Thromb Haemost 2022; 122(09): 1558-1566
DOI: 10.1055/a-1754-7551
Stroke, Systemic or Venous Thromboembolism

Bleeding Risk Assessment in End-Stage Kidney Disease: Validation of Existing Risk Scores and Evaluation of a Machine Learning-Based Approach

Stephan Nopp
1   Clinical Division of Haematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
,
Clemens P. Spielvogel
2   Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
3   Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
,
Sabine Schmaldienst
4   Department of Medicine I, Clinic Favoriten, Vienna, Austria
,
Renate Klauser-Braun
5   Department of Medicine III, Clinic Donaustadt, Vienna, Austria
,
Matthias Lorenz
6   Vienna Dialysis Centre, Vienna, Austria
,
Benedikt N. Bauer
1   Clinical Division of Haematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
,
Ingrid Pabinger
1   Clinical Division of Haematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
,
Marcus Säemann
7   Department of Medicine VI, Clinic Ottakring, Vienna, Austria
,
Oliver Königsbrügge
1   Clinical Division of Haematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
,
Cihan Ay
1   Clinical Division of Haematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
› Author Affiliations
Funding The VIVALDI study was supported by an unrestricted grant from the Austrian National Bank (Jubiläumsfond Österreichische Nationalbank, Project Number 16433), Joseph-Skoda-Prize of the Austrian Association of Internal Medicine (ÖGIM), and the Austrian Science Fund (FWF), Special Research Program (SFB) 54.

Abstract

Background Patients with end-stage kidney disease (ESKD) on hemodialysis (HD) are at increased risk for bleeding. However, despite relevant clinical implications regarding dialysis modalities or anticoagulation, no bleeding risk assessment strategy has been established in this challenging population.

Methods Analyses on bleeding risk assessment models were performed in the population-based Vienna InVestigation of Atrial fibrillation and thromboemboLism in patients on hemoDialysIs (VIVALDI) study including 625 patients. In this cohort study, patients were prospectively followed for a median observation period of 3.5 years for the occurrence of major bleeding. First, performances of existing bleeding risk scores (i.e., HAS-BLED, HEMORR2HAGES, ATRIA, and four others) were evaluated in terms of discrimination and calibration. Second, four machine learning-based prediction models that included clinical, dialysis-specific, and laboratory parameters were developed and tested using Monte Carlo cross-validation.

Results Of 625 patients (median age: 66 years, 37% women), 89 (14.2%) developed major bleeding, with a 1-year, 2-year, and 3-year cumulative incidence of 6.1% (95% confidence interval [CI]: 4.2–8.0), 10.3% (95% CI: 8.0–12.8), and 13.5% (95% CI: 10.8–16.2), respectively. C-statistics of the seven contemporary bleeding risk scores ranged between 0.54 and 0.59 indicating poor discriminatory performance. The HAS-BLED score showed the highest C-statistic of 0.59 (95% CI: 0.53–0.66). Similarly, all four machine learning-based predictions models performed poorly in internal validation (C-statistics ranging from 0.49 to 0.55).

Conclusion Existing bleeding risk scores and a machine learning approach including common clinical parameters fail to assist in bleeding risk prediction of patients on HD. Therefore, new approaches, including novel biomarkers, to improve bleeding risk prediction in patients on HD are needed.

Supplementary Material



Publication History

Received: 08 November 2021

Accepted: 27 January 2022

Accepted Manuscript online:
28 January 2022

Article published online:
19 June 2022

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