A new horizon for the use of non-invasive ventilation in patients with acute respiratory distress syndrome

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Validity of a clinical scale in predicting the failure of non-invasive ventilation in hypoxemic patients

INTRODUCTION

The HACOR scale is a clinical score that can predict early failure of NIV in hypoxemic acute respiratory failure (ARF) The aim of this study is to analyze the validity of the HACOR scale.

METHODS

A retrospective study of a cohort of over 2749 episodes on 2711 consecutive patients requiring NIV for hypoxemic ARF in a polyvalent intensive care unit. The scale was measured before starting NIV and at 1, 6, 12, 24 and 48 h after the initiation of NIV.

RESULTS

NIV failure occurred in 963 patients (35%). The value of the HACOR scale before NIV did not differ between success and failure. However, at 1, 6, 12, 24 and 48 h of NIV, the scale values clearly differed between the two groups. The HACOR scale at NIV initiation accurately predicts NIV failure in the first hour, with an optimal cut-off value of 8 points. The AUC for predicting NIV failure with HACOR at 1 h is greater than 0.9 in patients with pneumonia and adult respiratory distress syndrome (ARDS).

CONCLUSIONS

The HACOR scale measured at 1 h after NIV initiation accurately predicts NIV failure, especially in pneumonia and ARDS.

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

BACKGROUND

Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.

METHODS

This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.

RESULTS

A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO₂. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753-0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590-0.739]; p = 0.002 by Delong's test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669-0.817], p = 0.130 by Delong's test).

CONCLUSIONS

The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.

Acute Respiratory Failure and Management

Acute respiratory failure is a common reason for admission to the pediatric intensive care unit in oncology patients. Acute respiratory complications are also common after pediatric hematopoietic stem cell transplant (HSCT), accounting for a high proportion of HSCT-related morbidity and mortality. Evaluation of these patients requires a thorough workup that includes identification and treatment of infectious etiologies, and treatment for noninfectious causes once infectious causes are ruled out. These patients should be closely monitored for development of pediatric acute respiratory distress syndrome (PARDS) with early escalation of respiratory support. Patients undergoing a trial of noninvasive ventilation (NIV) should be continuously monitored to ensure they are responding. Prolonged delay of endotracheal intubation in patients who do not improve or worsen on NIV could worsen their outcome. Optimal treatment of immunocompromised patients with acute lung failure requires early and aggressive lung protective ventilation, prevention of fluid overload, and rapid diagnosis of underlying causes to facilitate prompt disease-directed therapy.