{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:30:02Z","timestamp":1740169802727,"version":"3.37.3"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Medical and Health Science and Technology Plan Project of Zhejiang Province","award":["2018RC079","JY2017205","20163011","2017jzk05"],"award-info":[{"award-number":["2018RC079","JY2017205","20163011","2017jzk05"]}]},{"name":"Youth Research Fund of Jinhua Hospital of Zhejiang University","award":["2018RC079","JY2017205","20163011","2017jzk05"],"award-info":[{"award-number":["2018RC079","JY2017205","20163011","2017jzk05"]}]},{"name":"Science and Technology Key Project of Jinhua City","award":["2018RC079","JY2017205","20163011","2017jzk05"],"award-info":[{"award-number":["2018RC079","JY2017205","20163011","2017jzk05"]}]},{"name":"Chinese Medicine Science and Technology project of Jinhua City","award":["2018RC079","JY2017205","20163011","2017jzk05"],"award-info":[{"award-number":["2018RC079","JY2017205","20163011","2017jzk05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2019,2,26]]},"abstract":"<jats:p>Endobronchial biopsy (EBB)-induced bleeding is fairly common; however, it can be potentially life-threatening due to difficult hemostasis following EBB. The aim of this study was to develop a predictive model of difficult hemostasis post-EBB. A total of 620 consecutive patients with primary lung cancer who had undergone EBB between 2014 and 2018 in a large tertiary hospital were enrolled in this retrospective single-center cohort study. Patients were classified into the difficult hemostasis group and the nondifficult hemostasis group according to hemostatic measures used following EBB. The LASSO regression method was used to select predictors and multivariate logistic regression was applied to develop the predictive model. The area under the curve (AUC) of the model was calculated. Bootstrapping method was applied for internal validation. Calibration curve analysis and decision curve analysis (DCA) were also performed. A nomogram was constructed to display the model. The incidence of difficult hemostasis post-EBB was 11.9% (74\/620). Eight variables were selected by the LASSO regression analysis and seven (histological type of cancer, lesion location, neutrophil percentage, activated partial thromboplastin time, low density lipoprotein cholesterol, apolipoprotein-E, and pulmonary infection) of them were finally included in the predictive model. The AUC of the model was 0.822 (95% CI, 0.777-0.868), and it was 0.808 (95% CI, 0.761-0.856) in the internal validation. The predictive model was well calibrated and DCA indicated its potential clinical usefulness, which suggests that the model has great potential to predict lung cancer patients with a more difficult post-EBB hemostasis.<\/jats:p>","DOI":"10.1155\/2019\/1656890","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T18:31:35Z","timestamp":1551205895000},"page":"1-7","source":"Crossref","is-referenced-by-count":3,"title":["Development of a Predictive Model of Difficult Hemostasis following Endobronchial Biopsy in Lung Cancer Patients"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1749-1200","authenticated-orcid":true,"given":"Saibin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Respiratory Medicine, Jinhua Municipal Central Hospital, No. 365, East Renmin Road, Jinhua 321000, Zhejiang Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.06.00013906"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.2147\/CMAR.S164315"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1378\/chest.127.6.1875"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.4103\/1817-1737.69117"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.12439"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1378\/chest.122.4.1461"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1378\/chest.115.2.397"},{"issue":"3","key":"8","first-page":"243","volume":"54","year":"2006","journal-title":"T\u00fcberk\u00fcloz ve Toraks"},{"issue":"1","key":"9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1186\/s12944-018-0821-6","volume":"17","year":"2018","journal-title":"Lipids in Health and Disease"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1097\/LBR.0000000000000102"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1159\/000085370"},{"issue":"53","key":"12","volume":"8","year":"2008","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.7326\/M14-0697"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.5588\/ijtld.11.0030"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0052-2017"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1378\/chest.100.4.1141"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1378\/chest.70.5.584"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2013-203618"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1080\/00015458.2005.11679793"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.65.9128"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1177\/0272989x06295361"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(14)71116-7"}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2019\/1656890.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2019\/1656890.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2019\/1656890.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T18:31:45Z","timestamp":1551205905000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/bmri\/2019\/1656890\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,26]]},"references-count":23,"alternative-id":["1656890","1656890"],"URL":"https:\/\/doi.org\/10.1155\/2019\/1656890","relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"type":"print","value":"2314-6133"},{"type":"electronic","value":"2314-6141"}],"subject":[],"published":{"date-parts":[[2019,2,26]]}}}