Runnan Shen1, Ming Gao2,1, Yangu Tao3,1, Qinchang Chen4,1, Guitao Wu1, Xushun Guo1, Zuqi Xia1, Guochang You1, Zilin Hong1, Kai Huang5,6. 1. Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Rd.2, Guangzhou, 510080, Guangdong Province, China. 2. Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 33, Yingfeng Road, Haizhu District, Guangzhou, 510000, Guangdong Province, China. 3. Department of Traditional Chinese Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 33, Yingfeng Road, Haizhu District, Guangzhou, 510000, Guangdong Province, China. 4. The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Rd.2, Guangzhou, 510080, Guangdong Province, China. 5. Department of Cardiovascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 33, Yingfeng Road, Haizhu District, Guangzhou, 510000, Guangdong Province, China. huangk37@mail.sysu.edu.cn. 6. Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Rd.2, Guangzhou, 510080, Guangdong Province, China. huangk37@mail.sysu.edu.cn.
Abstract
BACKGROUND: We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU). METHODS: Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation. RESULTS: We obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI] > 0; Integrated discrimination improvement [IDI] > 0; P < 0.05). The Brier scores of two new models in training set were 0.091 and 0.108. After internal validation, corrected area under the curves for two models were 0.850 and 0.830, while corrected Brier scores were 0.108 and 0.114. The more concise model was chosen to make the nomogram. CONCLUSIONS: The nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.
BACKGROUND: We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU). METHODS: Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation. RESULTS: We obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI] > 0; Integrated discrimination improvement [IDI] > 0; P < 0.05). The Brier scores of two new models in training set were 0.091 and 0.108. After internal validation, corrected area under the curves for two models were 0.850 and 0.830, while corrected Brier scores were 0.108 and 0.114. The more concise model was chosen to make the nomogram. CONCLUSIONS: The nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.
Entities:
Keywords:
Deep vein thrombosis; Intensive care unit; Nomogram; Prognosis
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