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

A study of machine learning to predict NRDS severity based on lung ultrasound score and clinical indicators

Objective

To develop predictive models for neonatal respiratory distress syndrome (NRDS) using machine learning algorithms to improve the accuracy of severity predictions.

Methods

This double-blind cohort study included 230 neonates admitted to the neonatal intensive care unit (NICU) of Yantaishan Hospital between December 2020 and June 2023. Of these, 119 neonates were diagnosed with NRDS and placed in the NRDS group, while 111 neonates with other conditions formed the non-NRDS (N-NRDS) group. All neonates underwent lung ultrasound and various clinical assessments, with data collected on the oxygenation index (OI), sequential organ failure assessment (SOFA), respiratory index (RI), and lung ultrasound score (LUS). An independent sample test was used to compare the groups' LUS, OI, RI, SOFA scores, and clinical data. Use Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify predictor variables, and construct a model for predicting NRDS severity using logistic regression (LR), random forest (RF), artificial neural network (NN), and support vector machine (SVM) algorithms. The importance of predictive variables and performance metrics was evaluated for each model.

Results

The NRDS group showed significantly higher LUS, SOFA, and RI scores and lower OI values than the N-NRDS group (p < 0.01). LUS, SOFA, and RI scores were significantly higher in the severe NRDS group compared to the mild and moderate groups, while OI was markedly lower (p < 0.01). LUS, OI, RI, and SOFA scores were the most impactful variables for the predictive efficacy of the models. The RF model performed best of the four models, with an AUC of 0.894, accuracy of 0.808, and sensitivity of 0.706. In contrast, the LR, NN, and SVM models have lower AUC values than the RF model with 0.841, 0.828, and 0.726, respectively.

Conclusion

Four predictive models based on machine learning can accurately assess the severity of NRDS. Among them, the RF model exhibits the best predictive performance, offering more effective support for the treatment and care of neonates.

Association between cardiopulmonary bypass time and mortality among patients with acute respiratory distress syndrome after cardiac surgery

BACKGROUND

Cardiopulmonary bypass (CPB) can lead to lung injury and even acute respiratory distress syndrome (ARDS) through triggering systemic inflammatory response. The objective of this study was to investigate the impact of CPB time on clinical outcomes in patients with ARDS after cardiac surgery.

METHODS

Totally, patients with ARDS after cardiac surgery in Beijing Anzhen Hospital from January 2005 to December 2015 were retrospectively included and were further divided into three groups according to the median time of CPB. The primary endpoints were the ICU mortality and in-hospital mortality, and ICU and hospital stay. Restricted cubic spline (RCS), logistic regression, cox regression model, and receiver operating characteristic (ROC) curve were adopted to explore the relationship between CPB time and clinical endpoints.

RESULTS

A total of 54,217 patients underwent cardiac surgery during the above period, of whom 210 patients developed ARDS after surgery and were finally included. The ICU mortality and in-hospital mortality were 21.0% and 41.9% in all ARDS patients after cardiac surgery respectively. Patients with long CPB time (CPB time ≥ 173 min) had longer length of ICU stay (P = 0.011), higher ICU (P < 0.001) mortality and in-hospital(P = 0.002) mortality compared with non-CPB patients (CPB = 0). For each ten minutes increment in CPB time, the hazards of a worse outcome increased by 13.3% for ICU mortality and 9.3% for in-hospital mortality after adjusting for potential factors. ROC curves showed CPB time presented more satisfactory power to predict mortality compared with APCHEII score. The optimal cut-off value of CPB time were 160.5 min for ICU mortality and in-hospital mortality.

CONCLUSIONS

Our findings demonstrated the significant prognostic value of CPB time in patients with ARDS after cardiac surgery. Longer time of CPB was associated with poorer clinical outcomes, and could be served as an indicator to predict short-term mortality in patients with ARDS after cardiac surgery.

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.

Development and Internal Validation of a Novel Prognostic Score to Predict Mortality in Acute Respiratory Distress Syndrome - Driving Pressure, Oxygenation and Nutritional Evaluation - "DRONE Score"

Introduction

There are few scores for mortality prediction in acute respiratory distress syndrome (ARDS) incorporating comprehensive ventilatory, acute physiological, organ dysfunction, oxygenation, and nutritional parameters. This study aims to determine the risk factors of ARDS mortality from the above-mentioned parameters at 48 h of invasive mechanical ventilation (IMV), which are feasible across most intensive care unit settings.

Methods

Prospective, observational, single-center study with 150 patients with ARDS defined by Berlin definition, receiving IMV with lung protective strategy.

Results

Our study had a mortality of 41.3% (62/150). We developed a 9-point novel prediction score, the driving pressure oxygenation and nutritional evaluation (DRONE) score comprising of driving pressure (DP), oxygenation accessed by the ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO2/FiO2) ratio and nutritional evaluation using the modified nutrition risk in the critically ill (mNUTRIC) score. Each component of the DRONE score with the cutoff value to predict mortality was assigned a particular score (the lowest DP within 48 h in a patient being always ≥15 cmH2O a score of 2, the highest achievable PaO2/FiO2 <208 was assigned a score of 4 and the mNUTRIC score ≥4 was assigned a score of (3). We obtained the DRONE score ≥4, area under the curve 0.860 to predict mortality. Cox regression for the DRONE score >4 was highly associated with mortality (P < 0.001, hazard ratio 5.43, 95% confidence interval [2.94-10.047]). Internal validation was done by bootstrap analysis. The clinical utility of the DRONE score ≥4 was assessed by Kaplan-Meier curve which showed significance.

Conclusions

The DRONE score ≥4 could be a reliable predictor of mortality at 48 h in ARDS patients receiving IMV.

Acute Respiratory Distress Syndrome in Patients with Subarachnoid Hemorrhage: Incidence, Predictive Factors, and Impact on Mortality

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is a known predictor of poor outcomes in critically ill patients. We sought to examine the role ARDS plays in outcomes in subarachnoid hemorrhage (SAH) patients. Prior studies investigating the incidence of ARDS in SAH patients did not control for SAH severity. Hence, we sought to determine the incidence ARDS in patients diagnosed with aneurysmal SAH and investigate the predisposing risk factors and impact upon outcomes.

METHODS

A retrospective cohort study was conducted using the National Inpatient Sample (NIS) database for the years 2008 to 2014. Multivariate stepwise regression analysis was performed to identify the risk factors and outcome associated with developing ARDS in the setting of SAH.

RESULTS

We identified 170,869 patients with non-traumatic subarachnoid hemorrhage, of whom 6962 were diagnosed with ARDS and of those 4829 required mechanical ventilation. ARDS more frequently developed in high grade SAH patients (1.97 ± 0.05 vs. 1.15 ± 0.01; p < 0.0001). Neurologic predictors of ARDS included cerebral edema (OR 1.892, CI 1.180-3.034, p = 0.0035) and medical predictors included cardiac arrest (OR 4.642, CI 2.273-9.482, p < 0.0001) and cardiogenic shock (OR 2.984, CI 1.157-7.696, p = 0.0239). ARDS was associated with significantly worse outcomes (15.5% vs. 52.9% discharged home, 63.0% vs. 40.8% discharged to rehabilitation facility and 21.5% vs. 6.3% in-hospital mortality).

CONCLUSION

Patients with SAH who developed ARDS were less likely to be discharged home, more likely to need rehabilitation and had a significantly higher risk of mortality. The identification of risk factors contributing to ARDS is helpful for improving outcomes and resource utilization.

