Implications of Early Respiratory Support Strategies on Disease Progression in Critical COVID-19

A Matched Subanalysis of the Prospective RISC-19-ICU Cohort

Pedro D. Wendel Garcia; Hernán Aguirre-Bermeo; Philipp K. Buehler; Mario Alfaro-Farias; Bernd Yuen; Sascha David; Thomas Tschoellitsch; Tobias Wengenmayer; Anita Korsos; Alberto Fogagnolo; Gian-Reto Kleger; Maddalena A. Wu; Riccardo Colombo; Fabrizio Turrini; Antonella Potalivo; Emanuele Rezoagli; Raquel Rodríguez-Garcia; Pedro Castro; Arantxa Lander-Azcona; Maria C. Martín-Delgado; Herminia Lozano-Gómez; Rolf Ensner; Marc P. Michot; Nadine Gehring; Peter Schott; Martin Siegemund; Lukas Merki; Jan Wiegand; Marie M. Jeitziner; Marcus Laube; Petra Salomon; Frank Hillgaertner; Alexander Dullenkopf; Hatem Ksouri; Sara Cereghetti; Serge Grazioli; Christian Bürkle; Julien Marrel; Isabelle Fleisch; Marie-Helene Perez; Anja Baltussen Weber; Samuele Ceruti; Katharina Marquardt; Tobias Hübner; Hermann Redecker; Michael Studhalter; Michael Stephan; Daniela Selz; Urs Pietsch; Anette Ristic; Antje Heise; Friederike Meyer zu Bentrup; Marilene Franchitti Laurent; Patricia Fodor; Tomislav Gaspert; Christoph Haberthuer; Elif Colak; Dorothea M. Heuberger; Thierry Fumeaux; Jonathan Montomoli; Philippe Guerci; Reto A. Schuepbach; Matthias P. Hilty; Ferran Roche-Campoon

Disclosures

Crit Care. 2021;25(175) 

In This Article

Methods

This was a retrospective subanalysis of data from the prospective RISC-19-ICU registry, which contains a standardized dataset of all critically ill COVID-19 patients admitted to the collaborating centers during the ongoing pandemic.

The RISC-19-ICU registry was deemed exempt from the need for additional ethics approval and patient informed consent by the ethics committee of the canton of Zurich (KEK 2020–00322, ClinicalTrials.gov Identifier: NCT04357275). The present study complies with the tenets of the Declaration of Helsinki, the Guidelines on Good Clinical Practice (GCP-Directive) issued by the European Medicines Agency, as well as Swiss law and Swiss regulatory authority requirements. All collaborating centers have complied with all local legal and ethical requirements. As of October 1, 2020, 63 collaborating centers in 10 countries, were actively contributing to the RISC-19-ICU registry. For further specifications on the RISC-19-ICU registry structure and data collection, see Additional file 1: e-Appendix 1.

Inclusion and Exclusion Criteria

Patients were included in the present substudy if they required SOT (≥10 L/min[20]), HFNC, NIV, or IMV at the time point of admission to the ICU defined as day 0. Patients without a full ICU outcome data set, with SOT <10 L/min, or with a do-not-intubate order at day 0 were excluded. For the days ensuing ICU admission, the daily respiratory support therapy was defined as the main strategy used during the chart day.

Initial Ventilation Support Group Definitions

For study purposes, patients were categorized into four groups according to their maximal respiratory support at ICU admission (day 0), as follows: (1) SOT group: patients receiving SOT with an oxygen flow of ≥10 L/min (FiO2 was approximated based on the delivered oxygen flow as described by Farias et al.[21]); (2) HFNC group: patients receiving HFNC, defined as a device delivering humidified and heated oxygen at a flow rate above 30 L/min; (3) NIV group: patients receiving NIV, irrespective of interface, mode and ventilator type employed; and (4) IMV group: intubated patients receiving IMV.

Statistical Analysis

Missing data handling is described in Additional file 1: e-Appendix 2. Comparisons of population characteristics were performed using the analysis of variance or Kruskal–Wallis test, as appropriate, and the Chi-squared test for categorical variables. Nearest neighbor matching with a propensity score caliper distance of 0.1 was employed to select IMV patients with ICU admission characteristics comparable to those of the patients in the SOT, HFNC and NIV groups. Patients having received IMV in another institutions ICU before admission to the RISC-19-ICU center were excluded from the matching process. To enable comparability between IMV and the noninvasive respiratory support strategies, Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology II (SAPS II) scores were used without the mechanical ventilation and neurologic sub-scores for the matching process. An optimal quality match was defined as a standardized mean difference (SMD) ≤0.1 per matching variable between patients in the IMV group and the other groups (SOT, HFNC and NIV).[22]

Univariable Cox proportional hazard models coupled to the Kaplan–Meier estimator were employed to analyze the effects, represented by hazard ratios (HR), of the different respiratory support strategies on the incidence of intubation, ICU mortality and discharge from ICU. Multivariable adjusted HRs were calculated for every model independently by means of an iterative, step-wise, maximum likelihood optimizing algorithm, controlling for collinearity, interactions, and effect size variation in every iteration. The maximum number of covariates per model was chosen to ensure 1 to 10 events per covariate. Comparison of survival distributions among the various respiratory support strategies was approached by means of the log-rank test. Proportional hazard assumptions were assessed through inspection of Schoenfeld residuals.

Generalized linear regression model (GLM) analysis, considering all recorded baseline characteristics at ICU admission, was employed to determine the best predictive model for mortality in patients initially receiving HFNC and NIV and requiring delayed IMV. Multivariable GLM analysis was performed by means of an iterative, step-wise, maximum likelihood optimizing algorithm initially considering all variables with p<0.1 on the univariable analysis. First-order interaction terms between the predictor variables were tested for all models, and excluded if not improving the final model fit. For the final GLM model, a prognostic score and nomogram were generated, and receiver operating characteristics (ROC) analysis was employed alongside minimal Euclidean distance fitting to the (0, 1) point to determine the optimal cut-off value for the generated score. 95% confidence intervals (CI) and p values comparing the prognostic score to classic severity scores were generated by means of the bootstrap percentile method.

Statistical analysis was performed through a fully scripted data management pathway using the R environment for statistical computing version 3.6 .1. Due to the observational, prospective nature of this cohort study no power calculations were performed. A two-sided p <0.05 was considered statistically significant. Values are given as medians with interquartile ranges (IQR) or counts and percentages as appropriate.

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