Discharge Disposition and Loss of Independence Among Survivors of COVID-19 Admitted to Intensive Care: Results From the SCCM Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS)

OBJECTIVES

To describe incidence and risk factors of loss of previous independent living through nonhome discharge or discharge home with health assistance in survivors of intensive care unit (ICU) admission for coronavirus disease 2019 (COVID-19).

DESIGN

Multicenter observational study including patients admitted to the ICU from January 2020 till June 30, 2021.

HYPOTHESIS

We hypothesized that there is a high risk of nonhome discharge in patients surviving ICU admission due to COVID-19.

SETTING

Data were included from 306 hospitals in 28 countries participating in the SCCM Discovery Viral Infection and Respiratory Illness Universal Study COVID-19 registry.

PATIENTS

Previously independently living adult ICU survivors of COVID-19.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was nonhome discharge. Secondary outcome was the requirement of health assistance among patients who were discharged home. Out of 10 820 patients, 7101 (66%) were discharged alive; 3791 (53%) of these survivors lost their previous independent living status, out of those 2071 (29%) through nonhome discharge, and 1720 (24%) through discharge home requiring health assistance. In adjusted analyses, loss of independence on discharge among survivors was predicted by patient age ≥ 65 years (adjusted odds ratio [aOR] 2.78, 95% confidence interval [CI] 2.47-3.14, P < .0001), former and current smoking status (aOR 1.25, 95% CI 1.08-1.46, P  =  .003 and 1.60 (95% CI 1.18-2.16), P  =  .003, respectively), substance use disorder (aOR 1.52, 95% CI 1.12-2.06, P  =  .007), requirement for mechanical ventilation (aOR 4.17, 95% CI 3.69-4.71, P < .0001), prone positioning (aOR 1.19, 95% CI 1.03-1.38, P  =  .02), and requirement for extracorporeal membrane oxygenation (aOR 2.28, 95% CI 1.55-3.34, P < .0001).

CONCLUSIONS

More than half of ICU survivors hospitalized for COVID-19 are unable to return to independent living status, thereby imposing a significant secondary strain on health care systems worldwide.

Extracting Aggregation Free Energies of Mixed Clusters from Simulations of Small Systems: Application to Ionic Surfactant Micelles

Micelle cluster distributions from molecular dynamics simulations of a solvent-free coarse-grained model of sodium octyl sulfate (SOS) were analyzed using an improved method to extract equilibrium association constants from small-system simulations containing one or two micelle clusters at equilibrium with free surfactants and counterions. The statistical-thermodynamic and mathematical foundations of this partition-enabled analysis of cluster histograms (PEACH) approach are presented. A dramatic reduction in computational time for analysis was achieved through a strategy similar to the selector variable method to circumvent the need for exhaustive enumeration of the possible partitions of surfactants and counterions into clusters. Using statistics from a set of small-system (up to 60 SOS molecules) simulations as input, equilibrium association constants for micelle clusters were obtained as a function of both number of surfactants and number of associated counterions through a global fitting procedure. The resulting free energies were able to accurately predict micelle size and charge distributions in a large (560 molecule) system. The evolution of micelle size and charge with SOS concentration as predicted by the PEACH-derived free energies and by a phenomenological four-parameter model fit, along with the sensitivity of these predictions to variations in cluster definitions, are analyzed and discussed.