Effects of changes in veno-venous extracorporeal membrane oxygenation blood flow on the measurement of intrathoracic blood volume and extravascular lung water index: a prospective interventional study

In severe acute respiratory distress syndrome (ARDS), veno-venous extracorporeal membrane oxygenation (V-V ECMO) has been proposed as a therapeutic strategy to possibly reduce mortality. Transpulmonary thermodilution (TPTD) enables monitoring of the extravascular lung water index (EVLWI) and cardiac preload parameters such as intrathoracic blood volume index (ITBVI) in patients with ARDS, but it is not generally recommended during V-V ECMO. We hypothesized that the amount of extracorporeal blood flow (ECBF) influences the calculation of EVLWI and ITBVI due to recirculation of indicator, which affects the measurement of the mean transit time (MTt), the time between injection and passing of half the indicator, as well as downslope time (DSt), the exponential washout of the indicator. EVLWI and ITBVI were measured in 20 patients with severe ARDS managed with V-V ECMO at ECBF rates from 6 to 4 and 2 l/min with TPTD. MTt and DSt significantly decreased when ECBF was reduced, resulting in a decreased EVLWI (26.1 [22.8-33.8] ml/kg at 6 l/min ECBF vs 22.4 [15.3-31.6] ml/kg at 4 l/min ECBF, p < 0.001; and 13.2 [11.8-18.8] ml/kg at 2 l/min ECBF, p < 0.001) and increased ITBVI (840 [753-1062] ml/m2 at 6 l/min ECBF vs 886 [658-979] ml/m2 at 4 l/min ECBF, p < 0.001; and 955 [817-1140] ml/m2 at 2 l/min ECBF, p < 0.001). In patients with severe ARDS managed with V-V ECMO, increasing ECBF alters the thermodilution curve, resulting in unreliable measurements of EVLWI and ITBVI. German Clinical Trials Register (DRKS00021050). Registered 14/08/2018. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00021050.

Predicting respiratory decompensation in mechanically ventilated adult ICU patients

Introduction: Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model. Methods: We constructed a random forest model to predict occurrence of VACs using health records and chart events from adult patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. We trained the machine learning models on two patient populations of 1921 and 464 based on low and high frequency data availability. Model features were generated using both basic statistical summaries and tsfresh, a python library that generates a large number of derived time-series features. Classification to determine whether a patient will experience VAC one hour after 35 h of ventilation was performed using a random forest classifier. Two different sample spaces conditioned on five varying feature extraction techniques were evaluated to identify the most optimal selection of features resulting in the best VAC discrimination. Each dataset was assessed using K-folds cross-validation (k = 10), giving average area under the receiver operating characteristic curves (AUROCs) and accuracies. Results: After feature selection, hyperparameter tuning, and feature extraction, the best performing model used automatically generated features on high frequency data and achieved an average AUROC of 0.83 ± 0.11 and an average accuracy of 0.69 ± 0.10. Discussion: Results show the potential viability of predicting VACs using machine learning, and indicate that higher-resolution data and the larger feature set generated by tsfresh yield better AUROCs compared to lower-resolution data and manual statistical features.

Determination of PaO2/FiO2 after 24 h of invasive mechanical ventilation and ΔPaO2/FiO2 at 24 h as predictors of survival in patients diagnosed with ARDS due to COVID-19

Introduction

Acute respiratory distress syndrome (ARDS) due to Coronavirus Disease 2019 (COVID-19) causes high mortality. The objective of this study is to determine whether the arterial pressure of oxygen/inspiratory fraction of oxygen (PaO2/FiO2) 24 h after invasive mechanical ventilation (IMV) and the difference between PaO2/FiO2 at 24 h after IMV and PaO2/FiO2 before admission to IMV (ΔPaO2/FiO2 24 h) are predictors of survival in patients with ARDS due to COVID-19.

Methods

A retrospective cohort study was conducted that included patients with ARDS due to COVID-19 in IMV admitted to the intensive care unit (ICU) of a hospital in southern Peru from April 2020 to April 2021. The ROC curves and the Youden index were used to establish the cut-off point for PaO2/FiO2 at 24 h of IMV and ΔPaO2/FiO2 at 24 h associated with mortality. The association with mortality was determined by Cox regression, calculating the crude (cHR) and adjusted (aHR) risk ratios, with their respective 95% confidence intervals (95% CI).

