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Chin Med J (Engl)
Chin Med J (Engl)
CM9
Chinese Medical Journal
0366-6999
2542-5641
Lippincott Williams & Wilkins Hagerstown, MD

CMJ-2024-730
10.1097/CM9.0000000000003272
00017
3
Correspondence
Development and validation of a predictive nomogram for venous thromboembolism in adult patients undergoing orthotopic liver transplantation
Li Younan 1
Zhu Rongrong 2
Zhao Junlai 2
Cao Zhanjiang 2
Song Jiyong 3
Tang Rui 3
Li Ang 3
Tong Xuan 3
Hou Yucheng 3
Lu Qian 3
Wu Weiwei 2
Dong Jiahong 3
Ji Yuanyuan
1 School of Clinical Medicine, Tsinghua University, Beijing 100084, China
2 Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
3 Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
Correspondence to: Dr. Weiwei Wu, Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China E-Mail: weiwei.wu@btch.edu.cn;
Dr. Qian Lu, Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China E-Mail: luqianbtch@163.com
27 8 2024
20 9 2024
137 18 22542256
10 3 2024
Copyright © 2024 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

OPEN-ACCESSTRUE
SDCT
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pmcTo the Editor: Venous thromboembolism (VTE) is a common complication following orthotopic liver transplantation (OLT), with an incidence of 2.8–8.6%,[123] which affects the quality of life of post-transplant patients. Current VTE risk assessment tools, such as the Caprini score, have limitations when applied to this population, underscoring the necessity of developing an early VTE risk assessment model tailored for OLT recipients.

This single-center retrospective study aimed to identify the risk factors of VTE and to develop a predictive nomogram model. This study was approved by the Ethics Committee of Beijing Tsinghua Changgung Hospital (No. 19242-401) and was registered at ClinicalTrials.gov (NCT05209048). Informed consent was waived by Institutional Review Board because of the retrospective nature of our study. The study included 280 adult OLT recipients in the training cohort (August 2018–December 2020) and 76 in the validation cohort (January–December 2021). Supplementary Figure 1, http://links.lww.com/CM9/C126 shows the participant selection criteria and the study design process. The cohort was divided into case and control groups based on the occurrence of VTE within 30 days post-OLT. Post-OLT VTE events primarily include deep vein thrombosis (DVT) and pulmonary embolism (PE). VTE screening involved daily ultrasound or CT venography within seven days post-surgery, with PE clinical suspicion confirmed using CT pulmonary angiography. Patients received prophylactic, mechanical, or pharmacological treatment.

In this study, the overall incidence of VTE after OLT was 11.5% (41/356). The incidence of DVT was 11.0% (39/356), with 38 cases of peripheral DVT and 1 case of mixed-type DVT, whereas the incidence of PE was 0.6% (2/356). Among DVT cases, 74.4% (29/39) were asymptomatic and incidentally diagnosed during routine postoperative ultrasonographic screening, all involving isolated calf muscle vein thrombosis, whereas the rest presented with limb swelling. The onset of VTE ranged from postoperative day 1 to 20, with a median onset on day 9.

Demographic and perioperative clinical data, such as surgical information and laboratory test results, were collected, and logistic regression was used to identify the posttransplant VTE risk factors. Before univariate logistic regression analysis, the variables were transformed and processed to enhance the applicability of the predictive model. Optimal cutoff values for continuous variables were determined using receiver operating characteristic (ROC) curves: age (60 years), body mass index (BMI) (25 kg/m2), Model for End-Stage Liver Disease (MELD) score (25), central venous pressure (CVP) ≤5 cmH2O time (1 h), and surgical duration (12 h). This finding led to the conversion of these variables into dichotomous ones. Multicategory variables, such as the American Society of Anesthesiologists (ASA) Physical Status and Child–Pugh classifications, were converted into dichotomous variables using optimal scaling regression.

