Dataset Information

Acquired agitation in acute respiratory distress syndrome with COVID-19 compared to influenza patients: a propensity score matching observational study.

ABSTRACT:

Background

A growing body of evidence reports that agitation and encephalopathy are frequent in critically ill Covid-19 patients. We aimed to assess agitation's incidence and risk factors in critically ill ARDS patients with Covid-19. For that purpose, we compared SARS-CoV-2 acute respiratory distress syndrome (ARDS) patients with a population of influenza ARDS patients, given that the influenza virus is also known for its neurotropism and ability to induce encephalopathy.

Methods

We included all the patients with laboratory-confirmed Covid-19 infection and ARDS admitted to our medical intensive care unit (ICU) between March 10th, 2020 and April 16th, 2021, and all the patients with laboratory-confirmed influenza infection and ARDS admitted to our ICU between April 10th, 2006 and February 8th, 2020. Clinical and biological data were prospectively collected and retrospectively analyzed. We also recorded previously known factors associated with agitation (ICU length of stay, length of invasive ventilation, SOFA score and SAPS II at admission, sedative and opioids consumption, time to defecation). Agitation was defined as a day with Richmond Agitation Sedation Scale greater than 0 after exclusion of other causes of delirium and pain. We compared the prevalence of agitation among Covid-19 patients during their ICU stay and in those with influenza patients.

Results

We included 241 patients (median age 62 years [53-70], 158 males (65.5%)), including 146 patients with Covid-19 and 95 patients with Influenza. One hundred eleven (46.1%) patients had agitation during their ICU stay. Patients with Covid-19 had significantly more agitation than patients with influenza (respectively 80 patients (54.8%) and 31 patients (32.6%), p < 0.01). After matching with a propensity score, Covid-19 patients remained more agitated than influenza patients (49 (51.6% vs 32 (33.7%), p = 0.006). Agitation remained independently associated with mortality after adjustment for other factors (HR = 1.85, 95% CI 1.37-2.49, p < 0.001).

Conclusion

Agitation in ARDS Covid-19 patients was more frequent than in ARDS influenza patients and was not associated with common risk factors, such as severity of illness or sedation. Systemic hyperinflammation might be responsible for these neurological manifestations, but there is no specific management to our knowledge.

SUBMITTER: Maamar A

PROVIDER: S-EPMC9463051 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Acquired agitation in acute respiratory distress syndrome with COVID-19 compared to influenza patients: a propensity score matching observational study.

Maamar Adel A Liard Clémence C Doucet Willelm W Reizine Florian F Painvin Benoit B Delamaire Flora F Coirier Valentin V Quelven Quentin Q Guillot Pauline P Lesouhaitier Mathieu M Tadié Jean Marc JM Gacouin Arnaud A

Virology journal 20220910 1

<h4>Background</h4>A growing body of evidence reports that agitation and encephalopathy are frequent in critically ill Covid-19 patients. We aimed to assess agitation's incidence and risk factors in critically ill ARDS patients with Covid-19. For that purpose, we compared SARS-CoV-2 acute respiratory distress syndrome (ARDS) patients with a population of influenza ARDS patients, given that the influenza virus is also known for its neurotropism and ability to induce encephalopathy.<h4>Methods</h4 ...[more]

PMID: 36085163

Similar Datasets

Project description:BackgroundCardinality matching (CM), a novel matching technique, finds the largest matched sample meeting prespecified balance criteria thereby overcoming limitations of propensity score matching (PSM) associated with limited covariate overlap, which are especially pronounced in studies with small sample sizes. The current study proposes a framework for large-scale CM (LS-CM); and compares large-scale PSM (LS-PSM) and LS-CM in terms of post-match sample size, covariate balance and residual confounding at progressively smaller sample sizes.MethodsEvaluation of LS-PSM and LS-CM within a comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and thiazide or thiazide-like diuretic monotherapy identified from a U.S. insurance claims database. Candidate covariates included patient demographics, and all observed prior conditions, drug exposures and procedures. Propensity scores were calculated using LASSO regression, and candidate covariates with non-zero beta coefficients in the propensity model were defined as matching covariates for use in LS-CM. One-to-one matching was performed using progressively tighter parameter settings. Covariate balance was assessed using standardized mean differences. Hazard ratios for negative control outcomes perceived as unassociated with treatment (i.e., true hazard ratio of 1) were estimated using unconditional Cox models. Residual confounding was assessed using the expected systematic error of the empirical null distribution of negative control effect estimates compared to the ground truth. To simulate diverse research conditions, analyses were repeated within 10 %, 1 and 0.5 % subsample groups with increasingly limited covariate overlap.ResultsA total of 172,117 patients (ACEI: 129,078; thiazide: 43,039) met the study criteria. As compared to LS-PSM, LS-CM was associated with increased sample retention. Although LS-PSM achieved balance across all matching covariates within the full study population, substantial matching covariate imbalance was observed within the 1 and 0.5 % subsample groups. Meanwhile, LS-CM achieved matching covariate balance across all analyses. LS-PSM was associated with better candidate covariate balance within the full study population. Otherwise, both matching techniques achieved comparable candidate covariate balance and expected systematic error.ConclusionsLS-CM found the largest matched sample meeting prespecified balance criteria while achieving comparable candidate covariate balance and residual confounding. We recommend LS-CM as an alternative to LS-PSM in studies with small sample sizes or limited covariate overlap.

Dataset Information

Acquired agitation in acute respiratory distress syndrome with COVID-19 compared to influenza patients: a propensity score matching observational study.

Background

Methods

Results

Conclusion

Publications

Acquired agitation in acute respiratory distress syndrome with COVID-19 compared to influenza patients: a propensity score matching observational study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets