| Creators: |
Strobl, Ralf and Misailovski, Martin and Blaschke, Sabine and Berens, Milena and Beste, Andreas and Krone, Manuel and Eisenmann, Michael and Ebert, Sina and Hoehn, Anna and Mees, Juliane and Kaase, Martin and Chackalackal, Dhia J. and Koller, Daniela and Chrampanis, Julia and Kosub, Jana-Michelle and Srivastava, Nikita and Albashiti, Fady and Groß, Uwe and Fischer, Andreas and Grill, Eva and Scheithauer, Simone |
| Title: |
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 |
| Item Type: |
Article or issue of a publication series |
| Journal or Series Title: |
Infection Control & Hospital Epidemiology |
| Page Range: |
pp. 746-753 |
| Additional Information: |
Open Access |
| Date: |
14 February 2024 |
| Divisions: |
Gesundheitsmanagement |
| Abstract (ENG): |
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. |
| Forthcoming: |
No |
| Language: |
English |
| Link eMedia: |
Download |
| Citation: |
Strobl, Ralf and Misailovski, Martin and Blaschke, Sabine and Berens, Milena and Beste, Andreas and Krone, Manuel and Eisenmann, Michael and Ebert, Sina and Hoehn, Anna and Mees, Juliane and Kaase, Martin and Chackalackal, Dhia J. and Koller, Daniela and Chrampanis, Julia and Kosub, Jana-Michelle and Srivastava, Nikita and Albashiti, Fady and Groß, Uwe and Fischer, Andreas and Grill, Eva and Scheithauer, Simone
(2024)
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.
Infection Control & Hospital Epidemiology, 45 (6).
pp. 746-753.
ISSN 1559-6834
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