Observational studyBritish journal of anaesthesia2021
Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19.
Observational study in British journal of anaesthesia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.Pulmonary medicine · 2026Pooled it
- Machine learning-based prediction models for noninvasive respiratory support failure in acute respiratory failure: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Association Between Mood Disorders and Risk of COVID-19 Infection, Hospitalization, and Death: A Systematic Review and Meta-analysis.JAMA psychiatry · 2021Pooled it
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Calculating the Risk of Admission to Intensive Care Units in COVID-19 Patients Using Machine Learning.Journal of clinical medicine · 2025Article
- Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19.Frontiers in artificial intelligence · 2025Article
- Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.Healthcare technology letters · 2024Article
- A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19.Biotech (Basel (Switzerland)) · 2024Article
- Development and validation of the COVID-19 Hospitalized Patient Deterioration Index.The American journal of managed care · 2023Observational
- Machine Learning for Benchmarking Critical Care Outcomes.Healthcare informatics research · 2023Article
- Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients.Journal of medical systems · 2023Article
- Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19.Frontiers in public health · 2023Article
- Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19.Frontiers in public health · 2023Article
- Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma.Frontiers in genetics · 2023Article
- Comparison of static and rolling logistic regression models on predicting invasive mechanical ventilation or death from COVID-19-A retrospective, multicentre study.The clinical respiratory journal · 2023Article
- Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study.Intensive care medicine experimental · 2022Article
- A machine learning model for predicting deterioration of COVID-19 inpatients.Scientific reports · 2022Article
- A novel method for handling pre-existing conditions in multivariate prediction model development for COVID-19 death in the Department of Veterans Affairs.Biology methods & protocols · 2022Article
- Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPatients with coronavirus disease 2019 (COVID-19) requiring mechanical ventilation have high mortality and resource utilisation. The ability to predict which patients may require mechanical ventilation allows increased acuity of care and targeted interventions to potentially mitigate deterioration.
methodsWe included hospitalised patients with COVID-19 in this single-centre retrospective observational study. Our primary outcome was mechanical ventilation or death within 24 h. As clinical decompensation is more recognisable, but less modifiable, as the prediction window shrinks, we also assessed 4, 8, and 48 h prediction windows. Model features included demographic information, laboratory results, comorbidities, medication administration, and vital signs. We created a Random Forest model, and assessed performance using 10-fold cross-validation. The model was compared with models derived from generalised estimating equations using discrimination.
resultsNinety-three (23%) of 398 patients required mechanical ventilation or died within 14 days of admission. The Random Forest model predicted pending mechanical ventilation with good discrimination (C-statistic=0.858; 95% confidence interval, 0.841-0.874), which is comparable with the discrimination of the generalised estimating equation regression. Vitals sign data including SpO
conclusionMachine learning techniques can be leveraged to improve the ability to predict which patients with COVID-19 are likely to require mechanical ventilation, identifying unrecognised bellwethers and providing insight into the constellation of accompanying signs of respiratory failure in COVID-19.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.