Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models
In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to corre...
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| פורמט: | Online |
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Firenze University Press
2022
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| גישה מקוונת: | ONIX_20220915_9788855184618_23 |
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| _version_ | 1869514316483395584 |
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| author | Ferrante, Pasquale Di Maso, Matteo Ferraroni, Monica Delbue, Serena Ambrogi, Federico |
| author_browse | Ambrogi, Federico Delbue, Serena Di Maso, Matteo Ferrante, Pasquale Ferraroni, Monica |
| author_facet | Ferrante, Pasquale Di Maso, Matteo Ferraroni, Monica Delbue, Serena Ambrogi, Federico |
| author_sort | Ferrante, Pasquale |
| collection | Directory of Open Access Books |
| description | In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years. |
| format | Online |
| id | doab-20.500.12854ir-92240 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-922402025-08-13T13:42:27Z Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models Ferrante, Pasquale Di Maso, Matteo Ferraroni, Monica Delbue, Serena Ambrogi, Federico Endogenous time-varying covariates Time-dependent Cox model Joint models Inflammatory biomarkers Covid-19 mortality thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years. 2022-09-22T04:02:58Z 2022-09-22T04:02:58Z 2022-09-15T20:05:45Z 2021 chapter ONIX_20220915_9788855184618_23 2704-5846 https://library.oapen.org/handle/20.500.12657/58227 9788855184618 https://directory.doabooks.org/handle/20.500.12854/92240 eng Proceedings e report open access image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/58227/1/978-88-5518-461-8_36.pdf https://library.oapen.org/bitstream/20.500.12657/58227/1/978-88-5518-461-8_36.pdf Firenze University Press 10.36253/978-88-5518-461-8.36 10.36253/978-88-5518-461-8.36 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855184618 6 Florence open access |
| spellingShingle | Endogenous time-varying covariates Time-dependent Cox model Joint models Inflammatory biomarkers Covid-19 mortality thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics Ferrante, Pasquale Di Maso, Matteo Ferraroni, Monica Delbue, Serena Ambrogi, Federico Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title | Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title_full | Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title_fullStr | Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title_full_unstemmed | Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title_short | Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
| title_sort | chapter longitudinal profile of a set of biomarkers in predicting covid 19 mortality using joint models |
| topic | Endogenous time-varying covariates Time-dependent Cox model Joint models Inflammatory biomarkers Covid-19 mortality thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics |
| topic_facet | Endogenous time-varying covariates Time-dependent Cox model Joint models Inflammatory biomarkers Covid-19 mortality thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics |
| url | ONIX_20220915_9788855184618_23 |
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