Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study
The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID‐19)...
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| Veröffentlicht in: | Cancer science 2021-06, Vol.112 (6), p.2522-2532 |
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| creator | Xu, Bin Song, Ke‐Han Yao, Yi Dong, Xiao‐Rong Li, Lin‐Jun Wang, Qun Yang, Ji‐Yuan Hu, Wei‐Dong Xie, Zhi‐Bin Luo, Zhi‐Guo Luo, Xiu‐Li Liu, Jing Rao, Zhi‐Guo Zhang, Hui‐Bo Wu, Jie Li, Lan Gong, Hong‐Yun Chu, Qian Song, Qi‐Bin Wang, Jie |
| description | The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID‐19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID‐19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C‐index and time‐dependent area under the receiver operating characteristic curve (t‐AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C‐reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d‐dimer) were significantly associated with symptomatic deterioration. The C‐index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t‐AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low‐risk (total points ≤ 9.98) and high‐risk (total points > 9.98) group. The Kaplan‐Meier deterioration‐free survival of COVID‐19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID‐19 in patients with cancer.
This is the first study to present a nomogram model to individually predict the deterioration of COVID‐19 in cancer patients. Clinical symptoms, computed tomography image features, cancer types, and comorbidities were incorporated in the model. Risk stratification was carried out targeting cancer populations for COVID‐19 deterioration. Cancer type was a critical factor affecting symptomatic dete |
| doi_str_mv | 10.1111/cas.14882 |
| format | Article |
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This is the first study to present a nomogram model to individually predict the deterioration of COVID‐19 in cancer patients. Clinical symptoms, computed tomography image features, cancer types, and comorbidities were incorporated in the model. Risk stratification was carried out targeting cancer populations for COVID‐19 deterioration. Cancer type was a critical factor affecting symptomatic deterioration. The COVID‐19 patients with lymphoma had the highest risk score for symptomatic deterioration.</description><identifier>ISSN: 1347-9032</identifier><identifier>ISSN: 1349-7006</identifier><identifier>EISSN: 1349-7006</identifier><identifier>DOI: 10.1111/cas.14882</identifier><identifier>PMID: 33728806</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Aged ; Analysis ; Area Under Curve ; Aspartate aminotransferase ; Bilirubin ; Cancer ; Cancer patients ; Cancer therapies ; Cell number ; China ; Comorbidity ; Computed tomography ; Coronaviruses ; COVID-19 ; COVID-19 - mortality ; Decision Support Techniques ; deterioration ; Diabetes ; Disease ; Disease Progression ; Disease susceptibility ; Female ; Fever ; Health aspects ; Humans ; Hypertension ; Infections ; Laboratories ; Lymphocytes ; Male ; Medical prognosis ; Middle Aged ; Neoplasms - mortality ; Neoplasms - virology ; Nomograms ; Oncology, Experimental ; Original ; Patients ; Precision Medicine ; Prediction models ; Prognosis ; Regression analysis ; Retrospective Studies ; retrospective study ; Risk Factors ; Risk groups ; risk model ; Serum levels ; Severe acute respiratory syndrome coronavirus 2 ; Survival Analysis ; Urea</subject><ispartof>Cancer science, 2021-06, Vol.112 (6), p.2522-2532</ispartof><rights>2021 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.</rights><rights>2021 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5622-e9ee041e270da9084ecc388034e53a1179fe47eaf1ada62ffb59bc45c8cf62fe3</citedby><cites>FETCH-LOGICAL-c5622-e9ee041e270da9084ecc388034e53a1179fe47eaf1ada62ffb59bc45c8cf62fe3</cites><orcidid>0000-0002-0499-0430 ; 0000-0002-5602-0487 ; 0000-0003-3646-8572</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/PMC8177766/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177766/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,1418,11564,27926,27927,45576,45577,46054,46478,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33728806$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Bin</creatorcontrib><creatorcontrib>Song, Ke‐Han</creatorcontrib><creatorcontrib>Yao, Yi</creatorcontrib><creatorcontrib>Dong, Xiao‐Rong</creatorcontrib><creatorcontrib>Li, Lin‐Jun</creatorcontrib><creatorcontrib>Wang, Qun</creatorcontrib><creatorcontrib>Yang, Ji‐Yuan</creatorcontrib><creatorcontrib>Hu, Wei‐Dong</creatorcontrib><creatorcontrib>Xie, Zhi‐Bin</creatorcontrib><creatorcontrib>Luo, Zhi‐Guo</creatorcontrib><creatorcontrib>Luo, Xiu‐Li</creatorcontrib><creatorcontrib>Liu, Jing</creatorcontrib><creatorcontrib>Rao, Zhi‐Guo</creatorcontrib><creatorcontrib>Zhang, Hui‐Bo</creatorcontrib><creatorcontrib>Wu, Jie</creatorcontrib><creatorcontrib>Li, Lan</creatorcontrib><creatorcontrib>Gong, Hong‐Yun</creatorcontrib><creatorcontrib>Chu, Qian</creatorcontrib><creatorcontrib>Song, Qi‐Bin</creatorcontrib><creatorcontrib>Wang, Jie</creatorcontrib><title>Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study</title><title>Cancer science</title><addtitle>Cancer Sci</addtitle><description>The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID‐19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID‐19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C‐index and time‐dependent area under the receiver operating characteristic curve (t‐AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C‐reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d‐dimer) were significantly associated with symptomatic deterioration. The C‐index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t‐AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low‐risk (total points ≤ 9.98) and high‐risk (total points > 9.98) group. The Kaplan‐Meier deterioration‐free survival of COVID‐19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID‐19 in patients with cancer.
