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Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population
Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were ret...
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| Published in: | Scientific reports 2024-10, Vol.14 (1), p.24259-11, Article 24259 |
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| creator | Li, Wenbo Liu, Xiaonan Liu, Yang Liu, Jie Guo, Qirui Li, Jing Zheng, Wei Zhang, Longyou Zhang, Ying Hong, Yin Wang, Anxin Zheng, Huaguang |
| description | Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model’s discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted
P
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| doi_str_mv | 10.1038/s41598-024-74393-6 |
| format | article |
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P
< 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73–0.82) and 0.65 (95% CI: 0.56–0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-024-74393-6</identifier><identifier>PMID: 39414835</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/308 ; 692/499 ; 692/53 ; 692/617 ; 692/699 ; 692/700 ; Adult ; Aged ; Arteriosclerosis ; Asymptomatic ; Asymptomatic Diseases ; Asymptomatic intracranial atherosclerotic stenosis ; Atherosclerosis ; Blood Pressure ; Calibration ; Female ; Humanities and Social Sciences ; Humans ; Hypertension ; Hypertension - diagnosis ; Hypertension - epidemiology ; Intracranial Arteriosclerosis - diagnosis ; Intracranial Arteriosclerosis - epidemiology ; LASSO-Logistic regression ; Male ; Middle Aged ; multidisciplinary ; Neurologically healthy population ; Nomogram ; Nomograms ; Population studies ; Regression analysis ; Retrospective Studies ; Risk Factors ; ROC Curve ; Science ; Science (multidisciplinary) ; Stenosis</subject><ispartof>Scientific reports, 2024-10, Vol.14 (1), p.24259-11, Article 24259</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. 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><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c422t-f8fd039287f8963f7a8bc5a14e505d8f769e61366d3c00d961541865632841b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3117209716/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3117209716?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39414835$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Wenbo</creatorcontrib><creatorcontrib>Liu, Xiaonan</creatorcontrib><creatorcontrib>Liu, Yang</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><creatorcontrib>Guo, Qirui</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Zheng, Wei</creatorcontrib><creatorcontrib>Zhang, Longyou</creatorcontrib><creatorcontrib>Zhang, Ying</creatorcontrib><creatorcontrib>Hong, Yin</creatorcontrib><creatorcontrib>Wang, Anxin</creatorcontrib><creatorcontrib>Zheng, Huaguang</creatorcontrib><title>Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model’s discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted
P
< 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73–0.82) and 0.65 (95% CI: 0.56–0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population.</description><subject>692/308</subject><subject>692/499</subject><subject>692/53</subject><subject>692/617</subject><subject>692/699</subject><subject>692/700</subject><subject>Adult</subject><subject>Aged</subject><subject>Arteriosclerosis</subject><subject>Asymptomatic</subject><subject>Asymptomatic Diseases</subject><subject>Asymptomatic intracranial atherosclerotic stenosis</subject><subject>Atherosclerosis</subject><subject>Blood Pressure</subject><subject>Calibration</subject><subject>Female</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypertension - diagnosis</subject><subject>Hypertension - epidemiology</subject><subject>Intracranial Arteriosclerosis - diagnosis</subject><subject>Intracranial Arteriosclerosis - epidemiology</subject><subject>LASSO-Logistic