Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use
Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. We enrolled 2191 consecutive hospitalized patients with COVID-19...
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| Veröffentlicht in: | PloS one 2021-01, Vol.16 (1), p.e0245281-e0245281 |
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| creator | Magro, Bianca Zuccaro, Valentina Novelli, Luca Zileri, Lorenzo Celsa, Ciro Raimondi, Federico Gori, Mauro Cammà, Giulia Battaglia, Salvatore Genova, Vincenzo Giuseppe Paris, Laura Tacelli, Matteo Mancarella, Francesco Antonio Enea, Marco Attanasio, Massimo Senni, Michele Di Marco, Fabiano Lorini, Luca Ferdinando Fagiuoli, Stefano Bruno, Raffaele Cammà, Calogero Gasbarrini, Antonio |
| description | Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19.
We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission |
| doi_str_mv | 10.1371/journal.pone.0245281 |
| format | Article |
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We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp).
A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0245281</identifier><identifier>PMID: 33444411</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Antiviral drugs ; Blood ; Blood coagulation ; Bone marrow ; Bone marrow transplantation ; Business ; C-reactive protein ; Cardiology ; Child care ; Children ; Coagulation ; Cohort Studies ; Computed tomography ; Computer programs ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - mortality ; Creatine ; Creatine kinase ; Diabetes ; Diagnostic systems ; Drafting software ; Drug dosages ; Economic analysis ; Economics ; Editing ; Epidemics ; Female ; Gastroenterology ; Health promotion ; Health risks ; Hematology ; Hepatology ; Hospital Mortality ; Hospital patients ; Hospitalization ; Hospitalization - statistics & numerical data ; Hospitals ; Humans ; Infectious diseases ; Internal medicine ; Italy - epidemiology ; Kinases ; L-Lactate dehydrogenase ; Laboratories ; Lactate dehydrogenase ; Lactic acid ; Male ; Medical examination ; Medicine ; Medicine and Health Sciences ; Methodology ; Middle Aged ; Mobile Applications ; Mortality ; Pandemics ; Patients ; Public health ; Radiography ; Respiratory diseases ; Retrospective Studies ; Risk analysis ; Risk Assessment - methods ; Risk Factors ; ROC Curve ; SARS-CoV-2 - isolation & purification ; Severe acute respiratory syndrome ; Severe acute respiratory syndrome coronavirus 2 ; Software ; Statistical analysis ; Statistics ; Survival analysis ; Transaminase ; Transplantation ; Transplants & implants ; Viral diseases ; Web applications</subject><ispartof>PloS one, 2021-01, Vol.16 (1), p.e0245281-e0245281</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Magro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Magro et al 2021 Magro et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-c7cf75196b20410183ad733b5e0af62dc95102ae5ae21cfc907ba420971584193</citedby><cites>FETCH-LOGICAL-c692t-c7cf75196b20410183ad733b5e0af62dc95102ae5ae21cfc907ba420971584193</cites><orcidid>0000-0002-5662-2162 ; 0000-0002-2705-248X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808616/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808616/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33444411$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Di Gennaro, Francesco</contributor><creatorcontrib>Magro, Bianca</creatorcontrib><creatorcontrib>Zuccaro, Valentina</creatorcontrib><creatorcontrib>Novelli, Luca</creatorcontrib><creatorcontrib>Zileri, Lorenzo</creatorcontrib><creatorcontrib>Celsa, Ciro</creatorcontrib><creatorcontrib>Raimondi, Federico</creatorcontrib><creatorcontrib>Gori, Mauro</creatorcontrib><creatorcontrib>Cammà, Giulia</creatorcontrib><creatorcontrib>Battaglia, Salvatore</creatorcontrib><creatorcontrib>Genova, Vincenzo Giuseppe</creatorcontrib><creatorcontrib>Paris, Laura</creatorcontrib><creatorcontrib>Tacelli, Matteo</creatorcontrib><creatorcontrib>Mancarella, Francesco Antonio</creatorcontrib><creatorcontrib>Enea, Marco</creatorcontrib><creatorcontrib>Attanasio, Massimo</creatorcontrib><creatorcontrib>Senni, Michele</creatorcontrib><creatorcontrib>Di Marco, Fabiano</creatorcontrib><creatorcontrib>Lorini, Luca Ferdinando</creatorcontrib><creatorcontrib>Fagiuoli, Stefano</creatorcontrib><creatorcontrib>Bruno, Raffaele</creatorcontrib><creatorcontrib>Cammà, Calogero</creatorcontrib><creatorcontrib>Gasbarrini, Antonio</creatorcontrib><title>Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19.
