Nomogram for individualized prediction of inferior vena cava filter retrieval difficulty

Abstract

Inferior vena cava filter (IVCF) could reduce the risk of fatal pulmonary embolism. However, procedural difficulties often exist during IVCF retrieval, requiring extra devices and venous access. Thus, an effective prediction model is essential to predicting the difficulties in preoperative planning, hence aiding efficient intraoperative cooperation. This study retrospectively analyzed 477 cases of IVCF retrievals in the center of the Third Xiangya Hospital of Central South University from 2011 to 2020, among which 344 cases were defined non-difficult retrieval and 133 cases as difficult retrieval (including 35 failed retrievals). The cases before 2017 were classified as training cohort (TC), while the rest as validation cohort (VC). A nomogram was generated to predict IVCF retrieval difficulty with risk factors validated by univariate and multivariate logistic regression analysis. The study then evaluated the model performance with calibration plot, receiver operating characteristic curve (ROC) and decision curve analysis (DCA). It is shown that IVCF retrieval difficulty increases significantly when factors of embedded top of the filter, leg penetration, and irregular anticoagulation occur. Moreover, the nomogram shows the predictive accuracy values of TC and VC are 0.819 and 0.817, respectively. The calibration curve of TC and VC indicates that the model can effectively predict the risk of difficult retrieval. This nomogram has good predictive effect and low generalization error, which can provide evidence for surgical decision of IVCF retrieval.

Keywords

nomogram / inferior vena cava filter retrieval / univariate and multivariate logistic regression analysis / decision curve analysis / hospital information system

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