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10.1055/a-1167-7596

http://scihub22266oqcxt.onion/10.1055/a-1167-7596
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32422662!ä!32422662

suck abstract from ncbi


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pmid32422662      Dtsch+Med+Wochenschr 2020 ; 145 (15): e87-e92
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  • COVID-19-Triage: Wer bleibt stationar? Das Modell Essen #MMPMID32422662
  • Fistera D; Risse J; Manegold R; Pabst D; Konik M; Dolff S; Witzke O; Schaarschmidt BM; Taube C; Kill C; Holzner C
  • Dtsch Med Wochenschr 2020[Jul]; 145 (15): e87-e92 PMID32422662show ga
  • INTRODUCTION: Data about optimal initial assessment in patients with suspicion for COVID19-infection or already confirmed infection are sparse. Especially, in preparation for expected mass casualty incident it is necessary to distinguish early and efficiently between outpatient and inpatient treatment including the need for intensive care therapy. METHODS: We present a model for a safe and efficient triage, which is established and used in the university hospital of Essen, Germany. It is intended for a non-disaster situation. This model is a combination of clinical assessment by using vital parameters and Manchester triage scale (MTS). Possible additional parameters are POCT (point-of-care-testing) values, electrocardiogram, CT pulmonary angiography, SARS-Cov2-PCR as well as detailed diagnostic of laboratory values. The model was validated by 100 consecutive patients. We demonstrate three patients to illustrate this model. RESULTS: During the first two weeks after implementing this model in our normal operation at the emergency department, we had an efficient selectivity between need for inpatient and outpatient treatment. 16 patients were classified as "inpatients" according to initial assessment. Among 84 patients who were initially classified as "outpatients", 7 patients returned to our emergency department within 14 days. Three of these patients returned due to complaints other than COVID19. One female patient had to be admitted due to progressive dyspnea. CONCLUSIONS: This introduced triage-model seems to be an efficient concept. Adjustment might be necessary after further experience and after a growing number of patients.
  • |*Coronavirus Infections/diagnosis/therapy[MESH]
  • |*Pandemics[MESH]
  • |*Pneumonia, Viral/diagnosis/therapy[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |Decision Support Techniques[MESH]
  • |Hospitalization/*statistics & numerical data[MESH]
  • |Humans[MESH]
  • |Models, Theoretical[MESH]
  • |Radiography, Thoracic[MESH]
  • |SARS-CoV-2[MESH]
  • |Triage/*methods[MESH]


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