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...
Ausführliche Beschreibung
Autor*in: |
Ferrante, Pasquale [verfasserIn] Di Maso, Matteo [verfasserIn] Ferraroni, Monica [verfasserIn] Delbue, Serena [verfasserIn] Ambrogi, Federico [verfasserIn] |
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Format: |
E-Book |
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Sprache: |
Englisch |
Erschienen: |
Florence: Firenze University Press ; 2021 |
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Rechteinformationen: |
Open Access Namensnennung 4.0 International ; CC BY 4.0 |
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Schlagwörter: |
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Umfang: |
1 Online-Ressource (6 p.) |
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Reihe: |
Proceedings e report ; 132 |
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Links: | |
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ISBN: |
978-88-5518-461-8 |
Katalog-ID: |
1905040253 |
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9788855184618 36 978-88-5518-461-8 (DE-627)1905040253 (DE-599)KEP108439399 (OCoLC)1366223106 (OAPEN)58227 (EBP)108439399 DE-627 ger DE-627 rda eng JHBC bicssc Ferrante, Pasquale verfasserin aut Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models Florence Firenze University Press 2021 1 Online-Ressource (6 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Proceedings e report 132 Open Access Unrestricted online access star 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 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ English Social research and statistics Di Maso, Matteo verfasserin aut Ferraroni, Monica verfasserin aut Delbue, Serena verfasserin aut Ambrogi, Federico verfasserin aut https://library.oapen.org/bitstream/id/dba95397-89bd-47ab-9ce9-a5e719fdf69d/978-88-5518-461-8_36.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/58227 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460626182X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606767480 OLR-OAPEN f z 29-10-24 30 01 0104 460710020X GBV-OAPEN z 29-10-24 31 01 0027 4606428058 z 29-10-24 34 01 3551 4604748977 OLR-OAPEN zi002 28-10-24 39 01 0547 460726637X GBV-OAPEN ke 29-10-24 40 01 0007 4606095520 OLR-OAPEN xsn 29-10-24 63 01 3401 4607602213 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607768820 OLR-OAPEN z 29-10-24 70 01 0089 4606601167 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604510407 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4605006141 OLR-OAL Vervielfältigungen (z.B. 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Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 161 01 0960 4603956606 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 187 01 3520 460432154X JSTOR Open Access eBook z 28-10-24 213 01 0551 4605430636 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 230 01 0552 460559678X OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 283 01 3283 4605929320 OLR-OAL z 29-10-24 293 01 3293 4604134189 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 370 01 4370 4605264418 OLR-FREE Kostenloser Zugriff z 29-10-24 603 01 4603 4603787847 OLR-OAPEN Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers. z 28-10-24 808 01 4808 4606933771 OLR-OAPEN OAPEN Library zg 29-10-24 22 01 0018 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 23 01 0830 https://library.oapen.org/handle/20.500.12657/58227 30 01 0104 Open Access https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 eBook GBV-OAPEN https://library.oapen.org/handle/20.500.12657/58227 34 01 3551 OpenAccess https://library.oapen.org/handle/20.500.12657/58227 40 01 0007 Volltext, Open Access https://library.oapen.org/handle/20.500.12657/58227 63 01 3401 E-Book https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Open Access https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Dieser Titel ist Teil einer Datenbank http://www.bibliothek.uni-regensburg.de/dbinfo/detail.php?titel_id=10728&bib_id=ulb_hal 70 01 0089 https://library.oapen.org/handle/20.500.12657/58227 LF 72 01 0035 https://library.oapen.org/handle/20.500.12657/58227 95 01 3095 https://library.oapen.org/handle/20.500.12657/58227 110 01 3110 Open Access https://library.oapen.org/handle/20.500.12657/58227 136 01 3526 Open Access https://library.oapen.org/handle/20.500.12657/58227 147 01 3528 https://library.oapen.org/handle/20.500.12657/58227 161 01 0960 https://library.oapen.org/handle/20.500.12657/58227 LF 187 01 3520 https://library.oapen.org/handle/20.500.12657/58227 213 01 0551 https://library.oapen.org/handle/20.500.12657/58227 230 01 0552 https://library.oapen.org/handle/20.500.12657/58227 283 01 3283 https://library.oapen.org/handle/20.500.12657/58227 293 01 3293 https://library.oapen.org/handle/20.500.12657/58227 LF 370 01 4370 https://library.oapen.org/handle/20.500.12657/58227 603 01 4603 https://library.oapen.org/handle/20.500.12657/58227 808 01 4808 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 00 eBook GBV-OAPEN 22 01 0018 OLR-ZDB-94-OAL 23 01 0830 OLR-OAPEN 30 01 0104 GBV-OAPEN 34 01 3551 OLR-OAPEN 39 01 0547 GBV-OAPEN 39 01 0547 LF 40 01 0007 OLR-OAPEN 63 01 3401 E-Books LF GBV-OAPEN 65 01 0003 OLR-OAPEN 70 01 0089 OLR-OAPEN-OA 72 01 0035 OLR-OAPEN-OA 95 01 3095 OLR-OAL 110 01 3110 OLR-GBV-OAPEN 136 01 3526 OLR-OAPEN 147 01 3528 OLR-OAPEN 161 01 0960 OLR-OAPEN 213 01 0551 OLR-OAPEN 230 01 0552 OLR-OAPEN 283 01 3283 OLR-OAL 293 01 3293 OLR-OAPEN 370 01 4370 OLR-FREE 370 01 4370 olr-ebook GBV-OAPEN 603 01 4603 OLR-OAPEN 808 01 4808 OLR-OAPEN |
