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. 2023 Dec 14:14:1242132.
doi: 10.3389/fphys.2023.1242132. eCollection 2023.

Nomogram model combined thrombelastography for venous thromboembolism risk in patients undergoing lung cancer surgery

Da Qin  1   2 , Hongfei Cai  1 , Qing Liu  1 , Tianyu Lu  1 , Ze Tang  1 , Yuhang Shang  1 , Youbin Cui  1   2 , Rui Wang  1   2
Affiliations

Nomogram model combined thrombelastography for venous thromboembolism risk in patients undergoing lung cancer surgery

Da Qin et al. Front Physiol. .

Abstract

Background: The aim of this study was to develop a nomogram model in combination with thromboelastography (TEG) to predict the development of venous thromboembolism (VTE) after lung cancer surgery. Methods: The data of 502 patients who underwent surgical treatment for lung cancer from December 2020 to December 2022 were retrospectively analyzed. Patients were then randomized into training and validation groups. Univariate and multivariate logistic regression analyses were carried out in the training group and independent risk factors were included in the nomogram to construct risk prediction models. The predictive capability of the model was assessed by the consistency index (C-index), receiver operating characteristic curves (ROC), the calibration plot and decision curve analysis (DCA). Results: The nomogram risk prediction model comprised of the following five independent risk factors: age, operation time, forced expiratory volume in one second and postoperative TEG parameters k value(K) and reaction time(R). The nomogram model demonstrated better predictive power than the modified Caprini model, with the C-index being greater. The calibration curve verified the consistency of nomogram between the two groups. Furthermore, DCA demonstrated the clinical value and potential for practical application of the nomogram. Conclusion: This study is the first to combine TEG and clinical risk factors to construct a nomogram to predict the occurrence of VTE in patients after lung cancer surgery. This model provides a simple and user-friendly method to assess the probability of VTE in postoperative lung cancer patients, enabling clinicians to develop individualized preventive anticoagulation strategies to reduce the incidence of such complications.

Keywords: decision curve analysis; lung; surgery; tumor models; venous thrombosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Include exclusion flow chart. VTE, venous thromboembolism.
FIGURE 2
FIGURE 2
Nomogram for predicting postoperative VTE in patients with lung cancer. Post_K, postoperative TEG parameters k value; Post_R, postoperative TEG parameters reaction time; FEV1, forced expiratory volume in one second.
FIGURE 3
FIGURE 3
Receiver operating characteristic (ROC) curves in training and validation cohorts. (A) The area under the ROC curve for the nomogram (blue line), nomogram consisting of Caprini scores and TEG parameters (green line), modified Caprini model (red line) in the training cohort were 0.913 (0.867–0.958), 0.882 (0.827–0.936) and 0.681 (0.600–0.761), respectively. (B) The area under the ROC curve for the nomogram (blue line), nomogram consisting of Caprini scores and TEG parameters (green line), modified Caprini model (red line) in the validation cohort were 0.955 (0.917–0.993), 0.925 (0.875–0.975) and 0.728 (0.630–0.826), respectively.
FIGURE 4
FIGURE 4
Clinical impact curve of nomogram in training cohort.
FIGURE 5
FIGURE 5
The calibration plot of nomogram in training and validation cohorts (A,B). Bootstrapping method with 1000 resamples was utilized.
FIGURE 6
FIGURE 6
Decision curve analysis of nomogram and the modified Caprini model.
See this image and copyright information in PMC

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References

    1. Agzarian J., Hanna W. C., Schneider L., Schieman C., Finley C. J., Peysakhovich Y., et al. (2016). Postdischarge venous thromboembolic complications following pulmonary oncologic resection: an underdetected problem. thorac cardiov 151 (4), 992–999. 10.1016/j.jtcvs.2015.11.038 - DOI - PubMed
    1. Artang R., Brod C., Nielsen J. D. (2022). Application of activators ecarin and factor xa in thrombelastography for measurement of anticoagulant effect of direct oral anticoagulants using TEG 5000. Semin. Thromb. Hemost. 48 (7), 808–813. 10.1055/s-0042-1756699 - DOI - PubMed
    1. Bischof D., Dalbert S., Zollinger A., Ganter M. T., Gantner M. T., Hofer C. K. (2010). Thrombelastography in the surgical patient. Minerva Anestesiol. 76 (2), 131–137. - PubMed
    1. Cai Y., Dong H., Li X., Liu Y., Hu B., Li H., et al. (2023). Development and validation of a nomogram to assess postoperative venous thromboembolism risk in patients with stage IA non-small cell lung cancer. Cancer Med. 12 (2), 1217–1227. 10.1002/cam4.4982 - DOI - PMC - PubMed
    1. Corrales-Rodriguez L., Blais N. (2012). Lung cancer associated venous thromboembolic disease: a comprehensive review. Lung cancer 75 (1), 1–8. 10.1016/j.lungcan.2011.07.004 - DOI - PubMed

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