Referring to the diagnostic and grading criteria for postoperative acute kidney injury (AKI) proposed by the Kidney Disease: Improving Global Outcomes (KDIGO) organization[4] in 2012, and considering the availability of observational indicators in retrospective data, this study defined postoperative AKI Grade 1 as an increase in serum creatinine by ≥26.5 μmol/L or 1.5–1.9 times above baseline within 48 h postoperatively; AKI Grade 2 as an increase of 2.0–2.9 times above baseline within seven days postoperatively; and AKI Grade 3 as an increase in serum creatinine by ≥353.6 μmol/L or ≥3.0 times above baseline within seven days postoperatively. The baseline creatinine level was defined as the lowest recorded serum creatinine level in the week before surgery. The occurrence of postoperative AKI was transformed into a dichotomous variable using the optimal scaling regression, categorizing it as “Not occurred or Grade 1” and “Grade 2 or higher”.

The D-dimer and fibrinogen ratio (DFR) has a higher sensitivity and specificity in predicting thrombosis than relying solely on D-dimer or fibrinogen (FIB). A higher DFR indicates a greater likelihood of thrombosis. In this study, the average DFR value in the week before surgery and the DFR value on the first postoperative day were compared, and the increase in DFR on the first postoperative day compared with the preoperative levels was used as a dichotomous variable for analysis.

Supplementary Table 1, http://links.lww.com/CM9/C126 shows the univariate analysis of perioperative clinical data of the patients in the training cohort. We found significant correlations (P <0.05) between the occurrence of VTE and several factors, including age ≥60 years (odds ratio [OR] = 2.59, 95% confidence interval [CI]: 1.22–5.49), preoperative comorbidities (hypertension, diabetes, coronary heart disease, or hyperlipidemia) (OR = 3.31, 95% CI: 1.55–7.04), MELD score ≥25 (OR = 2.73, 95% CI: 1.28–5.84), postoperative AKI grade ≥2 (OR = 6.92, 95% CI: 2.23–21.50), and an increase in DFR on postoperative day 1 compared with preoperative levels (OR = 3.31, 95% CI: 1.47–7.45).

Variables showing significant correlations in the univariate analysis were included in a multiple-logistic regression analysis using a forward stepwise selection method with the Akaike information criterion (AIC) as the stopping rule. Based on the results of the multiple-logistic regression analysis in Supplementary Table 2, http://links.lww.com/CM9/C126, four independent risk factors were identified as predictors for post-liver transplantation (LT) VTE: preoperative comorbidities (hypertension and/or diabetes and/or coronary heart disease and/or hyperlipidemia) (OR = 3.21, 95% CI: 1.45–7.33, P = 0.004), MELD score ≥25 (OR = 2.91, 95% CI: 1.23–6.82, P = 0.014), postoperative AKI grade ≥2 (OR = 5.38, 95% CI: 1.45–19.23, P = 0.009), and postoperative day 1 DFR increased compared with preoperative levels (OR = 3.81, 95% CI: 1.64–9.63, P = 0.003). These variables were then incorporated into the nomogram. These variables were treated as independent variables, with VTE occurrence as the dependent variable. The regression coefficients from the multiple-logistic regression analysis for each variable were used as weights to establish a predictive nomogram for post-LT VTE, as presented in Figure 1A, and made available online as Figure 1B.

Figure 1 (A) Nomogram for predicting VTE in patients after OLT. (B) Screenshot of the dynamic nomogram predictive tool onhttps://liyounan.shinyapps.io/my-liver-vte/. AKI: Acute kidney injury; DFR: D-dimer and fibrinogen ratio; MELD: Model for End-Stage Liver Disease; OLT: Orthotopic liver transplantation; VTE: Venous thromboembolism.

The performance and reliability of the nomograms were evaluated. Discrimination was quantified using the area under the ROC curve [Supplementary Figure 2, http://links.lww.com/CM9/C126], which was 0.833 (95% CI: 0.749–0.917, P <0.05) in the training cohort and 0.775 (95% CI: 0.575–0.976, P <0.05) in the validation cohort, indicating a strong discriminatory ability of the model. The area under the curve (AUC) was 0.624 (95% CI: 0.521–0.727, P <0.05) for the Caprini score.

