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Abstract

Background:

The current predictive models for venous thromboembolism (VTE) have limitations in predicting VTE risk in patients with urological cancer.

Objectives:

To establish and validate a risk stratification model for in-hospital VTE in patients with urological cancer.

Design:

Retrospective, multicenter study.

Methods:

The clinical data of 735 patients with urological cancer in the Department of Urology at four hospitals in China between January 2019 and December 2024 were analyzed. VTE (n = 147) and non-VTE (n = 588) groups were formed based on inclusion and exclusion criteria to develop a predictive model for VTE in patients with urological cancer.

Results:

In this study, we developed a risk stratification model using a logistic regression based on variables selected by LASSO and constructed a nomogram to visualize the model. The areas under the receiver operating characteristic curve for the training, validation, and external validation cohorts were 0.933 (95% confidence interval (CI): 0.909–0.957), 0.900 (95% CI: 0.850–0.950), and 0.857 (95% CI: 0.776–0.938), respectively. The corresponding Brier scores for them were 0.070, 0.089, and 0.200. The calibration curve indicated good model performance. Decision curve analysis evaluated the clinical utility of the model and showed that it provided a higher net benefit than the “treat-all” and “treat-none” strategies across a wide range of threshold probabilities, supporting its potential clinical usefulness.

Conclusion:

A predictive model was developed to estimate the risk of in-hospital VTE in patients with urological cancer, enabling personalized assessment and guiding preventive strategies. Further studies are needed to better validate our model.

Keywords

D-dimer predictive models risk factors urological cancers venous thromboembolism

Introduction

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), represents two clinical manifestations of the same disease at different stages and locations.¹ Acute PE is particularly insidious—characterized by high rates of mortality, misdiagnosis, and missed diagnoses—and is a significant cause of unexpected in-hospital and perioperative mortality.^2,3 Moreover, VTE is the second leading cause of death in patients with cancer and a significant cause of increased mortality in patients with urological cancer.^4–6 The incidence of VTE in patients with urological cancer remains relatively high at 2.5%–7.9% in those with bladder cancer, 1.7%–5.3% in kidney cancer, 2.0%–2.2% in prostate cancer, and 5.4%–6.2% in urothelial cancer (including renal pelvis and ureteral cancer).^7–10 Evidence suggests that the risk of VTE varies across different types of cancers, including among urological malignancies themselves.^11,12 For instance, patients with renal cell carcinoma are prone to developing renal vein or inferior vena cava tumor thrombi, which may increase their VTE risk owing to factors such as the tumor stage and treatment of the tumor thrombus.¹³ In bladder cancer, major surgery, prolonged immobilization, and chemotherapy can contribute to an elevated incidence of VTE, with the risk becoming particularly pronounced in metastatic cancer.¹⁴ In prostate cancer, the risk of VTE increases by approximately 50% within 5 years of diagnosis, with the highest incidence occurring in the first 6 months; androgen deprivation therapy (ADT) may further amplify this risk.^15,16 Several risk prediction models for VTE have been developed, such as the Caprini and Khorana models, which can effectively guide clinical practice.^17,18 However, urological cancers have a unique pathogenesis and treatment protocols, and the Caprini model does not differentiate between tumor types or treatment-related factors, resulting in a limited discriminative ability. Most patients undergoing major abdominal or pelvic urological surgery are classified as high risk; however, their VTE risk differs substantially, making this model insufficient to distinguish VTE risk in urological surgery.^19,20 The Khorana score, proposed by Khorana et al., is a predictive model for assessing the risk of VTE in patients with cancer receiving chemotherapy. However, some studies have suggested that its predictive performance for urological cancer-related VTE may be limited.^21–23 Existing models pay insufficient attention to individual characteristics and dynamic changes in patients with urological cancer, which may reduce their effectiveness in risk stratification and restrict their clinical applicability.¹⁷ Therefore, it is necessary to develop a risk assessment model based on the individual differences of patients with urological cancer.

