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Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission

Background Coronavirus-2019 (COVID-19) patients admitted to the intensive care unit (ICU) have mortality rates between 30%-50%. Identifying patient factors associated with mortality can help identify critical patients early and treat them accordingly. Patients and methods In this retrospective study...

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Autores principales: Ganesan, Rajarajan, Mahajan, Varun, Singla, Karan, Konar, Sushant, Samra, Tanvir, Sundaram, Senthil K, Suri, Vikas, Garg, Mandeep, Kalra, Naveen, Puri, Goverdhan D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8681888/
https://www.ncbi.nlm.nih.gov/pubmed/34976472
http://dx.doi.org/10.7759/cureus.19690
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author Ganesan, Rajarajan
Mahajan, Varun
Singla, Karan
Konar, Sushant
Samra, Tanvir
Sundaram, Senthil K
Suri, Vikas
Garg, Mandeep
Kalra, Naveen
Puri, Goverdhan D
author_facet Ganesan, Rajarajan
Mahajan, Varun
Singla, Karan
Konar, Sushant
Samra, Tanvir
Sundaram, Senthil K
Suri, Vikas
Garg, Mandeep
Kalra, Naveen
Puri, Goverdhan D
author_sort Ganesan, Rajarajan
collection PubMed
description Background Coronavirus-2019 (COVID-19) patients admitted to the intensive care unit (ICU) have mortality rates between 30%-50%. Identifying patient factors associated with mortality can help identify critical patients early and treat them accordingly. Patients and methods In this retrospective study, the records of patients admitted to the COVID-19 ICU in a single tertiary care hospital from April 2020 to September 2020 were analysed. The clinical and laboratory parameters between patients who were discharged from the hospital (survival cohort) and those who died in the hospital (mortality cohort) were compared. A multivariate logistic regression model was constructed to identify parameters associated with mortality.  Results A total of 147 patients were included in the study. The age of the patients was 55 (45, 64), median (IQR), years. At admission, 23 (16%) patients were on mechanical ventilation and 73 (50%) were on non-invasive ventilation. Sixty patients (40%, 95% CI: 32.8 to 49.2%) had died. Patients who died had a higher Charlson comorbidity index (CCI): 3 (2, 4) vs. 2 (1, 3), p = 0.0019, and a higher admission sequential organ failure assessment (SOFA) score: 5 (4, 7) vs. 4 (3, 4), p < 0.001. Serum urea, serum creatinine, neutrophils on differential leukocyte count, neutrophil to lymphocyte ratio (N/L ratio), D-dimer, serum lactate dehydrogenase (LDH), and C-reactive protein were higher in the mortality cohort. The ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, platelet count, lymphocytes on differential leukocyte count, and absolute lymphocyte count was lower in the mortality cohort. The parameters and cut-off values used for the multivariate logistic regression model included CCI > 2, SOFA score > 4, D-dimer > 1346 ng/mL, LDH > 514 U/L and N/L ratio > 27. The final model had an area under the curve of 0.876 (95% CI: 0.812 to 0.925), p < 0.001 with an accuracy of 78%. All five parameters were found to be independently associated with mortality.  Conclusions CCI, SOFA score, D-dimer, LDH, and N/L ratio are independently associated with mortality. A model incorporating the combination of these clinical and laboratory parameters at admission can predict COVID-19 ICU mortality with good accuracy.
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spelling pubmed-86818882021-12-30 Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission Ganesan, Rajarajan Mahajan, Varun Singla, Karan Konar, Sushant Samra, Tanvir Sundaram, Senthil K Suri, Vikas Garg, Mandeep Kalra, Naveen Puri, Goverdhan D Cureus Infectious Disease Background Coronavirus-2019 (COVID-19) patients admitted to the intensive care unit (ICU) have mortality rates between 30%-50%. Identifying patient factors associated with mortality can help identify critical patients early and treat them accordingly. Patients and methods In this retrospective study, the records of patients admitted to the COVID-19 ICU in a single tertiary care hospital from April 2020 to September 2020 were analysed. The clinical and laboratory parameters between patients who were discharged from the hospital (survival cohort) and those who died in the hospital (mortality cohort) were compared. A multivariate logistic regression model was constructed to identify parameters associated with mortality.  Results A total of 147 patients were included in the study. The age of the patients was 55 (45, 64), median (IQR), years. At admission, 23 (16%) patients were on mechanical ventilation and 73 (50%) were on non-invasive ventilation. Sixty patients (40%, 95% CI: 32.8 to 49.2%) had died. Patients who died had a higher Charlson comorbidity index (CCI): 3 (2, 4) vs. 2 (1, 3), p = 0.0019, and a higher admission sequential organ failure assessment (SOFA) score: 5 (4, 7) vs. 4 (3, 4), p < 0.001. Serum urea, serum creatinine, neutrophils on differential leukocyte count, neutrophil to lymphocyte ratio (N/L ratio), D-dimer, serum lactate dehydrogenase (LDH), and C-reactive protein were higher in the mortality cohort. The ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, platelet count, lymphocytes on differential leukocyte count, and absolute lymphocyte count was lower in the mortality cohort. The parameters and cut-off values used for the multivariate logistic regression model included CCI > 2, SOFA score > 4, D-dimer > 1346 ng/mL, LDH > 514 U/L and N/L ratio > 27. The final model had an area under the curve of 0.876 (95% CI: 0.812 to 0.925), p < 0.001 with an accuracy of 78%. All five parameters were found to be independently associated with mortality.  Conclusions CCI, SOFA score, D-dimer, LDH, and N/L ratio are independently associated with mortality. A model incorporating the combination of these clinical and laboratory parameters at admission can predict COVID-19 ICU mortality with good accuracy. Cureus 2021-11-18 /pmc/articles/PMC8681888/ /pubmed/34976472 http://dx.doi.org/10.7759/cureus.19690 Text en Copyright © 2021, Ganesan et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Infectious Disease
Ganesan, Rajarajan
Mahajan, Varun
Singla, Karan
Konar, Sushant
Samra, Tanvir
Sundaram, Senthil K
Suri, Vikas
Garg, Mandeep
Kalra, Naveen
Puri, Goverdhan D
Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title_full Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title_fullStr Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title_full_unstemmed Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title_short Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission
title_sort mortality prediction of covid-19 patients at intensive care unit admission
topic Infectious Disease
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8681888/
https://www.ncbi.nlm.nih.gov/pubmed/34976472
http://dx.doi.org/10.7759/cureus.19690
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