Acute lung injury prediction score: derivation and validation in a population-based sample

Outpatient insulin use in type 2 diabetes mellitus and acute respiratory distress syndrome outcomes: A retrospective cohort study

BACKGROUND

The impact of type 2 diabetes mellitus (T2DM) on acute respiratory distress syndrome (ARDS) is debatable. T2DM was suspected to reduce the risk and complications of ARDS. However, during coronavirus disease 2019 (COVID-19), T2DM predisposed patients to ARDS, especially those who were on insulin at home.

AIM

To evaluate the impact of outpatient insulin use in T2DM patients on non-COVID-19 ARDS outcomes.

METHODS

We conducted a retrospective cohort analysis using the Nationwide Inpatient Sample database. Adult patients diagnosed with ARDS were stratified into insulin-dependent diabetes mellitus (DM) (IDDM) and non-insulin-dependent DM (NIDDM) groups. After applying exclusion criteria and matching over 20 variables, we compared cohorts for mortality, duration of mechanical ventilation, incidence of acute kidney injury (AKI), length of stay (LOS), hospitalization costs, and other clinical outcomes.

RESULTS

Following 1:1 propensity score matching, the analysis included 274 patients in each group. Notably, no statistically significant differences emerged between the IDDM and NIDDM groups in terms of mortality rates (32.8% vs 31.0%, P = 0.520), median hospital LOS (10 d, P = 0.537), requirement for mechanical ventilation, incidence rates of sepsis, pneumonia or AKI, median total hospitalization costs, or patient disposition upon discharge.

CONCLUSION

Compared to alternative anti-diabetic medications, outpatient insulin treatment does not appear to exert an independent influence on in-hospital morbidity or mortality in diabetic patients with non-COVID-19 ARDS.

Effect of a physician-nurse integrated lung protection care model in neurocritical patients

Background

Lung injury resulting from diffuse pulmonary interstitial and other lung-related complications is a significant contributor to poor prognosis and mortality in patients with critical neurological diseases. To enhance patient outcomes, it is essential to investigate a lung protection model that involves the collaboration of doctors, nurses, and other medical professionals.

Methods

Patients receiving different care styles were divided into two groups: routine care (RC) and lung function protection care (LFPC). The LFPC group included airway and posture management, sedation and analgesia management, positive end-expiratory pressure titration in ventilation management, and fluid volume management, among others. Statistical analysis methods, such as chi-square, were used to compare the incidence of acute lung injury (ALI), neurogenic pulmonary edema (NPE), ventilator-associated pneumonia (VAP), acute respiratory distress syndrome (ARDS), and length of stay between the RC and LFPC groups.

Results

The RC group included 68 patients (33 males; 34-74 years of age). The LFPC group included 60 patients (29 males; 37-73 years of age). Compared with the RC group, the LFPC group had lower occurrence rates of ALI (20.0 % vs. 38.2 %, P = 0.024), NPE (8.3 % vs. 23.5 %, P = 0.021), VAP (8.3 % vs. 25.0 %, P = 0.013), and ARDS (1.7 % vs. 16.2 %, P = 0.015). The length of hospital stay was shorter in the LFPC group than in the RC group (11.3 ± 3.5 vs. 14.3 ± 4.4 days, P = 0.0001).

Conclusion

The physician-nurse integrated lung protection care model proved to be effective in improving outcomes, reducing complications, and shortening the hospital stay length for neurocritical patients.

Predictive value of combination of lung injury prediction score and receptor for advanced glycation end‑products for the occurrence of acute respiratory distress syndrome

The present study evaluated the predictive value of the combination of the lung injury prediction score (LIPS) and receptor for advanced glycation end-products (RAGE) for the occurrence of acute respiratory distress syndrome (ARDS) in critically ill patients with ARDS risk factors. A total of 551 patients with risk factors of ARDS were divided into an ARDS group and a non-ARDS group. LIPS was computed within 6 h of admission into the ICU, and the plasma concentration of RAGE was detected within 24 h of admission. Multivariate analysis was performed to identify independent associations, and the predictive values for ARDS occurrence were assessed with receiver operating characteristic (ROC) curve. Within 7 days after admission into the ICU, ARDS occurred in 176 patients (31.9%). Multivariate analysis demonstrated that LIPS [odds ratio (OR), 1.282; 95% confidence interval (CI), 1.108-1.604], RAGE levels (OR, 2.359; 95% CI, 1.351-4.813) and Acute Physiology and Chronic Health Evaluation II score (OR, 1.167; 95% CI, 1.074-1.485) were independently associated with ARDS occurrence. ROC curves demonstrated that the area under curve (AUC) of LIPS, RAGE levels and their combination was 0.714 [standard error (SE), 0.023; 95% CI, 0.670-0.759], 0.709 (SE, 0.025; 95% CI, 0.660-0.758) and 0.889 (SE, 0.014; 95% CI, 0.861-0.917), respectively. The AUC of LIPS combined with RAGE levels was significantly higher compared with those of LIPS (0.889 vs. 0.714; Z=6.499; P<0.001) and RAGE (0.889 vs. 0.709; Z=6.282; P<0.001) levels alone. In conclusion, both LIPS and RAGE levels were independently associated with ARDS occurrence in critically ill patients with ARDS risk factors, and had medium predictive values for ARDS occurrence. Combination of LIPS with RAGE levels increased the predictive value for ARDS occurrence.

Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta-analysis

AIM

To assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta-analysis.

METHODS

Eight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed.

RESULTS

A total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13/24), model calibration assessment (9/24), and dichotomization of continuous predictors (6/24).

CONCLUSIONS

This study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.

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

Objective

Based on the Montreux definition, we aim to develop and validate a predictive model for the early diagnosis of neonatal acute respiratory distress syndrome (ARDS).

Methods

A retrospective analysis of clinical data on 198 neonates with respiratory distress from January 2018 to January 2022 was conducted. Neonates meeting Montreux definition were classified as ARDS group (n = 79), while the rest were non-ARDS group (n = 119). Univariate analysis identified indicators for neonatal ARDS, followed by logistic regression to construct a predictive model for early diagnosis. The ability of predictors and models to predict neonatal ARDS was evaluated using area under the curve (AUC), and model performance was estimated through bootstrap resampling.

Results

Maternal prenatal fever, abnormal fetal heart beat, meconium-stained amniotic fluid (MSAF), white blood cell (WBC), absolute neutrophil count (ANC), neutrophil percentage (NE%), platelet count (PLT), C-reactive protein (CRP), procalcitonin (PCT), creatine kinase (CK), activated partial thromboplastin time (APTT), serum calcium (Ca) and sodium (Na)exhibited significant differences between the ARDS group and the non-ARDS group (P < 0.05). MSAF (OR=5.037; 95% CI: 1.523-16.657; P < 0.05), ANC (OR = 1.324; 95% CI: 1.172-1.495; P < 0.05), PLT (OR = 0.979; 95% CI: 0.971-0.986; P < 0.05), Ca (OR = 0.020; 95% CI: 0.004-0.088; P < 0.05) emerged as independent risk factors for the development of ARDS. The respective AUC values for MSAF, ANC, PLT, Ca, and the combined prediction models were 0.606, 0.691, 0.808, 0.761 and 0.931. Internal validation showed that the C-index for the model was 0.931.

Conclusions

Early application of the model combining MSAF, ANC, PLT and Ca may have a good predictive effect on the early diagnosis of neonatal ARDS.

Hypoalbuminemia: incidence and its impact on acute respiratory distress syndrome and 28-day outcome in trauma patients

OBJECTIVE

This prospective observational study explored the effect of early onset hypoalbuminemia (EOH) on the development of adult respiratory distress syndrome (ARDS) in orthopedic trauma victims.

METHODS

Serum albumin levels were measured for the initial 7 days of injury for adult trauma patients (18-65 years). Patients were recruited into group A (any serum albumin value < 3.5 mg/dl) and group B (all serum albumin ≥ 3.5 mg/dl), based on serum albumin values. Patients were followed for the development of ARDS and outcome until 28 days. The primary outcome of the study was to explore the effects of EOH on ARDS.

