; 95% CI 1.48-1.59), and increased hospitalization cost ($46 459 vs $29 441; aIRR 1.50; 95% CI 1.45-1.55). Conclusion: In CAP, ARDS was associated with worse in-patient outcomes in terms of mortality, LOS, and hospitalization cost. Future studies are needed to explore outcomes in patients with CAP with ARDS and explore risk factors for development of ARDS after CAP.

Risk Factors for Death of Burn Patients With Acute Respiratory Distress Syndrome

The aim of this study was to investigate factors independently affecting outcomes of post-burn ARDS patients at the time of ARDS onset. A prospective study was conducted on 66 patients with ARDS, treated in the ICU at the Le Huu Trac National Burns Hospital in Hanoi, Viet Nam, from 2014 to 2017. Patients were divided into a survivor and non-survivor group. Demographic criteria, burn severity, inhalation injury, clinical and subclinical features at ARDS onset were compared between the two groups. The results showed that overall mortality of ARDS patients was 62.12%. Logistic regression analysis indicated that at the time of ARDS onset, serum lactate level (OR=6.71), blood platelet count (OR=.99), static lung compliance (OR=.73) and driving pressure (OR=1.69) were independent risk factors for death, while patients' demographics, burn severity and ARDS severity did not significantly affect the mortality rate.

Cause of Death by Race and Ethnicity in Minnesota Before and During the COVID-19 Pandemic, 2019-2020

OBJECTIVES

To measure changes in cause of death dynamics in 2019 and 2020 and the relationship between the concurrent occurrence of the COVID-19 pandemic and mortality outcome by race and ethnicity.

PATIENTS AND METHODS

We used resident mortality data from the Minnesota Department of Health (MDH) to conduct a retrospective statistical analysis of deaths in Minnesota in 2019 relative to 2020 to assess changes in mortality in a pre-pandemic and pandemic period.

RESULTS

COVID-19 strongly contributed to ethnicity-related mortality disparities in Minnesota. Not only was there a greater proportion of COVID-19 decedents within Black and Hispanic populations, but their average decedent age was markedly lower relative to the White population. The Black population experienced a disproportionate increase in decedents with a 34% increase during 2020 compared to 2019.

CONCLUSIONS

This retrospective analysis of death dynamics and mortality outcomes in Minnesota from 2019 to 2020 demonstrated an increase in adverse mortality outcomes relative to the pre-pandemic period that disproportionately impacted Black and Hispanic minority populations.

Cause of Death by Race and Ethnicity in Minnesota Before and During the COVID-19 Pandemic, 2019-2020

Objectives

To measure changes in cause of death dynamics in 2019 and 2020 and the relationship between concurrent occurrence of the COVID-19 pandemic and mortality outcome by race and ethnicity.

Patients and Methods

We used resident mortality data from the Minnesota Department of Health (MDH) to conduct retrospective statistical analysis of deaths in Minnesota in 2019 relative to 2020 to assess changes in mortality in a pre-pandemic and pandemic period.

Results

COVID-19 strongly contributed to ethnicity-related mortality disparities in Minnesota. Not only was there a greater proportion of COVID-19 decedents within the Black and Hispanic populations, but their average decedent age was markedly lower relative to the White population. The Black population experienced a disproportionate increase in decedents with a 34% increase during 2020 compared to 2019.

Conclusions

This retrospective analysis of death dynamics and mortality outcomes in Minnesota from 2019 to 2020 demonstrated an increase in adverse mortality outcomes relative to the pre-pandemic period that disproportionately impacted Black and Hispanic minority populations. Access to non-pharmaceutical interventions combating COVID-19 infection in Black and Hispanic communities should be expanded in Minnesota.

Prognostic value of lactate in cats presented in respiratory distress to the emergency room

Studies demonstrating the prognostic utility of plasma lactate concentration and lactate clearance in cats are limited. The objective of this study was to determine the prognostic utility of plasma lactate concentration upon admission and plasma lactate clearance in cats presented to the emergency room with respiratory distress attributed to underlying cardiac or respiratory disease. Additionally, we sought to determine if plasma lactate concentration on admission was significantly associated with the underlying cause of respiratory distress (cardiac vs. respiratory), type of respiratory disease, and vital parameters. Seventy-one cats presented in respiratory distress to the ER at a university teaching hospital were enrolled in this retrospective study. Admission lactate concentration was not associated with survival, duration of hospitalization, vital parameters, or underlying etiology for respiratory distress. In contrast, lactate clearance was significantly associated with survival and length of hospitalization. While a statistically significant association between lactate clearance and length of hospitalization was identified, this finding lacked clinical significance where an increase in lactate concentration by 1% was associated with an increased length of hospitalization by 11.4 min (p = 0.035). Results of this study suggest that lactate clearance may have prognostic utility in this population of cats. Further studies including the larger population of cats with underlying cardiac or respiratory disease are warranted.

