scholarly journals Pediatric respiratory distress: California out‐of‐hospital protocols and evidence‐based recommendations

2020 ◽  
Vol 1 (5) ◽  
pp. 955-964
Author(s):  
Tabitha Cheng ◽  
Jennifer Farah ◽  
Nicholas Aldridge ◽  
Sharon Tamir ◽  
J. Joelle Donofrio‐Odmann
Keyword(s):  
Respiratory Distress ◽  
Evidence Based ◽  
Hospital Protocols

scholarly journals Promoting Evidence-Based Practice in Acute Respiratory Distress Syndrome: A Systematic Review

2021 ◽  
Vol 3 (4) ◽  
pp. e0391
Author(s):  
Shewit P. Giovanni ◽  
Ann L. Jennerich ◽  
Tessa L. Steel ◽  
Sharukh Lokhandwala ◽  
Waleed Alhazzani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Evidence Based Practice ◽  
Evidence Based

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

scholarly journals Adult Patients with Respiratory Distress: Current Evidence-based Recommendations for Prehospital Care

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Sammy Hodroge ◽  
Melody Glenn ◽  
Amelia Breyre ◽  
Bennett Lee ◽  
Nick Aldridge ◽  
...  
Keyword(s):  
Respiratory Distress ◽  
Prehospital Care ◽  
Adult Patients ◽  
Current Evidence ◽  
Evidence Based

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

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