Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis
Purpose To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters. Methods In this retrospective IRB-approved study, we i...
Gespeichert in:
| Veröffentlicht in: | Abdominal imaging 2021-04, Vol.46 (4), p.1651-1658 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1658 |
|---|---|
| container_issue | 4 |
| container_start_page | 1651 |
| container_title | Abdominal imaging |
| container_volume | 46 |
| creator | Hectors, Stefanie J. Riyahi, Sadjad Dev, Hreedi Krishnan, Karthik Margolis, Daniel J. A. Prince, Martin R. |
| description | Purpose
To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters.
Methods
In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set;
n
= 45, mean age 65 years, M/F 23/22) and at a second institution (validation set;
n
= 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis.
Results
AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI (
P
= 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89,
P
|
| doi_str_mv | 10.1007/s00261-020-02823-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7584857</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2521818930</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-4ed2935fe45431ef101d5f70ff8f9ddd30262888bab2178086c3fe06f94e62803</originalsourceid><addsrcrecordid>eNp9Uk1v1DAQjRCIVqV_gAOyxIVDA-OPJA4HJFgoVCrqpSBultcep16ycbCTVvtj-K_1dsvyceBgzUjz5s2b8SuKpxReUoDmVQJgNS2BQX6S8fLmQXHIeF2XAJV8uM_Ft4PiOKUVANC6opRVj4sDzqGVopGHxc_Pcz_5ax29npDoQfeb5BMJjiwuiV_rzg_dCen1MkQ9hbg5yRhLLK5DF_V45Y3uiUM9zRETcSGSMaL1ZvJh2JJoM2fa794OuCF-WM1xG8ji4uvZ-5K2ZNSTx2FKr4km73xpch692et4Ujxyuk94fB-Pii-nHy4Xn8rzi49ni7fnpRGNmEqBlrW8cigqwSk6CtRWrgHnpGuttTyfikkpl3rJaCNB1oY7hNq1AnMB-FHxZsc7zss12jsZuldjzBeIGxW0V39XBn-lunCtmkoKWTWZ4MU9QQw_ZkyTWvtksO_1gGFOimVlFGoQNEOf_wNdhTnmhTOqYlRS2fKtIrZDmRhSiuj2YiiorQHUzgAqG0DdGUDd5KZnf66xb_n13RnAd4CUS0OH8ffs_9DeAqInvgM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2521818930</pqid></control><display><type>article</type><title>Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Hectors, Stefanie J. ; Riyahi, Sadjad ; Dev, Hreedi ; Krishnan, Karthik ; Margolis, Daniel J. A. ; Prince, Martin R.</creator><creatorcontrib>Hectors, Stefanie J. ; Riyahi, Sadjad ; Dev, Hreedi ; Krishnan, Karthik ; Margolis, Daniel J. A. ; Prince, Martin R.</creatorcontrib><description>Purpose
To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters.
Methods
In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set;
n
= 45, mean age 65 years, M/F 23/22) and at a second institution (validation set;
n
= 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis.
Results
AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI (
P
= 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89,
P
< 0.001) and good performance in the validation set (AUC 0.78,
P
= 0.030).
