Differentiating patients admitted primarily due to coronavirus disease 2019 (COVID-19) from those admitted with incidentally detected severe acute respiratory syndrome corona-virus type 2 (SARS-CoV-2) at hospital admission: A cohort analysis of German hospital records.
Infect Control Hosp Epidemiol 2024;
45:746-753. [PMID:
38351873 PMCID:
PMC11102825 DOI:
10.1017/ice.2024.3]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/11/2023] [Accepted: 12/01/2023] [Indexed: 05/18/2024]
Abstract
OBJECTIVE
The number of hospitalized patients with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) does not differentiate between patients admitted due to coronavirus disease 2019 (COVID-19) (ie, primary cases) and incidental SARS-CoV-2 infection (ie, incidental cases). We developed an adaptable method to distinguish primary cases from incidental cases upon hospital admission.
DESIGN
Retrospective cohort study.
SETTING
Data were obtained from 3 German tertiary-care hospitals.
PATIENTS
The study included patients of all ages who tested positive for SARS-CoV-2 by a standard quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay upon admission between January and June 2022.
METHODS
We present 2 distinct models: (1) a point-of-care model that can be used shortly after admission based on a limited range of parameters and (2) a more extended point-of-care model based on parameters that are available within the first 24-48 hours after admission. We used regression and tree-based classification models with internal and external validation.
RESULTS
In total, 1,150 patients were included (mean age, 49.5±28.5 years; 46% female; 40% primary cases). Both point-of-care models showed good discrimination with area under the curve (AUC) values of 0.80 and 0.87, respectively. As main predictors, we used admission diagnosis codes (ICD-10-GM), ward of admission, and for the extended model, we included viral load, need for oxygen, leucocyte count, and C-reactive protein.
CONCLUSIONS
We propose 2 predictive algorithms based on routine clinical data that differentiate primary COVID-19 from incidental SARS-CoV-2 infection. These algorithms can provide a precise surveillance tool that can contribute to pandemic preparedness. They can easily be modified to be used in future pandemic, epidemic, and endemic situations all over the world.
Collapse