The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study
Open accessAbstract
Background Nationwide, unbiased, and unselected data of hospitalised patients with COVID-19 are scarce. Our aim was to provide a detailed account of case characteristics, resource use, and outcomes of hospitalised patients with COVID-19 in Germany, where the health-care system has not been overwhelmed by the pandemic. Methods In this observational study, adult patients with a confirmed COVID-19 diagnosis, who were admitted to hospital in Germany between Feb 26 and April 19, 2020, and for whom a complete hospital course was available (ie, the patient was discharged or died in hospital) were included in the study cohort. Claims data from the German Local Health Care Funds were analysed. The data set included detailed information on patient characteristics, duration of hospital stay, type and duration of ventilation, and survival status. Patients with adjacent completed hospital stays were grouped into one case. Patients were grouped according to whether or not they had received any form of mechanical ventilation. To account for comorbidities, we used the Charlson comorbidity index. Findings Of 10 021 hospitalised patients being treated in 920 different hospitals, 1727 (17%) received mechanical ventilation (of whom 422 [24%] were aged 18–59 years, 382 [22%] were aged 60–69 years, 535 [31%] were aged 70–79 years, and 388 [23%] were aged ≥80 years). The median age was 72 years (IQR 57–82). Men and women were equally represented in the non-ventilated group, whereas twice as many men than women were in the ventilated group. The likelihood of being ventilated was 12% for women (580 of 4822) and 22% for men (1147 of 5199). The most common comorbidities were hypertension (5575 [56%] of 10 021), diabetes (2791 [28%]), cardiac arrhythmia (2699 [27%]), renal failure (2287 [23%]), heart failure (1963 [20%]), and chronic pulmonary disease (1358 [14%]). Dialysis was required in 599 (6%) of all patients and in 469 (27%) of 1727 ventilated patients. The Charlson comorbidity index was 0 for 3237 (39%) of 8294 patients without ventilation, but only 374 (22%) of 1727 ventilated patients. The mean duration of ventilation was 13·5 days (SD 12·1). In-hospital mortality was 22% overall (2229 of 10 021), with wide variation between patients without ventilation (1323 [16%] of 8294) and with ventilation (906 [53%] of 1727; 65 [45%] of 145 for non-invasive ventilation only, 70 [50%] of 141 for non-invasive ventilation failure, and 696 [53%] of 1318 for invasive mechanical ventilation). In-hospital mortality in ventilated patients requiring dialysis was 73% (342 of 469). In-hospital mortality for patients with ventilation by age ranged from 28% (117 of 422) in patients aged 18–59 years to 72% (280 of 388) in patients aged 80 years or older. Interpretation In the German health-care system, in which hospital capacities have not been overwhelmed by the COVID-19 pandemic, mortality has been high for patients receiving mechanical ventilation, particularly for patients aged 80 years or older and those requiring dialysis, and has been considerably lower for patients younger than 60 years. Funding None.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Comorbidity measures for use with administrative data.
Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998) · Medical Care · doi:10.1097/00005650-199801000-00004 · Cited by 9,921
Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., and the Northwell COVID-19 Research Consortium, Barnaby, D. P., Becker, L. B., Chelico, J. D., et al. (2020) · JAMA · doi:10.1001/jama.2020.6775 · Cited by 9,254
Grasselli, G., Zangrillo, A., Zanella, A., Antonelli, M., Cabrini, L., Castelli, A., Cereda, D., Coluccello, A., Foti, G., Fumagalli, R., et al. (2020) · JAMA · doi:10.1001/jama.2020.5394 · Cited by 5,469
Docherty, A. B., Harrison, E. M., Green, C. A., Hardwick, H. E., Pius, R., Norman, L., Holden, K. A., Read, J. M., Dondelinger, F., Carson, G., et al. (2020) · BMJ · doi:10.1136/bmj.m1985 · Cited by 3,481
Multiorgan and Renal Tropism of SARS-CoV-2
Puelles, V. G., Lütgehetmann, M., Lindenmeyer, M. T., Sperhake, J. P., Wong, M. N., Allweiss, L., Chilla, S., Heinemann, A., Wanner, N., Liu, S., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2011400 · Cited by 1,775
Self-citationNepogodiev, D., Bhangu, A., Glasbey, J. C., Li, E., Omar, O. M., Simoes, J. F., Abbott, T. E., Alser, O., Arnaud, A. P., Bankhead-Kendall, B. K., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31182-x · Cited by 1,711
Estimating the burden of SARS-CoV-2 in France
Salje, H., Tran Kiem, C., Lefrancq, N., Courtejoie, N., Bosetti, P., Paireau, J., Andronico, A., Hozé, N., Richet, J., Dubost, C.-L., et al. (2020) · Science · doi:10.1126/science.abc3517 · Cited by 1,038
Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., Noursadeghi, M., Pillay, D., Sebire, N., Holmes, C., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30854-0 · Cited by 518
Cited by
Vijenthira, A., Gong, I. Y., Fox, T. A., Booth, S., Cook, G., Fattizzo, B., Martín-Moro, F., Razanamahery, J., Riches, J. C., Zwicker, J., et al. (2020) · Blood · doi:10.1182/blood.2020008824 · Cited by 625
Ranzani, O. T., Bastos, L. S. L., Gelli, J. G. M., Marchesi, J. F., Baião, F., Hamacher, S., & Bozza, F. A. (2021) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30560-9 · Cited by 513
Global pandemics interconnected — obesity, impaired metabolic health and COVID-19
Stefan, N., Birkenfeld, A. L., & Schulze, M. B. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00462-1 · Cited by 452
Ivermectin for preventing and treating COVID-19
Popp, M., Reis, S., Schießer, S., Hausinger, R. I., Stegemann, M., Metzendorf, M.-I., Kranke, P., Meybohm, P., Skoetz, N., & Weibel, S. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd015017.pub3 · Cited by 169
Gender inequality and health disparity amid COVID-19
Su, Z., Cheshmehzangi, A., McDonnell, D., Šegalo, S., Ahmad, J., & Bennett, B. (2022) · Nursing Outlook · doi:10.1016/j.outlook.2021.08.004 · Cited by 50
Metadata
- Authors
- Karagiannidis, C., Mostert, C., Hentschker, C., Voshaar, T., Malzahn, J., Schillinger, G., Klauber, J., Janssens, U., Marx, G., Weber-Carstens, S., Kluge, S., Pfeifer, M., Grabenhenrich, L., Welte, T., & Busse, R.
- Venue
- The Lancet Respiratory Medicine
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 8
- Issue
- 9
- Pages
- 853-862
- ISSN
- 2213-2600 (ISSN-L)
- OpenAlex
- W3046101830
- Cited by
- 785
- References
- 10
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. https://app.cochat.ai/papers/10.1016/s2213-2600(20)30316-7
- The paper
- Karagiannidis, C., Mostert, C., Hentschker, C., Voshaar, T., Malzahn, J., Schillinger, G., Klauber, J., Janssens, U., Marx, G., Weber-Carstens, S., Kluge, S., Pfeifer, M., Grabenhenrich, L., Welte, T., & Busse, R. (2020). Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. The Lancet Respiratory Medicine. https://doi.org/10.1016/s2213-2600(20)30316-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.