Validation of venous thromboembolism predictive model in hematologic malignancies

被引:2
|
作者
Sacerio, Agnerys Lopez [1 ]
Ramon, Mara Caridad Tejeda
Helguera, Aliuska Morales [2 ]
Castillo, Yunierkis Perez [3 ,4 ]
Rodriguez, Javier Cruz [5 ]
Rodriguez, Jorge Felix Guerra [1 ]
Falanga, Anna [6 ,7 ]
机构
[1] Arnaldo Milian Univ Hosp, Hematol Dept, Santa Clara, Cuba
[2] Cent Univ Martha Abreu De Las Villas, Chem Bioact Ctr, Santa Clara, Cuba
[3] Univ Las Amer, Biochemoinformat Grp, Quito, Ecuador
[4] Univ Las Amer, Sch Phys & Math Sci, Quito, Ecuador
[5] Arnaldo Milian Univ Hosp, Surg Dept, Santa Clara, Cuba
[6] Hosp Papa Giovanni XXIII, Dept Transfus Med & Hematol, Bergamo, Italy
[7] Univ Milano Bicocca, Monza, Italy
关键词
Venous thromboembolism; Hematologic malignancy; Risk assessment model; Primary prevention; RISK; THROMBOSIS;
D O I
10.1007/s00277-023-05463-4
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milian University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.
引用
收藏
页码:3613 / 3620
页数:8
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