Results

Two hundred patients were analyzed. The average age was 54.29 years, 79% were men, and 25.5% (n = 51) died. The cut-off point calculated for PaO2/FiO2 24 h after IMV and ΔPaO2/FiO2 24 h was 222.5 and 109.5, respectively. Those participants with a value below the cut-off point of ΔPaO2/FiO2 24 h and PaO2/FiO2 24 h after IMV had higher mortality, aHR = 3.32 (CI 95% [1.82-6.07]) and aHR = 2.87 (CI 95% [1.48-5.57]) respectively.

Conclusion

PaO2/FiO2 24 h after IMV and ΔPaO2/FiO2 24 h in patients diagnosed with ARDS due to COVID-19 on IMV were associated with higher hospital mortality. These findings are helpful to identify those patients with a higher risk of dying on admission to the ICU.

Prognostic value of the novel P/FPE index to classify ARDS severity: A cohort study

OBJECTIVE

To evaluate the impact of the novel P/FP_E index to classify ARDS severity on mortality of patients with ARDS.

DESIGN

A retrospective cohort study.

SETTING

Twelve-bed medical and surgical intensive care unit from January 2018 to December 2020.

PATIENTS

A total of 217 ARDS patients managed with invasive mechanical ventilation >48h.

INTERVENTIONS

None.

VARIABLES

ARDS severity on day 1 and day 3 was measured based on PaO₂/FiO₂ ratio and P/FP_E index [PaO₂/(FiO₂×PEEP)]. Primary outcome was the hospital mortality.

RESULTS

Hospital mortality rate was 59.9%. Relative to PaO₂/FiO₂ ratio, 31.8% of patients on day 1 and 77.0% on day 3 were reclassified into a different category of ARDS severity by P/FP_E index. The level of PEEP was lower by P/FP_E index-based ARDS severity classification than by using PaO₂/FiO₂ ratio. The performance for predicting mortality of P/FP_E index was superior to PaO₂/FiO₂ ratio in term of AROC (day 1: 0.72 vs. 0.62; day 3: 0.87 vs. 0.68) and CORR (day 1: 0.370 vs. 0.213; day 3: 0.634 vs. 0.301). P/FP_E index improved prediction of risk of death compared to PaO₂/FiO₂ ratio as showed by the qNRI (day 1: 72.0%, p<0.0001; day 3: 132.4%, p<0.0001) and IDI (day 1: 0.09, p<0.0001; day 3: 0.31, p<0.0001).

CONCLUSIONS

Assessment of ARDS severity based on P/FP_E index seems better than PaO₂/FiO₂ ratio for predicting mortality. The value of P/FP_E index for clinical decision-making requires confirmation by randomized controlled trials.

Respiratory Subsets in Patients with Moderate to Severe Acute Respiratory Distress Syndrome for Early Prediction of Death