Conclusion
Our results show diminished renal perfusion preceding AKI and a promising role of CAEI, combined with laboratory and demographic markers, for prediction of AKI in COVID-19.</description><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-020-02823-w</identifier><identifier>PMID: 33098478</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Acute Kidney Injury - diagnostic imaging ; Aged ; Aorta ; Bladder ; Computed tomography ; Coronaviruses ; COVID-19 ; COVID-19 Testing ; Demographic variables ; Gastroenterology ; Hepatology ; Humans ; Imaging ; Injury analysis ; Kidneys ; Kidneys, Ureters, Bladder, Retroperitoneum ; Laboratories ; Medicine ; Medicine & Public Health ; Middle Aged ; Multivariate Analysis ; Neuroimaging ; Parameter identification ; Perfusion ; Prediction models ; Radiology ; Regression models ; Renal cortex ; Retroperitoneum ; Retrospective Studies ; Risk Factors ; SARS-CoV-2 ; Tomography, X-Ray Computed ; Training ; Urea ; Ureters</subject><ispartof>Abdominal imaging, 2021-04, Vol.46 (4), p.1651-1658</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-4ed2935fe45431ef101d5f70ff8f9ddd30262888bab2178086c3fe06f94e62803</citedby><cites>FETCH-LOGICAL-c474t-4ed2935fe45431ef101d5f70ff8f9ddd30262888bab2178086c3fe06f94e62803</cites><orcidid>0000-0001-9457-6606</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00261-020-02823-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00261-020-02823-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33098478$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hectors, Stefanie J.</creatorcontrib><creatorcontrib>Riyahi, Sadjad</creatorcontrib><creatorcontrib>Dev, Hreedi</creatorcontrib><creatorcontrib>Krishnan, Karthik</creatorcontrib><creatorcontrib>Margolis, Daniel J. A.</creatorcontrib><creatorcontrib>Prince, Martin R.</creatorcontrib><title>Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters.
Methods
In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set;
n
= 45, mean age 65 years, M/F 23/22) and at a second institution (validation set;
n
= 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis.
Results
AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI (
P
= 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89,
P
< 0.001) and good performance in the validation set (AUC 0.78,
P
= 0.030).
Conclusion
Our results show diminished renal perfusion preceding AKI and a promising role of CAEI, combined with laboratory and demographic markers, for prediction of AKI in COVID-19.</description><subject>Acute Kidney Injury - diagnostic imaging</subject><subject>Aged</subject><subject>Aorta</subject><subject>Bladder</subject><subject>Computed tomography</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 Testing</subject><subject>Demographic variables</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Injury analysis</subject><subject>Kidneys</subject><subject>Kidneys, Ureters, Bladder, Retroperitoneum</subject><subject>Laboratories</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Neuroimaging</subject><subject>Parameter identification</subject><subject>Perfusion</subject><subject>Prediction models</subject><subject>Radiology</subject><subject>Regression models</subject><subject>Renal cortex</subject><subject>Retroperitoneum</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>SARS-CoV-2</subject><subject>Tomography, X-Ray Computed</subject><subject>Training</subject><subject>Urea</subject><subject>Ureters</subject><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</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><recordid>eNp9Uk1v1DAQjRCIVqV_gAOyxIVDA-OPJA4HJFgoVCrqpSBultcep16ycbCTVvtj-K_1dsvyceBgzUjz5s2b8SuKpxReUoDmVQJgNS2BQX6S8fLmQXHIeF2XAJV8uM_Ft4PiOKUVANC6opRVj4sDzqGVopGHxc_Pcz_5ax29npDoQfeb5BMJjiwuiV_rzg_dCen1MkQ9hbg5yRhLLK5DF_V45Y3uiUM9zRETcSGSMaL1ZvJh2JJoM2fa794OuCF-WM1xG8ji4uvZ-5K2ZNSTx2FKr4km73xpch692et4Ujxyuk94fB-Pii-nHy4Xn8rzi49ni7fnpRGNmEqBlrW8cigqwSk6CtRWrgHnpGuttTyfikkpl3rJaCNB1oY7hNq1AnMB-FHxZsc7zss12jsZuldjzBeIGxW0V39XBn-lunCtmkoKWTWZ4MU9QQw_ZkyTWvtksO_1gGFOimVlFGoQNEOf_wNdhTnmhTOqYlRS2fKtIrZDmRhSiuj2YiiorQHUzgAqG0DdGUDd5KZnf66xb_n13RnAd4CUS0OH8ffs_9DeAqInvgM</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Hectors, Stefanie J.</creator><creator>Riyahi, Sadjad</creator><creator>Dev, Hreedi</creator><creator>Krishnan, Karthik</creator><creator>Margolis, Daniel J. A.</creator><creator>Prince, Martin R.