Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage
Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:98 |
|---|---|
| Enthalten in: |
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases - 98(2020) vom: 10. Sept., Seite 494-500 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
De Nardo, Pasquale [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
COVID-19 |
|---|
| Anmerkungen: |
Date Completed 21.09.2020 Date Revised 10.01.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.ijid.2020.06.082 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM311974856 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM311974856 | ||
| 003 | DE-627 | ||
| 005 | 20231225143416.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ijid.2020.06.082 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1039.xml |
| 035 | |a (DE-627)NLM311974856 | ||
| 035 | |a (NLM)32619766 | ||
| 035 | |a (PII)S1201-9712(20)30516-6 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a De Nardo, Pasquale |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 21.09.2020 | ||
| 500 | |a Date Revised 10.01.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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%) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Multi-Criteria Decision Analysis | |
| 650 | 4 | |a Pandemic | |
| 650 | 4 | |a SARS CoV-2 | |
| 700 | 1 | |a Gentilotti, Elisa |e verfasserin |4 aut | |
| 700 | 1 | |a Mazzaferri, Fulvia |e verfasserin |4 aut | |
| 700 | 1 | |a Cremonini, Eleonora |e verfasserin |4 aut | |
| 700 | 1 | |a Hansen, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Goossens, Herman |e verfasserin |4 aut | |
| 700 | 1 | |a Tacconelli, Evelina |e verfasserin |4 aut | |
| 700 | 0 | |a members of the COVID-19MCDA Group |e verfasserin |4 aut | |
| 700 | 1 | |a Durante Mangoni, E |e investigator |4 oth | |
| 700 | 1 | |a Florio, L L |e investigator |4 oth | |
| 700 | 1 | |a Zampino, R |e investigator |4 oth | |
| 700 | 1 | |a Mele, F |e investigator |4 oth | |
| 700 | 1 | |a Gentile, I |e investigator |4 oth | |
| 700 | 1 | |a Pinchera, B |e investigator |4 oth | |
| 700 | 1 | |a Coppola, N |e investigator |4 oth | |
| 700 | 1 | |a Pisaturo, M |e investigator |4 oth | |
| 700 | 1 | |a Luzzati, R |e investigator |4 oth | |
| 700 | 1 | |a Petrosillo, N |e investigator |4 oth | |
| 700 | 1 | |a Nicastri, E |e investigator |4 oth | |
| 700 | 1 | |a Corpolongo, A |e investigator |4 oth | |
| 700 | 1 | |a Cataldo, M A |e investigator |4 oth | |
| 700 | 1 | |a D'Abramo, A |e investigator |4 oth | |
| 700 | 1 | |a Maffongelli, G |e investigator |4 oth | |
| 700 | 1 | |a Scorzolini, L |e investigator |4 oth | |
| 700 | 1 | |a Palazzolo, C |e investigator |4 oth | |
| 700 | 1 | |a Boumis, E |e investigator |4 oth | |
| 700 | 1 | |a Pan, A |e investigator |4 oth | |
| 700 | 1 | |a D'Arminio Monforte, A |e investigator |4 oth | |
| 700 | 1 | |a Bai, F |e investigator |4 oth | |
| 700 | 1 | |a Antinori, S |e investigator |4 oth | |
| 700 | 1 | |a De Rosa, F G |e investigator |4 oth | |
| 700 | 1 | |a Corcione, S |e investigator |4 oth | |
| 700 | 1 | |a Lupia, T |e investigator |4 oth | |
| 700 | 1 | |a Pinna, S M |e investigator |4 oth | |
