Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19
Descripción del Articulo
Introduction: The pandemic caused by the coronavirus disease 2019 represented a framework of health, social and economic crisis at a global level, generating great human losses, which is why the need arises to determine the prognostic factors, including some accessible, rapid and low-cost, which may...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2025 |
| Institución: | Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo |
| Repositorio: | Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo |
| Lenguaje: | español |
| OAI Identifier: | oai:cmhnaaa_ojs_cmhnaaa.cmhnaaa.org.pe:article/2426 |
| Enlace del recurso: | https://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/2426 |
| Nivel de acceso: | acceso abierto |
| Materia: | COVID-19 neutrófilos linfocitos biomarcadores neutrophils lymphocytes biomarkers |
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Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo |
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Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 Ratio neutrófilo linfocito como predictor de gravedad en pacientes con COVID-19 |
| title |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| spellingShingle |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 Vásquez-Sáenz, Nuria Ximena COVID-19 neutrófilos linfocitos biomarcadores COVID-19 neutrophils lymphocytes biomarkers |
| title_short |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| title_full |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| title_fullStr |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| title_full_unstemmed |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| title_sort |
Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19 |
| dc.creator.none.fl_str_mv |
Vásquez-Sáenz, Nuria Ximena Valverde-Soriano, Harold Bryce Alpaca-Salvador, Hugo Aurelio Vasquez-Saenz, Ruddy Jacqueline |
| author |
Vásquez-Sáenz, Nuria Ximena |
| author_facet |
Vásquez-Sáenz, Nuria Ximena Valverde-Soriano, Harold Bryce Alpaca-Salvador, Hugo Aurelio Vasquez-Saenz, Ruddy Jacqueline |
| author_role |
author |
| author2 |
Valverde-Soriano, Harold Bryce Alpaca-Salvador, Hugo Aurelio Vasquez-Saenz, Ruddy Jacqueline |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
COVID-19 neutrófilos linfocitos biomarcadores COVID-19 neutrophils lymphocytes biomarkers |
| topic |
COVID-19 neutrófilos linfocitos biomarcadores COVID-19 neutrophils lymphocytes biomarkers |
| description |
Introduction: The pandemic caused by the coronavirus disease 2019 represented a framework of health, social and economic crisis at a global level, generating great human losses, which is why the need arises to determine the prognostic factors, including some accessible, rapid and low-cost, which may be useful in predicting potentially serious cases. Objective: Determine if the neutrophil-lymphocyte ratio predicts the severity of COVID-19 according to the severity classification of the Ministry of Health. Materials and methods: A retrospective cohort study was carried out, using consecutive sampling, 200 patients with a confirmed diagnosis of COVID-19 were included upon admission for emergencies at Hospital III EsSalud Chimbote, whose recruitment period was from February to May 2021 with a 30-day follow-up. An multivariate analysis was performed with binary logistic regression in the R Commander version 4.0.5 program, developing a training model to predict the risk of COVID-19 severity during hospitalization. Results: With a stepwise construction strategy from back to front, a model was obtained that included the neutrophil lymphocyte ratio (OR: 1.14) adjusted to lactate dehydrogenase and age as predictors of severe COVID-19; showing an accuracy of 74% for a high-risk threshold greater than 40% and an area under the curve (AUC) of 0.83 (95%CI:0.78-0.88). Conclusion: The neutrophil-lymphocyte ratio was a independent predictor of the risk of developing severe COVID-19. |
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2025 |
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2025-03-14 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion texto |
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https://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/2426 10.35434/rcmhnaaa.2024.174.2426 |
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https://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/2426 |
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10.35434/rcmhnaaa.2024.174.2426 |
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spa |
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spa |
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https://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/2426/1024 |
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https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
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openAccess |
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Cuerpo Médico del Hospital Nacional Almanzor Aguinaga Asenjo |
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Cuerpo Médico del Hospital Nacional Almanzor Aguinaga Asenjo |