Introduction: In patients with acute respiratory distress syndrome (ARDS), the PaO2/FiO2 ratio at the time of ARDS diagnosis is weakly associated with mortality. We hypothesized that setting a PaO2/FiO2 threshold in 150 mm Hg at 24 h from moderate/severe ARDS diagnosis would improve predictions of death in the intensive care unit (ICU). Methods: We conducted an ancillary study in 1303 patients with moderate to severe ARDS managed with lung-protective ventilation enrolled consecutively in four prospective multicenter cohorts in a network of ICUs. The first three cohorts were pooled (n = 1000) as a testing cohort; the fourth cohort (n = 303) served as a confirmatory cohort. Based on the thresholds for PaO2/FiO2 (150 mm Hg) and positive end-expiratory pressure (PEEP) (10 cm H2O), the patients were classified into four possible subsets at baseline and at 24 h using a standardized PEEP-FiO2 approach: (I) PaO2/FiO2 ≥ 150 at PEEP < 10, (II) PaO2/FiO2 ≥ 150 at PEEP ≥ 10, (III) PaO2/FiO2 < 150 at PEEP < 10, and (IV) PaO2/FiO2 < 150 at PEEP ≥ 10. Primary outcome was death in the ICU. Results: ICU mortalities were similar in the testing and confirmatory cohorts (375/1000, 37.5% vs. 112/303, 37.0%, respectively). At baseline, most patients from the testing cohort (n = 792/1000, 79.2%) had a PaO2/FiO2 < 150, with similar mortality among the four subsets (p = 0.23). When assessed at 24 h, ICU mortality increased with an advance in the subset: 17.9%, 22.8%, 40.0%, and 49.3% (p < 0.0001). The findings were replicated in the confirmatory cohort (p < 0.0001). However, independent of the PEEP levels, patients with PaO2/FiO2 < 150 at 24 h followed a distinct 30-day ICU survival compared with patients with PaO2/FiO2 ≥ 150 (hazard ratio 2.8, 95% CI 2.2−3.5, p < 0.0001). Conclusions: Subsets based on PaO2/FiO2 thresholds of 150 mm Hg assessed after 24 h of moderate/severe ARDS diagnosis are clinically relevant for establishing prognosis, and are helpful for selecting adjunctive therapies for hypoxemia and for enrolling patients into therapeutic trials.

Relation of Ischemic Heart Disease to Outcomes in Patients With Acute Respiratory Distress Syndrome

Patients with ischemic heart disease (IHD) are often excluded from acute respiratory distress syndrome (ARDS) clinical trials. As a result, little is known about the impact of IHD in this population. We sought to assess the association between IHD and clinical outcomes in patients with ARDS. Participants from 4 ARDS randomized controlled trials with shared study criteria, definitions, and end points were included. Using multivariable logistic regression, we assessed for the association between IHD and a primary outcome of 60-day mortality. Secondary outcomes included 90-day mortality, 28-day ventilator-free days, and 28-day organ failure. Among 1,909 patients, 102 had a history of IHD (5.4%). Patients with IHD were more likely to be older and male (p <0.05). Noncardiac co-morbidities, severity of illness, and other markers of ARDS severity were not statistically different (all, p >0.05). Patients with IHD had a higher 60-day (39.2% vs 23.3%, p <0.001) and 90-day (40.2% vs 24.0%, p <0.001) mortality, and experienced more frequent renal (45.1% vs 32.0%, p = 0.006) and hepatic (35.3% vs 25.2%, p = 0.023) failure. After multivariable adjustment, 60-day (odds ratio [OR] 1.76; 95% confidence interval [CI]: 1.07 to 2.89, p = 0.025) and 90-day (OR 1.74; 95% CI: 1.06 to 2.85, p = 0.028) mortality remained higher. IHD was associated with 10% fewer ventilator-free days (incidence rate ratio 0.90; 95% CI: 0.85 to 0.96, p = 0.001). In conclusion, co-morbid IHD was associated with higher mortality and fewer ventilator-free days in patients with ARDS. Future studies are needed to identify predictors of mortality and improve treatment paradigms in this critically ill subgroup of patients.

Extravascular lung water levels are associated with mortality: a systematic review and meta-analysis

BACKGROUND

The prognostic value of extravascular lung water (EVLW) measured by transpulmonary thermodilution (TPTD) in critically ill patients is debated. We performed a systematic review and meta-analysis of studies assessing the effects of TPTD-estimated EVLW on mortality in critically ill patients.

METHODS

Cohort studies published in English from Embase, MEDLINE, and the Cochrane Database of Systematic Reviews from 1960 to 1 June 2021 were systematically searched. From eligible studies, the values of the odds ratio (OR) of EVLW as a risk factor for mortality, and the value of EVLW in survivors and non-survivors were extracted. Pooled OR were calculated from available studies. Mean differences and standard deviation of the EVLW between survivors and non-survivors were calculated. A random effects model was computed on the weighted mean differences across the two groups to estimate the pooled size effect. Subgroup analyses were performed to explore the possible sources of heterogeneity.

