Evidence map›Paper›PMID 37166617›Full record

ArticleInfection2023

Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19: a prospective study.

Ujjwal Mukund Mahajan, Johanna Erber, Parichehr Shamsrizi, Florian Voit, Jakob Vielhauer, Anna-Lena Johlke, Christopher Benesch, Najib Ben Khaled, Felix Reinecke, Wolf-Stephan Rudi and 7 more

Open access · hybridAbstract readMulticenter Study
In one paragraph

Article in Infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 8 institutions in 1 country.

Ujjwal Mukund Mahajan *Department of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Johanna Erber *Department of Internal Medicine II, Technical University of Munich, School of Medicine, University Hospital Rechts Der Isar, Munich, Germany.
Parichehr ShamsriziDepartment for Clinical Immunology of Infectious Diseases, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
Florian VoitDepartment of Internal Medicine II, Technical University of Munich, School of Medicine, University Hospital Rechts Der Isar, Munich, Germany.
Jakob VielhauerDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Anna-Lena JohlkeDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Christopher BeneschDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Najib Ben KhaledDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Felix ReineckeDepartment of Anesthesiology, Hospital of the LMU Munich, Munich, Germany.
Wolf-Stephan RudiDepartment of Medicine I, Hospital of the LMU Munich, Munich, Germany.
Matthias KleinDepartment of Neurology, Hospital of the LMU Munich, Munich, Germany.
Carolin JakobDepartment I of Internal Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany.
Marcus OswaldInstitute for Infectious Diseases and Infection Control, RG Systemsbiology, Jena University Hospital, Jena, Germany.
Rainer KönigInstitute for Infectious Diseases and Infection Control, RG Systemsbiology, Jena University Hospital, Jena, Germany.
Christian SchulzDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Julia MayerleDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany.
Hans Christian StubbeDepartment of Medicine II, Medizinische Klinik und Poliklinik II, Hospital of the LMU Munich, LMU Klinikum, Campus Großhadern, Marchioninistr. 15, 81377, Munich, Germany. Hans_Christian.Stubbe@med.uni-muenchen.de.
German Center for Infection Research · DELMU Klinikum · DELudwig-Maximilians-Universität München · DEJena University Hospital · DEBernhard Nocht Institute for Tropical Medicine · DEKlinikum rechts der Isar · DETechnical University of Munich · DEUniversity Hospital Cologne · DE

Funding

Bundesministerium für Bildung und Forschung 01EO1502
6 · The paper itself

Abstract

purposeIdentification 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.

methodsIn 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.

resultsThe 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.

conclusionThe SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.

Indexed as

COVID-19AdultHospitalizationHospitalsHumansProspective StudiesSARS-CoV-2Artificial intelligenceCOVID-19Decision supportProspective clinical studySACOV-19SARS-CoV-2

Identifiers

PMID37166617
PMCPMC10173210
OpenAlexW4376133046

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.