</creator><general>Springer US</general><general>Springer Nature B.V</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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9457-6606</orcidid></search><sort><creationdate>20210401</creationdate><title>Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis</title><author>Hectors, Stefanie J. ; Riyahi, Sadjad ; Dev, Hreedi ; Krishnan, Karthik ; Margolis, Daniel J. A. ; Prince, Martin R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-4ed2935fe45431ef101d5f70ff8f9ddd30262888bab2178086c3fe06f94e62803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Acute Kidney Injury - diagnostic imaging</topic><topic>Aged</topic><topic>Aorta</topic><topic>Bladder</topic><topic>Computed tomography</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 Testing</topic><topic>Demographic variables</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Imaging</topic><topic>Injury analysis</topic><topic>Kidneys</topic><topic>Kidneys, Ureters, Bladder, Retroperitoneum</topic><topic>Laboratories</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Neuroimaging</topic><topic>Parameter identification</topic><topic>Perfusion</topic><topic>Prediction models</topic><topic>Radiology</topic><topic>Regression models</topic><topic>Renal cortex</topic><topic>Retroperitoneum</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>SARS-CoV-2</topic><topic>Tomography, X-Ray Computed</topic><topic>Training</topic><topic>Urea</topic><topic>Ureters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hectors, Stefanie J.</creatorcontrib><creatorcontrib>Riyahi, Sadjad</creatorcontrib><creatorcontrib>Dev, Hreedi</creatorcontrib><creatorcontrib>Krishnan, Karthik</creatorcontrib><creatorcontrib>Margolis, Daniel J. A.</creatorcontrib><creatorcontrib>Prince, Martin R.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</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>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hectors, Stefanie J.</au><au>Riyahi, Sadjad</au><au>Dev, Hreedi</au><au>Krishnan, Karthik</au><au>Margolis, Daniel J. A.</au><au>Prince, Martin R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2021-04-01</date><risdate>2021</risdate><volume>46</volume><issue>4</issue><spage>1651</spage><epage>1658</epage><pages>1651-1658</pages><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters.
Methods
In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set;
n
= 45, mean age 65 years, M/F 23/22) and at a second institution (validation set;
n
= 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis.
Results
AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI (
P
= 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89,
P
< 0.001) and good performance in the validation set (AUC 0.78,
P
= 0.030).
Conclusion
Our results show diminished renal perfusion preceding AKI and a promising role of CAEI, combined with laboratory and demographic markers, for prediction of AKI in COVID-19.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>33098478</pmid><doi>10.1007/s00261-020-02823-w</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-9457-6606</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2366-004X |
| ispartof | Abdominal imaging, 2021-04, Vol.46 (4), p.1651-1658 |
| issn | 2366-004X 2366-0058 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7584857 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Acute Kidney Injury - diagnostic imaging Aged Aorta Bladder Computed tomography Coronaviruses COVID-19 COVID-19 Testing Demographic variables Gastroenterology Hepatology Humans Imaging Injury analysis Kidneys Kidneys, Ureters, Bladder, Retroperitoneum Laboratories Medicine Medicine & Public Health Middle Aged Multivariate Analysis Neuroimaging Parameter identification Perfusion Prediction models Radiology Regression models Renal cortex Retroperitoneum Retrospective Studies Risk Factors SARS-CoV-2 Tomography, X-Ray Computed Training Urea Ureters |
| title | Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T10%3A45%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multivariate%20analysis%20of%20CT%20imaging,%20laboratory,%20and%20demographical%20features%20for%20prediction%20of%20acute%20kidney%20injury%20in%20COVID-19%20patients:%20a%20Bi-centric%20analysis&rft.jtitle=Abdominal%20imaging&rft.au=Hectors,%20Stefanie%20J.&rft.date=2021-04-01&rft.volume=46&rft.issue=4&rft.spage=1651&rft.epage=1658&rft.pages=1651-1658&rft.issn=2366-004X&rft.eissn=2366-0058&rft_id=info:doi/10.1007/s00261-020-02823-w&rft_dat=%3Cproquest_pubme%3E2521818930%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2521818930&rft_id=info:pmid/33098478&rfr_iscdi=true |