Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model
Objective To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19). Methods In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2021-08-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605211040263 |
| _version_ | 1819034635879841792 |
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| author | Gayathri Thiruvengadam Ravanan Ramanujam Lakshmi Marappa |
| author_facet | Gayathri Thiruvengadam Ravanan Ramanujam Lakshmi Marappa |
| author_sort | Gayathri Thiruvengadam |
| collection | DOAJ |
| description | Objective To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19). Methods In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model. Results A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease. Conclusions The log-logistic AFT model accurately described the recovery time of patients with COVID-19. |
| first_indexed | 2024-12-21T07:36:52Z |
| format | Article |
| id | doaj.art-df26683d072942c1a5713e326ad51c6b |
| institution | Directory Open Access Journal |
| issn | 1473-2300 |
| language | English |
| last_indexed | 2024-12-21T07:36:52Z |
| publishDate | 2021-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj.art-df26683d072942c1a5713e326ad51c6b2022-12-21T19:11:26ZengSAGE PublishingJournal of International Medical Research1473-23002021-08-014910.1177/03000605211040263Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time modelGayathri ThiruvengadamRavanan RamanujamLakshmi MarappaObjective To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19). Methods In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model. Results A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease. Conclusions The log-logistic AFT model accurately described the recovery time of patients with COVID-19.https://doi.org/10.1177/03000605211040263 |
| spellingShingle | Gayathri Thiruvengadam Ravanan Ramanujam Lakshmi Marappa Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model Journal of International Medical Research |
| title | Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| title_full | Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| title_fullStr | Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| title_full_unstemmed | Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| title_short | Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| title_sort | modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model |
| url | https://doi.org/10.1177/03000605211040263 |
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