This study analyzed the risk factors for in-hospital VTE in patients with urological cancer and explored an individualized risk stratification model. The aim was to enhance the predictive power of the model, accurately and efficiently identify patients at risk of VTE, implement precise preventive measures and treatments, and reduce VTE-associated complications and mortality in patients with urological cancer.

Materials and methods

Study participants

After applying the inclusion and exclusion criteria, a total of 147 VTE cases were identified from 19,254 patients with urological cancers in the Department of Urology at 4 hospitals in the People’s Republic of China (Second Hospital of Tianjin Medical University, Tianjin Hospital, Tianjin Medical University Chu Hsien-I Memorial Hospital, and Tianjin Third Central Hospital) from January 2019 to December 2024. In addition, 588 patients without VTE were randomly selected as controls in a 1:4 ratio, yielding a final study population of 735 patients.²⁴ Data were collected from standardized inpatient records in the electronic medical record system. And we additionally collected 109 patients to serve as the external validation cohort. Research staff were trained to minimize potential bias inherent to the retrospective study design. This study was approved by the Second Hospital of Tianjin Medical University’s review board (approval number: KY2024K211). The reporting of this study conforms to the statements of STROBE (File S1).²⁵

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) Urological cancers confirmed by pathology, (2) An imaging examination (ultrasound, color Doppler, and computed tomography angiography) revealed the presence of VTE, (3) No coagulopathy-related diseases, and (4) Relatively complete clinical data.

The exclusion criteria were as follows: (1) Venous tumor thrombus, (2) Diagnosis of VTE on admission, (3) Poor general condition with a short life expectancy (no more than 6 months), and (4) Active bleeding.

Data collection

The following clinical data of the enrolled patients were collected through the hospital’s electronic medical record system: (1) General clinical data: age; sex; body mass index (BMI); history of smoking, alcohol consumption, hypertension, and diabetes; tumor stage; surgical treatment; surgical grade; and drug treatment (chemotherapy, immunotherapy, or targeted therapy); (2) Laboratory test indices: D-dimer level (0–0.5 mg/L), prothrombin time (PT) (11–13.5 s), activated partial thromboplastin time (APTT; 25–35 s), thrombin time (TT; 14–21 s), fibrinogen level (2.0–4.0 g/L), hemoglobin (Hb) level (male: 130–175 g/L; female: 115–150 g/L), platelet (PLT) count (100–300 × 10⁹/L), and white blood cell (WBC) count (4.0–10.0 × 10⁹/L); and (3) VTE risk assessment data based on the Caprini score.

Statistical analysis

All statistical analyses were performed using R software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). Missing data were imputed using multiple imputation. Quantitative variables are expressed as mean ± standard deviation, and comparisons between groups were conducted using independent-samples t-tests. Categorical variables are summarized as frequencies and percentages, and differences between groups were compared using the Chi-square (χ²) test or Fisher’s exact test. Variables were selected using the LASSO method. A binomial logistic regression model was constructed based on the variables with statistical significance (p < 0.05) to develop a general risk prediction model. A nomogram was generated to provide a visual representation of the predictive model. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) was employed to confirm its clinical utility. Internal validation was performed with 1000 bootstrap resamples, and external validation was conducted using grouped calibration. Model calibration was assessed by plotting the calibration curve and calculating the Brier score.

Results

Clinical characteristics and laboratory parameters

In total, 735 patients were included in the study between January 2019 and December 2024, with 147 and 588 patients in the VTE and non-VTE groups, respectively. Age (71.08 ± 8.32 vs 65.72 ± 11.85 years), the Caprini score (6.54 ± 1.73 vs 4.94 ± 1.30), drug treatment, tumor stage, and surgical grade were significantly different between the VTE group and the non-VTE group (p < 0.05), whereas BMI, sex distribution, surgical treatment, and history of smoking, alcohol consumption, cancer type, and hypertension were not (p > 0.05). The differences in clinical characteristics between the two groups are shown in Table 1.

Table 1.

Clinical characteristics of patients enrolled in this study.