spelling |
9788855184618 36 978-88-5518-461-8 (DE-627)1905040253 (DE-599)KEP108439399 (OCoLC)1366223106 (OAPEN)58227 (EBP)108439399 DE-627 ger DE-627 rda eng JHBC bicssc Ferrante, Pasquale verfasserin aut Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models Florence Firenze University Press 2021 1 Online-Ressource (6 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Proceedings e report 132 Open Access Unrestricted online access star 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 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ English Social research and statistics Di Maso, Matteo verfasserin aut Ferraroni, Monica verfasserin aut Delbue, Serena verfasserin aut Ambrogi, Federico verfasserin aut https://library.oapen.org/bitstream/id/dba95397-89bd-47ab-9ce9-a5e719fdf69d/978-88-5518-461-8_36.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/58227 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460626182X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606767480 OLR-OAPEN f z 29-10-24 30 01 0104 460710020X GBV-OAPEN z 29-10-24 31 01 0027 4606428058 z 29-10-24 34 01 3551 4604748977 OLR-OAPEN zi002 28-10-24 39 01 0547 460726637X GBV-OAPEN ke 29-10-24 40 01 0007 4606095520 OLR-OAPEN xsn 29-10-24 63 01 3401 4607602213 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607768820 OLR-OAPEN z 29-10-24 70 01 0089 4606601167 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604510407 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4605006141 OLR-OAL Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 110 01 3110 4607432628 OLR-GBV-OAPEN Open Access z 29-10-24 136 01 3526 4605763066 OLR-OAPEN z 29-10-24 147 01 3528 4607936421 OLR-OAPEN frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 161 01 0960 4603956606 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 187 01 3520 460432154X JSTOR Open Access eBook z 28-10-24 213 01 0551 4605430636 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 230 01 0552 460559678X OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 283 01 3283 4605929320 OLR-OAL z 29-10-24 293 01 3293 4604134189 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 370 01 4370 4605264418 OLR-FREE Kostenloser Zugriff z 29-10-24 603 01 4603 4603787847 OLR-OAPEN Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers. z 28-10-24 808 01 4808 4606933771 OLR-OAPEN OAPEN Library zg 29-10-24 22 01 0018 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 23 01 0830 https://library.oapen.org/handle/20.500.12657/58227 30 01 0104 Open Access https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 eBook GBV-OAPEN https://library.oapen.org/handle/20.500.12657/58227 34 01 3551 OpenAccess https://library.oapen.org/handle/20.500.12657/58227 40 01 0007 Volltext, Open Access https://library.oapen.org/handle/20.500.12657/58227 63 01 3401 E-Book https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Open Access https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Dieser Titel ist Teil einer Datenbank http://www.bibliothek.uni-regensburg.de/dbinfo/detail.php?titel_id=10728&bib_id=ulb_hal 70 01 0089 https://library.oapen.org/handle/20.500.12657/58227 LF 72 01 0035 https://library.oapen.org/handle/20.500.12657/58227 95 01 3095 https://library.oapen.org/handle/20.500.12657/58227 110 01 3110 Open Access https://library.oapen.org/handle/20.500.12657/58227 136 01 3526 Open Access https://library.oapen.org/handle/20.500.12657/58227 147 01 3528 https://library.oapen.org/handle/20.500.12657/58227 161 01 0960 https://library.oapen.org/handle/20.500.12657/58227 LF 187 01 3520 https://library.oapen.org/handle/20.500.12657/58227 213 01 0551 https://library.oapen.org/handle/20.500.12657/58227 230 01 0552 https://library.oapen.org/handle/20.500.12657/58227 283 01 3283 https://library.oapen.org/handle/20.500.12657/58227 293 01 3293 https://library.oapen.org/handle/20.500.12657/58227 LF 370 01 4370 https://library.oapen.org/handle/20.500.12657/58227 603 01 4603 https://library.oapen.org/handle/20.500.12657/58227 808 01 4808 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 00 eBook GBV-OAPEN 22 01 0018 OLR-ZDB-94-OAL 23 01 0830 OLR-OAPEN 30 01 0104 GBV-OAPEN 34 01 3551 OLR-OAPEN 39 01 0547 GBV-OAPEN 39 01 0547 LF 40 01 0007 OLR-OAPEN 63 01 3401 E-Books LF GBV-OAPEN 65 01 0003 OLR-OAPEN 70 01 0089 OLR-OAPEN-OA 72 01 0035 OLR-OAPEN-OA 95 01 3095 OLR-OAL 110 01 3110 OLR-GBV-OAPEN 136 01 3526 OLR-OAPEN 147 01 3528 OLR-OAPEN 161 01 0960 OLR-OAPEN 213 01 0551 OLR-OAPEN 230 01 0552 OLR-OAPEN 283 01 3283 OLR-OAL 293 01 3293 OLR-OAPEN 370 01 4370 OLR-FREE 370 01 4370 olr-ebook GBV-OAPEN 603 01 4603 OLR-OAPEN 808 01 4808 OLR-OAPEN |