The Impact of Chronic Medical Conditions on Mortality in Acute Respiratory Distress Syndrome

PURPOSE

To examine the impact of chronic comorbidities on mortality in Acute Respiratory Distress Syndrome (ARDS).

MATERIALS AND METHODS

Retrospective cohort study of adults with ARDS (ICD-10-CM code J80) from the National Inpatient Sample between January, 2016 and December, 2018. For the primary outcome of mortality, we conducted weighted logistic regression adjusting for factors identified on univariate analysis as potentially significant or differing between the two groups at baseline. We used negative binomial regression adjusting for the same comorbidities to identify risk factors for longer length of stay (LOS) among ARDS survivors.

RESULTS

After exclusions, 1046 records were analyzed (3355 ARDS survivors and 1875 non-survivors.) The comorbidities examined included hypertension, diabetes mellitus, obesity, hypothyroidism, alcohol and drug use, chronic kidney disease (CKD), cardiovascular disease, chronic liver disease, chronic pulmonary disease and malignancy. In multivariate analysis, we found that malignancy (OR 2.26, 95% CI 1.84-2.78, p < 0.001), cardiovascular disease (OR 1.54, 95% CI 1.23-1.92, p < 0.001), and CKD (OR 1.75, 95% CI 1.22-2.50, p  =  0.002) increased the risk of death. In interaction analyses, cardiovascular disease combined with either malignancy or CKD conferred higher odds of death compared to either risk factor alone.

CONCLUSIONS

The comorbidity of malignancy confers the most reliable risk of poor outcomes in ARDS with higher odds of hospital death and a simultaneous association with longer hospital LOS among survivors.

Acute respiratory distress syndrome among patients with severe COVID-19 admitted to treatment center of Wollega University Referral Hospital, Western Ethiopia

Background

Acute respiratory distress syndrome is a life-threatening condition that has a significant effect on the occurrence of morbidity and mortality among patients with severe Coronavirus disease 2019 (COVID-19). To the best of researchers’ knowledge, there is no Study on ARDS of COVID-19 in Ethiopia. Therefore, this study aimed to identify the prevalence of ARDS and associated factors among severe COVID-19 patients at Wollega University Referral Hospital.

Methods

An institution-based retrospective cross-sectional study was conducted from September 20, 2020, to June 10, 2021. Real-Time Reverse transcription-polymerase Chain Reaction (rRT-PCR) test was used to test Patients for COVID-19. Epi-data version 3.2 was used for data entry, and the final data analysis was through STATA version 14. After checking the assumption P-value<0.25 in the bivariable analysis was used to select a candidate variable for multi-variable analysis, and a p-value of <0.05 was used to declare statistical significance.

Results

In this study, the prevalence of ARDS was 32%. Almost all the patients had the clinical feature of cough (93.7%), followed by shortness of breath (79.9%), fever (77.7%), and headache (67%). Age older than 65 years (AOR = 3.35, 95%CI = 1.31, 8.55), male gender (AOR = 5.63, 95%CI = 2.15, 14.77), and low oxygen saturation level (AOR = 4.60, 95%CI = 1.15, 18.35) were the independent predictors of ARDS among severe COVID-19 patients.

Conclusion

The prevalence of ARDS among patients with severe COVID-19 was high in the study area. Therefore, elders and patients with critical conditions (low oxygen saturation) better to get special attention during COVID-19 case management to enhance good care and monitoring of the patients.

Central Venous Pressure Measurement Is Associated With Improved Outcomes in Patients With or at Risk for Acute Respiratory Distress Syndrome: An Analysis of the Medical Information Mart for Intensive Care IV Database

Background

Central venous pressure (CVP) monitoring is widely used in the intensive care unit (ICU). However, the formal utility of CVP measurement to altering patient outcomes among ICU patients with or at risk for acute respiratory distress syndrome (ARDS) has never been investigated. Our study aimed to explore the association of CVP measurement with 28-day mortality specifically in that population.

