Development and internal validation of the Cleveland Clinic Bleeding Model to predict major bleeding risk at admission in medical inpatients.

Autor:	Mittman BG; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA. Electronic address: bg.mittman@gmail.com., Sheehan M; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA., Kojima L; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA., Casacchia NJ; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA., Lisheba O; Enterprise Analytics eResearch Department, Cleveland Clinic, Cleveland, Ohio, USA., Hu B; Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA., Pappas MA; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA., Rothberg MB; Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA.
Jazyk:	angličtina
Zdroj:	Journal of thrombosis and haemostasis : JTH [J Thromb Haemost] 2024 Oct; Vol. 22 (10), pp. 2855-2863. Date of Electronic Publication: 2024 Jul 11.
DOI:	10.1016/j.jtha.2024.06.025
Abstrakt:	Background: Guidelines recommend pharmacologic VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment. Objectives: We developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model with IMPROVE. Methods: We selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Haemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a least absolute shrinkage selection operator logistic regression model in the training set and compared the discrimination and calibration of our model with the IMPROVE model in the validation set. Results: Among 46 314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (odds ratio [OR] = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model had better discrimination than IMPROVE (area under the receiver operating characteristics curve = 0.85 vs 0.70; P < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high risk (7.2% vs 11.8%; P < .001). Calibration was adequate (Brier score = 0.0057). Conclusion: Using a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model. Competing Interests: Declaration of competing interests Authors have no conflicts of interest to declare. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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