Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study

This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral...

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Veröffentlicht in:PloS one 2022-04, Vol.17 (4), p.e0263548-e0263548
Hauptverfasser: Giacomelli, Andrea, Ridolfo, Anna Lisa, Pezzati, Laura, Oreni, Letizia, Carrozzo, Giorgia, Beltrami, Martina, Poloni, Andrea, Caloni, Beatrice, Lazzarin, Samuel, Colombo, Martina, Pozza, Giacomo, Pagano, Simone, Caronni, Stefania, Fusetti, Chiara, Gerbi, Martina, Petri, Francesco, Borgonovo, Fabio, D'Aloia, Fabiana, Negri, Cristina, Rizzardini, Giuliano, Antinori, Spinello
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creator Giacomelli, Andrea
Ridolfo, Anna Lisa
Pezzati, Laura
Oreni, Letizia
Carrozzo, Giorgia
Beltrami, Martina
Poloni, Andrea
Caloni, Beatrice
Lazzarin, Samuel
Colombo, Martina
Pozza, Giacomo
Pagano, Simone
Caronni, Stefania
Fusetti, Chiara
Gerbi, Martina
Petri, Francesco
Borgonovo, Fabio
D'Aloia, Fabiana
Negri, Cristina
Rizzardini, Giuliano
Antinori, Spinello
description This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020-31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59-0.95, and 0.58, 95% CI 0.44-0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65-1.33, and 0.88, 95% CI 0.59-1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43-0.88, and 0.44, 95% CI 0.28-0.70). Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years.
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This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020-31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59-0.95, and 0.58, 95% CI 0.44-0.77). Among the patients aged &gt;75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65-1.33, and 0.88, 95% CI 0.59-1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43-0.88, and 0.44, 95% CI 0.28-0.70). Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged &gt;75 years.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0263548</identifier><identifier>PMID: 35404963</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Comorbidity ; Confidence intervals ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Death ; Epidemics ; Fatalities ; Health risks ; Hospitalization ; Humans ; Medicine and Health Sciences ; Mortality ; Observational studies ; Patients ; People and places ; Prospective Studies ; Risk management ; Risk reduction ; Statistical analysis ; Statistics ; Time dependence ; Viral diseases</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0263548-e0263548</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Giacomelli 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>2022 Giacomelli et al 2022 Giacomelli et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-1aa2c10cb3722289cdd2157ec3117eecffbc129c15e4ffb0ff8ef9d818c6ebb03</citedby><cites>FETCH-LOGICAL-c692t-1aa2c10cb3722289cdd2157ec3117eecffbc129c15e4ffb0ff8ef9d818c6ebb03</cites><orcidid>0000-0003-3685-4289</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/PMC9000097/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9000097/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2106,2932,23875,27933,27934,53800,53802</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35404963$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Lazzeri, Chiara</contributor><creatorcontrib>Giacomelli, Andrea</creatorcontrib><creatorcontrib>Ridolfo, Anna Lisa</creatorcontrib><creatorcontrib>Pezzati, Laura</creatorcontrib><creatorcontrib>Oreni, Letizia</creatorcontrib><creatorcontrib>Carrozzo, Giorgia</creatorcontrib><creatorcontrib>Beltrami, Martina</creatorcontrib><creatorcontrib>Poloni, Andrea</creatorcontrib><creatorcontrib>Caloni, Beatrice</creatorcontrib><creatorcontrib>Lazzarin, Samuel</creatorcontrib><creatorcontrib>Colombo, Martina</creatorcontrib><creatorcontrib>Pozza, Giacomo</creatorcontrib><creatorcontrib>Pagano, Simone</creatorcontrib><creatorcontrib>Caronni, Stefania</creatorcontrib><creatorcontrib>Fusetti, Chiara</creatorcontrib><creatorcontrib>Gerbi, Martina</creatorcontrib><creatorcontrib>Petri, Francesco</creatorcontrib><creatorcontrib>Borgonovo, Fabio</creatorcontrib><creatorcontrib>D'Aloia, Fabiana</creatorcontrib><creatorcontrib>Negri, Cristina</creatorcontrib><creatorcontrib>Rizzardini, Giuliano</creatorcontrib><creatorcontrib>Antinori, Spinello</creatorcontrib><title>Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020-31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59-0.95, and 0.58, 95% CI 0.44-0.77). Among the patients aged &gt;75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65-1.33, and 0.88, 95% CI 0.59-1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43-0.88, and 0.44, 95% CI 0.28-0.70). Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged &gt;75 years.</description><subject>Biology and Life Sciences</subject><subject>Comorbidity</subject><subject>Confidence intervals</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Death</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Health risks</subject><subject>Hospitalization</subject><subject>Humans</subject><subject>Medicine and Health Sciences</subject><subject>Mortality</subject><subject>Observational studies</subject><subject>Patients</subject><subject>People and places</subject><subject>Prospective Studies</subject><subject>Risk management</subject><subject>Risk reduction</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Time dependence</subject><subject>Viral diseases</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9uO0zAQhiMEYpfCGyCwhIRAosV20qTmAqkqp0q7qsRhby3HGbdepXGwnULfgwdmethVg_aC-CIj-5t_xuOZJHnK6IilBXt77TrfqHrUugZGlOfpOJvcS86ZSPkw5zS9f2KfJY9CuKZ0nE7y_GFyhizNRJ6eJ38unY-qtnFLvIoQiFq7Zklmi6v5hyETpFXRQhMDWbnQ2h0ZoCJV5y1ScQXEWB8iWh6A_FIbVHBmfwCtrWBtNbENubS1at6QOfpv35EpaT2qgY52A8SVAfwGwzi8DQmxq7aPkwdG1QGeHP-D5Menj99nX4YXi8_z2fRiqHPB45ApxTWjukwLzvlE6KribFyAThkrALQxpWZcaDaGDG1qzASMqCZsonMoS5oOkucH3bZ2QR4LGiTPM8EzjkVGYn4gKqeuZevtWvmtdMrK_YbzS6l8tLoGmeV5UZiccUp5hgmISnCNSjo1ldHVLtr7Y7SuXEOlsaxe1T3R_kljV3LpNlJQ_ESBAq-OAt797CBEubZBQ421Bdcd8h4Lzvd5v_gHvft2R2qp8AK2MQ7j6p2onBaUMpan2DKDZHQHhWv_vNh9xuJ-z-F1zwGZCL_jUnUhyPm3r__PLq767MsTdgWqjqvg6m7XOqEPZgdQY58FD-a2yIzK3fDcVEPuhkcehwfdnp0-0K3TzbSkfwFFYBX8</recordid><startdate>20220411</startdate><enddate>20220411</enddate><creator>Giacomelli, Andrea</creator><creator>Ridolfo, Anna Lisa</creator><creator>Pezzati, Laura</creator><creator>Oreni, Letizia</creator><creator>Carrozzo, Giorgia</creator><creator>Beltrami, Martina</creator><creator>Poloni, Andrea</creator><creator>Caloni, Beatrice</creator><creator>Lazzarin, Samuel</creator><creator>Colombo, Martina</creator><creator>Pozza, Giacomo</creator><creator>Pagano, Simone</creator><creator>Caronni, Stefania</creator><creator>Fusetti, Chiara</creator><creator>Gerbi, Martina</creator><creator>Petri, Francesco</creator><creator>Borgonovo, Fabio</creator><creator>D'Aloia, Fabiana</creator><creator>Negri, Cristina</creator><creator>Rizzardini, Giuliano</creator><creator>Antinori, Spinello</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3685-4289</orcidid></search><sort><creationdate>20220411</creationdate><title>Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study</title><author>Giacomelli, Andrea ; 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Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</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>Giacomelli, Andrea</au><au>Ridolfo, Anna Lisa</au><au>Pezzati, Laura</au><au>Oreni, Letizia</au><au>Carrozzo, Giorgia</au><au>Beltrami, Martina</au><au>Poloni, Andrea</au><au>Caloni, Beatrice</au><au>Lazzarin, Samuel</au><au>Colombo, Martina</au><au>Pozza, Giacomo</au><au>Pagano, Simone</au><au>Caronni, Stefania</au><au>Fusetti, Chiara</au><au>Gerbi, Martina</au><au>Petri, Francesco</au><au>Borgonovo, Fabio</au><au>D'Aloia, Fabiana</au><au>Negri, Cristina</au><au>Rizzardini, Giuliano</au><au>Antinori, Spinello</au><au>Lazzeri, Chiara</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-04-11</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>e0263548</spage><epage>e0263548</epage><pages>e0263548-e0263548</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020-31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59-0.95, and 0.58, 95% CI 0.44-0.77). Among the patients aged &gt;75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65-1.33, and 0.88, 95% CI 0.59-1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43-0.88, and 0.44, 95% CI 0.28-0.70). Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged &gt;75 years.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35404963</pmid><doi>10.1371/journal.pone.0263548</doi><tpages>e0263548</tpages><orcidid>https://orcid.org/0000-0003-3685-4289</orcidid><oa>free_for_read</oa></addata></record>
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1932-6203
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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 Biology and Life Sciences
Comorbidity
Confidence intervals
Coronaviruses
COVID-19
COVID-19 - epidemiology
Death
Epidemics
Fatalities
Health risks
Hospitalization
Humans
Medicine and Health Sciences
Mortality
Observational studies
Patients
People and places
Prospective Studies
Risk management
Risk reduction
Statistical analysis
Statistics
Time dependence
Viral diseases
title Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study
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