RESULTS

EOH (any serum albumin value < 3.5 g/dl within 7 days of injury) was present in 205/386 (53.1%) patients. The majority of 174/205 (84.9%) patients had EOH by the fourth day after the injury, with the mean time for development of EOH being 2.15 ± 1.87 days. ARDS manifested in 87/205 (42.4%) and 15/181 (8.3%) patients in group A and group B, respectively (p < 0.001). EOH had 8.2 times greater odds of ARDS (OD 8.2 95% CL 4.7-14.0, p = 0.000). The mean time for the onset of ARDS was 5.63 ± 2.62 days. No statistically significant causal relationship occurred between the onset of EOH and the development of ARDS (Pearson's correlation coefficient = 0.14, p = 0.16). At serum albumin cutoff concentrations of 3.4 gm/dl on D1 (AUC 0.68, 95% CI: 0.61-0.74, p = 0.000), ARDS may be anticipated in 62.8% of patients. The commencement of ARDS was independently correlated with EOH (p = 0.000), Respiratory rate on admission (p = 0.000), inotrope use (p = 0.000), and soft tissue injury (p = 0.000) (R2 = 0.466). The odds of 28-day all-cause death were 7.7 times higher in EOH (OD 7.7 95% CL 3.5-16.7, p = 0.00) and 9 times higher in ARDS (OD 9 95% CL 4.9-16.16, p = 0.00).

CONCLUSION

EOH is a frequent occurrence and has a strong influence development of ARDS and 28-day mortality in trauma patients.

Early plasma proteomic biomarkers and prediction model of acute respiratory distress syndrome after cardiopulmonary bypass: a prospective nested cohort study

BACKGROUND

Early recognition of the risk of acute respiratory distress syndrome (ARDS) after cardiopulmonary bypass (CPB) may improve clinical outcomes. The main objective of this study was to identify proteomic biomarkers and develop an early prediction model for CPB-ARDS.

METHODS

The authors conducted three prospective nested cohort studies of all consecutive patients undergoing cardiac surgery with CPB at Union Hospital of Tongji Medical College Hospital. Plasma proteomic profiling was performed in ARDS patients and matched controls (Cohort 1, April 2021-July 2021) at multiple timepoints: before CPB (T1), at the end of CPB (T2), and 24 h after CPB (T3). Then, for Cohort 2 (August 2021-July 2022), biomarker expression was measured and verified in the plasma. Furthermore, lung ischemia/reperfusion injury (LIRI) models and sham-operation were established in 50 rats to explore the tissue-level expression of biomarkers identified in the aforementioned clinical cohort. Subsequently, a machine learning-based prediction model incorporating protein and clinical predictors from Cohort 2 for CPB-ARDS was developed and internally validated. Model performance was externally validated on Cohort 3 (January 2023-March 2023).

RESULTS

A total of 709 proteins were identified, with 9, 29, and 35 altered proteins between ARDS cases and controls at T1, T2, and T3, respectively, in Cohort 1. Following quantitative verification of several predictive proteins in Cohort 2, higher levels of thioredoxin domain containing 5 (TXNDC5), cathepsin L (CTSL), and NPC intracellular cholesterol transporter 2 (NPC2) at T2 were observed in CPB-ARDS patients. A dynamic online predictive nomogram was developed based on three proteins (TXNDC5, CTSL, and NPC2) and two clinical risk factors (CPB time and massive blood transfusion), with excellent performance (precision: 83.33%, sensitivity: 93.33%, specificity: 61.16%, and F1 score: 85.05%). The mean area under the receiver operating characteristics curve (AUC) of the model after 10-fold cross-validation was 0.839 (95% CI: 0.824-0.855). Model discrimination and calibration were maintained during external validation dataset testing, with an AUC of 0.820 (95% CI: 0.685-0.955) and a Brier Score of 0.177 (95% CI: 0.147-0.206). Moreover, the considerably overexpressed TXNDC5 and CTSL proteins identified in the plasma of patients with CPB-ARDS, exhibited a significant upregulation in the lung tissue of LIRI rats.

CONCLUSIONS

This study identified several novel predictive biomarkers, developed and validated a practical prediction tool using biomarker and clinical factor combinations for individual prediction of CPB-ARDS risk. Assessing the plasma TXNDC5, CTSL, and NPC2 levels might identify patients who warrant closer follow-up and intensified therapy for ARDS prevention following major surgery.

The role of artificial neural networks in prediction of severe acute pancreatitis associated acute respiratory distress syndrome: A retrospective study

Early identification and intervention of acute respiratory distress syndrome (ARDS) are particularly important. This study aimed to construct predictive models for ARDS following severe acute pancreatitis (SAP) by artificial neural networks and logistic regression. The artificial neural networks model was constructed using clinical data from 214 SAP patients. The patient cohort was randomly divided into a training set and a test set, with 149 patients allocated to the training set and 65 patients assigned to the test set. The artificial neural networks and logistic regression models were trained by the training set, and then the performance of both models was evaluated using the test set. The sensitivity, specificity, PPV, NPV, accuracy, and AUC value of artificial neural networks model were 68.0%, 87.5%, 77.3%, 81.4%, 80.0%, 0.853 ± 0.054 (95% CI: 0.749-0.958). The sensitivity, specificity, PPV, NPV, accuracy and AUC value of logistic regression model were 48.7%, 85.3%, 65.5%, 74.4%, 72.0%, 0.799 ± 0.045 (95% CI: 0.710-0.888). There were no significant differences between the artificial neural networks and logistic regression models in predictive performance. Bedside Index of Severity in Acute Pancreatitis score, procalcitonin, prothrombin time, and serum calcium were the most important predictive variables in the artificial neural networks model. The discrimination abilities of logistic regression and artificial neural networks models in predicting SAP-related ARDS were similar. It is advisable to choose the model according to the specific research purpose.

Lung Ultrasound Prediction Model for Acute Respiratory Distress Syndrome: A Multicenter Prospective Observational Study

Rationale: Lung ultrasound (LUS) is a promising tool for diagnosis of acute respiratory distress syndrome (ARDS), but adequately sized studies with external validation are lacking. Objectives: To develop and validate a data-driven LUS score for diagnosis of ARDS and compare its performance with that of chest radiography (CXR). Methods: This multicenter prospective observational study included invasively ventilated ICU patients who were divided into a derivation cohort and a validation cohort. Three raters scored ARDS according to the Berlin criteria, resulting in a classification of "certain no ARDS," or "certain ARDS" when experts agreed or "uncertain ARDS" when evaluations conflicted. Uncertain cases were classified in a consensus meeting. Results of a 12-region LUS exam were used in a logistic regression model to develop the LUS-ARDS score. Measurements and Main Results: Three hundred twenty-four (16% certain ARDS) and 129 (34% certain ARDS) patients were included in the derivation cohort and the validation cohort, respectively. With an ARDS diagnosis by the expert panel as the reference test, the LUS-ARDS score, including the left and right LUS aeration scores and anterolateral pleural line abnormalities, had an area under the receiver operating characteristic (ROC) curve of 0.90 (95% confidence interval [CI], 0.85-0.95) in certain patients of the derivation cohort and 0.80 (95% CI, 0.72-0.87) in all patients of the validation cohort. Within patients who had imaging-gold standard chest computed tomography available, diagnostic accuracy of eight independent CXR readers followed the ROC curve of the LUS-ARDS score. Conclusions: The LUS-ARDS score can be used to accurately diagnose ARDS also after external validation. The LUS-ARDS score may be a useful adjunct to a diagnosis of ARDS after further validation, as it showed performance comparable with that of the current practice with experienced CXR readers but more objectifiable diagnostic accuracy at each cutoff.

Effect of a low versus intermediate tidal volume strategy on pulmonary complications in patients at risk of acute respiratory distress syndrome-a randomized clinical trial

Introduction

There is no consensus on whether invasive ventilation should use low tidal volumes (V_T) to prevent lung complications in patients at risk of acute respiratory distress syndrome (ARDS). The purpose of this study is to determine if a low V_T strategy is more effective than an intermediate V_T strategy in preventing pulmonary complications.