Methods

This study was based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients were divided into CVP and no CVP groups according to whether they had CVP measurement within 24 h of admission to the ICU. The primary outcome was 28-day mortality. Multivariate regression was used to elucidate the association between CVP measurement and 28-day mortality, and propensity score matching (PSM) and propensity score-based overlap weighting (OW) were employed to verify the stability of our results.

Results

A total of 10,198 patients with or at risk for ARDS were included in our study, of which 4,647 patients (45.6%) belonged to the CVP group. Multivariate logistic regression showed that the early measurement of CVP was independently associated with lower 28-day mortality (OR: 0.49; 95% CI: 0.42–0.57; p < 0.001). This association remained robust after PSM and OW (both p < 0.001). Patients in the CVP group had shorter ICU stay, lower in-hospital mortality, more fluid on day 1 and higher clearance of blood lactate than those in the no CVP group.

Conclusion

Early CVP measurement is associated with an improvement in 28-day mortality among a general population of critically ill patients with or at risk for ARDS.

Efficacy and safety of corticosteroids in acute respiratory distress syndrome: An overview of meta-analyses

BACKGROUND

Evidence-based recommendations on the efficacy and safety of corticosteroids in acute respiratory distress syndrome (ARDS) remain a therapeutic challenge. Findings from several systematic reviews and meta-analyses are inconsistent. We aimed to assess the published meta-analyses through a systematic review approach and provide further insight into the current uncertainty and also to perform an updated meta-analysis from all the available primary studies.

METHODOLOGY

We followed the Preferred Reporting Items for Systematic Review (PRISMA) guidelines to establish the patients, intervention, control and outcome (PICO) for reviewing published meta-analyses. Data sources such as PubMed/MEDLINE, SCOPUS, Cochrane and Google Scholar from inception to February 2021 were accessed. Prevention of ARDS, mortality, ventilator-free days, ICU stay and safety in terms of occurrence of adverse effects were the patient-related outcomes. The review also assessed meta-analysis design-related outcomes which includes the quality of meta-analysis, factors contributing to the risk of bias, extent and sources of heterogeneity, publication bias and robustness of findings. AMSTAR-2 checklist assessed the quality of published meta-analyses.

RESULTS

A total of 18 meta-analyses were reviewed comprising a total of 38 primary studies and 3760 patients. Fourteen studies were in ARDS, three in community-acquired pneumonia and one in critical care. The overall quality of meta-analyses was observed to be critically low to high. A non-significant risk of publication bias and non-significant level of heterogeneity was observed in the reviewed meta-analysis. Corticosteroid was significantly effective in preventing ARDS among CAP patients. The effect of corticosteroids on mortality was observed to be still inconsistent, whereas significant improvement was observed with ICU and ventilator outcomes compared with the control group. Our meta-analysis observed a significant reduction of mortality in RCTs (RR: 0.78; 95% CI: 0.61 to 0.99) and the duration of mechanical ventilation (MD: -4.75; 95% CI: -7.63 to -1.88); and a significant increase in ventilator-free days (MD: 6.03; 95% CI: 3.59 to 8.47) and ICU-free days (MD: 8.04; 95% CI: 2.70 to 13.38) in ARDS patients treated with corticosteroids compared with the control group.

CONCLUSION

The quality of included studies ranged from critically low to high demonstrating inconsistency in risk of bias. While older studies found no significant effect, recent meta-analyses of RCTs found a significant mortality reduction in the corticosteroid group with considerable levels of heterogeneity. The updated meta-analysis by our team found a significant reduction in mortality in the pooled estimation of RCTs but not in cohort studies. Corticosteroid therapy was effective in terms of ICU and ventilator outcomes with minimal safety concerns. Future meta-analyses should be well executed with specific research questions and well performed with minimal risk of bias to produce good quality evidence.

Biochemistry tests in hospitalized COVID-19 patients: Experience from a Canadian tertiary care centre

BACKGROUND

Coronavirus Disease 2019 (COVID-19) has variable clinical presentation, from asymptomatic to severe disease leading to death. Biochemical markers may help with management and prognostication of COVID-19 patients; however, their utility is still under investigation.

METHODS

A retrospective study was conducted to evaluate alanine aminotransferase, C-reactive protein (CRP), ferritin, lactate, and high sensitivity troponin T (TnT) levels in 67 patients who were admitted to a Canadian tertiary care centre for management of COVID-19. Logistic, cause-specific Cox proportional-hazards, and accelerated failure time regression modelling were performed to assess the associations of initial analyte concentrations with in-hospital death and length of stay in hospital; joint modelling was performed to assess the associations of the concentrations over the course of the hospital stay with in-hospital death.

