ArticleBMC cancer2024
The Alfalfa-inpatient-CAT assessment model: a thrombotic risk assessment model for inpatient cancer patients.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a risk assessment model for hospital-acquired venous thrombosis in medical in-patients with cancer.Journal of thrombosis and haemostasis : JTH · 2026Article
- Development of a risk prediction model for central venous catheter insertion-related thrombosis in critically ill pediatric patients.Frontiers in pediatrics · 2026Article
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Authors and funding
8 authors.
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Abstract
purposeTo construct a venous thromboembolism (VTE) risk assessment model specifically for inpatients with cancer.
methodPatients were included according to the inclusion criteria. Univariate and multivariate analyses of all variables were included to develop a VTE risk assessment model applicable to the derivation cohort. Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were used to test the fit degree and identification validity of the model. The patient data from separate validation cohorts verified the external population.
resultA total of 944 cancer patients were included in this study. Alfalfa-inpatient-CAT model, a risk assessment model for VTE in hospitalized cancer patients, was established, which mainly includes hypertension, surgical history (nearly one month), history of VTE, peripherally inserted central venous catheters (PICC), chemotherapy, PT < 12.85 s, D-dimer ≥ 1.805 μg/mL, hemoglobin ≤ 114.5 g/L, CRP ≥ 7.575 mg/L. Hosmer-Lemeshow test results showed P = 0.353 > 0.05, (χ
conclusionThis study developed a new VTE risk assessment model - Alfalfa-inpatient-CAT model - for hospitalized cancer patients at high risk of thrombosis. This model has a good fitting degree and discriminant validity. It is expected to provide some reference for the clinical treatment of inpatients with cancer through continuous optimization.
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