Methods

A randomized clinical trial was conducted in invasively ventilated patients with a lung injury prediction score (LIPS) of >4 performed in the intensive care units of 10 hospitals in Spain and one in the United States of America (USA) from 3 November 2014 to 30 August 2016. Patients were randomized to invasive ventilation using low V_T (≤ 6 mL/kg predicted body weight, PBW) (N = 50) or intermediate V_T (> 8 mL/kg PBW) (N = 48). The primary endpoint was the development of ARDS during the first 7 days after the initiation of invasive ventilation. Secondary endpoints included the development of pneumonia and severe atelectases; the length of intensive care unit (ICU) and hospital stay; and ICU, hospital, 28- and 90-day mortality.

Results

In total, 98 patients [67.3% male], with a median age of 65.5 years [interquartile range 55-73], were enrolled until the study was prematurely stopped because of slow recruitment and loss of equipoise caused by recent study reports. On day 7, five (11.9%) patients in the low V_T group and four (9.1%) patients in the intermediate V_T group had developed ARDS (risk ratio, 1.16 [95% CI, 0.62-2.17]; p = 0.735). The incidence of pneumonia and severe atelectasis was also not different between the two groups. The use of a low V_T strategy did neither affect the length of ICU and hospital stay nor mortality rates.

Conclusions

In patients at risk for ARDS, a low V_T strategy did not result in a lower incidence of ARDS than an intermediate V_T strategy.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02070666.

Predicting Persistent Acute Respiratory Failure in Acute Pancreatitis: The Accuracy of Two Lung Injury Indices

BACKGROUND/AIMS

Early and accurate identification of patients with acute pancreatitis (AP) at high risk of persistent acute respiratory failure (PARF) is crucial. We sought to determine the accuracy of simplified Lung Injury Prediction Score (sLIPS) and simplified Early Acute Lung Injury (sEALI) for predicting PARF in ward AP patients.

METHODS

Consecutive AP patients in a training cohort from West China Hospital of Sichuan University (n = 912) and a validation cohort from The First Affiliated Hospital of Nanchang University (n = 1033) were analyzed. PARF was defined as oxygen in arterial blood/fraction of inspired oxygen < 300 mmHg that lasts for > 48 h. The sLIPS was composed by shock (predisposing condition), alcohol abuse, obesity, high respiratory rate, low oxygen saturation, high oxygen requirement, hypoalbuminemia, and acidosis (risk modifiers). The sEALI was calculated from oxygen 2 to 6 L/min, oxygen > 6 L/min, and high respiratory rate. Both indices were calculated on admission.

RESULTS

PARF developed in 16% (145/912) and 22% (228/1033) (22%) of the training and validation cohorts, respectively. In these patients, sLIPS and sEALI were significantly increased. sLIPS ≥ 2 predicted PARF in the training (AUROC 0.87, 95% CI 0.84-0.89) and validation (AUROC 0.81, 95% CI 0.78-0.83) cohorts. sLIPS was significantly more accurate than sEALI and current clinical scoring systems in both cohorts (all P < 0.05).

CONCLUSIONS

Using routinely available clinical data, the sLIPS can accurately predict PARF in ward AP patients and outperforms the sEALI and current existing clinical scoring systems.

To Establish an Early Prediction Model for Acute Respiratory Distress Syndrome in Severe Acute Pancreatitis Using Machine Learning Algorithm

OBJECTIVE

To develop binary and quaternary classification prediction models in patients with severe acute pancreatitis (SAP) using machine learning methods, so that doctors can evaluate the risk of patients with acute respiratory distress syndrome (ARDS) and severe ARDS at an early stage.

METHODS

A retrospective study was conducted on SAP patients hospitalized in our hospital from August 2017 to August 2022. Logical Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and eXtreme Gradient Boosting (XGB) were used to build the binary classification prediction model of ARDS. Shapley Additive explanations (SHAP) values were used to interpret the machine learning model, and the model was optimized according to the interpretability results of SHAP values. Combined with the optimized characteristic variables, four-class classification models, including RF, SVM, DT, XGB, and Artificial Neural Network (ANN), were constructed to predict mild, moderate, and severe ARDS, and the prediction effects of each model were compared.

RESULTS

The XGB model showed the best effect (AUC = 0.84) in the prediction of binary classification (ARDS or non-ARDS). According to SHAP values, the prediction model of ARDS severity was constructed with four characteristic variables (PaO₂/FiO₂, APACHE II, SOFA, AMY). Among them, the overall prediction accuracy of ANN is 86%, which is the best.

CONCLUSIONS

Machine learning has a good effect in predicting the occurrence and severity of ARDS in SAP patients. It can also provide a valuable tool for doctors to make clinical decisions.

Novel models for early prediction and prevention of acute respiratory distress syndrome in patients following hepatectomy: A clinical translational study based on 1,032 patients

Background

Acute respiratory distress syndrome (ARDS) is a serious organ failure and postoperative complication. However, the incidence rate, early prediction and prevention of postoperative ARDS in patients undergoing hepatectomy remain unidentified.

Methods

A total of 1,032 patients undergoing hepatectomy between 2019 and 2020, at the Eastern Hepatobiliary Surgery Hospital were included. Patients in 2019 and 2020 were used as the development and validation cohorts, respectively. The incidence rate of ARDS was assessed. A logistic regression model and a least absolute shrinkage and selection operator (LASSO) regression model were used for constructing ARDS prediction models.

Results

The incidence of ARDS was 8.8% (43/490) in the development cohort and 5.7% (31/542) in the validation cohort. Operation time, postoperative aspartate aminotransferase (AST), and postoperative hemoglobin (Hb) were all critical predictors identified by the logistic regression model, with an area under the curve (AUC) of 0.804 in the development cohort and 0.752 in the validation cohort. Additionally, nine predictors were identified by the LASSO regression model, with an AUC of 0.848 in the development cohort and 0.786 in the validation cohort.

Conclusion

We reported the incidence of ARDS in patients undergoing hepatectomy and developed two simple and practical prediction models for early predicting postoperative ARDS in patients undergoing hepatectomy. These tools may improve clinicians' ability to early estimate the risk of postoperative ARDS and timely prevent its emergence.

Association of the C-terminal 42-peptide fragment of alpha-1 antitrypsin with the severity of ARDS: A pilot study

BACKGROUND

Acute respiratory distress syndrome is a life-threatening condition with a hospital mortality rate of up to 40%. Biomarkers related to the pathophysiology of ARDS may not only identify patients at risk but may also serve as potential therapeutic targets. This study examined the association between the proteolytic C-terminal 42-peptide fragment of alpha-1 antitrypsin and ARDS severity.

METHODS

The 42-peptide fragment and interleukin-6 levels were measured in 21 patients with mild-to-moderate ARDS and 47 patients with moderate-to-severe ARDS on days 1, 3, and 5 after diagnosis/admission to the intensive care unit. To elucidate the association between both biomarkers and the PaO₂/FiO₂ ratio, the concentrations of both biomarkers were compared between the two groups, and a multivariate regression analysis was performed.

RESULTS

The concentrations of both biomarkers were higher in patients with moderate-to-severe ARDS. While the PaO₂/FiO₂ ratio increased from day 1 to day 3, the concentrations of both biomarkers decreased. Multivariate regression analysis revealed negative associations between the PaO₂/FiO₂ ratio and both the C-terminal 42-peptide of alpha-1 antitrypsin and interleukin-6 on day 1 (beta: -0.138, p = 0.052; beta: -0.096, p = 0.004) and on day 3 (beta: -0.157, p = 0.045; beta: -0.106, p = 0.043).

INTERPRETATION

The C-terminal 42-peptide of alpha-1 antitrypsin is a new biomarker associated with ARDS severity. Its predictive value in identifying patients at risk of developing moderate-to-severe ARDS must be investigated in additional, independent prospective studies.

Effect of Uncomplicated Diabetes Mellitus on Acute Respiratory Distress Syndrome Among COVID-19 Patients in Aseer Region, Saudi Arabia

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; an ssRNA virus), which mainly affects the respiratory system but can also cause damage to other body systems. Acute respiratory distress syndrome (ARDS) is a serious complication of COVID-19 that requires early recognition and comprehensive management. ARDS is a diffuse inflammatory process that causes diffuse alveolar damage in the lung.  Aim: The study aimed to assess the effect of uncomplicated diabetes mellitus on ARDS among COVID-19 patients in the Aseer region.