We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp).
A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Antiviral drugs</subject><subject>Blood</subject><subject>Blood coagulation</subject><subject>Bone marrow</subject><subject>Bone marrow transplantation</subject><subject>Business</subject><subject>C-reactive protein</subject><subject>Cardiology</subject><subject>Child care</subject><subject>Children</subject><subject>Coagulation</subject><subject>Cohort Studies</subject><subject>Computed tomography</subject><subject>Computer programs</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - mortality</subject><subject>Creatine</subject><subject>Creatine kinase</subject><subject>Diabetes</subject><subject>Diagnostic systems</subject><subject>Drafting software</subject><subject>Drug dosages</subject><subject>Economic analysis</subject><subject>Economics</subject><subject>Editing</subject><subject>Epidemics</subject><subject>Female</subject><subject>Gastroenterology</subject><subject>Health promotion</subject><subject>Health risks</subject><subject>Hematology</subject><subject>Hepatology</subject><subject>Hospital Mortality</subject><subject>Hospital patients</subject><subject>Hospitalization</subject><subject>Hospitalization - statistics & numerical data</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Internal medicine</subject><subject>Italy - epidemiology</subject><subject>Kinases</subject><subject>L-Lactate dehydrogenase</subject><subject>Laboratories</subject><subject>Lactate dehydrogenase</subject><subject>Lactic acid</subject><subject>Male</subject><subject>Medical examination</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methodology</subject><subject>Middle Aged</subject><subject>Mobile Applications</subject><subject>Mortality</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Public health</subject><subject>Radiography</subject><subject>Respiratory diseases</subject><subject>Retrospective Studies</subject><subject>Risk analysis</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>SARS-CoV-2 - isolation & purification</subject><subject>Severe acute respiratory syndrome</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Survival analysis</subject><subject>Transaminase</subject><subject>Transplantation</subject><subject>Transplants & implants</subject><subject>Viral diseases</subject><subject>Web 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in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use</title><author>Magro, Bianca ; Zuccaro, Valentina ; Novelli, Luca ; Zileri, Lorenzo ; Celsa, Ciro ; Raimondi, Federico ; Gori, Mauro ; Cammà, Giulia ; Battaglia, Salvatore ; Genova, Vincenzo Giuseppe ; Paris, Laura ; Tacelli, Matteo ; Mancarella, Francesco Antonio ; Enea, Marco ; Attanasio, Massimo ; Senni, Michele ; Di Marco, Fabiano ; Lorini, Luca Ferdinando ; Fagiuoli, Stefano ; Bruno, Raffaele ; Cammà, Calogero ; Gasbarrini, Antonio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-c7cf75196b20410183ad733b5e0af62dc95102ae5ae21cfc907ba420971584193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Antiviral drugs</topic><topic>Blood</topic><topic>Blood coagulation</topic><topic>Bone marrow</topic><topic>Bone marrow transplantation</topic><topic>Business</topic><topic>C-reactive protein</topic><topic>Cardiology</topic><topic>Child care</topic><topic>Children</topic><topic>Coagulation</topic><topic>Cohort Studies</topic><topic>Computed tomography</topic><topic>Computer programs</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 - mortality</topic><topic>Creatine</topic><topic>Creatine kinase</topic><topic>Diabetes</topic><topic>Diagnostic systems</topic><topic>Drafting software</topic><topic>Drug dosages</topic><topic>Economic analysis</topic><topic>Economics</topic><topic>Editing</topic><topic>Epidemics</topic><topic>Female</topic><topic>Gastroenterology</topic><topic>Health promotion</topic><topic>Health