RESULTS

Initial TnT and CRP concentrations were associated with length of stay in hospital. Eighteen patients died (27%), and the median initial TnT concentration was higher in patients who died (55 ng/L) than those who lived (16 ng/L; P < 0.0001). There were no survivors with an initial TnT concentration > 64 ng/L. While the initial TnT concentration was predictive of death, later measurements were not. Only CRP had prognostic value with both the initial and subsequent measurements: a 20% increase in the initial CRP concentration was associated with a 14% (95% confidence interval (CI): 1-29%) increase in the odds of death, and the hazard of death increased 14% (95% CI: 5-25%) for each 20% increase in the current CRP value. While the initial lactate concentration was not predictive of death, subsequent measurements were.

CONCLUSION

CRP, lactate and TnT were associated with poorer outcomes and appear to be useful biochemical markers for monitoring COVID-19 patients.

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4–9.8 days) in MIMIC-III and 5.0 days (IQR 3.0–9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10–6.41 days, and it was externally validated in eICU with RMSE of 5.87–6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.

Major Insights in Dynamics of Host Response to SARS-CoV-2: Impacts and Challenges

The coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization on March 11, 2020, is caused by the infection of highly transmissible species of a novel coronavirus called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As of July 25, 2021, there are 194,372,584 cases and 4,167,937 deaths with high variability in clinical manifestations, disease burden, and post-disease complications among different people around the globe. Overall, COVID-19 is manifested as mild to moderate in almost 90% of the cases and only the rest 10% of the cases need hospitalization. However, patients with older age and those having different comorbidities have made worst the pandemic scenario. The variability of pathological consequences and clinical manifestations of COVID-19 is associated with differential host-SARS-CoV-2 interactions, which are influenced by the factors that originated from the SARS-CoV-2 and the host. These factors usually include the genomic attributes and virulent factors of the SARS-CoV-2, the burden of coinfection with other viruses and bacteria, age and gender of the individuals, different comorbidities, immune suppressions/deficiency, genotypes of major histocompatibility complex, and blood group antigens and antibodies. We herein retrieved and reviewed literatures from PubMed, Scopus, and Google relevant to clinical complications and pathogenesis of COVID-19 among people of different age, sex, and geographical locations; genomic characteristics of SARS-CoV-2 including its variants, host response under different variables, and comorbidities to summarize the dynamics of the host response to SARS-CoV-2 infection; and host response toward approved vaccines and treatment strategies against COVID-19. After reviewing a large number of published articles covering different aspects of host response to SARS-CoV-2, it is clear that one aspect from one region is not working with the scenario same to others, as studies have been done separately with a very small number of cases from a particular area/region of a country. Importantly, to combat such a pandemic as COVID-19, a conclusive understanding of the disease dynamics is required. This review emphasizes on the identification of the factors influencing the dynamics of host responses to SARS-CoV-2 and offers a future perspective to explore the molecular insights of COVID-19.

Effect of different levels of PEEP on mortality in ICU patients without acute respiratory distress syndrome: systematic review and meta-analysis with trial sequential analysis

OBJECTIVE

To determine whether higher positive end- expiratory pressure (PEEP) could provide a survival advantage for patients without acute respiratory distress syndrome (ARDS) compared with lower PEEP.

METHODS

Eligible studies were identified through searches of Embase, Cochrane Library, Web of Science, Medline, and Wanfang database from inception up to 1 June 2021. Trial sequential analysis (TSA) was used in this meta-analysis.

DATA SYNTHESIS

Twenty-seven randomized controlled trials (RCTs) were identified for further evaluation. Higher and lower PEEP arms included 1330 patients and 1650 patients, respectively. A mean level of 9.6±3.4 cmH₂O was applied in the higher PEEP groups and 1.9±2.6 cmH₂O was used in the lower PEEP groups. Higher PEEP, compared with lower PEEP, was not associated with reduction of all-cause mortality (RR 1.03; 95% CI 0.91-1.18; P =0.627), and 28-day mortality (RR 1.07 ; 95% CI 0.92-1.24; P =0.365). In terms of risk of ARDS (RR 0.43; 95% CI 0.24-0.78; P =0.005), duration of intensive care unit (MD -1.04; 95%CI-1.36 to -0.73; P < 0.00001), and oxygenation (MD 40.30; 95%CI 0.94 to 79.65; P = 0.045), higher PEEP was superior to lower PEEP. Besides, the pooled analysis showed no significant differences between groups both in the duration of mechanical ventilation (MD 0.00; 95%CI-0.13 to 0.13; P = 0.996) and hospital stay (MD -0.66; 95%CI-1.94 to 0.61; P = 0.309). More importantly, lower PEEP did not increase the risk of pneumonia, atelectasis, barotrauma, hypoxemia, or hypotension among patients compared with higher PEEP. The TSA analysis showed that the results of all-cause mortality and 28-day mortality might be false-negative results.

