Observational studyPloS one2020
Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.
Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Coronavirus Disease 2019 as Cause of Viral Sepsis: A Systematic Review and Meta-Analysis.Critical care medicine · 2021Pooled it
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.Yonsei medical journal · 2025Article
- Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review.Journal of clinical medicine · 2024Article
- Moisture content in diets in the co-feeding phase of Pseudoplatystoma sp. influences performance and digestive process.Veterinary research communications · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Understanding contextual and practical factors to inform WHO recommendations on using chest imaging to monitor COVID-19 pulmonary sequelae: a qualitative study exploring stakeholders' perspective.Health research policy and systems · 2024Article
- Machine learning algorithm for ventilator mode selection, pressure and volume control.PloS one · 2024Article
- Reintubation After Lung Cancer Resection: Development and External Validation of a Predictive Score.The Annals of thoracic surgery · 2024Article
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Machine learning algorithm for early-stage prediction of severe morbidity in COVID-19 pneumonia patients based on bio-signals.BMC pulmonary medicine · 2023Article
- Spacer exchange in persistent periprosthetic joint infection: microbiological evaluation and survivorship analysis.Archives of orthopaedic and trauma surgery · 2023Article
- Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida.Frontiers in digital health · 2023Article
- The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer.Frontiers in oncology · 2023Article
- Editorial: Outcome of sepsis and prediction of mortality risk.Frontiers in medicine · 2023Article
- SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy.Journal of Raman spectroscopy : JRS · 2023Article
- A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients.PloS one · 2023Observational
- Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
- A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19.Scientific reports · 2022Article
- Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.PloS one · 2022Article
Corrections and comments
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Authors and funding
39 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia.
methodsThis was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome.
resultsA total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example.
conclusionThis study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.
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