METHODOLOGY

 A retrospective cohort study was conducted in Aseer Central Hospital between July 10, 2021 to Jan 15, 2022 where confirmed inpatient COVID-19 cases in the Aseer region were classified into two groups. The first group was diabetic patients without any diabetes-related complications and confirmed for COVID-19 infection (diabetes group). The second group was confirmed COVID-19 patients free from any chronic disease. Extracted data included patients' diabetes status, medical history, socio-demographic data, COVID-19 infection data and vaccination, experienced signs and symptoms, tachypnea, use of accessory muscles of respiration, nasal flaring, grunting, cyanosis, need for hospitalization, need for mechanical ventilation and ICU admission.  Results: The study included 144 patients with uncomplicated diabetes and 323 healthy patients with COVID-19 infection. The mean age of the diabetic group was 65.4 ± 12.9 years old compared to 40.2 ± 11.9 years old for the healthy group. Only one case of the diabetic group was vaccinated against COVID-19 at the study period versus two cases of the healthy group (P=.925). Also, 14 (9.7%) of the diabetic group were contacted with confirmed COVID-19 cases in comparison to 44 (13.6%) healthy cases (P=.238). A total of five (3.5%) diabetic cases needed mechanical ventilation during hospitalization compared to 23 (7.1%) healthy cases with no statistical significance (P=.125). Also, 12 (8.3%) diabetic cases admitted to ICU versus 42 (13%) of healthy cases (P=.145).  Conclusions: In conclusion, there is a great controversy regarding the effect of diabetes on the progression of COVID-19 infection to ARDS. The current study showed that there was no significant difference between diabetic and healthy COVID-19 infected cases regarding ARDS related clinical factors mainly need of ICU admission and mechanical ventilation.

Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study

Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.

Risk factors for acute respiratory distress syndrome in sepsis patients: a retrospective study from a tertiary hospital in China

Background

Less is known about the risk factors for acute respiratory distress syndrome (ARDS) in sepsis patients diagnosed according to sepsis 3.0 criteria. Moreover, the risk factors for ARDS severity remain unclear.

Methods

We retrospectively collected the characteristics of sepsis patients from the intensive care unit of the First Affiliated Hospital of China Medical University from January 2017 to September 2018. Logistic regression was used in determining the risk factors.

Results

529 patients with sepsis were enrolled and 179 developed ARDS. The most common infection sites were acute abdominal infection (n = 304) and pneumonia (n = 117). Multivariate analysis showed that patients with pancreatitis with local infection (odds ratio [OR], 3.601; 95% confidence interval [CI], 1.429–9.073, P = 0.007), pneumonia (OR 3.486; 95% CI 1.890–6.430, P < 0.001), septic shock (OR 2.163; 95% CI 1.429–3.275, P < 0.001), a higher sequential organ failure assessment (SOFA) score (OR 1.241; 95% CI 1.155–1.333, P < 0.001) and non-pulmonary SOFA score (OR 2.849; 95% CI 2.113–3.841, P < 0.001) were independent risk factors for ARDS. Moreover, pneumonia is associated with increased severity of ARDS (OR 2.512; 95% CI 1.039–6.067, P = 0.041).

Conclusions

We determined five risk factors for ARDS in sepsis patients. Moreover, pneumonia is significantly associated with an increased severity of ARDS.

Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning

Background

Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS), in addition, etiology-associated heterogeneity in ARDS has become an emerging topic quite recently; however, the intersection between the two, which is early prediction of target conditions in etiology-specific ARDS, has not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of moderate-to-severe condition of inhalation-induced ARDS.

Methods

Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 h that ended 6 h prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 200 mmHg).

Results

The trained random forest classifier was validated using two independent validation cohorts, with an area under the curve of 0.9127 (95% confidence interval 0.8713–0.9542) and 0.9026 (95% confidence interval 0.8075–1), respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min < 9, resp_96h_6h_mean ≥ 16.1, HR_96h_6h_mean ≥ 102, and temp_96h_6h_max > 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 h prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test).

Conclusions

This newly established random forest‑based interpretable model shows good predictive ability for moderate-to-severe inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01963-7.

Acute Respiratory Distress Syndrome: An Unexpected Outcome of Suspected Viral Gastroenteritis

Acute respiratory distress syndrome (ARDS) is a life-threatening manifestation of diffuse inflammation damaging the lung pleura. Risk factors for development are numerous with most cases arising in those already hospitalized for critical illness. We describe a unique case of a healthy 20-year-old female developing myocarditis and severe ARDS while hospitalized for septic shock after initially presenting with gastroenteritis from a suspected Coxsackie B infection in the setting of an overseas military deployment. After two transfers via land and air, she reached a facility that delivered definitive care and survived. This case highlights how a common disease can develop into something far more deadly and how early recognition of ARDS risk factors can improve clinical decision-making at the time of admission.

A hitchhiker's guide through the COVID-19 galaxy

Numerous reviews have summarized the epidemiology, pathophysiology and the various therapeutic aspects of Coronavirus disease 2019 (COVID-19), but a practical guide on "how to treat whom with what and when" based on an understanding of the immunological background of the disease stages remains missing. This review attempts to combine the current knowledge about the immunopathology of COVID-19 with published evidence of available and emerging treatment options. We recognize that the information about COVID-19 and its treatment is rapidly changing, but hope that this guide offers those on the frontline of this pandemic an understanding of the host response in COVID-19 patients and supports their ongoing efforts to select the best treatments tailored to their patient's clinical status.

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19

We present an interpretable machine learning algorithm called ‘eARDS’ for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner^® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88–0.90), sensitivity of 0.77 (95% CI = 0.75–0.78), specificity 0.85 (95% CI = 085–0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81–0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO₂), standard deviation of the systolic blood pressure (SBP), O₂ flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18–40) (AUROC = 0.93 [0.92–0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81–0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Linear Association Between Hypoalbuminemia and Increased Risk of Acute Respiratory Distress Syndrome in Critically Ill Adults

Supplemental Digital Content is available in the text.

We hypothesized that low serum albumin would contribute to pulmonary edema formation, thereby independently increasing the risk of developing acute respiratory distress syndrome in critically ill patients.

Diagnosis of acute respiratory distress syndrome (DARTS) by bedside exhaled breath octane measurements in invasively ventilated patients: protocol of a multicentre observational cohort study

Background

Acute respiratory distress syndrome (ARDS) is currently diagnosed by the Berlin Definition. Diagnosis is subjective and often late. Untargeted metabolomics analysis of exhaled breath with gas chromatography and mass spectrometry (GC-MS) showed that the breath concentration of octane has a high diagnostic accuracy for ARDS. To facilitate rapid bedside measurement of this biomarker, a point-of-care (POC) breath test was developed. A prototype already showed good reproducibility and repeatability for the detection of octane. In this study we aim to measure octane in exhaled breath of invasively ventilated intensive care unit (ICU) patients and validate the diagnostic accuracy of the breath test for the early diagnosis of ARDS.

Methods

This is a multicentre observational cohort study in patients admitted to the ICU receiving invasive ventilation for at least 24 hours. At least 500 patients in two academic hospitals in The Netherlands will be included. ARDS patients will be compared to patients without ARDS. ARDS diagnosis will be based on the Berlin Definition. Two diagnostic assessments will be performed during the first 72 hours of invasive ventilation, including breath sampling, arterial blood gas analysis and lung ultrasound (LUS). In patients fulfilling the criteria for ARDS, three additional breath samples will be taken to assess resolution. The primary endpoint is the diagnostic accuracy for ARDS, defined by the area under the receiver operating characteristics curve (AUROCC) of octane concentration in exhaled breath. Secondary endpoints are the association between exhaled breath octane and ARDS adjusted for confounders, and the added diagnostic accuracy of the breath test on top of the Lung Injury Prediction Score (LIPS).

Discussion

This is the first study that validates a metabolic biomarker of ARDS in an adequate sample size. The major novelty is the use of a POC breath test that has been specifically developed for the purpose of diagnosing ARDS. Strengths are; assessment in the early phase, in patients at risk for ARDS, longitudinal sampling and an expert panel to reliably diagnose ARDS. This study will provide a decisive answer on the question if exhaled breath metabolomics can be used to diagnose ARDS.

