AIM: The appropriate selection of hospitalized patients for venous thromboembolism(VTE) prophylaxis is an important unresolved issue. We sought to validate the Caprini model, a famous individual VTE risk assessment model(RAM), in hospitalized Chinese patients. METHODS: We performed a retrospective case-control study among unselected hospitalized patients admitted to a comprehensive hospital in China. A total of 347 patients were confirmed to have VTE during hospitalization, and 651 controls were randomly selected to match the patients according to medical service. Both the patients and controls were retrospectively assessed for the risk of VTE using the Caprini RAM. RESULTS: The average Caprini cumulative risk score in the patients was significantly higher than that observed in the controls(4.69±2.58 vs 3.16±1.82, p＜0.0001). Compared with that observed in the low-risk group, a classification of high-risk according to the Caprini model was associated with a 1.65-fold increased risk of VTE(95%CI 1.05-2.61), while that of highest-risk was associated with a 4.84-fold increased risk of VTE(95%CI 3.06-7.64). After further stratifying the highest risk level with a cumulative risk score of ≥5 into scores of 5-6, 7-8 and ≥9, the patients with a score of 5-6 were found to exhibit a 3.33-fold increased risk of VTE(95%CI 2.06-5.40), those with a score 7-8 exhibited a 9.41-fold increased risk of VTE(95%CI 4.90-18.08) and those with a score of ≥9 exhibited a 24.69-fold(95%CI 7.98-76.40) increased risk of VTE compared with their low-risk counterparts. CONCLUSIONS: Our study suggests that the Caprini RAM can be used to effectively stratify hospitalized Chinese patients into VTE risk categories based on individual risk factors. The classification of the highest risk level with a cumulative risk score of ≥5 provided significantly more clinical information, and further stratification of this group of patients is needed.
AIM: The appropriate selection of hospitalized patients for venous thromboembolism(VTE) prophylaxis is an important unresolved issue. We sought to validate the Caprini model, a famous individual VTE risk assessment model(RAM), in hospitalized Chinese patients. METHODS: We performed a retrospective case-control study among unselected hospitalized patients admitted to a comprehensive hospital in China. A total of 347 patients were confirmed to have VTE during hospitalization, and 651 controls were randomly selected to match the patients according to medical service. Both the patients and controls were retrospectively assessed for the risk of VTE using the Caprini RAM. RESULTS: The average Caprini cumulative risk score in the patients was significantly higher than that observed in the controls(4.69±2.58 vs 3.16±1.82, p＜0.0001). Compared with that observed in the low-risk group, a classification of high-risk according to the Caprini model was associated with a 1.65-fold increased risk of VTE(95%CI 1.05-2.61), while that of highest-risk was associated with a 4.84-fold increased risk of VTE(95%CI 3.06-7.64). After further stratifying the highest risk level with a cumulative risk score of ≥5 into scores of 5-6, 7-8 and ≥9, the patients with a score of 5-6 were found to exhibit a 3.33-fold increased risk of VTE(95%CI 2.06-5.40), those with a score 7-8 exhibited a 9.41-fold increased risk of VTE(95%CI 4.90-18.08) and those with a score of ≥9 exhibited a 24.69-fold(95%CI 7.98-76.40) increased risk of VTE compared with their low-risk counterparts. CONCLUSIONS: Our study suggests that the Caprini RAM can be used to effectively stratify hospitalized Chinese patients into VTE risk categories based on individual risk factors. The classification of the highest risk level with a cumulative risk score of ≥5 provided significantly more clinical information, and further stratification of this group of patients is needed.
Authors: Andrea J Darzi; Allen B Repp; Frederick A Spencer; Rami Z Morsi; Rana Charide; Itziar Etxeandia-Ikobaltzeta; Kenneth A Bauer; Allison E Burnett; Mary Cushman; Francesco Dentali; Susan R Kahn; Suely M Rezende; Neil A Zakai; Arnav Agarwal; Samer G Karam; Tamara Lotfi; Wojtek Wiercioch; Reem Waziry; Alfonso Iorio; Elie A Akl; Holger J Schünemann Journal: Blood Adv Date: 2020-10-13
Authors: Marcos de Bastos; Sandhi M Barreto; Jackson S Caiafa; Tânia Boguchi; José Luiz Padilha Silva; Suely M Rezende Journal: J Thromb Thrombolysis Date: 2016-05 Impact factor: 2.300