Background & aims: Venous thromboembolism (VTE) is a recognized complication of acutely ill patients, but its incidence and risk factors in those with cirrhosis are uncertain. Methods: We retrospectively studied a consecutive cohort of cirrhosis patients non-electively admitted to our medical unit to determine the rates of symptomatic VTE during hospitalization. Firstly, we explored associations with baseline, clinical and laboratory characteristics using logistic regression. Secondly, we developed a clinical prediction model that could predict the risk of in-hospital VTE. Results: We included 687 patients (median age 61 years old; 68% male; Child-Pugh A/B/C, 13%/40%/47%). During hospitalization, 34 patients (4.9%) experienced VTE. Multivariate analysis showed that male sex (OR: 2.56, p = 0.05), AKI (OR: 3.1, p = 0.001), bacterial infections (OR: 2.6, p = 0.008), Pugh score (OR: 1.6. p < 0.001), family history of thrombosis (OR: 3.1, p = 0.04), reduced mobility (OR: 4.6, p < 0.001), and C-reactive protein (OR: 1.1, p = 0.005) were independent predictors of VTE. We combined these variables in a prediction model (CirrhosisThrombosisModel) that accurately discriminated between high- and low-risk patients. The AUROC of CiThroModel was significantly higher than that of Padua prediction score (0.882 vs. 0.742). After validating the CiThroModel using bootstrapping, the adjusted model maintained optimal discrimination ability (0.862) and calibration. The adjusted formula to calculate the in-hospital risk of VTE was -9.00 + 0.82 [Male sex] + 1.14 [AKI] + 0.98 [Infection] + 0.48 * Child Pugh score + 1.14 [VTE family history] + 1.54 [Reduced mobility] + 0.15 * PCR/10. Conclusion: The CiThroModel seems a valuable tool for identifying hospitalized patients with cirrhosis at risk of VTE (https://majinzin.shinyapps.io/vterisk/).

CiThroModel Improves Prediction of Symptomatic Venous Thromboembolism in Hospitalized Patients With Cirrhosis Without Hepatocellular Carcinoma

Zanetto, Alberto
;
Vitale, Alessandro
;
Pelizzaro, Filippo;Campello, Elena;Russo, Francesco Paolo;Burra, Patrizia;Simioni, Paolo;
2025

Abstract

Background & aims: Venous thromboembolism (VTE) is a recognized complication of acutely ill patients, but its incidence and risk factors in those with cirrhosis are uncertain. Methods: We retrospectively studied a consecutive cohort of cirrhosis patients non-electively admitted to our medical unit to determine the rates of symptomatic VTE during hospitalization. Firstly, we explored associations with baseline, clinical and laboratory characteristics using logistic regression. Secondly, we developed a clinical prediction model that could predict the risk of in-hospital VTE. Results: We included 687 patients (median age 61 years old; 68% male; Child-Pugh A/B/C, 13%/40%/47%). During hospitalization, 34 patients (4.9%) experienced VTE. Multivariate analysis showed that male sex (OR: 2.56, p = 0.05), AKI (OR: 3.1, p = 0.001), bacterial infections (OR: 2.6, p = 0.008), Pugh score (OR: 1.6. p < 0.001), family history of thrombosis (OR: 3.1, p = 0.04), reduced mobility (OR: 4.6, p < 0.001), and C-reactive protein (OR: 1.1, p = 0.005) were independent predictors of VTE. We combined these variables in a prediction model (CirrhosisThrombosisModel) that accurately discriminated between high- and low-risk patients. The AUROC of CiThroModel was significantly higher than that of Padua prediction score (0.882 vs. 0.742). After validating the CiThroModel using bootstrapping, the adjusted model maintained optimal discrimination ability (0.862) and calibration. The adjusted formula to calculate the in-hospital risk of VTE was -9.00 + 0.82 [Male sex] + 1.14 [AKI] + 0.98 [Infection] + 0.48 * Child Pugh score + 1.14 [VTE family history] + 1.54 [Reduced mobility] + 0.15 * PCR/10. Conclusion: The CiThroModel seems a valuable tool for identifying hospitalized patients with cirrhosis at risk of VTE (https://majinzin.shinyapps.io/vterisk/).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3545876
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