Trial registration

The trial is registered at trialregister.nl (ID: NL8226) with the tag “DARTS”.

Acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is an acute respiratory illness characterised by bilateral chest radiographical opacities with severe hypoxaemia due to non-cardiogenic pulmonary oedema. The COVID-19 pandemic has caused an increase in ARDS and highlighted challenges associated with this syndrome, including its unacceptably high mortality and the lack of effective pharmacotherapy. In this Seminar, we summarise current knowledge regarding ARDS epidemiology and risk factors, differential diagnosis, and evidence-based clinical management of both mechanical ventilation and supportive care, and discuss areas of controversy and ongoing research. Although the Seminar focuses on ARDS due to any cause, we also consider commonalities and distinctions of COVID-19-associated ARDS compared with ARDS from other causes.

Predicting the mortality risk of acute respiratory distress syndrome: radial basis function artificial neural network model versus logistic regression model

To predict the mortality of acute respiratory distress syndrome (ARDS) by using a radial basis function (RBF) artificial neural network (ANN) model. This study included 217 patients who were admitted between June 2013 and November 2019. The RBF ANN model and logistic regression (LR) model were based on twelve factors related to ARDS. Statistical indexes were used to determine the value of the prediction in the two models. The sensitivity, specificity and accuracy of the RBF ANN model to predict mortality were 83.6%, 88.5% and 82.5%, respectively. Significant differences were found between the RBF ANN and LR models (P < 0.05). When the RBF ANN model was used to identify ARDS, the area under the ROC curve was 0.854 ± 0.029. LDH, organ failure, SP-D and PaO2/FiO2 were the most important independent variables. The RBF ANN model was more likely to predict the mortality of ARDS than the LR model. In addition, it can extract informative risk factors for ARDS.

Predictive Role of Lung Injury Prediction Score in the Development of Acute Respiratory Distress Syndrome in Korea

PURPOSE

Early recognition and therapeutic intervention are important in patients at high risk of acute respiratory distress syndrome (ARDS). The lung injury prediction score (LIPS) has been used to predict ARDS development; however, it was developed based on the previous definition of ARDS. We investigated the predictive role of LIPS in ARDS development according to its Berlin definition in the Korean population.

MATERIALS AND METHODS

This was a retrospective study that enrolled adult patients admitted to the intensive care unit (ICU) at a single university-affiliated hospital in Korea from September 1, 2018, to August 31, 2019. LIPS at the time of ICU admission and the development of ARDS were evaluated.

RESULTS

Of the 548 enrolled patients, 33 (6.0%) fulfilled the Berlin ARDS definition. The LIPS for non-ARDS and ARDS groups were 4.96±3.05 and 8.53±2.45, respectively (p<0.001); it was significantly associated with ARDS development (odds ratio 1.48, 95% confidence interval, 1.29-1.69; p<0.001). LIPS >6 predicted the development of ARDS with a sensitivity of 84.8% and a specificity of 67.2% [area under the curve (AUC)=0.82]. A modified LIPS model adjusted for age and severity at ICU admission predicted ICU mortality in patients with ARDS (AUC=0.80), but not in those without ARDS (AUC=0.54).

CONCLUSION

LIPS predicted the development of ARDS as diagnosed by the Berlin definition in the Korean population. LIPS provides useful information for managing patients with ARDS.

Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome

BACKGROUND

Impaired alveolar fluid clearance, determined in part by alveolar sodium transport, is associated with acute respiratory distress syndrome (ARDS). Nasal sodium transport may reflect alveolar transport. The primary objective of this prospective, observational study was to determine if reduced nasal sodium transport, as measured by nasal potential difference (NPD), was predictive of the development of and outcome from ARDS.

METHODS

NPD was measured in 15 healthy controls and in 88 patients: 40 mechanically ventilated patients defined as 'at-risk' for ARDS, 61 mechanically ventilated patients with ARDS (13 who were previously included in the 'at-risk' group) and 8 ARDS survivors on the ward.

RESULTS

In at-risk subjects, maximum NPD (mNPD) was greater in those who developed ARDS (difference -8.4 mV; 95% CI -13.8 to -3.7; p=0.005) and increased mNPD predicted the development of ARDS before its onset (area under the curve (AUC) 0.75; 95% CI 0.59 to 0.89). In the ARDS group, mNPD was not significantly different for survivors and non-survivors (p=0.076), and mNPD was a modest predictor of death (AUC 0.60; 95% CI 0.45 to 0.75). mNPD was greater in subjects with ARDS (-30.8 mV) than in at-risk subjects (-24.2 mV) and controls (-19.9 mV) (p<0.001). NPD values were not significantly different for survivors and controls (p=0.18).

CONCLUSIONS

Increased NPD predicts the development of ARDS in at-risk subjects but does not predict mortality. NPD increases before ARDS develops, is greater during ARDS, but is not significantly different for controls and survivors. These results may reflect the upregulated sodium transport necessary for alveolar fluid clearance in ARDS. NPD may be useful as a biomarker of endogenous mechanisms to stimulate sodium transport. Larger studies are now needed to confirm these associations and predictive performance.

Acute Respiratory Distress Syndrome Prediction Score: Derivation and Validation

BACKGROUND

Despite advances in treatment strategies, acute respiratory distress syndrome (ARDS) after cardiac surgery remains associated with high morbidity and mortality. A method of screening patients for risk of ARDS after cardiac surgery is needed.

OBJECTIVES

To develop and validate an ARDS prediction score designed to identify patients at high risk of ARDS after cardiac or aortic surgery.

METHODS

An ARDS prediction score was derived from a retrospective derivation cohort and validated in a prospective cohort. Discrimination and calibration of the score were assessed with area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test, respectively. A sensitivity analysis was conducted to assess model performance at different cutoff points.

RESULTS

The retrospective derivation cohort consisted of 201 patients with and 602 patients without ARDS who had undergone cardiac or aortic surgery. Nine routinely available clinical variables were included in the ARDS prediction score. In the derivation cohort, the score distinguished patients with versus without ARDS with area under the curve of 0.84 (95% CI, 0.81-0.88; Hosmer-Lemeshow P = .55). In the validation cohort, 46 of 1834 patients (2.5%) had ARDS develop within 7 days after cardiac or aortic surgery. Area under the curve was 0.78 (95% CI, 0.71-0.85), and the score was well calibrated (Hosmer-Lemeshow P = .53).

CONCLUSIONS

The ARDS prediction score can be used to identify high-risk patients from the first day after cardiac or aortic surgery. Patients with a score of 3 or greater should be closely monitored. The score requires external validation before clinical use.

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS)

PURPOSE

Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS.

MATERIALS AND METHODS

9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation.

RESULTS

On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively.

CONCLUSION

Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.

Descriptive Acute Respiratory Distress Syndrome (ARDS) in adults with imported severe Plasmodium falciparum malaria: A 10 year-study in a Portuguese tertiary care hospital

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a severe complication of malaria that remains largely unstudied. We aim to describe the development of ARDS associated with severe P. falciparum malaria, its management and impact on clinical outcome.

METHODS

Retrospective observational study of adult patients admitted with severe P. falciparum malaria in an Intensive Care Unit (ICU) of a tertiary care hospital from Portugal from 2008 to 2018. A multivariate logistic regression analysis was used to identify factors associated with the development of ARDS, defined according to Berlin Criteria. Prognosis was assessed by case-fatality ratio, nosocomial infection and length of stay.

RESULTS

98 patients were enrolled, of which 32 (33%) developed ARDS, a median of 2 days after starting antimalarial medication (IQR 0-4, range 0-6). Length of stay in ICU and in hospital were significantly longer in patients who developed ARDS: 13 days (IQR 10-18) vs 3 days (IQR 2-5) and 21 days (IQR 15-30.5) vs 7 days (IQR 6-10), respectively. Overall case-fatality ratio in ICU was 4.1% and did not differ between groups. The risk of ARDS development is difficult to establish.

CONCLUSION

ARDS is a hard to predict late complication of severe malaria. A low threshold for ICU admission and monitoring should be used. Ideally patients should be managed in a centre with experience and access to advanced techniques.

