Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19 : a prospective study
© 2023. The Author(s)..
PURPOSE: Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19.
METHODS: In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days.
RESULTS: The SACOV-19 score's AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores.
CONCLUSION: The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:51 |
|---|---|
| Enthalten in: |
Infection - 51(2023), 6 vom: 11. Dez., Seite 1669-1678 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Mahajan, Ujjwal Mukund [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 23.11.2023 Date Revised 23.11.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s15010-023-02041-8 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM356695107 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM356695107 | ||
| 003 | DE-627 | ||
| 005 | 20231226071014.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s15010-023-02041-8 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1188.xml |
| 035 | |a (DE-627)NLM356695107 | ||
| 035 | |a (NLM)37166617 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Mahajan, Ujjwal Mukund |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19 |b a prospective study |
| 264 | 1 | |c 2023 | |
| 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 23.11.2023 | ||
| 500 | |a Date Revised 23.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a PURPOSE: Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19 | ||
| 520 | |a METHODS: In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days | ||
| 520 | |a RESULTS: The SACOV-19 score's AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores | ||
| 520 | |a CONCLUSION: The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19 | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Decision support | |
| 650 | 4 | |a Prospective clinical study | |
| 650 | 4 | |a SACOV-19 | |
| 650 | 4 | |a SARS-CoV-2 | |
| 700 | 1 | |a Erber, Johanna |e verfasserin |4 aut | |
| 700 | 1 | |a Shamsrizi, Parichehr |e verfasserin |4 aut | |
| 700 | 1 | |a Voit, Florian |e verfasserin |4 aut | |
| 700 | 1 | |a Vielhauer, Jakob |e verfasserin |4 aut | |
| 700 | 1 | |a Johlke, Anna-Lena |e verfasserin |4 aut | |
| 700 | 1 | |a Benesch, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Khaled, Najib Ben |e verfasserin |4 aut | |
| 700 | 1 | |a Reinecke, Felix |e verfasserin |4 aut | |
| 700 | 1 | |a Rudi, Wolf-Stephan |e verfasserin |4 aut | |
| 700 | 1 | |a Klein, Matthias |e verfasserin |4 aut | |
| 700 | 1 | |a Jakob, Carolin |e verfasserin |4 aut | |
| 700 | 1 | |a Oswald, Marcus |e verfasserin |4 aut | |
| 700 | 1 | |a König, Rainer |e verfasserin |4 aut | |
| 700 | 1 | |a Schulz, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Mayerle, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Stubbe, Hans Christian |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Infection |d 1973 |g 51(2023), 6 vom: 11. Dez., Seite 1669-1678 |w (DE-627)NLM00001740X |x 1439-0973 |7 nnns |
| 773 | 1 | 8 | |g volume:51 |g year:2023 |g number:6 |g day:11 |g month:12 |g pages:1669-1678 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s15010-023-02041-8 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 51 |j 2023 |e 6 |b 11 |c 12 |h 1669-1678 | ||