allfields_unstemmed |
9788855184618 36 978-88-5518-461-8 (DE-627)1905040253 (DE-599)KEP108439399 (OCoLC)1366223106 (OAPEN)58227 (EBP)108439399 DE-627 ger DE-627 rda eng JHBC bicssc Ferrante, Pasquale verfasserin aut Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models Florence Firenze University Press 2021 1 Online-Ressource (6 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Proceedings e report 132 Open Access Unrestricted online access star 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 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ English Social research and statistics Di Maso, Matteo verfasserin aut Ferraroni, Monica verfasserin aut Delbue, Serena verfasserin aut Ambrogi, Federico verfasserin aut https://library.oapen.org/bitstream/id/dba95397-89bd-47ab-9ce9-a5e719fdf69d/978-88-5518-461-8_36.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/58227 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460626182X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606767480 OLR-OAPEN f z 29-10-24 30 01 0104 460710020X GBV-OAPEN z 29-10-24 31 01 0027 4606428058 z 29-10-24 34 01 3551 4604748977 OLR-OAPEN zi002 28-10-24 39 01 0547 460726637X GBV-OAPEN ke 29-10-24 40 01 0007 4606095520 OLR-OAPEN xsn 29-10-24 63 01 3401 4607602213 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607768820 OLR-OAPEN z 29-10-24 70 01 0089 4606601167 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604510407 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4605006141 OLR-OAL Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 110 01 3110 4607432628 OLR-GBV-OAPEN Open Access z 29-10-24 136 01 3526 4605763066 OLR-OAPEN z 29-10-24 147 01 3528 4607936421 OLR-OAPEN frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 161 01 0960 4603956606 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 187 01 3520 460432154X JSTOR Open Access eBook z 28-10-24 213 01 0551 4605430636 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 230 01 0552 460559678X OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 283 01 3283 4605929320 OLR-OAL z 29-10-24 293 01 3293 4604134189 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 370 01 4370 4605264418 OLR-FREE Kostenloser Zugriff z 29-10-24 603 01 4603 4603787847 OLR-OAPEN Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers. z 28-10-24 808 01 4808 4606933771 OLR-OAPEN OAPEN Library zg 29-10-24 22 01 0018 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 23 01 0830 https://library.oapen.org/handle/20.500.12657/58227 30 01 0104 Open Access https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 eBook GBV-OAPEN https://library.oapen.org/handle/20.500.12657/58227 34 01 3551 OpenAccess https://library.oapen.org/handle/20.500.12657/58227 40 01 0007 Volltext, Open Access https://library.oapen.org/handle/20.500.12657/58227 63 01 3401 E-Book https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Open Access https://library.oapen.org/handle/20.500.12657/58227 65 01 0003 Dieser Titel ist Teil einer Datenbank http://www.bibliothek.uni-regensburg.de/dbinfo/detail.php?titel_id=10728&bib_id=ulb_hal 70 01 0089 https://library.oapen.org/handle/20.500.12657/58227 LF 72 01 0035 https://library.oapen.org/handle/20.500.12657/58227 95 01 3095 https://library.oapen.org/handle/20.500.12657/58227 110 01 3110 Open Access https://library.oapen.org/handle/20.500.12657/58227 136 01 3526 Open Access https://library.oapen.org/handle/20.500.12657/58227 147 01 3528 https://library.oapen.org/handle/20.500.12657/58227 161 01 0960 https://library.oapen.org/handle/20.500.12657/58227 LF 187 01 3520 https://library.oapen.org/handle/20.500.12657/58227 213 01 0551 https://library.oapen.org/handle/20.500.12657/58227 230 01 0552 https://library.oapen.org/handle/20.500.12657/58227 283 01 3283 https://library.oapen.org/handle/20.500.12657/58227 293 01 3293 https://library.oapen.org/handle/20.500.12657/58227 LF 370 01 4370 https://library.oapen.org/handle/20.500.12657/58227 603 01 4603 https://library.oapen.org/handle/20.500.12657/58227 808 01 4808 Volltextzugang https://library.oapen.org/handle/20.500.12657/58227 31 01 0027 00 eBook GBV-OAPEN 22 01 0018 OLR-ZDB-94-OAL 23 01 0830 OLR-OAPEN 30 01 0104 GBV-OAPEN 34 01 3551 OLR-OAPEN 39 01 0547 GBV-OAPEN 39 01 0547 LF 40 01 0007 OLR-OAPEN 63 01 3401 E-Books LF GBV-OAPEN 65 01 0003 OLR-OAPEN 70 01 0089 OLR-OAPEN-OA 72 01 0035 OLR-OAPEN-OA 95 01 3095 OLR-OAL 110 01 3110 OLR-GBV-OAPEN 136 01 3526 OLR-OAPEN 147 01 3528 OLR-OAPEN 161 01 0960 OLR-OAPEN 213 01 0551 OLR-OAPEN 230 01 0552 OLR-OAPEN 283 01 3283 OLR-OAL 293 01 3293 OLR-OAPEN 370 01 4370 OLR-FREE 370 01 4370 olr-ebook GBV-OAPEN 603 01 4603 OLR-OAPEN 808 01 4808 OLR-OAPEN |
