Jiaping Huai1, Xiaohua Ye2, Jin Ding2. 1. Department of Critical Care Medicine, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang, People's Republic of China. 2. Department of Gastroenterology, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang, People's Republic of China.
Abstract
BACKGROUND: Delayed colorectal post-polypectomy bleeding (PPB) is a fairly common complication after polypectomy. The present study aimed to build a novel nomogram-based model of delayed PPB. METHODS: A cohort of 2494 patients who had undergone colonoscopic polypectomy between January 2016 and April 2020 were consecutively enrolled. The patient demographics, polyp characteristics, laboratory factors, and pathological parameters were collected. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting potential variables. Multivariate logistic regression was used to develop the nomogram. A bootstrapping method was employed for internal validation. The performance of the nomogram was evaluated on the basis of its calibration, discrimination, and clinical usefulness. RESULTS: Of 2494 patients undergoing colonoscopic polypectomy, 40 (1.6%) developed delayed PPB. The LASSO regression identified 6 variables (age, gender, polyp location, polyp morphology, antithrombotic medication use, and modality of polypectomy), and a predictive model was subsequently established. The area under the curve (AUC) of the predictive model and the internal validation were 0.838 (95% CI: 0.775-0.900) and 0.824 (95% CI: 0.759-0.889), respectively. The predictive model provided acceptable calibration, and a decision curve analysis (DCA) showed its clinical utility. CONCLUSION: This predictive model may enable clinicians to predict the risk of delayed PPB and optimize preoperative decision-making, for effective treatment.
BACKGROUND: Delayed colorectal post-polypectomy bleeding (PPB) is a fairly common complication after polypectomy. The present study aimed to build a novel nomogram-based model of delayed PPB. METHODS: A cohort of 2494 patients who had undergone colonoscopic polypectomy between January 2016 and April 2020 were consecutively enrolled. The patient demographics, polyp characteristics, laboratory factors, and pathological parameters were collected. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting potential variables. Multivariate logistic regression was used to develop the nomogram. A bootstrapping method was employed for internal validation. The performance of the nomogram was evaluated on the basis of its calibration, discrimination, and clinical usefulness. RESULTS: Of 2494 patients undergoing colonoscopic polypectomy, 40 (1.6%) developed delayed PPB. The LASSO regression identified 6 variables (age, gender, polyp location, polyp morphology, antithrombotic medication use, and modality of polypectomy), and a predictive model was subsequently established. The area under the curve (AUC) of the predictive model and the internal validation were 0.838 (95% CI: 0.775-0.900) and 0.824 (95% CI: 0.759-0.889), respectively. The predictive model provided acceptable calibration, and a decision curve analysis (DCA) showed its clinical utility. CONCLUSION: This predictive model may enable clinicians to predict the risk of delayed PPB and optimize preoperative decision-making, for effective treatment.
Authors: K Tim Buddingh; Thomas Herngreen; Jelle Haringsma; Wil C van der Zwet; Frank P Vleggaar; Ronald Breumelhof; Frank Ter Borg Journal: Am J Gastroenterol Date: 2011-01-25 Impact factor: 10.864
Authors: Ann G Zauber; Sidney J Winawer; Michael J O'Brien; Iris Lansdorp-Vogelaar; Marjolein van Ballegooijen; Benjamin F Hankey; Weiji Shi; John H Bond; Melvin Schapiro; Joel F Panish; Edward T Stewart; Jerome D Waye Journal: N Engl J Med Date: 2012-02-23 Impact factor: 91.245
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