Variables	Non-VTE group	VTE group	p
Age (years), mean ± SD (95% CI)	65.72 ± 11.85 (64.75–66.71)	71.08 ± 8.32 (69.73–72.51)	<0.001**
Sex, n (%)
Male	486 (82.7%)	114 (77.6%)	0.192
Female	102 (17.3%)	33 (22.4%)
BMI (kg/m²), mean ± SD (95% CI)	24.63 ± 3.77 (24.32–24.94)	24.38 ± 3.15 (23.86–24.91)	0.408
Smoking, n (%)	214 (36.4%)	60 (40.8%)	0.417
Alcohol consumption, n (%)	119 (20.8%)	33 (23.2%)	0.603
Hypertension, n (%)	251 (43.9%)	66 (46.5%)	0.643
Diabetes, n (%)	110 (18.7%)	35 (23.8%)	0.147
Caprini score, mean ± SD (95% CI)	4.94 ± 1.30 (3.83–5.05)	6.54 ± 1.73 (6.243–6.820)	<0.001**
Cancer type, n (%)
PC	80 (13.6%)	20 (13.6%)	1.000
KC	48 (8.2%)	12 (8.1%)
BC	404 (68.7%)	101 (68.7%)
RPC	24 (4.1%)	6 (4.1%)
UC	32 (5.4%)	8 (5.4%)
Surgical treatment, n (%)	382 (65.0%)	120 (81.6%)	0.162
Surgical grade, n (%)
Grade 1	248 (42.2%)	57 (39.9%)	<0.001**
Grade 2	4 (0.7%)	0 (0.0%)
Grade 3	17 (2.9%)	5 (3.5%)
Grade 4	113 (19.2%)	58 (40.6%)
Tumor stage, n (%)
0	3 (0.6%)	3 (2.4%)	<0.001**
I	245 (49.1%)	41 (32.8%)
II	162 (32.5%)	37 (29.6%)
III	68 (13.6%)	33 (26.4%)
IV	21 (4.2%)	11 (8.8%)
Drug treatment, n (%)	148 (25.2%)	73 (49.7%)	<0.001**
Chemotherapy	59 (10.4%)	33 (23.8%)	<0.001**
Targeted therapy	6 (1.1%)	0 (0.0%)
Immunotherapy	25 (4.3%)	11 (7.5%)
Immunotherapy and chemotherapy	33 (5.6%)	24 (16.3%)
Immunotherapy and targeted therapy	15 (2.7%)	3 (2.2%)

BC, bladder cancer; BMI, body mass index; CI, confidence interval; KC, kidney cancer; PC, prostate cancer; RPC, renal pelvis cancer; SD, standard deviation; UC, ureter cancer; VTE, venous thromboembolism.

p < 0.01;

The D-dimer level (2.24 ± 2.62 vs 0.46 ± 0.99 mg/L) in the VTE group was higher than that in the non-VTE group, whereas the PT (11.51 ± 1.39 vs 12.26 ± 1.61 s), APTT (28.85 ± 7.62 vs 31.46 ± 6.33 s), and Hb level (117.10 ± 26.53 vs 130.16 ± 21.96 g/L) were significantly lower in the VTE group than in the non-VTE group (p < 0.05). There were no significant differences in the TT, fibrinogen level, PLT count, or WBC count between the two groups (p > 0.05). The differences in laboratory parameters between the two groups are shown in Table 2.

Table 2.

Laboratory parameters of patients enrolled in this study.