| 700 | 1 | |a Scabini, S |e investigator |4 oth | |
| 700 | 1 | |a Canta, F |e investigator |4 oth | |
| 700 | 1 | |a Belloro, S |e investigator |4 oth | |
| 700 | 1 | |a Bisoffi, Z |e investigator |4 oth | |
| 700 | 1 | |a Angheben, A |e investigator |4 oth | |
| 700 | 1 | |a Gobbi, F |e investigator |4 oth | |
| 700 | 1 | |a Turcato, E |e investigator |4 oth | |
| 700 | 1 | |a Ronzoni, N |e investigator |4 oth | |
| 700 | 1 | |a Moro, L |e investigator |4 oth | |
| 700 | 1 | |a Calabria, S |e investigator |4 oth | |
| 700 | 1 | |a Rodari, P |e investigator |4 oth | |
| 700 | 1 | |a Bertoli, G |e investigator |4 oth | |
| 700 | 1 | |a Marasca, G |e investigator |4 oth | |
| 700 | 1 | |a Puoti, M |e investigator |4 oth | |
| 700 | 1 | |a Gori, A |e investigator |4 oth | |
| 700 | 1 | |a Bandera, A |e investigator |4 oth | |
| 700 | 1 | |a Mangioni, D |e investigator |4 oth | |
| 700 | 1 | |a Rizzi, M |e investigator |4 oth | |
| 700 | 1 | |a Castelli, F |e investigator |4 oth | |
| 700 | 1 | |a Montineri, A |e investigator |4 oth | |
| 700 | 1 | |a Coco, C A |e investigator |4 oth | |
| 700 | 1 | |a Maresca, M |e investigator |4 oth | |
| 700 | 1 | |a Frasca, M |e investigator |4 oth | |
| 700 | 1 | |a Aquilini, D |e investigator |4 oth | |
| 700 | 1 | |a Vincenzi, M |e investigator |4 oth | |
| 700 | 1 | |a Lambertenghi, L |e investigator |4 oth | |
| 700 | 1 | |a De Rui, M E |e investigator |4 oth | |
| 700 | 1 | |a Razzaboni, E |e investigator |4 oth | |
| 700 | 1 | |a Cattaneo, P |e investigator |4 oth | |
| 700 | 1 | |a Visentin, A |e investigator |4 oth | |
| 700 | 1 | |a Erbogasto, A |e investigator |4 oth | |
| 700 | 1 | |a Dalla Vecchia, I |e investigator |4 oth | |
| 700 | 1 | |a Coledan, I |e investigator |4 oth | |
| 700 | 1 | |a Vecchi, M |e investigator |4 oth | |
| 700 | 1 | |a Be, G |e investigator |4 oth | |
| 700 | 1 | |a Motta, L |e investigator |4 oth | |
| 700 | 1 | |a Zaffagnini, A |e investigator |4 oth | |
| 700 | 1 | |a Auerbach, N |e investigator |4 oth | |
| 700 | 1 | |a Del Bravo, P |e investigator |4 oth | |
| 700 | 1 | |a Azzini, A M |e investigator |4 oth | |
| 700 | 1 | |a Righi, E |e investigator |4 oth | |
| 700 | 1 | |a Carrara, E |e investigator |4 oth | |
| 700 | 1 | |a Savoldi, A |e investigator |4 oth | |
| 700 | 1 | |a Sibani, M |e investigator |4 oth | |
| 700 | 1 | |a Lattuada, E |e investigator |4 oth | |
| 700 | 1 | |a Carolo, G |e investigator |4 oth | |
| 700 | 1 | |a Cordioli, M |e investigator |4 oth | |
| 700 | 1 | |a Soldani, F |e investigator |4 oth | |
| 700 | 1 | |a Pezzani, M D |e investigator |4 oth | |
| 700 | 1 | |a Avallone, S |e investigator |4 oth | |
| 700 | 1 | |a Bruno, R |e investigator |4 oth | |
| 700 | 1 | |a Ricciardi, A |e investigator |4 oth | |
| 700 | 1 | |a Saggese, M P |e investigator |4 oth | |
| 700 | 1 | |a Malerba, G |e investigator |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases |d 1998 |g 98(2020) vom: 10. Sept., Seite 494-500 |w (DE-627)NLM094730857 |x 1878-3511 |7 nnns |
| 773 | 1 | 8 | |g volume:98 |g year:2020 |g day:10 |g month:09 |g pages:494-500 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.ijid.2020.06.082 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 98 |j 2020 |b 10 |c 09 |h 494-500 | ||