This is the first study to present a nomogram model to individually predict the deterioration of COVID‐19 in cancer patients. Clinical symptoms, computed tomography image features, cancer types, and comorbidities were incorporated in the model. Risk stratification was carried out targeting cancer populations for COVID‐19 deterioration. Cancer type was a critical factor affecting symptomatic deterioration. The COVID‐19 patients with lymphoma had the highest risk score for symptomatic deterioration.</description><subject>Aged</subject><subject>Analysis</subject><subject>Area Under Curve</subject><subject>Aspartate aminotransferase</subject><subject>Bilirubin</subject><subject>Cancer</subject><subject>Cancer patients</subject><subject>Cancer therapies</subject><subject>Cell number</subject><subject>China</subject><subject>Comorbidity</subject><subject>Computed tomography</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - mortality</subject><subject>Decision Support Techniques</subject><subject>deterioration</subject><subject>Diabetes</subject><subject>Disease</subject><subject>Disease Progression</subject><subject>Disease susceptibility</subject><subject>Female</subject><subject>Fever</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Infections</subject><subject>Laboratories</subject><subject>Lymphocytes</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Middle Aged</subject><subject>Neoplasms - mortality</subject><subject>Neoplasms - virology</subject><subject>Nomograms</subject><subject>Oncology, Experimental</subject><subject>Original</subject><subject>Patients</subject><subject>Precision Medicine</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>retrospective study</subject><subject>Risk Factors</subject><subject>Risk groups</subject><subject>risk model</subject><subject>Serum levels</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Survival Analysis</subject><subject>Urea</subject><issn>1347-9032</issn><issn>1349-7006</issn><issn>1349-7006</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><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>eNp9ks1u1DAQxyMEoqVw4AWQJS5wyNZfiRMOSKvla6VKPfBxtbz2eOsqsYOd7Go58Qg8I0-Ct1sKRYB98Nj-zX_G4ymKxwTPSB6nWqUZ4U1D7xTHhPG2FBjXd69sUbaY0aPiQUqXGLOat_x-ccSYoE2D6-Niu_TGbZyZVOe-gEF9MNAhGyIaIhinR-fXaHH-afnq-9dvpEUGRoguRDW64JHzaMgW-DGhrRsvkFZeQ3yB5qifutHpfAMRRRhjSANktQ2gNE5m97C4Z1WX4NH1elJ8fPP6w-JdeXb-drmYn5W6qiktoQXAnAAV2KgWNxy0ZjlzxqFiihDRWuAClCXKqJpau6raleaVbrTNW2AnxcuD7jCtejD7hKLq5BBdr-JOBuXk7RvvLuQ6bGRDhBB1nQWeXQvE8HmCNMreJQ1dpzyEKUlaYUoxYwxn9Okf6GWYos_Pk1Qw2uKKN-S_VMXqhola8F_UWnUgnbchZ6f3oeVcECw4wZxmavYXKk8DvdPBg3X5_JbD84ODzh-SItibShAs970kcy_Jq17K7JPfS3dD_myeDJwegG2Osvu3klzM3x8kfwDtr9Q_</recordid><startdate>202106</startdate><enddate>202106</enddate><creator>Xu, Bin</creator><creator>Song, Ke‐Han</creator><creator>Yao, Yi</creator><creator>Dong, Xiao‐Rong</creator><creator>Li, Lin‐Jun</creator><creator>Wang, Qun</creator><creator>Yang, Ji‐Yuan</creator><creator>Hu, Wei‐Dong</creator><creator>Xie, Zhi‐Bin</creator><creator>Luo, Zhi‐Guo</creator><creator>Luo, Xiu‐Li</creator><creator>Liu, Jing</creator><creator>Rao, Zhi‐Guo</creator><creator>Zhang, Hui‐Bo</creator><creator>Wu, Jie</creator><creator>Li, Lan</creator><creator>Gong, Hong‐Yun</creator><creator>Chu, Qian</creator><creator>Song, Qi‐Bin</creator><creator>Wang, Jie</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><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>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0499-0430</orcidid><orcidid>https://orcid.org/0000-0002-5602-0487</orcidid><orcidid>https://orcid.org/0000-0003-3646-8572</orcidid></search><sort><creationdate>202106</creationdate><title>Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study</title><author>Xu, Bin ; Song, Ke‐Han ; Yao, Yi ; Dong, Xiao‐Rong ; Li, Lin‐Jun ; Wang, Qun ; Yang, Ji‐Yuan ; Hu, Wei‐Dong ; Xie, Zhi‐Bin ; Luo, Zhi‐Guo ; Luo, Xiu‐Li ; Liu, Jing ; Rao, Zhi‐Guo ; Zhang, Hui‐Bo ; Wu, Jie ; Li, Lan ; Gong, Hong‐Yun ; Chu, Qian ; Song, Qi‐Bin ; Wang, Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5622-e9ee041e270da9084ecc388034e53a1179fe47eaf1ada62ffb59bc45c8cf62fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Analysis</topic><topic>Area Under Curve</topic><topic>Aspartate aminotransferase</topic><topic>Bilirubin</topic><topic>Cancer</topic><topic>Cancer