Variables	Non-VTE group		VTE group		p
Variables	Mean ± SD	95% CI	Mean ± SD	95% CI	p
PT (s)	12.26 ± 1.61	12.14–12.40	11.51 ± 1.39	11.28–11.75	<0.001**
APTT (s)	31.46 ± 6.33	30.94–31.99	28.85 ± 7.62	27.61–30.15	<0.001**
TT (s)	17.64 ± 1.97	17.51–17.83	17.54 ± 1.64	17.27–17.82	0.537
D-dimer (mg/L)	0.46 ± 0.99	0.38–0.54	2.24 ± 2.62	1.81–2.69	<0.001**
Fibrinogen (g/L)	3.39 ± 1.22	3.29–3.50	3.52 ± 1.10	3.34–3.70	0.236
Hb (g/L)	130.16 ± 21.96	128.40–132.03	117.10 ± 26.53	112.92–121.74	<0.001**
PLT (×10⁹/L)	225.00 ± 57.60	220.67–230.13	222.56 ± 77.90	209.63–235.66	0.727
WBC (×10⁹/L)	6.76 ± 2.20	6.58–6.94	7.40 ± 5.51	6.48–8.33	0.174

APTT, activated partial thromboplastin time; CI, confidence interval; Hb, hemoglobin; PLT, platelets; PT, prothrombin time; SD, standard deviation; TT, thrombin time; VTE, venous thromboembolism; WBC, white blood cell.

p < 0.01.

LASSO-based variable selection and multivariate logistic analysis

The dataset was randomly divided into a training set and a validation set at a 7:3 ratio, which were used for model development and internal validation, respectively. Missing data were imputed using the multiple imputation: PT (0.68%), TT (0.27%), fibrinogen (1.09%), WBC (0.41%), drug treatment (1.22%), and tumor stage (10.06%). Variable selection was performed using the LASSO method; in the figure, the two dashed lines represent log(λ_min) and log(λ_1se). Based on log(λ_1se), nine variables were ultimately selected (Figure 1; Table 3). After LASSO-based variable selection, those retained (PT, APTT, age, drug treatment, Caprini score, Hb level, tumor stage, and surgical grade) were subsequently entered into a multivariable logistic regression to develop the risk prediction model. The results of the logistic regression are as follows (Table 4).

Figure 1.

LASSO cross-validation plot.

Table 3.

Variables selected by LASSO regression and their coefficients.

Original variables	β coefficient	OR
Age	0.004	1
Caprini score	0.46	1.58
D-dimer	0.341	1.41
PT	−0.080	0.92
APTT	−0.003	1
Hb	−0.009	0.99
Tumor stage	−0.114	0.89
Surgical grade	0.304	1.35
Drug treatment	0.404	1.5

APTT, activated partial thromboplastin time; Hb, hemoglobin; OR, odds ratio; PT, prothrombin time.

Table 4.

Results of a binomial logistic regression for the training set.

Variables	β	OR	95% CI	p
Age	0.053	1.05	1.02–1.09	<0.01**
Caprini score	0.642	1.9	1.47–2.46	<0.01**
D-dimer	0.977	2.66	1.72–4.12	<0.01**
PT	−0.433	0.65	0.47–0.91	0.012*
APTT	−0.026	0.97	0.90–1.04	0.358
Hb	−0.020	0.98	0.97–0.99	0.017*
Drug treatment	1.12	3.11	1.58–6.09	<0.01**
Tumor stage
I	−2.75	0.06	0.004–0.89	<0.01**
II	−1.89	0.15	0.01–1.77	0.014*
III	−1.89	0.15	0.01–1.81	0.025*
IV	−1.06	0.35	0.02–5.29	0.128
Surgical grade
Grade 1	0.56	1.76	0.75–4.13	0.145
Grade 2	–	–	–	0.989
Grade 3	0.57	1.77	0.27–11.6	0.646
Grade 4	1.57	4.83	1.96–11.9	<0.01**

APTT, activated partial thromboplastin time; CI, confidence interval; Hb, hemoglobin; OR, odds ratio; PT, prothrombin time.

p < 0.05. **p < 0.01.

Construction of a nomogram to visually represent the model

To visually represent the model, a nomogram was constructed based on the results of logistic regression analysis. The nomogram incorporated nine risk factors: PT, APTT, age, D-dimer level, Hb level, Caprini score, tumor stage, and drug treatment. Each risk factor was assigned a corresponding score based on the “Points” scale. The corresponding risk of VTE was determined based on the total score (Figure 2).

Figure 2.