Acute respiratory distress syndrome in acute pancreatitis

Development of organ failure is one of the major determinants of mortality in patients with acute pancreatitis (AP). Acute respiratory distress syndrome (ARDS) is an important cause of respiratory failure in AP and is associated with high mortality. Pathogenesis of ARDS in AP is incompletely understood. Release of various cytokines plays an important role in development of ARDS in AP. Increased gut permeability due to various toxins, inflammatory mediators, and pancreatic enzymes potentiates lung injury by gut-lymph-lung axis leading on to increased translocation of bacterial endotoxins. Various scoring systems, serum levels of various cytokines and lung ultrasound have been evaluated for prediction of development of ARDS in AP with varying results. Various drugs have shown encouraging results in prevention of ARDS in animal models but these encouraging results in animal models are yet to be confirmed in clinical studies. There is no specific effective treatment for ARDS. Treatment of sepsis and local complications of AP should be done according to the standard management strategies. Lung protective ventilatory strategies are of paramount importance to improve outcome of patients of AP with ARDS and therefore effective coordination between gastroenterologists and intensivists is needed for effective management of these patients.

Injury Characteristics and von Willebrand Factor for the Prediction of Acute Respiratory Distress Syndrome in Patients With Burn Injury: Development and Internal Validation

OBJECTIVE

To derive and validate a prediction model for the development of ARDS in burn-injured patients.

SUMMARY BACKGROUND DATA

Burn injury carries the highest incidence of acute respiratory distress syndrome (ARDS) among all predisposing conditions, but few studies exist on risk factors in these patients. Studies employing biomarkers and clinical risk factors for predicting ARDS mortality have recently been examined but none exist for onset of ARDS nor in patients with burn injury.

METHODS

This was a prospective multicenter study of 113 patients with isolated burn injury or inhalation injury. Clinical variables and plasma biomarkers representative of endothelial injury, epithelial injury, or inflammation were collected within 24 hours of admission. The most parsimonious model was chosen by considering discrimination, calibration, and model fit.

RESULTS

Among the biomarkers measured in patients with burn injuries, a one-standard deviation increase in log-transformed levels of the A2 domain of von Willebrand factor in the first 24 hours was most strongly associated with the development of ARDS (OR 7.72; 95% CI: 1.64-36.28, P = 0.03). Of candidate models, a 3-variable model with %TBSA, inhalation injury, and von Willebrand factor-A2 had comparable discrimination to more complex models (area under the curve: 0.90; 95% CI 0.85-0.96). The 3-variable model had good model fit by Hosmer-Lemeshow test (P = 0.74) and maintained similar discrimination after accounting for performance optimism (Bootstrapped area under the curve: 0.90; 95% CI: 0.84-0.95).

CONCLUSIONS

The 3-variable model with %TBSA, inhalation injury, and von Willebrand factor could be used to better identify at-risk patients for both the study and prevention of ARDS in patients with burn injury.

Risk modifiers of acute respiratory distress syndrome in patients with non-pulmonary sepsis: a retrospective analysis of the FORECAST study

Background

Predisposing conditions and risk modifiers instead of causes and risk factors have recently been used as alternatives to identify patients at a risk of acute respiratory distress syndrome (ARDS). However, data regarding risk modifiers among patients with non-pulmonary sepsis is rare.

Methods

We conducted a secondary analysis of the multicenter, prospective, Focused Outcomes Research in Emergency Care in Acute Respiratory Distress Syndrome, Sepsis and Trauma (FORECAST) cohort study that was conducted in 59 intensive care units (ICUs) in Japan during January 2016–March 2017. Adult patients with severe sepsis caused by non-pulmonary infection were included, and the primary outcome was having ARDS, defined as meeting the Berlin definition on the first or fourth day of screening. Multivariate logistic regression modeling was used to identify risk modifiers associated with ARDS, and odds ratios (ORs) and their 95% confidence intervals were reported. The following explanatory variables were then assessed: age, sex, admission source, body mass index, smoking status, congestive heart failure, chronic obstructive pulmonary disease, diabetes mellitus, steroid use, statin use, infection site, septic shock, and acute physiology and chronic health evaluation (APACHE) II score.

Results

After applying inclusion and exclusion criteria, 594 patients with non-pulmonary sepsis were enrolled, among whom 85 (14.3%) had ARDS. Septic shock was diagnosed in 80% of patients with ARDS and 66% of those without ARDS (p = 0.01). APACHE II scores were higher in patients with ARDS [26 (22–33)] than in those without ARDS [21 (16–28), p < 0.01]. In the multivariate logistic regression model, the following were independently associated with ARDS: ICU admission source [OR, 1.89 (1.06–3.40) for emergency department compared with hospital wards], smoking status [OR, 0.18 (0.06–0.59) for current smoking compared with never smoked], infection site [OR, 2.39 (1.04–5.40) for soft tissue infection compared with abdominal infection], and APACHE II score [OR, 1.08 (1.05–1.12) for higher compared with lower score].

Conclusions

Soft tissue infection, ICU admission from an emergency department, and a higher APACHE II score appear to be the risk modifiers of ARDS in patients with non-pulmonary sepsis.

What factors predispose patients to acute respiratory distress syndrome?

Sepsis, pneumonia, and shock are the most common conditions predisposing to acute respiratory distress syndrome (ARDS) and certain host genetic variants have been associated with the development of ARDS. Risk modifiers include abuse of alcohol and tobacco, malnutrition, and obesity. The Lung Injury Prediction Score (LIPS) and the simplified Early Acute Lung Injury Score predict ARDS based on clinical and investigational criteria. Hospital-acquired ARDS may result from a medley factors of which high tidal volume ventilation, high oxygen concentration, and plasma transfusion are most commonly implicated. The Checklist for Lung Injury Prevention (CLIP) has been developed to ensure compliance with evidence-based practice that may affect ARDS occurrence. To date, no pharmacologic intervention has been shown to prevent ARDS

Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

BACKGROUND

To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters.

METHODS

A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission.

RESULTS

All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count.

CONCLUSIONS

This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.

How to optimize critical care resources in surgical patients: intensive care without physical borders

PURPOSE OF REVIEW

Timely identification of surgery patients at risk of postoperative complications is important to improve the care process, including critical care. This review discusses epidemiology and impact of postoperative complications; prediction scores used to identify surgical patients at risk of complications, and the role of critical care in the postoperative management. It also discusses how critical care may change, with respect to admission to the ICU.

RECENT FINDING

Optimization of postoperative outcome, next to preoperative and intraoperative optimization, consists of using risk scores to early identify patients at risk of developing complications. Critical care consultancy should be performed in the ward after surgery, if necessary. ICUs could work at different levels of intensity, but remain preferably multidisciplinary, combining care for surgical and medical patients. ICU admission should still be considered for those patients at very high risk of postoperative complications, and for those receiving complex or emergency interventions.

SUMMARY

To optimize critical care resources for surgery patients at high risk of postoperative complications, the care process should not only include critical care and monitoring in ICUs, but also strict monitoring in the ward. Prediction scores could help to timely identify patients at risk. More intense care (monitoring) outside the ICU could improve outcome. This concept of critical care without borders could be implemented in the near future to optimize the local resources and improve patient safety. Predict more, do less in ICUs, and more in the ward.

Diabetic lung disease: fact or fiction?

Diabetes mellitus is a chronic, progressive, incompletely understood metabolic disorder whose prevalence has been increasing steadily worldwide. Even though little attention has been paid to lung disorders in the context of diabetes, its prevalence has recently been challenged by newer studies of disease development. In this review, we summarize and discuss the role of diabetes mellitus involved in the progression of pulmonary diseases, with the main focus on pulmonary fibrosis, which represents a chronic and progressive disease with high mortality and limited therapeutic options.

