Construction and Verification of Risk Predicting Models to Evaluate the Possibility of Venous Thromboembolism After Robot-Assisted Radical Prostatectomy

Machine learning models for risk prediction of cancer-associated thrombosis: a systematic review and meta-analysis

BACKGROUND

Although the number of models for predicting the risk of cancer-associated thrombosis has been rising, there is still a lack of comprehensive assessment for machine learning prediction models.

OBJECTIVES

This study aimed to critically appraise and quantify the performance studies using machine learning to predict cancer-associated thrombosis.

METHODS

We conducted searches on PubMed, Embase, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and other related databases for the related publications (from inception to December 1, 2023). The Prediction Model Risk of Bias Assessment Tool checklist was employed to evaluate the risk of bias and applicability. The Grading of Recommendations Assessment, Development and Evaluation system was used to evaluate the quality of evidence in systematic reviews. Meta-analyses were conducted using R (version 4.3.2).

RESULTS

A total of 32 studies were included. Mostly included literature exhibited a high risk of bias, and the applicability of the prediction models was deemed acceptable. The 21 included studies in the meta-analysis demonstrated the high predictive capacity of the machine learning models for cancer-associated thrombosis.

CONCLUSION

Most of the prediction models included in the study showed good applicability and excellent prediction performance, but there was a high risk of bias.

Nomogram to Assess the Risk of Deep Venous Thrombosis After Posterior Lumbar Fusion: A Retrospective Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

Deep venous thrombosis (DVT) is a common complication following lumbar spine surgery, which can lead to adverse consequences such as venous thromboembolism and pulmonary embolism. This study aimed to investigate whether predictors of DVT can improve clinical interventions.

METHODS

The study included patients who underwent posterior lumbar fusion between 2012 and 2022. In the training cohort, stepwise logistic regression, based on the Akaike information criterion minimum, was used to identify variables for constructing the nomogram. The nomogram was evaluated and validated using calibration curves, Brier scores, receiver operating characteristic (ROC) curves, C-index, decision curve analyses (DCAs), clinical impact curves (CICs), and risk stratification analyses.

RESULTS

A total of 9216 patients were enrolled after screening. The nomogram included seven variables: cerebrovascular disease, diabetes, body mass index, age, pedicular screw quantity, D-dimer, and hypertension. Calibration plots demonstrated favorable agreement between predicted and observed probabilities. The C-index indicated satisfactory discriminatory ability of the nomogram (0.772 for the training cohort and 0.792 for the validation cohort). Additionally, the DCA and CIC revealed that the nomogram could provide clinical benefits for patients.

CONCLUSIONS

This study successfully developed and validated a nomogram that can assess the risk of DVT following posterior lumbar fusion. The nomogram will assist surgeons in making informed clinical decisions.

Developing and validating risk predicting models to assess venous thromboembolism risk after radical cystectomy

Background

Radical cystectomy (RC) patients are at significant risk for venous thromboembolism (VTE). Current predictive models, such as the Caprini risk assessment (CRA) model, have limitations. This research aimed to create a novel predictive model for forecasting the risk of VTE after RC.

Methods

This single-center study involved RC patients treated between January 1, 2010 and December 31, 2019. The individuals were divided into training and testing groups in a random manner. Multivariate and stepwise logistic regression were utilized to create two novel models. The models' performance was compared to the commonly used CRA model, employing metrics including net reclassification improvement (NRI), integrated discrimination improvement (IDI), and receiver operating characteristic (ROC) curve analyses.

Results

A total of 272 patients were enrolled, among whom 36 were diagnosed with VTE after RC. Model A and Model B were then conducted. The area under ROC of Model A and Model B is 0.806 [95% confidence interval (CI): 0.748-0.856] and 0.833 (95% CI: 0.777-0.880), respectively, which were also determined in the testing cohorts. The two new Models were superior both in classification ability and prediction ability (NRI >0, IDI >0, P<0.01). Model A and Model B had a concordance index (C-index) of 0.806 and 0.833, respectively. In decision curve analysis (DCA), the two new models provided a net benefit between 0.02 and 0.84, suggesting promising clinical utility.

Conclusions

Regarding predictive accuracy, both models surpass the existing CRA model, with Model A being advantageous due to its fewer variables. This easy-to-use model enables swift risk assessment and timely intervention for high-risk groups, yielding favorable patient outcomes.

Predictive accuracy of boosted regression model in estimating risk of venous thromboembolism following minimally invasive radical surgery in pharmacological prophylaxis-naïve men with prostate cancer

BACKGROUND

Venous thromboembolism (VTE) is a potentially life-threatening but preventable complication after urological surgery. Physicians are faced with the challenges of weighing the risks and benefits of thromboprophylaxis given scanty evidence for or against and practice variation worldwide.

OBJECTIVE

The primary objective of the study was to explore the possibility of a risk-stratified approach for thromboembolism prophylaxis following radical prostatectomy.

DESIGN, SETTING, AND PARTICIPANTS

A prospective database was accessed to cross-link venous thromboembolism events in 522 men who underwent minimally invasive prostatectomy between February 2010 and October 2021. A deterministic data linkage method was used to record events through electronic systems. Community Health Index (CHI) numbers were used to identify patients via electronic health records. Patient demographics and clinical characteristics such as age, comorbidities, Gleason staging, and readmission details accrued.

OUTCOMES

VTE within 90 days and development of a risk-stratified scoring system. All statistical analysis was performed using R-Statistical Software and the risk of VTE within 90 days of surgery was estimated via gradient-boosting decision trees (BRT) model.

RESULTS AND LIMITATIONS

1.1% (6/522) of patients developed deep vein thrombosis or pulmonary embolism within 3 months post-minimally invasive prostatectomy. Statistical analysis demonstrated a significant difference in the body mass index (p = 0.016), duration of hospital stay (p < 0.001), and number of readmissions (p = 0.036) between patients who developed VTE versus patients who did not develop VTE. BRT analysis found 8 variables that demonstrated relative importance in predicting VTE. The receiver operating curves (ROC) were constructed to assess the discrimination power of a new model. The model showed an AUC of 0.97 (95% confidence intervals [CI]: 0.945,0.999). For predicting VTE, a single-center study is a limitation.

CONCLUSIONS

The incidence of VTE post-minimally invasive prostatectomy in men who did not receive prophylaxis with low molecular weight heparin is low (1.1%). The proposed risk-scoring system may aid in the identification of higher-risk patients for thromboprophylaxis. In this report, we looked at the outcomes of venous thromboembolism following minimally invasive radical prostatectomy for prostate cancer in consecutive men. We developed a new scoring system using advanced statistical analysis. We conclude that the VTE risk is very low and our model, if applied, can risk stratify men for the development of VTE following radical surgery for prostate cancer.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.