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Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo; Vol. 17 No. 4 (2024): Rev. Cuerpo Med. HNAAA, October - December; e2426 Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo; Vol. 17 Núm. 4 (2024): Rev. Cuerpo Med. HNAAA, Octubre - Diciembre; e2426 2227-4731 2225-5109 10.35434/rcmhnaaa.2024.174 reponame:Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo instname:Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo instacron:HNAAA |
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Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo |
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Neutrophil lymphocyte ratio as a predictor of severity in patients with COVID-19Ratio neutrófilo linfocito como predictor de gravedad en pacientes con COVID-19Vásquez-Sáenz, Nuria XimenaValverde-Soriano, Harold BryceAlpaca-Salvador, Hugo AurelioVasquez-Saenz, Ruddy JacquelineCOVID-19neutrófiloslinfocitosbiomarcadoresCOVID-19neutrophilslymphocytesbiomarkersIntroduction: The pandemic caused by the coronavirus disease 2019 represented a framework of health, social and economic crisis at a global level, generating great human losses, which is why the need arises to determine the prognostic factors, including some accessible, rapid and low-cost, which may be useful in predicting potentially serious cases. Objective: Determine if the neutrophil-lymphocyte ratio predicts the severity of COVID-19 according to the severity classification of the Ministry of Health. Materials and methods: A retrospective cohort study was carried out, using consecutive sampling, 200 patients with a confirmed diagnosis of COVID-19 were included upon admission for emergencies at Hospital III EsSalud Chimbote, whose recruitment period was from February to May 2021 with a 30-day follow-up. An multivariate analysis was performed with binary logistic regression in the R Commander version 4.0.5 program, developing a training model to predict the risk of COVID-19 severity during hospitalization. Results: With a stepwise construction strategy from back to front, a model was obtained that included the neutrophil lymphocyte ratio (OR: 1.14) adjusted to lactate dehydrogenase and age as predictors of severe COVID-19; showing an accuracy of 74% for a high-risk threshold greater than 40% and an area under the curve (AUC) of 0.83 (95%CI:0.78-0.88). Conclusion: The neutrophil-lymphocyte ratio was a independent predictor of the risk of developing severe COVID-19.Introducción: La pandemia ocasionada por la enfermedad del coronavirus 2019 representó un marco de crisis sanitaria, social y económica a nivel global, generando grandes pérdidas humanas, por lo que surge la necesidad de determinar los factores pronósticos, entre ellos algunos biomarcadores accesibles, rápidos y de bajo costo, que puedan resultar útiles en predecir los casos potencialmente graves. Objetivo: Determinar si el ratio neutrófilo linfocito predice la gravedad por COVID-19 según la clasificación de severidad del Ministerio de Salud. Materiales y métodos: Se realizó un estudio de cohorte retrospectivo, mediante muestreo consecutivo se incluyó 200 pacientes con diagnóstico confirmado de COVID-19 al ingreso por emergencias del Hospital III EsSalud Chimbote, cuyo periodo de reclutamiento fue de febrero a mayo del año 2021 con un seguimiento de 30 días. Se realizó un análisis multivariante con regresión logística binaria en el programa R Commander versión 4.0.5 desarrollando un modelo de entrenamiento para predecir el riesgo de gravedad del COVID-19 durante la hospitalización. Resultados: Con una estrategia de construcción stepwise de atrás hacia adelante se obtuvo un modelo predictivo que incluyó el ratio neutrófilo linfocito (OR:1,14) ajustado a la lactato deshidrogenasa y la edad como predictores del COVID-19 grave; mostrando una exactitud del 74% para un umbral de alto riesgo mayor a 40% y un área bajo la curva (AUC) de 0,83 (IC95%:0,78-0,88). Conclusión: El ratio neutrófilo linfocito fue un predictor independiente del riesgo de desarrollar COVID-19 grave.Cuerpo Médico del Hospital Nacional Almanzor Aguinaga Asenjo2025-03-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontextoapplication/pdfhttps://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/242610.35434/rcmhnaaa.2024.174.2426Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo; Vol. 17 No. 4 (2024): Rev. Cuerpo Med. HNAAA, October - December; e2426Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo; Vol. 17 Núm. 4 (2024): Rev. Cuerpo Med. HNAAA, Octubre - Diciembre; e24262227-47312225-510910.35434/rcmhnaaa.2024.174reponame:Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjoinstname:Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjoinstacron:HNAAAspahttps://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/2426/1024Derechos de autor 2025 Nuria Ximena Vásquez Sáenz, Hugo Aurelio Alpaca Salvador, Ruddy Jacqueline Vasquez Saenz, Harold Bryce Valverde Sorianohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:cmhnaaa_ojs_cmhnaaa.cmhnaaa.org.pe:article/24262025-05-19T04:52:26Z |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).