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9788855184618 36 978-88-5518-461-8 (DE-627)1905040253 (DE-599)KEP108439399 (OCoLC)1366223106 (OAPEN)58227 (EBP)108439399 DE-627 ger DE-627 rda eng JHBC bicssc Ferrante, Pasquale verfasserin aut Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models Florence Firenze University Press 2021 1 Online-Ressource (6 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Proceedings e report 132 Open Access Unrestricted online access star 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 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ English Social research and statistics Di Maso, Matteo verfasserin aut Ferraroni, Monica verfasserin aut Delbue, Serena verfasserin aut Ambrogi, Federico verfasserin aut https://library.oapen.org/bitstream/id/dba95397-89bd-47ab-9ce9-a5e719fdf69d/978-88-5518-461-8_36.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/58227 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460626182X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606767480 OLR-OAPEN f z 29-10-24 30 01 0104 460710020X GBV-OAPEN z 29-10-24 31 01 0027 4606428058 z 29-10-24 34 01 3551 4604748977 OLR-OAPEN zi002 28-10-24 39 01 0547 460726637X GBV-OAPEN ke 29-10-24 40 01 0007 4606095520 OLR-OAPEN xsn 29-10-24 63 01 3401 4607602213 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607768820 OLR-OAPEN z 29-10-24 70 01 0089 4606601167 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604510407 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4605006141 OLR-OAL Vervielfältigungen (z.B. 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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 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ English Social research and statistics Di Maso, Matteo verfasserin aut Ferraroni, Monica verfasserin aut Delbue, Serena verfasserin aut Ambrogi, Federico verfasserin aut https://library.oapen.org/bitstream/id/dba95397-89bd-47ab-9ce9-a5e719fdf69d/978-88-5518-461-8_36.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/58227 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460626182X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606767480 OLR-OAPEN f z 29-10-24 30 01 0104 460710020X GBV-OAPEN z 29-10-24 31 01 0027 4606428058 z 29-10-24 34 01 3551 4604748977 OLR-OAPEN zi002 28-10-24 39 01 0547 460726637X GBV-OAPEN ke 29-10-24 40 01 0007 4606095520 OLR-OAPEN xsn 29-10-24 63 01 3401 4607602213 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607768820 OLR-OAPEN z 29-10-24 70 01 0089 4606601167 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604510407 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4605006141 OLR-OAL Vervielfältigungen (z.B. 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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">1905040253</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241031070325.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241008s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9788855184618</subfield><subfield code="c">36</subfield><subfield code="9">978-88-5518-461-8</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1905040253</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP108439399</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1366223106</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OAPEN)58227</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)108439399</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">JHBC</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ferrante, Pasquale</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Florence</subfield><subfield code="b">Firenze University Press</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (6 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Proceedings e report</subfield><subfield code="v">132</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">Open Access</subfield><subfield code="f">Unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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). 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Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
abstract |
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 |
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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 |
abstract_unstemmed |
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 |
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Chapter Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models |
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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). 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