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1000 Titel
  • Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage
1000 Autor/in
  1. De Nardo, Pasquale |
  2. Gentilotti, Elisa |
  3. Mazzaferri, Fulvia |
  4. Cremonini, Eleonora |
  5. Hansen, Paul |
  6. Goossens, Herman |
  7. Tacconelli, Evelina |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-30
1000 Erschienen in
1000 Quellenangabe
  • 98:P494-500
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1016/j.ijid.2020.06.082 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326449/ |
1000 Ergänzendes Material
  • https://www.sciencedirect.com/science/article/pii/S1201971220305166#sec0060 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • OBJECTIVE: To use Multi-Criteria Decision Analysis (MCDA) to determine weights for eleven criteria in order to prioritize COVID-19 non-critical patients for admission to hospital in healthcare settings with limited resources. METHODS: The MCDA was applied in two main steps: specification of criteria for prioritizing COVID-19 patients (and levels within each criterion); and determination of weights for the criteria based on experts’ knowledge and experience in managing COVID-19 patients, via an online survey. Criteria were selected based on available COVID-19 evidence with a focus on low- and middle-income countries (LMICs). RESULTS: The most important criteria (mean weights, summing to 100%), are: PaO2 (16.3%); peripheral O2 saturation (15.9%); chest X-ray (14.1%); Modified Early Warning Score-MEWS (11.4%); respiratory rate (9.5%); comorbidities (6.5%); living with vulnerable people (6.4%); body mass index (5.6%); duration of symptoms before hospital evaluation (5.4%); CRP (5.1%); and age (3.8%). CONCLUSIONS: At the beginning of a new pandemic, when evidence for disease predictors is limited or unavailable and effective national contingency plans are difficult to establish, the MCDA prioritization model could play a pivotal role in improving the response of health systems.
1000 Sacherschließung
lokal pandemic
gnd 1206347392 COVID-19
lokal Multi-Criteria Decision Analysis
lokal SARS CoV-2
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-4433-0138|https://frl.publisso.de/adhoc/uri/R2VudGlsb3R0aSwgRWxpc2E=|https://frl.publisso.de/adhoc/uri/TWF6emFmZXJyaSwgRnVsdmlh|https://frl.publisso.de/adhoc/uri/Q3JlbW9uaW5pLCBFbGVvbm9yYQ==|https://frl.publisso.de/adhoc/uri/SGFuc2VuLCBQYXVs|https://frl.publisso.de/adhoc/uri/R29vc3NlbnMsIEhlcm1hbg==|https://frl.publisso.de/adhoc/uri/VGFjY29uZWxsaSwgRXZlbGluYQ==
1000 Label
1000 Förderer
  1. Innovative Medicines Initiative |
  2. Horizon 2020 |
  3. European Federation of Pharmaceutical Industries and Associations |
  4. bioMérieux SA |
  5. Janssen Pharmaceuticals |
  6. Accelerate Diagnostics S.L. |
  7. Abbott Pharmaceuticals |
  8. Bio-Rad Laboratories |
  9. BD Switzerland Sàrl |
  10. Wellcome |
1000 Fördernummer
  1. 820755
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1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Innovative Medicines Initiative |
    1000 Förderprogramm -
    1000 Fördernummer 820755
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    1000 Förderer Horizon 2020 |
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    1000 Fördernummer -
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    1000 Förderer European Federation of Pharmaceutical Industries and Associations |
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    1000 Förderer bioMérieux SA |
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    1000 Förderer Janssen Pharmaceuticals |
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    1000 Förderer Accelerate Diagnostics S.L. |
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    1000 Förderer Abbott Pharmaceuticals |
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    1000 Förderer Bio-Rad Laboratories |
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    1000 Förderer BD Switzerland Sàrl |
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    1000 Fördernummer -
  10. 1000 joinedFunding-child
    1000 Förderer Wellcome |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6421747.rdf
1000 Erstellt am 2020-07-09T09:25:15.522+0200
1000 Erstellt von 122
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1000 Zuletzt bearbeitet Thu Dec 14 12:46:22 CET 2023
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1000 Vgl. frl:6421747
1000 Oai Id
  1. oai:frl.publisso.de:frl:6421747 |
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