scholarly journals Effective prediction model for preventing postoperative deep vein thrombosis during bladder cancer treatment

2022 ◽  
Vol 50 (1) ◽  
pp. 030006052110676
Author(s):  
Xing Liu ◽  
Abai Xu ◽  
Jingwen Huang ◽  
Haiyan Shen ◽  
Yazhen Liu
Keyword(s):  
Bladder Cancer ◽  
Deep Vein Thrombosis ◽  
Prediction Model ◽  
Model Evaluation ◽  
Cross Validation ◽  
High Accuracy ◽  
Hemostatic Agents ◽  
Vein Thrombosis ◽  
Deep Vein ◽  
Passive Muscle

Objective To begin to understand how to prevent deep vein thrombosis (DVT) after an innovative operation termed intracorporeal laparoscopic reconstruction of detenial sigmoid neobladder, we explored the factors that influence DVT following surgery, with the aim of constructing a model for predicting DVT occurrence. Methods This retrospective study included 151 bladder cancer patients who underwent intracorporeal laparoscopic reconstruction of detenial sigmoid neobladder. Data describing general clinical characteristics and other common parameters were collected and analyzed. Thereafter, we generated model evaluation curves and finally cross-validated their extrapolations. Results Age and body mass index were risk factors for DVT, whereas postoperative use of hemostatic agents and postoperative passive muscle massage were significant protective factors. Model evaluation curves showed that the model had high accuracy and little bias. Cross-validation affirmed the accuracy of our model. Conclusion The prediction model constructed herein was highly accurate and had little bias; thus, it can be used to predict the likelihood of developing DVT after surgery.

scholarly journals Deep Vein Thrombosis due to Metastatic Bladder Cancer

2015 ◽  
Vol 54 (19) ◽  
pp. 2527-2528
Author(s):  
Manami Shinotsuka ◽  
Seiichiro Shirai ◽  
Yasuharu Tokuda
Keyword(s):  
Bladder Cancer ◽  
Deep Vein Thrombosis ◽  
Vein Thrombosis ◽  
Metastatic Bladder Cancer ◽  
Deep Vein

scholarly journals Prediction of the Risk of Preoperative Deep Vein Thrombosis in Non-fractured Patients Awaiting Total Joint Arthroplasty Using a Support Vector Machine

2021 ◽  
Author(s):  
Haosheng Wang ◽  
Tingting Fan ◽  
Yanhua Chen ◽  
Wenle Li ◽  
Fujiang Zhao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Regression Analysis ◽  
Deep Vein Thrombosis ◽  
Prediction Model ◽  
Total Joint Arthroplasty ◽  
Ultrasound Examination ◽  
Joint Arthroplasty ◽  
Support Vector ◽  
Vein Thrombosis ◽  
Deep Vein

Abstract Background: We developed a potential useful alternative prediction model based on the support vector machine (SAM) algorithm to predict the risk of preoperative deep vein thrombosis (DVT) in non-fractured patients awaiting total joint arthroplasty (TJA). Methods: From March 2015 to August 2020, a retrospective review of the preoperative ultrasound examination findings of lower extremity venous vessels was performed on non-fractured patients of 369 elective TJA. Based on the ultrasound examination findings of preoperative lower extremely venous vessels, these patients were divided into two groups: the DVT group and the Non-DVT group. We collected the clinical, imaging, and laboratory findings from an electronic medical record system. These variables were imported into univariate, multivariate and logistic regression analysis to identify the risk factor for preoperative DVT. According to published literature and clinical experience, a series of variables were selected to construct a prediction model based on the SVM machine learning algorithm. Results: Among the 369 patients, preoperative DVT was observed in 21 patients (5.7%). The Multivariate regression analysis showed the following 5 independent factors associated with preoperative DVT: preoperative fibrinogen odds ratio [OR] = 7.306), age (OR = 1.133), history of hypertension (OR = 3.848), preoperative hematocrit (OR = 0.315), and D-dimer (OR = 2.032). The SVM model achieved a maximum and average area under the receiver operating characteristic curve (AUC) of 0.94 and 0.77 in the 10-fold cross-validation. Meanwhile, the accuracy, precision, and recall of the model were 0.98, 0.92, and 0.93, respectively. Additionally, the confusion matrix showed the classification results of the discriminant analysis.Conclusions: SVM machine modeling is a promising method for the prediction of the risk of DVT in non-fractured patients awaiting TJA. However, future external validation is needed.

scholarly journals Development and validation of a prediction model to estimate risk of acute pulmonary embolism in deep vein thrombosis patients

