Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study

INTRODUCTION

Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control's (CDC) discharge diagnosis-based algorithm and the Massachusetts state department's drug administration-based classification system on identifying admissions for COVID-19.

METHODS

In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity.

RESULTS

Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6-54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths.

CONCLUSION

Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04702945.

Light-Induced Anti-Bacterial Effect Against Staphylococcus aureus of Porphyrin Covalently Bonded to a Polyethylene Terephthalate Surface

Antimicrobial photodynamic inactivation represents a promising and potentially greener alternative to conventional antimicrobials, and a solution for multidrug-resistant strains. The current study reports the development and characterization of tetra-substituted diazirine porphyrin covalently bonded to polyethylene terephthalate (PET) and its use as an antimicrobial surface. The diazirine moiety on the porphyrin was activated using a temperature of 120 °C, which initiated a C-H insertion mechanism that irreversibly functionalized the PET surface. Activation of the surface with white LED light in phosphate-buffered saline (PBS) led to singlet oxygen generation, which was detected via the degradation of 9,10-anthracenediylbis(methylene)dimalonic acid (ADMA) over time. The bactericidal effect of the ¹O₂-producing surface against Staphylococcus aureus was determined qualitatively and quantitatively. The growth of the pathogen beneath porphyrin-functionalized PET coupons was reduced; moreover, the PET coupons resulted in a 1.76-log reduction in cell counts after exposure to white LED light for 6 h. This is a promising material and platform for the development of safer antimicrobial surfaces, with applications in healthcare, food packaging, marine surfaces, and other surfaces in the environment.