CONCLUSIONS

Our results suggest that a lower PEEP ventilation strategy was non-inferior to a higher PEEP ventilation strategy in ICU patients without ARDS, with no increased risk of all-cause mortality and 28-day mortality. Further high-quality RCTs should be performed to confirm these findings.

Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study

Background

Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases.

Methods

Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO₂/FiO₂, and plateau pressure), Simplified Acute Physiology Score II (SAPS-II), Sepsis-related Organ Failure Assessment (SOFA), OSI, and OI were calculated. With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. The performance of the proposed RF method was also validated with the combined MIMIC-III and eICU-CRD data. The performance of mortality prediction was evaluated by using the area under the receiver operating characteristics curve (AUROC) and performing calibration using the Hosmer-Lemeshow test.

Results

With the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality.

Conclusions

Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.

Novel criteria to classify ARDS severity using a machine learning approach

Background

Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO₂/(FiO₂xPEEP) or P/FP_E] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches.

Methods

We examined P/FP_E values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO₂/FiO₂ criteria with P/FP_E under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time.

Results

P/FP_E ratio outperformed PaO₂/FiO₂ ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745).

Conclusions

The novel P/FP_E ratio to assess ARDS severity after onset over time is markedly better than current PaO₂/FiO₂ criteria. The use of P/FP_E could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03566-w.

National incidence rate and related mortality for acute respiratory distress syndrome in France

INTRODUCTION

Despite many efforts to improve mechanical ventilation strategies and the use of rescue strategies, ARDS-related mortality remains high. The primary objective of this study was to determine the incidence and 90-day mortality of ARDS patients admitted to all French ICUs following the introduction of the Berlin definition of ARDS.

PATIENTS AND METHODS

The data source for this nationwide cohort study was the French national hospital database (Programme de Médicalisation des Systèmes d'Information (PMSI)), which systematically collects administrative and medical information related to all patients hospitalised and hospital stays. Patient-level data were obtained from the PMSI database for all patients admitted to an ICU from the 1^st of January 2017, through the 31^st of December 2017. The inclusion criteria were as follows: ICU patients ≥ 18 years old with at least one International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10) diagnosis code of J80 (ARDS), either as a primary diagnosis or a secondary diagnosis, during their ICU stay.

RESULTS

A total of 12,846 ICU adult patients with ARDS were included. The crude incidence of ARDS was 24.6 per 100,000 person-years, varying with age from 6.7 per 100,000 person-years for those 18 through 40 years of age to 51.9 per 100,000 person-years for those 68 through 76 years of age. The in-hospital mortality rate was 51.1%. Day-90 mortality (day-1 being the ICU admission) was 51.2% and increased with age from 29.0% for patients 18 through 40 years of age to 69.3% for patients 77 years of age or older (p < 0.001). Only 53.9% of the survivors were transferred home directly after hospital discharge.

CONCLUSIONS

The incidence and mortality of ARDS in adults in France are higher than that generally reported in other countries.

Factors leading to high morbidity and mortality of COVID-19 in patients with type 2 diabetes

Coronavirus disease 2019 (COVID-19) is a recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus. Diabetes (mostly type 2 diabetes mellitus, T2DM) and hyperglycemia are among the major comorbidities in patients with COVID-19 leading to poor outcomes. Reports show that patients with diabetes and COVID-19 are at an increased risk for developing severe complications including acute respiratory distress syndrome, multi-organ failure, and death. Here we explore potential mechanistic links that could explain the observed higher morbidity and mortality in this patient population. Patients with T2DM have an underlying increased level of inflammation associated with obesity and insulin resistance in addition to other comorbidities including hypertension, obesity, cardiovascular disease, dyslipidemia, and being older. We review evidence that T2DM with hyperglycemia are among factors that lead to elevated expression of angiotensin-converting enzyme 2 (ACE2) in lungs and other tissues; ACE2 is the cellular "receptor" and port of viral entry. The preexisting chronic inflammation with augmented inflammatory response to the infection and the increasing viral load leads to extreme systemic immune response ("cytokine storm") that is strongly associated with increased severity of COVID-19. Based on the available evidence, it is recommended by a panel of experts that safe but stringent control of blood glucose, blood pressure, and lipids be carried out in patients with T2DM, measures that could potentially serve to decrease the severity of COVID-19 should these patients contract the viral infection. Once the infection occurs, then attention should be directed to proper glycemic control with use of insulin and frequent monitoring of blood glucose levels.