According to the optimal cutoff value of 0.077 by the nomogram model, the population was divided into two risk levels: low (VTE risk possibility <7.7%) and high (VTE risk possibility ≥7.7%). A comparison of the model-predicted thrombosis events with the observed thrombosis events is presented in Supplementary Table 3, http://links.lww.com/CM9/C126. In the training cohort, the sensitivity of the model was 87.5% (28/32), specificity was 64.1% (159/248), positive predictive value (PPV) was 23.9% (28/117), and negative predictive value (NPV) was 97.5% (159/163). Similarly, in the validation cohort, the sensitivity, specificity, PPV, and NPV were 77.7% (7/9), 56.7% (38/67), 19.4% (7/36), and 95.0% (38/40), respectively.

Internal validation was conducted on the training cohort using 1000 bootstrap resampling iterations, whereas external validation was performed on the validation cohort. Calibration curves were generated by plotting the probabilities predicted by the nomogram against the actual occurrence of VTE, which served as the outcome variable. Calibration curves for the training and validation cohorts are shown in Supplementary Figure 3, http://links.lww.com/CM9/C126. The Hosmer-Lemeshow goodness-of-fit test yielded a P-value >0.05, indicating that the predicted probabilities of the model were statistically consistent with the observed outcomes. This finding suggests good overall agreement between predicted and actual probabilities, affirming the model’s reliability and performance.

Decision curve analysis (DCA) for the VTE nomogram model and Caprini score are presented in Supplementary Figure 4, http://links.lww.com/CM9/C126. The diagonal line represents the net benefit of the intervention, where all individuals predicted to be positive received anticoagulant therapy. In contrast, the horizontal line indicates the net benefit of no intervention for all individuals predicted to be negative. The slope of the diagonal line reflects the net benefit gained from the intervention, with a negative slope indicating a decrease in benefit. The farther the curve was from these reference lines, the higher the clinical utility of the model. The VTE prediction model showed a higher net benefit within a range of risk thresholds of above 5.0%. Conversely, the decision curve for the Caprini score closely resembles the lines representing “all receive or adjust anticoagulant therapy” and “none receive or adjust anticoagulant therapy,” suggesting a minimal net benefit across the range of risk thresholds.

The inclusion of comprehensive predictive factors in the model reflects various aspects of the patient’s preoperative disease status, postoperative coagulation function, and organ function recovery, thereby improving predictive accuracy. Patients with preoperative comorbidities, such as hypertension, hyperlipidemia, diabetes, and coronary heart disease, are at a significantly increased risk of postoperative VTE due to associated vascular damage, alterations in coagulation status, and hemodynamic changes. The MELD score serves as a standard for assessing the severity of end-stage liver disease and is often associated with poor prognosis. The complex changes in coagulation function during the perioperative period of LT make it unreliable to rely solely on single coagulation function indicators, such as FIB or D-dimer, to predict thrombotic risk. Instead, the D-dimer/FIB ratio can provide a more comprehensive reflection of the balance between fibrinolysis and coagulation processes. After surgery, the status of important organs, such as the liver and kidneys, is a decisive factor in systemic coagulation.

The model’s internal and external validations demonstrated good discrimination and calibration, outperforming the Caprini score, making it applicable in post-liver transplant clinical practice for identifying high-risk patients. The study’s limitations include a single center’s clinical data and the majority of VTE events being mild DVT, requiring caution when applying the model to different populations. The model should be used as a supplementary tool in liver transplant patients, not as a direct guide to clinical decisions due to the varying severity of VTE. Further research is needed to investigate the relationship between post-liver transplant immunotherapy regimens and postoperative VTE, as the use of immunosuppressive medications may increase the risk of platelet aggregation and thrombosis.[5] Prospective observational studies with multicenter data are required to confirm the model’s clinical applicability.

Acknowledgment

We would like to thank Editage (www.editage.cn) for English language editing.

Funding

None.

Conflicts of interest

None.

Supplementary Material

Younan Li and Rongrong Zhu contributed equally to this work.

How to cite this article: Li YN, Zhu RR, Zhao JL, Cao ZJ, Song JY, Tang R, Li A, Tong X, Hou YC, Lu Q, Wu WW, Dong JH. Development and validation of a predictive nomogram for venous thromboembolism in adult patients undergoing orthotopic liver transplantation. Chin Med J 2024;137:2254–2256. doi: 10.1097/CM9.0000000000003272
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