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dc.contributor.authorLycholip, Valentinas,
dc.contributor.authorPuronaitė, Roma,
dc.contributor.authorSkorniakov, Viktor,
dc.contributor.authorNavickas, Petras,
dc.contributor.authorTarutytė, Gabrielė,
dc.contributor.authorTrinkūnas, Justas,
dc.contributor.authorBurneikaitė, Greta,
dc.contributor.authorKazėnaitė, Edita,
dc.contributor.authorJankauskienė, Augustina,
dc.date.accessioned2023-12-22T07:07:17Z
dc.date.available2023-12-22T07:07:17Z
dc.date.issued2023.
dc.identifier.issn0928-7329
dc.identifier.other(crossref_id)153965032
dc.identifier.urihttps://etalpykla.vilniustech.lt/xmlui/handle/123456789/153825
dc.description.abstractBACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models’ performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient’s age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score – 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient’s status evaluation.eng
dc.formatPDF
dc.format.extentp. 2513-2524.
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.titleAssessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis /
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references27
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus universitetas Vilniaus universiteto ligoninė Santaros klinikos
dc.contributor.institutionVilniaus universitetas Vilniaus universiteto ligoninė Santaros klinikos Vilniaus universitetas
dc.contributor.institutionVilniaus universitetas
dc.contributor.institutionVilniaus universiteto ligoninė Santaros klinikos Vilniaus universitetas Valstybinis mokslinių tyrimų institutas Inovatyvios medicinos centras
dc.contributor.institutionVilniaus universiteto ligoninė Santaros klinikos Vilniaus universitetas
dc.contributor.institutionVilniaus universiteto ligoninė Santaros klinikos Vilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldN 009 - Informatika / Computer science
dc.subject.researchfieldM 001 - Medicina / Medicine
dc.subject.vgtuprioritizedfieldsMC0404 - Bionika ir biomedicinos inžinerinės sistemos / Bionics and Biomedical Engineering Systems
dc.subject.ltspecializationsL105 - Sveikatos technologijos ir biotechnologijos / Health technologies and biotechnologies
dcterms.sourcetitleTechnology and health care.
dc.description.issueno. 6
dc.description.volumevol. 31
dc.publisher.nameIOS Press
dc.publisher.cityAmsterdam
dc.identifier.doi153965032
dc.identifier.doi10.3233/THC-235016
dc.identifier.elaba181908200


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