Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis
Introduction. Although COVID-19 is not currently a public health emergency, it will affect susceptible individuals in the post-COVID-19 era. Hence, the present study aimed to develop a model for Iranian patients to identify at-risk groups based on past medical history (PMHx) and some other factors a...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Canadian Journal of Infectious Diseases and Medical Microbiology |
| Online Access: | http://dx.doi.org/10.1155/2024/3256108 |
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| author | Seyyed Amir Yasin Ahmadi Yeganeh Karimi Arash Abdollahi Ali Kabir |
| author_facet | Seyyed Amir Yasin Ahmadi Yeganeh Karimi Arash Abdollahi Ali Kabir |
| author_sort | Seyyed Amir Yasin Ahmadi |
| collection | DOAJ |
| description | Introduction. Although COVID-19 is not currently a public health emergency, it will affect susceptible individuals in the post-COVID-19 era. Hence, the present study aimed to develop a model for Iranian patients to identify at-risk groups based on past medical history (PMHx) and some other factors affecting the death of patients hospitalized with COVID-19. Methods. A secondary study was conducted with the existing data of hospitalized COVID-19 adult patients in the hospitals covered by Iran University of Medical Sciences. PMHx was extracted from the registered ICD-10 codes. Stepwise logistic regression was used to predict mortality by PMHx and background covariates such as intensive care unit (ICU) admission. Crude population attributable fraction (PAF) as well as crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were reported. Results. A total of 8879 patients were selected with 19.68% mortality. Infectious and parasitic diseases’ history showed the greatest association (OR = 5.72, 95% CI: 4.20, 7.82), while the greatest PAF was for cardiovascular system diseases (20.46%). According to logistic regression modeling, the largest effect, other than ICU admission and age, was for history of infectious and parasitic diseases (OR = 3.089, 95% CI: 2.13, 4.47). A good performance was achieved (area under curve = 0.875). Conclusion. Considering the prevalence of underlying diseases, many mortality cases of COVID-19 are attributable to the history of cardiovascular disease. Future studies are needed for policy making regarding reduction of COVID-19 mortality in susceptible groups in the post-COVID-19 era. |
| format | Article |
| id | doaj-art-da27901fed0842939afab6da465c3599 |
| institution | Kabale University |
| issn | 1918-1493 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Canadian Journal of Infectious Diseases and Medical Microbiology |
| spelling | doaj-art-da27901fed0842939afab6da465c35992025-02-03T01:30:21ZengWileyCanadian Journal of Infectious Diseases and Medical Microbiology1918-14932024-01-01202410.1155/2024/3256108Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary AnalysisSeyyed Amir Yasin Ahmadi0Yeganeh Karimi1Arash Abdollahi2Ali Kabir3Preventive Medicine and Public Health Research CenterTehran Heart CenterMinimally Invasive Surgery Research CenterMinimally Invasive Surgery Research CenterIntroduction. Although COVID-19 is not currently a public health emergency, it will affect susceptible individuals in the post-COVID-19 era. Hence, the present study aimed to develop a model for Iranian patients to identify at-risk groups based on past medical history (PMHx) and some other factors affecting the death of patients hospitalized with COVID-19. Methods. A secondary study was conducted with the existing data of hospitalized COVID-19 adult patients in the hospitals covered by Iran University of Medical Sciences. PMHx was extracted from the registered ICD-10 codes. Stepwise logistic regression was used to predict mortality by PMHx and background covariates such as intensive care unit (ICU) admission. Crude population attributable fraction (PAF) as well as crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were reported. Results. A total of 8879 patients were selected with 19.68% mortality. Infectious and parasitic diseases’ history showed the greatest association (OR = 5.72, 95% CI: 4.20, 7.82), while the greatest PAF was for cardiovascular system diseases (20.46%). According to logistic regression modeling, the largest effect, other than ICU admission and age, was for history of infectious and parasitic diseases (OR = 3.089, 95% CI: 2.13, 4.47). A good performance was achieved (area under curve = 0.875). Conclusion. Considering the prevalence of underlying diseases, many mortality cases of COVID-19 are attributable to the history of cardiovascular disease. Future studies are needed for policy making regarding reduction of COVID-19 mortality in susceptible groups in the post-COVID-19 era.http://dx.doi.org/10.1155/2024/3256108 |
| spellingShingle | Seyyed Amir Yasin Ahmadi Yeganeh Karimi Arash Abdollahi Ali Kabir Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis Canadian Journal of Infectious Diseases and Medical Microbiology |
| title | Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis |
| title_full | Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis |
| title_fullStr | Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis |
| title_full_unstemmed | Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis |
| title_short | Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis |
| title_sort | modeling for prediction of mortality based on past medical history in hospitalized covid 19 patients a secondary analysis |
| url | http://dx.doi.org/10.1155/2024/3256108 |
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