regression</subject><subject>Male</subject><subject>Middle Aged</subject><subject>multidisciplinary</subject><subject>Neurologically healthy population</subject><subject>Nomogram</subject><subject>Nomograms</subject><subject>Population studies</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Stenosis</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kk1v1DAQhiMEolXpH-CAInHhEvB37BNCFR-VKrjA2Zq1naxXjp3aCdL--3p3S2k54INted55PB6_TfMao_cYUfmhMMyV7BBhXc-oop141pwTxHhHKCHPH-3PmstSdqgOThTD6mVzRuvKJOXnze33NKUxw9QOKbdzdtabxcexhbKf5iVNsHjT-rhkMBmih9DCsnU5FRPqfAiWxcVUfKmqFtro1pxCGr2BEPbt1kFYtvt2TvMaKivFV82LAUJxl_frRfPry-efV9-6mx9fr68-3XSGEbJ0gxwsoorIfpBK0KEHuTEcMHMccSuHXignMBXCUoOQVQJzhqXgghLJ8AbTi-b6xLUJdnrOfoK81wm8Ph6kPGrItf7gNO57IJIDYdIytqGKc2e5GQzGUlqFKuvjiTWvm8lZ4w79CE-gTyPRb_WYfmtc28wUJ5Xw7p6Q0-3qyqInX4wLAaJLa9EU417UOiSr0rf_SHdpzbH26qgiSPVYVBU5qUz9i5Ld8FANRvrgEH1yiK4O0UeH6EPSm8fveEj544cqoCdBqaE4uvz37v9g7wBk68ga</recordid><startdate>20241016</startdate><enddate>20241016</enddate><creator>Li, Wenbo</creator><creator>Liu, Xiaonan</creator><creator>Liu, Yang</creator><creator>Liu, Jie</creator><creator>Guo, Qirui</creator><creator>Li, Jing</creator><creator>Zheng, Wei</creator><creator>Zhang, Longyou</creator><creator>Zhang, Ying</creator><creator>Hong, Yin</creator><creator>Wang, Anxin</creator><creator>Zheng, Huaguang</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20241016</creationdate><title>Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population</title><author>Li, Wenbo ; Liu, Xiaonan ; Liu, Yang ; Liu, Jie ; Guo, Qirui ; Li, Jing ; Zheng, Wei ; Zhang, Longyou ; Zhang, Ying ; Hong, Yin ; Wang, Anxin ; Zheng, Huaguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-f8fd039287f8963f7a8bc5a14e505d8f769e61366d3c00d961541865632841b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>692/308</topic><topic>692/499</topic><topic>692/53</topic><topic>692/617</topic><topic>692/699</topic><topic>692/700</topic><topic>Adult</topic><topic>Aged</topic><topic>Arteriosclerosis</topic><topic>Asymptomatic</topic><topic>Asymptomatic Diseases</topic><topic>Asymptomatic intracranial atherosclerotic stenosis</topic><topic>Atherosclerosis</topic><topic>Blood Pressure</topic><topic>Calibration</topic><topic>Female</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Hypertension - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Wenbo</au><au>Liu, Xiaonan</au><au>Liu, Yang</au><au>Liu, Jie</au><au>Guo, Qirui</au><au>Li, Jing</au><au>Zheng, Wei</au><au>Zhang, Longyou</au><au>Zhang, Ying</au><au>Hong, Yin</au><au>Wang, Anxin</au><au>Zheng, Huaguang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2024-10-16</date><risdate>2024</risdate><volume>14</volume><issue>1</issue><spage>24259</spage><epage>11</epage><pages>24259-11</pages><artnum>24259</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model’s discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted
P
< 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73–0.82) and 0.65 (95% CI: 0.56–0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>39414835</pmid><doi>10.1038/s41598-024-74393-6</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 692/308 692/499 692/53 692/617 692/699 692/700 Adult Aged Arteriosclerosis Asymptomatic Asymptomatic Diseases Asymptomatic intracranial atherosclerotic stenosis Atherosclerosis Blood Pressure Calibration Female Humanities and Social Sciences Humans Hypertension Hypertension - diagnosis Hypertension - epidemiology Intracranial Arteriosclerosis - diagnosis Intracranial Arteriosclerosis - epidemiology LASSO-Logistic regression Male Middle Aged multidisciplinary Neurologically healthy population Nomogram Nomograms Population studies Regression analysis Retrospective Studies Risk Factors ROC Curve Science Science (multidisciplinary) Stenosis |
| title | Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population |
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