State of the Art Review of Cell Therapy in the Treatment of Lung Disease, and the Potential for Aerosol Delivery

Respiratory and pulmonary diseases are among the leading causes of death globally. Despite tremendous advancements, there are no effective pharmacological therapies capable of curing diseases such as COPD (chronic obstructive pulmonary disease), ARDS (acute respiratory distress syndrome), and COVID-19. Novel and innovative therapies such as advanced therapy medicinal products (ATMPs) are still in early development. However, they have exhibited significant potential preclinically and clinically. There are several longitudinal studies published, primarily focusing on the use of cell therapies for respiratory diseases due to their anti-inflammatory and reparative properties, thereby hinting that they have the capability of reducing mortality and improving the quality of life for patients. The primary objective of this paper is to set out a state of the art review on the use of aerosolized MSCs and their potential to treat these incurable diseases. This review will examine selected respiratory and pulmonary diseases, present an overview of the therapeutic potential of cell therapy and finally provide insight into potential routes of administration, with a focus on aerosol-mediated ATMP delivery.

Prediction of outcome in patients with ARDS: A prospective cohort study comparing ARDS-definitions and other ARDS-associated parameters, ratios and scores at intubation and over time

Background

Early recognition of high-risk-patients with acute respiratory distress syndrome (ARDS) might improve their outcome by less protracted allocation to intensified therapy including extracorporeal membrane oxygenation (ECMO). Among numerous predictors and classifications, the American European Consensus Conferenece (AECC)- and Berlin-definitions as well as the oxygenation index (OI) and the Murray-/Lung Injury Score are the most common. Most studies compared the prediction of mortality by these parameters on the day of intubation and/or diagnosis of ARDS. However, only few studies investigated prediction over time, in particular for more than three days.

Objective

Therefore, our study aimed at characterization of the best predictor and the best day(s) to predict 28-days-mortality within four days after intubation of patients with ARDS.

Methods

In 100 consecutive patients with ARDS severity according to OI (mean airway pressure*F_iO₂/p_aO₂), modified Murray-score without radiological points (Murray_mod), AECC- and Berlin-definition, were daily documented for four days after intubation. In the subgroup of 49 patients with transpulmonary thermodilution (TPTD) monitoring (PiCCO), extravascular lung water index (EVLWI) was measured daily.

Primary endpoint

Prediction of 28-days-mortality (Area under the receiver-operating-characteristic curve (ROC-AUC)); IBM SPSS 26.

Results

In the totality of patients the best prediction of 28-days-mortality was found on day-1 and day-2 (mean ROC-AUCs for all predictors/scores: 0.632 and 0.620). OI was the best predictor among the ARDS-scores (AUC=0.689 on day-1; 4-day-mean AUC = 0.625). AECC and Murray_mod had 4-day-means AUCs below 0.6. Among the 49 patients with TPTD, EVLWI (4-day-mean AUC=0.696) and OI (4-day-mean AUC=0.695) were the best predictors. AUCs were 0.789 for OI on day-1, and 0.786 for EVLWI on day-2. In binary regression analysis of patients with TPTD, EVLWI (B=-0.105; Wald=7.294; p=0.007) and OI (B=0.124; Wald=7.435; p=0.006) were independently associated with 28-days-mortality. Combining of EVLWI and OI provided ROC-AUCs of 0.801 (day-1) and 0.824 (day-2). Among the totality of patients, the use of TPTD-monitoring „per se“ and a lower SOFA-score were independently associated with a lower 28-days-mortality.

Conclusions

Prognosis of ARDS-patients can be estblished within two days after intubation. The best predictors were EVLWI and OI and their combination. TPTD-monitoring „per se“ was independently associated with reduced mortality.

Cumulative oxygen deficit is a novel predictor for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress

BACKGROUND AND OBJECTIVES

The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification.

METHODS

The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models.

RESULTS

A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO₂ (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57-16.93]; p = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28-85.37]; p = 0.029).

CONCLUSIONS

The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.