Development and validation of a predictive model for bleeding after peripheral vascular intervention: A report from the National Cardiovascular Data Registry Peripheral Vascular Interventions Registry.

Journal Article (Journal Article)

OBJECTIVES: To develop a model to predict risk of in-hospital bleeding following endovascular peripheral vascular intervention. BACKGROUND: Peri-procedural bleeding is a common, potentially preventable complication of catheter-based peripheral vascular procedures and is associated with increased mortality. We used the National Cardiovascular Data Registry (NCDR) Peripheral Vascular Interventions (PVI) Registry to develop a novel risk-prediction model to identify patients who may derive the greatest benefit from application of strategies to prevent bleeding. METHODS: We examined all patients undergoing lower extremity PVI at 76 NCDR PVI hospitals from 2014 to 2017. Patients with acute limb ischemia (n = 1600) were excluded. Major bleeding was defined as overt bleeding with a hemoglobin (Hb) drop of ≥ 3 g/dl, any Hb decline of ≥ 4 g/dl, or a blood transfusion in patients with pre-procedure Hb ≥ 8 g/dl. Hierarchical multivariable logistic regression was used to develop a risk model to predict major bleeding. Model validation was performed using 1000 bootstrapped replicates of the population after sampling with replacement. RESULTS: Among 25,382 eligible patients, 1017 (4.0%) developed major bleeding. Predictors of bleeding included age, female sex, critical limb ischemia, non-femoral access, prior heart failure, and pre-procedure hemoglobin. The model demonstrated good discrimination (optimism corrected c-statistic = 0.67), calibration (corrected slope = 0.98, intercept of -0.04) and range of predicted risk (1%-18%). CONCLUSIONS: Post-procedural PVI bleeding risk can be predicted based upon pre- and peri-procedural patient characteristics. Further studies are needed to determine whether this model can be utilized to improve procedural safety through developing and targeting bleeding avoidance strategies.

Full Text

Duke Authors

Cited Authors

  • Salisbury, AC; Safley, DM; Kennedy, KF; Bhardwaj, B; Aronow, HD; Jones, WS; Feldman, DN; Secemsky, E; Tsai, TT; Attaran, RR; Spertus, JA

Published Date

  • December 1, 2021

Published In

Volume / Issue

  • 98 / 7

Start / End Page

  • 1363 - 1372

PubMed ID

  • 34569709

Electronic International Standard Serial Number (EISSN)

  • 1522-726X

Digital Object Identifier (DOI)

  • 10.1002/ccd.29961

Language

  • eng

Conference Location

  • United States