Dataset Information

Handling oxygenation targets in ICU patients with COVID-19-Protocol and statistical analysis plan in the HOT-COVID trial.

ABSTRACT:

Background

Coronavirus disease (COVID-19) primarily affects the lungs and lower airways and may present as hypoxaemic respiratory failure requiring admission to an intensive care unit (ICU) for supportive treatment. Here, supplemental oxygen remains essential for COVID-19 patient management, but the optimal dosage is not defined. We hypothesize that targeting an arterial partial pressure of oxygen of 8 kPa throughout ICU admission is superior to targeting 12 kPa.

Methods

The Handling Oxygenation Targets in ICU patients with COVID-19 (HOT-COVID) trial, is an investigator-initiated, pragmatic, multicentre, randomized, parallel-group trial comparing a lower oxygenation target versus a higher oxygenation target in adult ICU patients with COVID-19. The primary outcome is days alive without life-support (use of mechanical ventilation, renal replacement therapy or vasoactive therapy) at day 90. Secondary outcomes are 90-day and 1-year mortality, serious adverse events in the ICU and days alive and out of hospital in the 90-day period, health-related quality-of-life at 1 year, and health economic analyses. One-year follow-up of cognitive and pulmonary function is planned in a subgroup of Danish patients. We will include 780 patients to detect or reject an absolute increase in days alive without life-support of 7 days with an α of 5% and a β of 20%. An interim analysis is planned after 90-day follow-up of 390 patients.

Conclusions

The HOT-COVID trial will provide patient-important data on the effect of two oxygenation targets in ICU patients with COVID-19 and hypoxia. This protocol paper describes the background, design and statistical analysis plan for the trial.

SUBMITTER: Molgaard Nielsen F

PROVIDER: S-EPMC8441913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSince the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient's clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death.ObjectiveThis statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.MethodsAfter the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection.ResultsAs of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform.ConclusionsThe methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients.Trial registrationClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041.International registered report identifier (irrid)DERR1-10.2196/30083.