RESULTS

Of the 18 studies included (1296 patients), OR could be extracted from 11 studies including 905 patients (464 survivors vs. 441 non-survivors), and 17 studies reported EVLW values of survivors and non-survivors, including 1246 patients (680 survivors vs. 566 non-survivors). The pooled OR of EVLW for mortality from eleven studies was 1.69 (95% confidence interval (CI) [1.22; 2.34], p < 0.0015). EVLW was significantly lower in survivors than non-survivors, with a mean difference of -4.97 mL/kg (95% CI [-6.54; -3.41], p < 0.001). The results regarding OR and mean differences were consistent in subgroup analyses.

CONCLUSIONS

The value of EVLW measured by TPTD is associated with mortality in critically ill patients and is significantly higher in non-survivors than in survivors. This finding may also be interpreted as an indirect confirmation of the reliability of TPTD for estimating EVLW at the bedside. Nevertheless, our results should be considered cautiously due to the high risk of bias of many studies included in the meta-analysis and the low rating of certainty of evidence. Trial registration the study protocol was prospectively registered on PROSPERO: CRD42019126985.

Neutrophil gelatinase-associated lipocalin as a prognostic biomarker of severe acute respiratory distress syndrome

Neutrophil gelatinase-associated lipocalin (NGAL) is produced in the bronchial and alveolar cells of inflamed lungs and is regarded as a potential prognostic biomarker in various respiratory diseases. However, there are no studies on patients with acute respiratory distress syndrome (ARDS). NGAL levels in serum and bronchoalveolar lavage (BAL) were measured at baseline and on day 7 in 110 patients with ARDS. Baseline NGAL levels were significantly higher in ARDS patients than in healthy controls (serum 25 [14.5–41] vs. 214 [114.5–250.3] ng/mL; BAL 90 [65–115] vs. 211 [124–244] ng/mL). In ARDS, baseline NGAL levels in serum and BAL were significantly higher in non-survivors than in survivors (p < 0.001 and p = 0.021, respectively). Baseline NGAL levels showed a fair predictive power for intensive care unit (ICU) mortality (serum area under the curve (AUC) 0.747, p < 0.001; BAL AUC 0.768, p < 0.001). In a multivariate Cox regression analysis, the baseline serum NGAL level (> 240 ng/mL) was significantly associated with ICU mortality (hazard ratio [HR] 5.39, 95% confidence interval [CI] 2.67–10.85, p < 0.001). In particular, day 7 NGAL was significantly correlated with day 7 driving pressure (serum r = 0.388, BAL r = 0.702), and 28 ventilator-free days (serum r = − 0.298, BAL r = − 0.297). Baseline NGAL has good prognostic value for ICU mortality in patients with ARDS. NGAL can be a biomarker for ventilator requirement, as it may be indicative of potential alveolar epithelial injury.

Aspirin as a Treatment for ARDS: A Randomized, Placebo-Controlled Clinical Trial

BACKGROUND

There is no pharmacologic treatment for ARDS. Platelets play an important role in the pathophysiology of ARDS. Preclinical, observational, and clinically relevant models of ARDS indicate aspirin as a potential therapeutic option.

RESEARCH QUESTION

Is enteral aspirin (75 mg, once daily) safe and effective in improving surrogate outcomes in adult patients with ARDS?

STUDY DESIGN AND METHODS

This randomized, double-blind (patient and investigator), allocation-concealed, placebo-controlled phase 2 trial was conducted in five UK ICUs. Patients fulfilling the Berlin definition of ARDS were randomly assigned at a 1:1 ratio to receive enteral aspirin (75 mg) or placebo, for a maximum of 14 days, using a computer-generated randomization schedule, with variable block size, stratified by vasopressor requirement. The primary end point was oxygenation index (OI) on day 7. Secondary outcomes included safety parameters and other respiratory physiological markers. Analyses were by intention to treat.

