The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

Simple item page
dc.contributor.authorTorres Macho, Juan
dc.contributor.authorRyan Múrua, Pablo
dc.contributor.authorValencia, Jorge
dc.contributor.authorPérez-Butragueño, Mario
dc.contributor.authorJiménez González De Buitrago, Eva
dc.contributor.authorFontán-Vela, Mario
dc.contributor.authorIzquierdo García, Elsa
dc.contributor.authorFernandez-Jimenez, Inés
dc.contributor.authorÁlvaro-Alonso, Elena
dc.contributor.authorLazaro, Andrea
dc.contributor.authorAlvarado, Marta
dc.contributor.authorNotario, Helena
dc.contributor.authorResino, Salvador
dc.contributor.authorVélez Serrano, Daniel
dc.contributor.authorMeca, Alejandro
dc.date.accessioned2024-01-30T18:54:57Z
dc.date.available2024-01-30T18:54:57Z
dc.date.issued2020-09-23
dc.description.abstractThis study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient’s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.doi10.3390/jcm9103066
dc.identifier.issn2077-0383
dc.identifier.officialurlhttps://www.mdpi.com/2077-0383/9/10/3066
dc.identifier.urihttps://hdl.handle.net/20.500.14352/96786
dc.issue.number10
dc.journal.titleJournal of Clinical Medicine
dc.language.isoeng
dc.page.initial3066 (10)
dc.publisherMDPI
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.keywordSARS-CoV-2
dc.subject.keywordCOVID-19
dc.subject.keywordPrediction score
dc.subject.keywordMortality
dc.subject.ucmMatemáticas (Matemáticas)
dc.subject.ucmMedicina
dc.subject.unesco3202 Epidemiología
dc.titleThe PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
dc.typejournal article
dc.volume.number9
dspace.entity.typePublication
relation.isAuthorOfPublicatione6cd1c08-1e79-4f41-8964-aea4e8803bd1
relation.isAuthorOfPublicatione3fc131f-27b6-4093-b095-0581e9a555aa
relation.isAuthorOfPublication94fd4500-f8c1-48f7-99cc-0d4dec2c47a6
relation.isAuthorOfPublication313b0f5b-7157-4a81-9ccc-234b2645c7d5
relation.isAuthorOfPublication1375c631-ecbd-4b51-b213-c7d4148c3eba
relation.isAuthorOfPublication.latestForDiscoverye6cd1c08-1e79-4f41-8964-aea4e8803bd1
Download
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
jcm-09-03066.pdf
Size:
549.97 KB
Format:
Adobe Portable Document Format
downoload Download
Collections
Artículos