Nomogram Model for Prediction of Portal Vein Thrombosis in Patients with Liver Cirrhosis After Splenectomy: A Retrospective Analysis of 2 Independent Cohorts

Assessment of portal system hemodynamics for the prediction of portal vein thrombosis in cirrhosis-A systematic review and meta-analysis

BACKGROUND

The pathogenesis of portal vein thrombosis (PVT) in cirrhosis is multifactorial, with altered hemodynamics being proposed as a possible contributor. The present systematic review was conducted to study the role of assessment of portal hemodynamics for the prediction of PVT in patients with cirrhosis.

METHODS

Three databases (Medline, Embase, and Scopus) were searched from inception to February 2023 for studies comparing portal venous system parameters in patients with cirrhosis developing PVT with those not. Results were presented as mean difference (MD) or odds ratio (OR) with their 95% confidence intervals (CIs).

RESULTS

A total of 31 studies (patients with cirrhosis: 19 studies, patients with cirrhosis undergoing splenectomy: 12 studies) were included. On pooling the data from multivariable analyses of the included studies, a larger portal vein diameter was a significant predictor of PVT in patients with cirrhosis without or with splenectomy with OR 1.74 (1.12-2.69) and OR 1.55 (1.26-1.92), respectively. On the other hand, a lower portal vein velocity (PVV) was a significant predictor of PVT in cirrhotics without or with splenectomy with OR 0.93 (0.91-0.96) and OR 0.71 (0.61-0.83), respectively. A PVV of <15 cm/s was the most commonly used cut-off for the prediction of PVT. Patients developing PVT also had a significantly higher splenic length, thickness, and splenic vein velocity.

CONCLUSION

The assessment of portal hemodynamic parameters at baseline evaluation in patients with cirrhosis may predict the development of PVT. Further studies are required to determine the optimal cut-offs for various parameters.

A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study

OBJECTIVE

The implementation of more active anticoagulant prevention and treatment measures has indeed led to a significant reduction in the incidence of perioperative deep vein thrombosis (DVT) among patients with bone trauma. However, it is important to note that despite these efforts, the incidence of DVT still remains relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Stratifying the risk further within high-risk groups for DVT continues to present challenges. As a result, the commonly used Caprini score during the perioperative period is not applicable to orthopedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture.

METHODS

We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, a comparison was made between the Caprini score and the developed nomogram model.

RESULTS

The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC = 0.571, 95% CI 0.479-0.623), the calibration accuracy and identification ability of nomogram were higher (AUC = 0.865,95% CI 0.780-0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score.

CONCLUSIONS

The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. To further reduce the incidence, more specialized risk assessment models for DVT should take into account the unique risk factors and characteristics associated with specific patient populations.

Establishment and verification a nomogram for predicting portal vein thrombosis presence among admitted cirrhotic patients

Background

Portal vein thrombosis (PVT) is an increasingly recognized complication of cirrhosis and possibly associated with mortality. This study aims to evaluate provoking factors for PVT, then establish a concise and efficient nomogram for predicting PVT presence among admitted cirrhotic patients.

Materials and methods

All cirrhotic patients admitted in Hunan Provincial People's Hospital between January 2010 and September 2020 were retrospectively reviewed, the clinical and laboratory data were collected. Multivariate logistic regression analysis and the least absolute shrinkage and selection operator regression method were used for screening the independent predictors and constructing the nomogram. The calibration curve was plotted to evaluate the consistent degree between observed outcomes and predicted probabilities. The area under the receiver operating characteristics curve was used to assess the discriminant performance. The decision curve analysis (DCA) was carried out to evaluate the benefits of nomogram.

Results

A total of 4,479 patients with cirrhosis were enrolled and 281 patients were identified with PVT. Smoking history, splenomegaly, esophagogastric varices, surgical history, red blood cell transfusion, and D-dimer were independent risk factors for PVT in cirrhosis. A nomogram was established with a good discrimination capacity and predictive efficiency with an the area under the curve (AUC) of 0.704 (95% CI: 0.664-0.745) in the training set and 0.685 (95% CI: 0.615-0.754) in the validation set. DCA suggested the net benefit of nomogram had a superior risk threshold probability.

Conclusion

A concise and efficient nomogram was established with good performance, which may aid clinical decision making and guide best treatment measures.

Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration

Objective

Splenectomy is a vital treatment method for hypersplenism with portal hypertension. However, portal venous system thrombosis (PVST) is a serious problem after splenectomy. Therefore, constructing an effective visual risk prediction model is important for preventing, diagnosing, and treating early PVST in hepatolenticular degeneration (HLD) surgical patients.

Methods

Between January 2016 and December 2021, 309 HLD patients were selected. The data were split into a development set (215 cases from January 2016 to December 2019) and a validation set (94 cases from January 2019 to December 2021). Patients' clinical characteristics and laboratory examinations were obtained from electronic medical record system, and PVST was diagnosed using Doppler ultrasound. Univariate and multivariate logistic regression analyses were used to establish the prediction model by variables filtered by LASSO regression, and a nomogram was drawn. The area under the curve (AUC) of receiver operating characteristic (ROC) curve and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the differentiation and calibration of the model. Clinical net benefit was evaluated by using decision curve analysis (DCA). The 36-month survival of PVST was studied as well.

Results

Seven predictive variables were screened out using LASSO regression analysis, including grade, POD14D-dimer (Postoperative day 14 D-dimer), POD7PLT (Postoperative day 7 platelet), PVD (portal vein diameter), PVV (portal vein velocity), PVF (portal vein flow), and SVD (splenic vein diameter). Multivariate logistic regression analysis revealed that all seven predictive variables had predictive values (P < 0.05). According to the prediction variables, the diagnosis model and predictive nomogram of PVST cases were constructed. The AUC under the ROC curve obtained from the prediction model was 0.812 (95% CI: 0.756-0.869) in the development set and 0.839 (95% CI: 0.756-0.921) in the validation set. Hosmer-Lemeshow goodness-of-fit test fitted well (P = 0.858 for development set; P = 0.137 for validation set). The nomogram model was found to be clinically useful by DCA. The 36-month survival rate of three sites of PVST was significantly different from that of one (P = 0.047) and two sites (P = 0.023).

Conclusion

The proposed nomogram-based prediction model can predict postoperative PVST. Meanwhile, an earlier intervention should be performed on three sites of PVST.

Nomogram for Predicting Portal Vein Thrombosis in Cirrhotic Patients: A Retrospective Cohort Study

AIM

Portal vein thrombosis (PVT) is a common complication in cirrhotic patients and will aggravate portal hypertension, thus leading to a series of severe complications. The aim of this study was to develop a nomogram based on a simple and effective model to predict PVT in cirrhotic patients.

METHODS

Clinical data of 656 cirrhotic patients with or without PVT in the First Affiliated Hospital of Soochow University and The Third Affiliated Hospital of Nantong University from January 2017 to March 2022 were retrospectively collected, and all patients were divided into training, internal and external validation cohorts. SPSS and R software were used to identify the independent risk factors and construct a predictive model. We evaluated the predictive value of the model by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. The feasibility of the model was further validated in the internal and external cohorts. All enrolled patients were followed up to construct the survival curves and calculate the incidence of complications.

RESULTS

The predictors of PVT included serum albumin, D-dimer, portal vein diameter, splenectomy, and esophageal and gastric varices. Based on the clinical and imaging findings, the final model served as a potential tool for predicting PVT in cirrhotic patients, with an AUC of 0.806 (0.766 in the internal validation cohort and 0.845 in the external validation cohort). The decision curve analysis revealed that the model had a high level of concordance between different medical centers. There was a significant difference between the PVT and non-PVT groups in survival analyses, with p values of 0.0477 and 0.0319 in the training and internal validation groups, respectively, along with p value of 0.0002 in the external validation group according to log-rank test; meanwhile, the median survival times of the PVT group were 54, 43, and 40 months, respectively. The incidence of recurrent esophageal and gastric variceal bleeding (EGVB) during the follow-up showed significant differences among the three cohorts (p = 0.009, 0.048, and 0.001 in the training, internal validation, and external validation cohorts, respectively).

CONCLUSION

The nomogram based on our model provides a simple and convenient method for predicting PVT in cirrhotic patients. Cirrhotic patients with PVT had a shorter survival time and were prone to recurrent EGVB compared with those in the non-PVT group.

Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models

BACKGROUND

For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.

AIM

To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.

METHODS

We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).

RESULTS

Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.

CONCLUSION

PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.