Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement

Parreco, Joshua; Hidalgo, Antonio E.; Parks, Jonathan; Kozol, Robert A.; Rattan, Rishi

doi:10.1016/j.jss.2018.03.028

Cited by 74 publications

(77 citation statements)

References 25 publications

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“…An AI Sepsis Expert algorithm for early prediction of sepsis has been engineered and achieved AUC values ranging from 0.83-0.85 according to the time of the prediction. An AI method for predicting prolonged mechanical ventilation achieved an AUC of 0.82 [23]. How can it be that AI or machine-learning predictive algorithms that can already automatically drive cars or successfully understand human speech failed to predict the microbial cause of pneumonia accurately?…”

Section: Discussionmentioning

confidence: 99%

“…Step 1: patient data collection Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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Background: Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?Methods: We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. Results: We included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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“…They found that the most relevant features were the pulmonary logistic organ dysfunction scores (LODS) for PMV, and, unexpectedly, a diagnosis of cardiac arrhythmia for tracheostomy. These models may have important implications for prognostication, and have the potential to improve outcomes by proceeding to tracheostomy sooner in select patients, thereby facilitating earlier mobilization [11].…”

Section: Mechanical Ventilationmentioning

confidence: 99%

“…Researchers using ML have pioneered the development of illness severity scores in the intensive care unit (ICU) [9,10]. More recent studies have leveraged the abundance of data present in the ICU to predict the course of mechanical ventilation and the occurrence of sepsis, as well as to support decision making in this context (e.g., fluid vs. vasopressor choices in sepsis care) [11][12][13][14][15][16][17]. This article is based on previously conducted studies and does not contain any studies with human participants or animals performed by any of the authors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

2020

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Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

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“…Use of AI to identify patients who will require > 7 days of mechanical ventilation has been shown to improve outcomes. 33 Teams are also using AI to aid ventilator weaning by targeting the success of extubation. Kuo et al 34 used neural networks to create a model with an accuracy of 80% and improved on traditional prediction by rapid shallow breathing index.…”

Section: Predictive Analyticsmentioning

confidence: 99%

Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

Rajagopalan

Kulkarni²

2020

Journal of Neuroanaesthesiology and Critical Care

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Artificial intelligence (AI) already influences almost every sector of our daily life, including the rapidly evolving technologies and datasets of healthcare delivery. The applications in medicine have significantly evolved over the past few decades and have shown promising results. Despite constant efforts to incorporate AI into the field of anesthesiology since its inception, it is still not commonplace. Neuroanesthesiology and neurocritical care is a discipline of medicine that deals with patients having disorders of the nervous system comprising a complex combination of both medical and surgical disease conditions. AI can be used for better monitoring, treatment, and outcome prediction, thereby reducing healthcare costs, minimizing delays in patient management, and avoiding medical errors. In this review, we have discussed the applications of AI and its potential in aiding the clinician’s judgment in several aspects of neuroanesthesiology and neurocritical care, some of the barriers to its implementation, and the future trends in improving education in this field, all of which will require further work to understand its exact scope.

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Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement

Cited by 74 publications

References 25 publications

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

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