Evidence map›Paper›PMID 41723547›Full record

ArticleJournal of anesthesia, analgesia and critical care2026

High-flow nasal therapy vs conventional oxygen therapy in mild COVID-19 hypoxaemia: a Bayesian reanalysis of the COVID-HIGH Trial.

Claudia Crimi, Salvatore Sardo, Alberto Noto, Fabiana Madotto, Mariachiara Ippolito, Santi Nolasco, Raffaele Campisi, Giuseppe Fiorentino, Ioannis Pantazopoulos, Athanasios Chalkias and 12 more

Registry-linked trialAbstract read
In one paragraph

Article in Journal of anesthesia, analgesia and critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04655638 (High-Flow Nasal Therapy Versus Conventional Oxygen Therapy in Patients With COVID-19), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT04655638 nacompletednot on this map

High-Flow Nasal Therapy Versus Conventional Oxygen Therapy in Patients With COVID-19: A Randomized Controlled Trial (The COVID-HIGH Trial)

TypeinterventionalSponsorAzienda Ospedaliera Universitaria Policlinico Paolo Giaccone PalermoRan2021 to 2021Enrolled364ConditionsCovid19, Acute Respiratory FailureArmsHigh Flow Nasal Therapy, Conventional Oxygen Therapy
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Claudia Crimi *Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
Salvatore Sardo *Department of Medical Sciences and Public Health, University of Cagliari, Monserrato, Italy.
Alberto NotoDepartment of Human Pathology of the Adult and Evolutive Age "Gaetano Barresi", Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy.
Fabiana MadottoDepartment of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Mariachiara IppolitoDepartment of Precision Medicine in Medical, Surgical and Critical Care Area (Me.Pre.C.C.), University of Palermo, Palermo, Italy.
Santi NolascoRespiratory Intensive Care Unit, Respiratory Medicine Unit, Policlinico "G. Rodolico-San Marco" University Hospital, Catania, Italy.
Raffaele CampisiRespiratory Intensive Care Unit, Respiratory Medicine Unit, Policlinico "G. Rodolico-San Marco" University Hospital, Catania, Italy.
Giuseppe FiorentinoUOC Fisiopatologia E Riabilitazione Respiratoria AO Dei Colli, Naples, Italy.
Ioannis PantazopoulosDepartment of Emergency Medicine, Faculty of Medicine, University of Thessaly, Larisa, Greece.
Athanasios ChalkiasInstitute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.
Alessio MatteiDepartment of "Medical Area", Respiratory Medicine Unit, Santa Croce E Carle Hospital, Cuneo, AO, Italy.
Raffaele ScalaPulmonology and Respiratory Intensive Care Unit, S. Donato Hospital, Arezzo, Italy.
Enrico CliniRespiratory Diseases Unit, Department of Medical and Surgical Sciences SMECHIMAI, University Hospital of Modena Policlinico, University of Modena Reggio Emilia, Modena, Italy.
Begum ErganDepartment of Pulmonary and Critical Care, Dokuz Eylul University, İzmir, Turkey.
Manel LujanDepartment of Respiratory Medicine, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain.
João Carlos WinckCardiovascular R&D Centre (UniC), Faculdade de Medicina da Universidade Do Porto, Porto, Portugal.
Antonino GiarratanoDepartment of Precision Medicine in Medical, Surgical and Critical Care Area (Me.Pre.C.C.), University of Palermo, Palermo, Italy.
Annalisa CarlucciDepartment of Experimental Medicine, University of Salento, Lecce, Italy.
Cesare GregorettiIntensive Care Unit, Fondazione 'Giglio', Cefalù, Palermo, Italy.
Paolo GroffEmergency Department, "S. Maria Della Misericordia" Hospital, Perugia, Italy.
Andrea CortegianiDepartment of Precision Medicine in Medical, Surgical and Critical Care Area (Me.Pre.C.C.), University of Palermo, Palermo, Italy. andrea.cortegiani@unipa.it.
COVID-HIGH Investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical effectiveness of high-flow nasal therapy (HFNT) over conventional oxygen therapy (COT) in patients with mild COVID-19-related acute hypoxaemic respiratory failure (AHRF) remains uncertain. The COVID-HIGH trial did not demonstrate statistically significant benefits of HFNT over COT. However, the trial was slightly underpowered, and the event rate lower-than-expected. Bayesian methods provide deeper insight by incorporating prior knowledge and quantifying uncertainty intuitively. This analysis aimed to quantify the probability of benefit or harm associated with HFNT, adopting a Bayesian approach.

methodsWe performed a Bayesian reanalysis of the COVID-HIGH trial (NCT, which randomised 364 patients with PaO₂/FiO₂ between 200-300 mmHg to receive HFNT or COT. The primary outcome was escalation of respiratory support (continuous positive airway pressure, noninvasive ventilation or invasive mechanical ventilation) within 28 days. A key secondary outcome was clinical recovery at day 14. Bayesian logistic models with noninformative and informative priors were used to estimate the posterior probability of treatment effects.

resultsEscalation of respiratory support occurred in 23.6% (HFNT) versus 30.2% (COT) (risk difference - 6.6%, 95% CI - 15.1 to 2.1; p = 0.14). Across a wide range of priors, the posterior probability mass on the beneficial side remained high, generally > 70%, while the proportion on the harm side remained consistently low at ≤ 6% for all models, underscoring a favourable benefit-risk profile. The acute respiratory failure meta-analysis model (OR 0.76, 95% CrI 0.60-0.97), the COVID-19 randomised evidence model (OR 0.76, 95% CrI 0.60-0.97), the COVID-19 observational evidence model (OR 0.60, 95% CrI 0.45-0.80), and the COVID-19 Bayesian meta-analysis mixed evidence model (OR 0.66, 95% CrI 0.52-0.86) showed posterior probability mass on the beneficial side of 70%-94%. Clinical recovery at day 14 occurred in 61.5% (HFNT) versus 53.3% (COT), with 61-73% of posterior probability mass on the clinical benefit side.

conclusionsThis Bayesian re-analysis of the COVID-HIGH trial suggests that HFNT likely reduces escalation of respiratory support and improves clinical recovery in patients with COVID-19 pneumonia and mild hypoxaemia, although the magnitude of benefit remains uncertain and sensitive to prior assumptions.

trial registrationThe trial was prospectively registered in ClinicalTrials.gov on December 7, 2020 (NCT04655638).

Indexed as

Acute hypoxemic respiratory failureBayesian statisticsCOVID-19 pneumoniaHigh-flow nasal therapyMild hypoxemia

Identifiers

PMID41723547
PMCPMC13032249

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.