Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities
Background: The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed como...
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Elsevier
2024-12-01
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| Series: | Journal of Infection and Public Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876034124003009 |
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| author | Mahdieh Shokrollahi Barough Mohammad Darzi Masoud Yunesian Danesh Amini Panah Yekta Ghane Sam Mottahedan Sohrab Sakinehpour Tahereh Kowsarirad Zahra Hosseini-Farjam Mohammad Reza Amirzargar Samaneh Dehghani Fahimeh Shahriyary Mohammad Mahdi Kabiri Marzieh Nojomi Neda Saraygord-Afshari Seyedeh Ghazal Mostofi Zeynab Yassin Nazanin Mojtabavi |
| author_facet | Mahdieh Shokrollahi Barough Mohammad Darzi Masoud Yunesian Danesh Amini Panah Yekta Ghane Sam Mottahedan Sohrab Sakinehpour Tahereh Kowsarirad Zahra Hosseini-Farjam Mohammad Reza Amirzargar Samaneh Dehghani Fahimeh Shahriyary Mohammad Mahdi Kabiri Marzieh Nojomi Neda Saraygord-Afshari Seyedeh Ghazal Mostofi Zeynab Yassin Nazanin Mojtabavi |
| author_sort | Mahdieh Shokrollahi Barough |
| collection | DOAJ |
| description | Background: The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model. Method: A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. Results: The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05). Conclusion: COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes. |
| format | Article |
| id | doaj-art-eb656246b5d24cf5b8d780dc09a58bc6 |
| institution | Kabale University |
| issn | 1876-0341 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Infection and Public Health |
| spelling | doaj-art-eb656246b5d24cf5b8d780dc09a58bc62024-11-27T05:02:01ZengElsevierJournal of Infection and Public Health1876-03412024-12-011712102566Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbiditiesMahdieh Shokrollahi Barough0Mohammad Darzi1Masoud Yunesian2Danesh Amini Panah3Yekta Ghane4Sam Mottahedan5Sohrab Sakinehpour6Tahereh Kowsarirad7Zahra Hosseini-Farjam8Mohammad Reza Amirzargar9Samaneh Dehghani10Fahimeh Shahriyary11Mohammad Mahdi Kabiri12Marzieh Nojomi13Neda Saraygord-Afshari14Seyedeh Ghazal Mostofi15Zeynab Yassin16Nazanin Mojtabavi17Department of Immunology, School of Medicine Iran University of Medical Sciences, Tehran, Iran; Immunology research center institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, IranGenetics Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, IranDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IranAntimicrobial resistance research center, institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; Department of Infectious Disease, School of Medicine, Antimicrobial Resistance Research Center, Iran University of Medical Sciences, Tehran, IranAntimicrobial resistance research center, institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; Department of Infectious Disease, School of Medicine, Antimicrobial Resistance Research Center, Iran University of Medical Sciences, Tehran, IranDepartment of Immunology, School of Medicine Iran University of Medical Sciences, Tehran, IranRadiation Sciences Department, School of paramedicine, Iran University of Medical Sciences, Tehran, IranRadiation Sciences Department, School of paramedicine, Iran University of Medical Sciences, Tehran, IranATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, IranDepartment of Hematology & Blood Banking, School of Allied Medicine, Iran University of Medical Sciences, Tehran, IranDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Hematology & Blood Banking, School of Allied Medicine, Iran University of Medical Sciences, Tehran, IranSchool of engineering, the University of Warwick, Coventry, United KingdomPreventive Medicine and Public Health Research Center, Iran University of Medical Sciences, IranDepartment of Medical Biotechnology, Faculty of Allied Medical sciences Iran University of Medical sciences, IranDepartment of Immunology, School of Medicine Iran University of Medical Sciences, Tehran, IranAntimicrobial resistance research center, institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; Corresponding author.Department of Immunology, School of Medicine Iran University of Medical Sciences, Tehran, Iran; Immunology research center institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; Correspondence to: Antimicrobial resistance research center, institute of Immunology and infectious disease, Iran University of Medical Sciences, Tehran, Iran.Background: The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model. Method: A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. Results: The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05). Conclusion: COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes.http://www.sciencedirect.com/science/article/pii/S1876034124003009COVID-19Laboratory dataComorbidityFeature selectionMachine learning |
| spellingShingle | Mahdieh Shokrollahi Barough Mohammad Darzi Masoud Yunesian Danesh Amini Panah Yekta Ghane Sam Mottahedan Sohrab Sakinehpour Tahereh Kowsarirad Zahra Hosseini-Farjam Mohammad Reza Amirzargar Samaneh Dehghani Fahimeh Shahriyary Mohammad Mahdi Kabiri Marzieh Nojomi Neda Saraygord-Afshari Seyedeh Ghazal Mostofi Zeynab Yassin Nazanin Mojtabavi Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities Journal of Infection and Public Health COVID-19 Laboratory data Comorbidity Feature selection Machine learning |
| title | Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities |
| title_full | Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities |
| title_fullStr | Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities |
| title_full_unstemmed | Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities |
| title_short | Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities |
| title_sort | retrospective analysis of covid 19 clinical and laboratory data constructing a multivariable model across different comorbidities |
| topic | COVID-19 Laboratory data Comorbidity Feature selection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1876034124003009 |
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