RESULTS

The trial was stopped early, due to slow recruitment, after 49 of a planned 60 patients were recruited. Twenty-four patients were allocated to aspirin and 25 to placebo. There was no significant difference in day 7 OI [aspirin group: unadjusted mean, 54.4 (SD 26.8); placebo group: 42.4 (SD 25); mean difference, 12.0; 95% CI, -6.1 to 30.1; P = .19]. Aspirin did not significantly impact the secondary outcomes. There was no difference in the number of adverse events between the groups (13 in each; OR, 1.04; 95% CI, 0.56-1.94; P = .56).

INTERPRETATION

Aspirin was well tolerated but did not improve OI or other physiological outcomes; a larger trial is not feasible in its current design.

TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT02326350; URL: www.

CLINICALTRIALS

gov.

Prognostication using SpO2/FiO2 in invasively ventilated ICU patients with ARDS due to COVID-19 - Insights from the PRoVENT-COVID study

Background

The SpO₂/FiO₂ is a useful oxygenation parameter with prognostic capacity in patients with ARDS. We investigated the prognostic capacity of SpO₂/FiO₂ for mortality in patients with ARDS due to COVID–19.

Methods

This was a post-hoc analysis of a national multicenter cohort study in invasively ventilated patients with ARDS due to COVID–19. The primary endpoint was 28–day mortality.

Results

In 869 invasively ventilated patients, 28–day mortality was 30.1%. The SpO₂/FiO₂ on day 1 had no prognostic value. The SpO₂/FiO₂ on day 2 and day 3 had prognostic capacity for death, with the best cut-offs being 179 and 199, respectively. Both SpO₂/FiO₂ on day 2 (OR, 0.66 [95%–CI 0.46–0.96]) and on day 3 (OR, 0.70 [95%–CI 0.51–0.96]) were associated with 28–day mortality in a model corrected for age, pH, lactate levels and kidney dysfunction (AUROC 0.78 [0.76–0.79]). The measured PaO₂/FiO₂ and the PaO₂/FiO₂ calculated from SpO₂/FiO₂ were strongly correlated (Spearman's r = 0.79).

Conclusions

In this cohort of patients with ARDS due to COVID–19, the SpO₂/FiO₂ on day 2 and day 3 are independently associated with and have prognostic capacity for 28–day mortality. The SpO₂/FiO₂ is a useful metric for risk stratification in invasively ventilated COVID–19 patients.

Transpulmonary thermodilution in patients treated with veno-venous extracorporeal membrane oxygenation

BACKGROUND

We tested the effect of different blood flow levels in the extracorporeal circuit on the measurements of cardiac stroke volume (SV), global end-diastolic volume index (GEDVI) and extravascular lung water index derived from transpulmonary thermodilution (TPTD) in 20 patients with severe acute respiratory distress syndrome (ARDS) treated with veno-venous extracorporeal membrane oxygenation (ECMO).

METHODS

Comparative SV measurements with transesophageal echocardiography and TPTD were performed at least 5 times during the treatment of the patients. The data were interpreted with a Bland-Altman analysis corrected for repeated measurements. The interchangeability between both measurement modalities was calculated and the effects of extracorporeal blood flow on SV measurements with TPTD was analysed with a linear mixed effect model. GEDVI and EVLWI measurements were performed immediately before the termination of the ECMO therapy at a blood flow of 6 l/min, 4 l/min and 2 l/min and after the disconnection of the circuit in 7 patients.

RESULTS

170 pairs of comparative SV measurements were analysed. Average difference between the two modalities (bias) was 0.28 ml with an upper level of agreement of 40 ml and a lower level of agreement of -39 ml within a 95% confidence interval and an overall interchangeability rate between TPTD and Echo of 64%. ECMO blood flow did not influence the mean bias between Echo and TPTD (0.03 ml per l/min of ECMO blood flow; p = 0.992; CI - 6.74 to 6.81). GEDVI measurement was not significantly influenced by the blood flow in the ECMO circuit, whereas EVLWI differed at a blood flow of 6 l/min compared to no ECMO flow (25.9 ± 10.1 vs. 11.0 ± 4.2 ml/kg, p = 0.0035).