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
You Li ◽  
Yuncong He ◽  
Yan Meng ◽  
Bowen Fu ◽  
Shuanglong Xue ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Deep Vein Thrombosis ◽  
Prediction Model ◽  
Acute Pulmonary Embolism ◽  
Predictive Accuracy ◽  
Pulmonary Angiography ◽  
Brier Score ◽  
Axis Deviation ◽  
Vein Thrombosis ◽  
Deep Vein

AbstractVenous thromboembolism (VTE), clinically presenting as deep vein thrombosis (DVT) or pulmonary embolism (PE). Not all DVT patients carry the same risk of developing acute pulmonary embolism (APE). To develop and validate a prediction model to estimate risk of APE in DVT patients combined with past medical history, clinical symptoms, physical signs, and the sign of the electrocardiogram. We analyzed data from a retrospective cohort of patients who were diagnosed as symptomatic VTE from 2013 to 2018 (n = 1582). Among them, 122 patients were excluded. All enrolled patients confirmed by pulmonary angiography or computed tomography pulmonary angiography (CTPA) and compression venous ultrasonography. Using the LASSO and logistics regression, we derived a predictive model with 16 candidate variables to predict the risk of APE and completed internal validation. Overall, 52.9% patients had DVT + APE (773 vs 1460), 47.1% patients only had DVT (687 vs 1460). The APE risk prediction model included one pre-existing disease or condition (respiratory failure), one risk factors (infection), three symptoms (dyspnea, hemoptysis and syncope), five signs (skin cold clammy, tachycardia, diminished respiration, pulmonary rales and accentuation/splitting of P2), and six ECG indicators (SIQIIITIII, right axis deviation, left axis deviation, S1S2S3, T wave inversion and Q/q wave), of which all were positively associated with APE. The ROC curves of the model showed AUC of 0.79 (95% CI, 0.77–0.82) and 0.80 (95% CI, 0.76–0.84) in the training set and testing set. The model showed good predictive accuracy (calibration slope, 0.83 and Brier score, 0.18). Based on a retrospective single-center population study, we developed a novel prediction model to identify patients with different risks for APE in DVT patients, which may be useful for quickly estimating the probability of APE before obtaining definitive test results and speeding up emergency management processes.

Incidence and location of perioperative deep vein thrombosis in patients with bladder cancer undergoing radical cystectomy

2021 ◽  
Author(s):  
Ryo Yamashita ◽  
Masafumi Nakamura ◽  
Yukiko Okayama ◽  
Mizuki Kawase ◽  
Nao Muraoka ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Deep Vein Thrombosis ◽  
Radical Cystectomy ◽  
Vein Thrombosis ◽  
Deep Vein

scholarly journals Derivation and Validation of a Prediction Model for Risk Stratification of Post-Thrombotic Syndrome in Elderly Patients with a First Deep Vein Thrombosis

2018 ◽  
Vol 118 (08) ◽  
pp. 1419-1427 ◽  
Author(s):  
Marie Méan ◽  
Andreas Limacher ◽  
Adriano Alatri ◽  
Drahomir Aujesky ◽  
Lucia Mazzolai
Keyword(s):  
Deep Vein Thrombosis ◽  
High Risk ◽  
Risk Stratification ◽  
Prediction Model ◽  
Prediction Rule ◽  
Brier Score ◽  
Risk Category ◽  
Vein Thrombosis ◽  
Post Thrombotic Syndrome ◽  
Deep Vein

Background Not all patients carry the same risk of developing a post-thrombotic syndrome (PTS), we therefore aimed to derive a prediction rule for risk stratification of PTS in patients with deep vein thrombosis (DVT). Methods Our derivation sample included 276 patients with a first acute symptomatic lower limb DVT enrolled in a prospective cohort. We derived our prediction rule using regression analysis, with the occurrence of PTS within 24 months of a DVT based on the Villalta score as outcome, and 11 candidate variables as predictors. We used bootstrapping methods for internal validation. Results Overall, 161 patients (58.3%) developed a PTS within 24 months of a DVT. Our prediction rule was based on five predictors (age ≥ 75 years, prior varicose vein surgery, multi-level thrombosis, concomitant antiplatelet/non-steroidal anti-inflammatory drug therapy and the number of leg symptoms and signs). Overall, 16.3, 31.2 and 52.5% of patients were classified as low- (score, 0–3), moderate (score, 4–5) and high-risk (score, ≥ 6), for developing a PTS. Within 24 months of the index DVT, 24.4% of the patients in the low-risk category developed a PTS, 38.4% in the moderate and 80.7% in the high-risk category. The prediction model showed good predictive accuracy (area under the curve, 0.77; 95% confidence interval, 0.71–0.82, calibration slope, 0.90 and Brier score, 0.20). Conclusion This easy-to-use clinical prediction rule accurately identifies patients with DVT who are at high risk of developing PTS within 24 months who could potentially benefit from special educational or therapeutic measures to limit the risk of PTS.

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