ArticleInfection control and hospital epidemiology2024
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.
Article in Infection control and hospital epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Remdesivir: Real-World Effectiveness and Safety in Individuals Hospitalized for Non-COVID Reasons and Non-Hospitalized High-Risk Patients During the Omicron Era in Greece.Microorganisms · 2026Article
- Frequency, lethality, and demographic trends of acute respiratory viruses in hospitalized patients: insights from a German tertiary care hospital from july 2022 to april 2023.BMC infectious diseases · 2025Article
- Trends in COVID-19-Attributable Hospitalizations Among Adults With Laboratory-Confirmed SARS-CoV-2-COVID-NET, June 2020 to September 2023.Influenza and other respiratory viruses · 2024Article
- Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry.Respiratory research · 2024Article
- Refining the hospitalization rate: A mixed methods approach to differentiate primary COVID-19 from incidental cases.Infection prevention in practice · 2024Article
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Abstract
objectiveThe 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.
designRetrospective cohort study.
settingData 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.
methodsWe 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.
resultsIn 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.
conclusionsWe 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.
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