patients</topic><topic>Cancer therapies</topic><topic>Cell number</topic><topic>China</topic><topic>Comorbidity</topic><topic>Computed tomography</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cancer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Bin</au><au>Song, Ke‐Han</au><au>Yao, Yi</au><au>Dong, Xiao‐Rong</au><au>Li, Lin‐Jun</au><au>Wang, Qun</au><au>Yang, Ji‐Yuan</au><au>Hu, Wei‐Dong</au><au>Xie, Zhi‐Bin</au><au>Luo, Zhi‐Guo</au><au>Luo, Xiu‐Li</au><au>Liu, Jing</au><au>Rao, Zhi‐Guo</au><au>Zhang, Hui‐Bo</au><au>Wu, Jie</au><au>Li, Lan</au><au>Gong, Hong‐Yun</au><au>Chu, Qian</au><au>Song, Qi‐Bin</au><au>Wang, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study</atitle><jtitle>Cancer science</jtitle><addtitle>Cancer Sci</addtitle><date>2021-06</date><risdate>2021</risdate><volume>112</volume><issue>6</issue><spage>2522</spage><epage>2532</epage><pages>2522-2532</pages><issn>1347-9032</issn><issn>1349-7006</issn><eissn>1349-7006</eissn><abstract>The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID‐19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID‐19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C‐index and time‐dependent area under the receiver operating characteristic curve (t‐AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C‐reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d‐dimer) were significantly associated with symptomatic deterioration. The C‐index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t‐AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low‐risk (total points ≤ 9.98) and high‐risk (total points > 9.98) group. The Kaplan‐Meier deterioration‐free survival of COVID‐19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID‐19 in patients with cancer.
This is the first study to present a nomogram model to individually predict the deterioration of COVID‐19 in cancer patients. Clinical symptoms, computed tomography image features, cancer types, and comorbidities were incorporated in the model. Risk stratification was carried out targeting cancer populations for COVID‐19 deterioration. Cancer type was a critical factor affecting symptomatic deterioration. The COVID‐19 patients with lymphoma had the highest risk score for symptomatic deterioration.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>33728806</pmid><doi>10.1111/cas.14882</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0499-0430</orcidid><orcidid>https://orcid.org/0000-0002-5602-0487</orcidid><orcidid>https://orcid.org/0000-0003-3646-8572</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Cancer science, 2021-06, Vol.112 (6), p.2522-2532 |
| issn | 1347-9032 1349-7006 1349-7006 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8177766 |
| source | Wiley Online Library - AutoHoldings Journals; MEDLINE; DOAJ Directory of Open Access Journals; Wiley Online Library Open Access; PubMed Central |
| subjects | Aged Analysis Area Under Curve Aspartate aminotransferase Bilirubin Cancer Cancer patients Cancer therapies Cell number China Comorbidity Computed tomography Coronaviruses COVID-19 COVID-19 - mortality Decision Support Techniques deterioration Diabetes Disease Disease Progression Disease susceptibility Female Fever Health aspects Humans Hypertension Infections Laboratories Lymphocytes Male Medical prognosis Middle Aged Neoplasms - mortality Neoplasms - virology Nomograms Oncology, Experimental Original Patients Precision Medicine Prediction models Prognosis Regression analysis Retrospective Studies retrospective study Risk Factors Risk groups risk model Serum levels Severe acute respiratory syndrome coronavirus 2 Survival Analysis Urea |
| title | Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T07%3A36%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Individualized%20model%20for%20predicting%20COVID%E2%80%9019%20deterioration%20in%20patients%20with%20cancer:%20A%20multicenter%20retrospective%20study&rft.jtitle=Cancer%20science&rft.au=Xu,%20Bin&rft.date=2021-06&rft.volume=112&rft.issue=6&rft.spage=2522&rft.epage=2532&rft.pages=2522-2532&rft.issn=1347-9032&rft.eissn=1349-7006&rft_id=info:doi/10.1111/cas.14882&rft_dat=%3Cgale_pubme%3EA710741042%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2536837674&rft_id=info:pmid/33728806&rft_galeid=A710741042&rfr_iscdi=true |