Guidelines on the management of acute respiratory distress syndrome

The Faculty of Intensive Care Medicine and Intensive Care Society Guideline Development Group have used GRADE methodology to make the following recommendations for the management of adult patients with acute respiratory distress syndrome (ARDS). The British Thoracic Society supports the recommendations in this guideline. Where mechanical ventilation is required, the use of low tidal volumes (<6 ml/kg ideal body weight) and airway pressures (plateau pressure <30 cmH₂O) was recommended. For patients with moderate/severe ARDS (PF ratio<20 kPa), prone positioning was recommended for at least 12 hours per day. By contrast, high frequency oscillation was not recommended and it was suggested that inhaled nitric oxide is not used. The use of a conservative fluid management strategy was suggested for all patients, whereas mechanical ventilation with high positive end-expiratory pressure and the use of the neuromuscular blocking agent cisatracurium for 48 hours was suggested for patients with ARDS with ratio of arterial oxygen partial pressure to fractional inspired oxygen (PF) ratios less than or equal to 27 and 20 kPa, respectively. Extracorporeal membrane oxygenation was suggested as an adjunct to protective mechanical ventilation for patients with very severe ARDS. In the absence of adequate evidence, research recommendations were made for the use of corticosteroids and extracorporeal carbon dioxide removal.

Acute Respiratory Distress Syndrome Phenotypes

The acute respiratory distress syndrome (ARDS) phenotype was first described over 50 years ago and since that time significant progress has been made in understanding the biologic processes underlying the syndrome. Despite this improved understanding, no pharmacologic therapies aimed at the underlying biology have been proven effective in ARDS. Increasingly, ARDS has been recognized as a heterogeneous syndrome characterized by subphenotypes with distinct clinical, radiographic, and biologic differences, distinct outcomes, and potentially distinct responses to therapy. The Berlin Definition of ARDS specifies three severity classifications: mild, moderate, and severe based on the PaO₂ to FiO₂ ratio. Two randomized controlled trials have demonstrated a potential benefit to prone positioning and neuromuscular blockade in moderate to severe phenotypes of ARDS only. Precipitating risk factor, direct versus indirect lung injury, and timing of ARDS onset can determine other clinical phenotypes of ARDS after admission. Radiographic phenotypes of ARDS have been described based on a diffuse versus focal pattern of infiltrates on chest imaging. Finally and most promisingly, biologic subphenotypes or endotypes have increasingly been identified using plasma biomarkers, genetics, and unbiased approaches such as latent class analysis. The potential of precision medicine lies in identifying novel therapeutics aimed at ARDS biology and the subpopulation within ARDS most likely to respond. In this review, we discuss the challenges and approaches to subphenotype ARDS into clinical, radiologic, severity, and biologic phenotypes with an eye toward the future of precision medicine in critical care.

Lung injury prediction scores: Clinical validation and C-reactive protein involvement in high risk patients

OBJECTIVE

A study was made to validate two previously derived lung injury prediction scores (LIPS) for the prediction of acute respiratory distress syndrome (ARDS) in high risk intensive care patients, with the incorporation of C-reactive protein (CRP) for improving score accuracy.

DESIGN

A prospective, observational cohort study was carried out.

PATIENTS

A total of 200 patients with APACHE II score ≥15 and at least one ARDS risk factor upon ICU admission were included.

INTERVENTIONS

Calculation of LIPS using formulas developed by Cartin-Ceba et al. (2009) and Trillo-Alvarez et al. (2011) (LIPS-2009 and LIPS-2011). C-reactive protein was measured upon admission (CRP-0) and after 48h (CRP-48).

MAIN VARIABLES OF INTEREST

Independent variables: LIPS-2009, LIPS-2011 and CRP values. Dependent variable: development of ARDS.

RESULTS

Eighty-eight patients (44%) developed ARDS after a median (Q₁-Q₃) of 2.5 (1.3-6.8) days. The LIPS-2009 and LIPS-2011 scores were 4 (3-6) and 5 (3.6-6.5) in ARDS patients compared to 2 (1-4) and 3.5 (1.5-4.5) in non-ARDS patients (p<0.001). CRP-48 was 96 (67.5-150.3)mg/L and 48 (24-96)mg/L in the two groups, respectively (p<0.001). ΔCRP (i.e., CRP-48 minus CRP-0) was significantly higher in the ARDS patients (p<0.001). The AUC was 0.740 and 0.738 for LIPS-2011 and LIPS-2009, respectively - the difference being nonsignificant (p=0.9, 0.9 and 0.8 for pairwise comparison of the different ROC curves). Integrating ΔCRP with LIPS-2011 using binary logistic regression analysis identified a new score (LIPS-N) with AUC 0.803, which was significantly higher than the AUC of LIPS-2011 (p=0.01).

CONCLUSIONS

Both LIPS scores are equally effective in predicting ARDS in high risk ICU patients. Integrating the change in CRP within the score might improve its accuracy.

Rationale and design of a prospective, multicentre, randomised, conventional treatment-controlled, parallel-group trial to evaluate the efficacy and safety of ulinastatin in preventing acute respiratory distress syndrome in high-risk patients

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is challenging in the intensive care unit (ICU). Although pharmacotherapy for ARDS has gained increasing attention, most trials have yielded negative results. Patients with ARDS have usually been recruited as subjects; the inflammatory reaction has already expanded into a cascade at this point, and its severity is sufficient to damage the lung parenchyma. This raises the question of whether early treatment can prevent ARDS and the associated lung injury. We hypothesise that ARDS is preventable in high-risk patients by administration of ulinastatin as an anti-inflammatory drug before ARDS onset, and we are performing a study to test ulinastatin, a protease inhibitor, versus treatment-as-usual in a group of patients at increased risk for ARDS.

METHODS AND ANALYSIS

This report presents the protocol for a multicentre, randomised, conventional treatment-controlled, parallel group study to prevent the development of ARDS using ulinastatin in high-risk patients. The study population will comprise patients at risk of ARDS in the ICU (≥18 years of age and Lung Injury Prediction Score of >4); patients with confirmed ARDS and some other conditions (immunodeficiency, use of some drugs, etc.) will be excluded. The enrolled patients will be randomly allocated to an ulinastatin group (ulinastatin will be intravenously administered every 8 hours for a total of 600 000 U/day for five consecutive days) or control group. The efficacy of ulinastatin in preventing ARDS development will be evaluated by the incidence rate of ARDS as the primary outcome; the secondary outcomes include the severity of ARDS, clinical outcome, extrapulmonary organ function and adverse events incurred by ulinastatin. Based on the results of preliminary studies and presuming the incidence of ARDS will decrease by 9% in high-risk patients, 880 patients are needed to obtain statistical power of 80%.

ETHICS AND DISSEMINATION

This study has been approved by the Peking University Third Hospital Medical Science Research Ethics Committee. The findings will be published in peer-reviewed journals and presented at national and international conferences.

TRIAL REGISTRATION NUMBER

NCT03089957; Pre-results.

Risk factors and outcomes of acute respiratory distress syndrome in critically ill patients with cirrhosis

AIM

Prior randomized controlled trials of acute respiratory distress syndrome (ARDS) excluded critically ill patients with cirrhosis. Data regarding risk factors for ARDS development and outcomes from ARDS in patients with cirrhosis are scarce. We sought to characterize outcomes from ARDS in patients with cirrhosis.

METHODS

An observational cohort of patients with cirrhosis admitted to an intensive care unit at a high-volume liver transplant center between 1 January 2012 and 31 December 2014 were reviewed. ARDS cases were identified according to the Berlin definition. Potential risk factors were examined in multivariable logistic regression analysis for ARDS development. Outcomes including in-hospital mortality were compared between ARDS and non-ARDS patients.

RESULTS

A total of 559 patients met the inclusion criteria and 45 (8.1%) developed ARDS. Differences between ARDS and non-ARDS patients included sepsis, Model for End-Stage Liver Disease - Sodium score, and Sequential Organ Failure Assessment score. In-hospital mortality was higher in cirrhotic patients with ARDS compared with those without ARDS (82.2% vs. 27.6%, P < 0.001). In multivariable analysis, acute-on-chronic liver failure (OR 8.69, 95% CI 2.28-33.18, P < 0.01) and shock on intensive care unit admission (OR 3.13, 95% CI 1.57-6.24, P = 0.001) were associated with ARDS development, whereas etiology of cirrhosis or alcohol use were not.

CONCLUSIONS

Acute-on-chronic liver failure and shock on intensive care unit admission were risk factors for ARDS development, whereas etiology of cirrhosis and alcohol were not. Mortality from ARDS was markedly increased in patients with cirrhosis. Early recognition and treatment for infection might be important for improving the high mortality in this group of patients.