CONCLUSIONS

Irrespectively of an established ECMO therapy, comparative SV measurements with Echo and TPTD are not interchangeable. Such caveats also apply to the interpretation of EVLWI, especially with a high blood flow in the extracorporeal circulation. In such situations, the clinician should rely on other methods of evaluation of the amount of lung oedema with the haemodynamic situation, vasopressor support and cumulative fluid balance in mind.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00021050). Registered 03/30/2020 https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017237.

Increased extravascular lung water index (EVLWI) reflects rapid non-cardiogenic oedema and mortality in COVID-19 associated ARDS

Nearly 5% of patients suffering from COVID-19 develop acute respiratory distress syndrome (ARDS). Extravascular lung water index (EVLWI) is a marker of pulmonary oedema which is associated with mortality in ARDS. In this study, we evaluate whether EVLWI is higher in patients with COVID-19 associated ARDS as compared to COVID-19 negative, ventilated patients with ARDS and whether EVLWI has the potential to monitor disease progression. EVLWI and cardiac function were monitored by transpulmonary thermodilution in 25 patients with COVID-19 ARDS subsequent to intubation and compared to a control group of 49 non-COVID-19 ARDS patients. At intubation, EVLWI was noticeably elevated and significantly higher in COVID-19 patients than in the control group (17 (11–38) vs. 11 (6–26) mL/kg; p < 0.001). High pulmonary vascular permeability index values (2.9 (1.0–5.2) versus 1.9 (1.0–5.2); p = 0.003) suggested a non-cardiogenic pulmonary oedema. By contrast, the cardiac parameters SVI, GEF and GEDVI were comparable in both cohorts. High EVLWI values were associated with viral persistence, prolonged intensive care treatment and in-hospital mortality (23.2 ± 6.7% vs. 30.3 ± 6.0%, p = 0.025). Also, EVLWI showed a significant between-subjects (r = − 0.60; p = 0.001) and within-subjects correlation (r = − 0.27; p = 0.028) to Horowitz index. Compared to non COVID-19 ARDS, COVID-19 results in markedly elevated EVLWI-values in patients with ARDS. High EVLWI reflects a non-cardiogenic pulmonary oedema in COVID-19 ARDS and could serve as parameter to monitor ARDS progression on ICU.

COVID-19-Associated Pneumonia: Radiobiological Insights

The evolution of SARS-CoV-2 pneumonia to acute respiratory distress syndrome is linked to a virus-induced “cytokine storm”, associated with systemic inflammation, coagulopathies, endothelial damage, thrombo-inflammation, immune system deregulation and disruption of angiotensin converting enzyme signaling pathways. To date, the most promising therapeutic approaches in COVID-19 pandemic are linked to the development of vaccines. However, the fight against COVID-19 pandemic in the short and mid-term cannot only rely on vaccines strategies, in particular given the growing proportion of more contagious and more lethal variants among exposed population (the English, South African and Brazilian variants). As long as collective immunity is still not acquired, some patients will have severe forms of the disease. Therapeutic perspectives also rely on the implementation of strategies for the prevention of secondary complications resulting from vascular endothelial damage and from immune system deregulation, which contributes to acute respiratory distress and potentially to long term irreversible tissue damage. While the anti-inflammatory effects of low dose irradiation have been exploited for a long time in the clinics, few recent physiopathological and experimental data suggested the possibility to modulate the inflammatory storm related to COVID-19 pulmonary infection by exposing patients to ionizing radiation at very low doses. Despite level of evidence is only preliminary, these preclinical findings open therapeutic perspectives and are discussed in this article.

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.