risks</topic><topic>Hematology</topic><topic>Hepatology</topic><topic>Hospital Mortality</topic><topic>Hospital patients</topic><topic>Hospitalization</topic><topic>Hospitalization - statistics & 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2</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Survival analysis</topic><topic>Transaminase</topic><topic>Transplantation</topic><topic>Transplants & implants</topic><topic>Viral diseases</topic><topic>Web applications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Magro, Bianca</creatorcontrib><creatorcontrib>Zuccaro, Valentina</creatorcontrib><creatorcontrib>Novelli, Luca</creatorcontrib><creatorcontrib>Zileri, Lorenzo</creatorcontrib><creatorcontrib>Celsa, Ciro</creatorcontrib><creatorcontrib>Raimondi, Federico</creatorcontrib><creatorcontrib>Gori, Mauro</creatorcontrib><creatorcontrib>Cammà, Giulia</creatorcontrib><creatorcontrib>Battaglia, Salvatore</creatorcontrib><creatorcontrib>Genova, Vincenzo Giuseppe</creatorcontrib><creatorcontrib>Paris, Laura</creatorcontrib><creatorcontrib>Tacelli, Matteo</creatorcontrib><creatorcontrib>Mancarella, Francesco 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C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Magro, Bianca</au><au>Zuccaro, Valentina</au><au>Novelli, Luca</au><au>Zileri, Lorenzo</au><au>Celsa, Ciro</au><au>Raimondi, Federico</au><au>Gori, Mauro</au><au>Cammà, Giulia</au><au>Battaglia, Salvatore</au><au>Genova, Vincenzo Giuseppe</au><au>Paris, Laura</au><au>Tacelli, Matteo</au><au>Mancarella, Francesco Antonio</au><au>Enea, Marco</au><au>Attanasio, Massimo</au><au>Senni, Michele</au><au>Di Marco, Fabiano</au><au>Lorini, Luca Ferdinando</au><au>Fagiuoli, Stefano</au><au>Bruno, Raffaele</au><au>Cammà, Calogero</au><au>Gasbarrini, Antonio</au><au>Di Gennaro, Francesco</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-01-14</date><risdate>2021</risdate><volume>16</volume><issue>1</issue><spage>e0245281</spage><epage>e0245281</epage><pages>e0245281-e0245281</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19.
We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp).
A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33444411</pmid><doi>10.1371/journal.pone.0245281</doi><tpages>e0245281</tpages><orcidid>https://orcid.org/0000-0002-5662-2162</orcidid><orcidid>https://orcid.org/0000-0002-2705-248X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2021-01, Vol.16 (1), p.e0245281-e0245281 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2478022617 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Aged Aged, 80 and over Antiviral drugs Blood Blood coagulation Bone marrow Bone marrow transplantation Business C-reactive protein Cardiology Child care Children Coagulation Cohort Studies Computed tomography Computer programs Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - mortality Creatine Creatine kinase Diabetes Diagnostic systems Drafting software Drug dosages Economic analysis Economics Editing Epidemics Female Gastroenterology Health promotion Health risks Hematology Hepatology Hospital Mortality Hospital patients Hospitalization Hospitalization - statistics & numerical data Hospitals Humans Infectious diseases Internal medicine Italy - epidemiology Kinases L-Lactate dehydrogenase Laboratories Lactate dehydrogenase Lactic acid Male Medical examination Medicine Medicine and Health Sciences Methodology Middle Aged Mobile Applications Mortality Pandemics Patients Public health Radiography Respiratory diseases Retrospective Studies Risk analysis Risk Assessment - methods Risk Factors ROC Curve SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Software Statistical analysis Statistics Survival analysis Transaminase Transplantation Transplants & implants Viral diseases Web applications |
| title | Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use |
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