The effect of diabetes on the risk and mortality of acute lung injury/acute respiratory distress syndrome: A meta-analysis

BACKGROUND

The role of pre-existing diabetes in acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is still controversial. This systematic review and meta-analysis of observational studies aimed to evaluate the effect of diabetes on the risk and mortality of ALI/ARDS.

METHODS

A comprehensive literature search was performed in PubMed, Scopus, Cochrane Central Register of Controlled Trails and Web of Science for their inception to September 2018. Summary risk estimates were calculated with a DerSimonian and Laird random-effects model. Heterogeneity was evaluated using Cochran chi-square test and the I statistic.

RESULTS

Ultimately, 14 studies with a total of 6613 ALI/ARDS cases were included. The risk of ALI/ARDS was not significantly reduced in diabetes patients (OR 0.82, 95% CI 0.57-1.18, P = .283), with obvious heterogeneity across studies (I = 72.5%, P < .001). Further analyses in the meta-analysis also showed no statistically significant associations between pre-existing diabetes and in-hospital mortality (OR 0.79, 95% CI 0.51-1.21, P = .282) or 60-day mortality of ALI/ARDS (OR 0.91, 95% CI 0.75-1.11, P = .352).

CONCLUSION

This systematic review and meta-analysis of observational studies indicates that pre-existing diabetes have no effect on the risk and mortality of ALI/ARDS.

Outcomes of Children With Critical Bronchiolitis Meeting at Risk for Pediatric Acute Respiratory Distress Syndrome Criteria

OBJECTIVES

New definitions of pediatric acute respiratory distress syndrome include criteria to identify a subset of children "at risk for pediatric acute respiratory distress syndrome." We hypothesized that, among PICU patients with bronchiolitis not immediately requiring invasive mechanical ventilation, those meeting at risk for pediatric acute respiratory distress syndrome criteria would have worse clinical outcomes, including higher rates of pediatric acute respiratory distress syndrome development.

DESIGN

Single-center, retrospective chart review.

SETTING

Mixed medical-surgical PICU within a tertiary academic children's hospital.

PATIENTS

Children 24 months old or younger admitted to the PICU with a primary diagnosis of bronchiolitis from September 2013 to April 2014. Children intubated before PICU arrival were excluded.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Collected data included demographics, respiratory support, oxygen saturation, and chest radiograph interpretation by staff radiologist. Oxygen flow (calculated as FIO2 × flow rate [L/min]) was calculated when oxygen saturation was 88-97%. The median age of 115 subjects was 5 months (2-11 mo). Median PICU length of stay was 2.8 days (1.5-4.8 d), and median hospital length of stay was 5 days (3-10 d). The criteria for at risk for pediatric acute respiratory distress syndrome was met in 47 of 115 subjects (40.9%). Children who were at risk for pediatric acute respiratory distress syndrome were more likely to develop pediatric acute respiratory distress syndrome (15/47 [31.9%] vs 1/68 [1.5%]; p < 0.001), had longer PICU length of stay (4.6 d [2.8-10.2 d] vs 1.9 d [1.0-3.1 d]; p < 0.001) and hospital length of stay (8 d [5-16 d] vs 4 d [2-6 d]; p < 0.001), and increased need for invasive mechanical ventilation (16/47 [34.0%] vs 2/68 [2.9%]; p < 0.001), compared with those children who did not meet at risk for pediatric acute respiratory distress syndrome criteria.

CONCLUSIONS

Our data suggest that the recent definition of at risk for pediatric acute respiratory distress syndrome can successfully identify children with critical bronchiolitis who have relatively unfavorable clinical courses.

Immature granulocytes: A novel biomarker of acute respiratory distress syndrome in patients with acute pancreatitis

PURPOSE

To investigate the relationship between immature granulocyte percentage (IG%) and acute respiratory distress syndrome (ARDS) in patients with acute pancreatitis (AP).

MATERIALS AND METHODS

A cohort of 2289 patients with AP was screened; 1933 were enrolled in this prospective multicenter study. Blood samples for IG% analysis were collected on admission and processed using a hematology analyzer. Demographic, radiological, and clinical laboratory data were prospectively collected and reviewed retrospectively.

RESULTS

Increased IG% reflected significant upward tendency of ARDS incidence and severity. Multivariable logistic regression revealed that Acute Physiology and Chronic Health Evaluation (APACHE) II, CT severity index, C-reactive protein, white blood cells, granulocytes, lymphocytes, and IG% (OR 1.297 [95% CI 1.230-1.368]) were independent factors predicting ARDS onset in patients with AP. Receiver operating characteristic curve analysis revealed that area under the curve for APACHE II and IG% were 0.837 (95% CI 0.798-0.876) and 0.821 (95% CI 0.794-0.849), respectively. The combination of APACHE II score and IG% demonstrated excellent predictive power for ARDS incidence.

CONCLUSIONS

IG% is a new type of biomarker for ARDS in patients with AP, which may promote timely and efficient identification of individuals at high risk for ARDS in the early stages of disease.

Identifying associations between diabetes and acute respiratory distress syndrome in patients with acute hypoxemic respiratory failure: an analysis of the LUNG SAFE database

BACKGROUND

Diabetes mellitus is a common co-existing disease in the critically ill. Diabetes mellitus may reduce the risk of acute respiratory distress syndrome (ARDS), but data from previous studies are conflicting. The objective of this study was to evaluate associations between pre-existing diabetes mellitus and ARDS in critically ill patients with acute hypoxemic respiratory failure (AHRF).

METHODS

An ancillary analysis of a global, multi-centre prospective observational study (LUNG SAFE) was undertaken. LUNG SAFE evaluated all patients admitted to an intensive care unit (ICU) over a 4-week period, that required mechanical ventilation and met AHRF criteria. Patients who had their AHRF fully explained by cardiac failure were excluded. Important clinical characteristics were included in a stepwise selection approach (forward and backward selection combined with a significance level of 0.05) to identify a set of independent variables associated with having ARDS at any time, developing ARDS (defined as ARDS occurring after day 2 from meeting AHRF criteria) and with hospital mortality. Furthermore, propensity score analysis was undertaken to account for the differences in baseline characteristics between patients with and without diabetes mellitus, and the association between diabetes mellitus and outcomes of interest was assessed on matched samples.

RESULTS

Of the 4107 patients with AHRF included in this study, 3022 (73.6%) patients fulfilled ARDS criteria at admission or developed ARDS during their ICU stay. Diabetes mellitus was a pre-existing co-morbidity in 913 patients (22.2% of patients with AHRF). In multivariable analysis, there was no association between diabetes mellitus and having ARDS (OR 0.93 (0.78-1.11); p = 0.39), developing ARDS late (OR 0.79 (0.54-1.15); p = 0.22), or hospital mortality in patients with ARDS (1.15 (0.93-1.42); p = 0.19). In a matched sample of patients, there was no association between diabetes mellitus and outcomes of interest.

CONCLUSIONS

In a large, global observational study of patients with AHRF, no association was found between diabetes mellitus and having ARDS, developing ARDS, or outcomes from ARDS.

TRIAL REGISTRATION

NCT02010073 . Registered on 12 December 2013.

Endocan, sepsis, pneumonia, and acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) and hospital-acquired pneumonia (HAP) are major problems of public health in intensive care units (ICUs), occurring in 15% of critically ill patients. Among the factors explaining ARDS development, sepsis is known as a frequent cause. Sepsis, ARDS, and HAP increase morbidity, mortality, length of stay in the ICU, and the overall costs of healthcare. The major challenge remains to identify accurately among critically ill patients those at risk of poor outcomes who could benefit from novel therapies. Endocan is released by the pulmonary endothelium in response to local or systemic injury. It inhibits mainly leukocyte diapedesis rather than leukocyte rolling or adhesion to the endothelial cells both in vitro and in vivo. Endocan was evaluated in 25 clinical reports, including 2454 critically ill patients and 452 healthy controls. The diagnostic value of endocan for sepsis or sepsis severity was equal to procalcitonin but its prognostic value was better. A predictive value for postoperative pneumonia was evidenced in two studies, and a predictive value for ARDS in four studies from three independent centers. This review presents an overview of the structure, expression, and functions of endocan. We also hereby summarize the potential applications of endocan in the prediction and prognosis of ARDS and HAP, as well as in the prognosis of sepsis.