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. 2021 Jul 21:8:783-794.
doi: 10.2147/JHC.S311970. eCollection 2021.

Construction and Validation of a Nomogram for Predicting the Risk of Deep Vein Thrombosis in Hepatocellular Carcinoma Patients After Laparoscopic Hepatectomy: A Retrospective Study

Yao Chen #  1 , Jianping Zhao #  1 , Zhanguo Zhang  1 , Zeyang Ding  1 , Yifa Chen  1 , Xiaoping Chen  1 , Wanguang Zhang  1
Affiliations

Construction and Validation of a Nomogram for Predicting the Risk of Deep Vein Thrombosis in Hepatocellular Carcinoma Patients After Laparoscopic Hepatectomy: A Retrospective Study

Yao Chen et al. J Hepatocell Carcinoma. .

Abstract

Background: The incidence of deep vein thrombosis (DVT) in hepatocellular carcinoma (HCC) patients after laparoscopic hepatectomy (LH) is unclear, and there is no effective method for DVT risk assessment in these patients.

Methods: The data from the total of 355 consecutive HCC patients who underwent LH were included. A DVT risk algorithm was developed using a training set (TS) of 243 patients, and its predictive performance was evaluated in both the TS and a validation set (VS) of 112 patients. The model was then used to develop a DVT risk nomogram (TRN).

Results: The incidence of DVT in the present study was 18.6%. Age, sex, body mass index (BMI), comorbidities and operative position were independent risk factors for DVT in the TS. The model based on these factors had a good predictive ability. In the TS, it had an area under the receiver operating characteristic (AUC) curve of 0.861, Hosmer-Lemeshow (H-L) goodness of fit p value of 0.626, sensitivity of 44.4%, specificity of 96.5%, positive predictive value (PPV) of 74.1%, negative predictive value (NPV) of 88.4%, and accuracy of 86.8%. In the VS, it had an AUC of 0.818, H-L p value of 0.259, sensitivity of 38.1%, specificity of 98.9%, PPV of 88.9%, NPV of 87.4%, and accuracy of 87.5%. The TRN performed well in both the internal and the external validation, indicating a good clinical application value. The TRN had a better predictive value of DVT than the Caprini score (p < 0.001).

Conclusion: The incidence of DVT after LH was high, and should not be neglected in HCC patients. The TRN provides an efficacious method for DVT risk evaluation and individualized pharmacological thromboprophylaxis.

Keywords: deep vein thrombosis; hepatocellular carcinoma; laparoscopic hepatectomy; nomogram.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Flowchart of study design.
Figure 2
Figure 2
The ROC curves of DVT risk score algorithm and Caprini score.
Figure 3
Figure 3
The nomogram based on the DVT risk score algorithm and the calibration curves of the TRN. The nomogram based on DVT risk score algorithm (A), the calibration curves of TRN in the TS (B) and in the VS (C). To use the TRN, the predicted DVT risk is on the scale of 0 to 400, and a vertical line is drawn upward to the points line to determine the score received for each variable (eg sex female = 100 points). Then the total score is obtained, which corresponds to a predictive probability of DVT on the predicted incidence line at the bottom of the nomogram.
Figure 4
Figure 4
The decision curve analysis for TRN and Caprini score.
See this image and copyright information in PMC

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