Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19?
Background: COVID-19 acute respiratory distress syndrome (ARDS) is associated with high morbidity and mortality. Identification of clinical prognostic factors at admission is crucial in the triage and therapeutic selection of patients in resource-poor settings. The study was done to identify clinica...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2023-01-01
|
| Series: | Current Medical Issues |
| Subjects: | |
| Online Access: | http://www.cmijournal.org/article.asp?issn=0973-4651;year=2023;volume=21;issue=3;spage=168;epage=173;aulast=Ninan |
| _version_ | 1827862179228942336 |
|---|---|
| author | K Fibi Ninan Ramya Iyadurai Justin K Varghese J Jonathan Arul Jeevan Karthik Gunasekaran Reka Karuppusami Binila Chacko K Jacob Johnson Amit Mandal Nivin Stanley David |
| author_facet | K Fibi Ninan Ramya Iyadurai Justin K Varghese J Jonathan Arul Jeevan Karthik Gunasekaran Reka Karuppusami Binila Chacko K Jacob Johnson Amit Mandal Nivin Stanley David |
| author_sort | K Fibi Ninan |
| collection | DOAJ |
| description | Background: COVID-19 acute respiratory distress syndrome (ARDS) is associated with high morbidity and mortality. Identification of clinical prognostic factors at admission is crucial in the triage and therapeutic selection of patients in resource-poor settings. The study was done to identify clinical parameters at admission to prognosticate patients who required intensive care unit (ICU) admission. Methods: In this retrospective study, the clinical parameters and outcomes of critically ill patients admitted from a single medical unit during the second wave of COVID-19 were studied. Patients were categorized as survivors and nonsurvivors. Factors associated with mortality were explored using Fisher's exact and t-test as appropriate. Results: The study population included 62 patients with a male: female ratio of 43 (69.3%):19 (30.7%) with a mean (standard deviation [SD]) age of 50.97 (±9.9) years. The mean (SD) O2 saturation was 82% (±10%) and median (interquartile range) PaO2/FiO2 ratio was 161 (89–214) on arrival to the emergency department. Forty-two (66%) required mechanical ventilation and the mean (SD) duration of hospital stay was 20 (±15) days. Thirty-six patients died, and the overall mortality was 58.1%. Increasing age, low SpO2 at presentation to the hospital, and need for mechanical ventilation were noted to be independent predictors of mortality with an odds ratio of 5.1 (95% confidence interval) (1.61–16.2) (P = 0.006) and 25 (3.70–180.19) (P = 0.001), respectively. Admission respiratory rate >36/min (P = 0.009) and SpO2 ≤83% (P = 0.001) were predictive of increased mortality among ICU patients. Conclusion: Low SpO2 at presentation (<83%), high respiratory rate (>36/min), and requirement of mechanical ventilation were strong predictors of mortality in patients admitted to ICU with COVID-19 ARDS. |
| first_indexed | 2024-03-12T13:49:33Z |
| format | Article |
| id | doaj.art-ee57ca06d50246b8a1eddf8f752f1907 |
| institution | Directory Open Access Journal |
| issn | 0973-4651 2666-4054 |
| language | English |
| last_indexed | 2024-03-12T13:49:33Z |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Current Medical Issues |
| spelling | doaj.art-ee57ca06d50246b8a1eddf8f752f19072023-08-23T07:29:00ZengWolters Kluwer Medknow PublicationsCurrent Medical Issues0973-46512666-40542023-01-0121316817310.4103/cmi.cmi_6_23Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19?K Fibi NinanRamya IyaduraiJustin K VargheseJ Jonathan Arul JeevanKarthik GunasekaranReka KaruppusamiBinila ChackoK Jacob JohnsonAmit MandalNivin Stanley DavidBackground: COVID-19 acute respiratory distress syndrome (ARDS) is associated with high morbidity and mortality. Identification of clinical prognostic factors at admission is crucial in the triage and therapeutic selection of patients in resource-poor settings. The study was done to identify clinical parameters at admission to prognosticate patients who required intensive care unit (ICU) admission. Methods: In this retrospective study, the clinical parameters and outcomes of critically ill patients admitted from a single medical unit during the second wave of COVID-19 were studied. Patients were categorized as survivors and nonsurvivors. Factors associated with mortality were explored using Fisher's exact and t-test as appropriate. Results: The study population included 62 patients with a male: female ratio of 43 (69.3%):19 (30.7%) with a mean (standard deviation [SD]) age of 50.97 (±9.9) years. The mean (SD) O2 saturation was 82% (±10%) and median (interquartile range) PaO2/FiO2 ratio was 161 (89–214) on arrival to the emergency department. Forty-two (66%) required mechanical ventilation and the mean (SD) duration of hospital stay was 20 (±15) days. Thirty-six patients died, and the overall mortality was 58.1%. Increasing age, low SpO2 at presentation to the hospital, and need for mechanical ventilation were noted to be independent predictors of mortality with an odds ratio of 5.1 (95% confidence interval) (1.61–16.2) (P = 0.006) and 25 (3.70–180.19) (P = 0.001), respectively. Admission respiratory rate >36/min (P = 0.009) and SpO2 ≤83% (P = 0.001) were predictive of increased mortality among ICU patients. Conclusion: Low SpO2 at presentation (<83%), high respiratory rate (>36/min), and requirement of mechanical ventilation were strong predictors of mortality in patients admitted to ICU with COVID-19 ARDS.http://www.cmijournal.org/article.asp?issn=0973-4651;year=2023;volume=21;issue=3;spage=168;epage=173;aulast=Ninanacute respiratory distress syndromecovid-19prognostic factors |
| spellingShingle | K Fibi Ninan Ramya Iyadurai Justin K Varghese J Jonathan Arul Jeevan Karthik Gunasekaran Reka Karuppusami Binila Chacko K Jacob Johnson Amit Mandal Nivin Stanley David Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? Current Medical Issues acute respiratory distress syndrome covid-19 prognostic factors |
| title | Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? |
| title_full | Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? |
| title_fullStr | Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? |
| title_full_unstemmed | Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? |
| title_short | Can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with COVID-19? |
| title_sort | can clinical parameters at admission predict severity and intensive care unit mortality outcomes in patients with covid 19 |
| topic | acute respiratory distress syndrome covid-19 prognostic factors |
| url | http://www.cmijournal.org/article.asp?issn=0973-4651;year=2023;volume=21;issue=3;spage=168;epage=173;aulast=Ninan |
| work_keys_str_mv | AT kfibininan canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT ramyaiyadurai canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT justinkvarghese canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT jjonathanaruljeevan canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT karthikgunasekaran canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT rekakaruppusami canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT binilachacko canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT kjacobjohnson canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT amitmandal canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 AT nivinstanleydavid canclinicalparametersatadmissionpredictseverityandintensivecareunitmortalityoutcomesinpatientswithcovid19 |