Annals of vascular surgery · 2026
Predictive Model for Thrombosis in Mature Autologous Arteriovenous Fistula.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhang Yan, Liu Fang, Li Xuejiao, Xu Yongcang, Jiang Huili
Аннотация
BACKGROUND: Based on the "high-risk stenosis-thrombosis" theory, this study developed a risk prediction model for thrombosis in mature autogenous arteriovenous fistulas (AVFs).
METHODS: Clinical data from 521 maintenance hemodialysis patients treated at the vascular access clinic of Central Hospital Affiliated to Shandong First Medical University between January 2023 and December 2023 were retrospectively analyzed. The dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Logistic regression (LR) and extreme gradient boosting (XGBoost) prediction models were constructed using the training cohort, and their predictive performance was subsequently compared in the validation set.
RESULTS: The results indicated that recent fistula dysfunction within the past month, large short-term fluctuations in diastolic blood pressure during dialysis, low hemoglobin levels, a history of 2 or more prior AVF failures, and regional or buttonhole cannulation were identified as risk factors for AVF thrombosis. The XGBoost model outperformed the LR model across all performance metrics in the training set. However, in the validation set, the LR model achieved a slightly higher area under the curve (AUC) of 0.955 (95% CI: 0.924-0.985) compared with the XGBoost model's AUC of 0.954 (95% CI: 0.921-0.988).
CONCLUSION: Although both the LR and XGBoost models showed favorable predictive performance, the LR model is recommended as a baseline screening tool for identifying patients at high risk of AVF thrombosis, given the clinical principle of favoring an appropriate over a more complex model.
METHODS: Clinical data from 521 maintenance hemodialysis patients treated at the vascular access clinic of Central Hospital Affiliated to Shandong First Medical University between January 2023 and December 2023 were retrospectively analyzed. The dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Logistic regression (LR) and extreme gradient boosting (XGBoost) prediction models were constructed using the training cohort, and their predictive performance was subsequently compared in the validation set.
RESULTS: The results indicated that recent fistula dysfunction within the past month, large short-term fluctuations in diastolic blood pressure during dialysis, low hemoglobin levels, a history of 2 or more prior AVF failures, and regional or buttonhole cannulation were identified as risk factors for AVF thrombosis. The XGBoost model outperformed the LR model across all performance metrics in the training set. However, in the validation set, the LR model achieved a slightly higher area under the curve (AUC) of 0.955 (95% CI: 0.924-0.985) compared with the XGBoost model's AUC of 0.954 (95% CI: 0.921-0.988).
CONCLUSION: Although both the LR and XGBoost models showed favorable predictive performance, the LR model is recommended as a baseline screening tool for identifying patients at high risk of AVF thrombosis, given the clinical principle of favoring an appropriate over a more complex model.
Предметные рубрики MeSH
HumansArteriovenous Shunt, SurgicalRisk FactorsThrombosisRenal DialysisGraft Occlusion, VascularPredictive Value of TestsRisk AssessmentRetrospective StudiesFemaleDecision Support TechniquesMaleMiddle AgedTreatment OutcomeVascular PatencyReproducibility of ResultsBoosting Machine Learning AlgorithmsTime FactorsAgedAdultPrediction AlgorithmsPredictive Learning Models