Nomogram for the Prediction of Delayed Colorectal Post-Polypectomy Bleeding
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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.
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.