Nomogram for VTE risk in patients with urological cancer.

Evaluation of model performance and clinical utility

ROC curve analysis revealed that the model achieved an area under the ROC curve (AUC) of 0.933 (95% confidence interval (CI): 0.909–0.957) in the training cohort, with an AUC of 0.900 (95% CI: 0.850–0.950) in the validation cohort and 0.857 (95% CI: 0.776–0.938) in the external validation cohort, indicating favorable discriminatory performance (Figure 3). VTE is a serious and potentially life-threatening condition if not promptly identified and managed, highlighting the need for a balance between sensitivity and specificity.²⁶ Using the maximum Youden index, an optimal cutoff value was identified. The corresponding sensitivity and specificity were 0.822 (95% CI: 0.743–0.891) and 0.928 (95% CI: 0.903–0.953) in the training cohort, 0.609 (95% CI: 0.548–0.833) and 0.865 (95% CI: 0.813–0.912) in the validation cohort, and 0.804 (95% CI: 0.728–0.880) and 0.765 (95% CI: 0.587–0.941) in the external validation cohort.²⁷ These results suggest that the predictive model demonstrates promising clinical utility in identifying patients at a high risk of VTE, supporting its potential role in guiding individualized thromboprophylaxis strategies (Table 5).

Figure 3.

ROC curves for the training, validation, and external validation cohorts.

Table 5.

Model performance metrics with 95% CI.

Variables	Point estimate	95% CI
Training cohort
AUC	0.933	0.904–0.963
Sensitivity	0.822	0.743–0.891
Specificity	0.928	0.903–0.953
Validation cohort
AUC	0.900	0.850–0.950
Sensitivity	0.609	0.548–0.833
Specificity	0.865	0.813–0.912
External validation cohort
AUC	0.857	0.776–0.938
Sensitivity	0.804	0.728–0.880
Specificity	0.765	0.587–0.941

AUC, area under the ROC curve; CI, confidence interval.

To assess the clinical utility of the model, DCA showed that across a wide range of threshold probabilities, our model provided a higher net benefit compared with the “All” and “None” strategies. The external cohort showed consistently higher net benefit across most threshold probabilities, while the training and validation cohorts also exhibited favorable clinical utility within clinically relevant threshold ranges. This suggests that the model has practical clinical value and may assist in guiding VTE risk management strategies (Figure 4).

Figure 4.

DCA curves for the training, validation, and external validation cohorts.

Calibration curves were constructed to assess the model calibration. In the training and validation cohorts, the dashed line represents the ideal reference line, indicating perfect agreement between the predicted and observed probabilities. The dotted line denotes the apparent performance of the model, whereas the solid line indicates the bootstrap-corrected performance (B = 1000). External validation was conducted using grouped calibration. The calibration curves showed good agreement between predicted and observed risks in the training and validation cohorts, while a moderate deviation from the ideal line was observed in the external validation cohort (Figure 5). Model reliability was further assessed using the Brier score, which indicated good predictive performance in the training (0.070) and validation (0.089) cohorts, and acceptable performance in the external validation cohort (0.200).

Figure 5.

Calibration curve for the training, validation, and external validation cohorts.

Discussion

VTE is a common complication in patients with cancer, including urological cancers.^28,29 A series of clinical risk stratifications for VTE in patients with cancer has been established, which provides a basis for healthcare professionals to identify high-risk patients, assess the risk of VTE at an early stage, and perform personalized prevention in a targeted manner. Although various risk assessment models, such as the Caprini and Khorana scores, have been widely adopted in different clinical settings, each model has certain limitations.^30–32 The Caprini score is a general risk-assessment tool applicable to a wide range of surgical patients. However, it only considers the presence of malignancy without further distinguishing between tumor types or treatment-related factors, which may limit its discriminatory power in patients with urological cancer.³¹ The Khorana score is primarily applicable for patients with cancer undergoing chemotherapy, and studies have indicated that its predictive performance may be limited when applied to assess the VTE risk in patients with urological malignancies.^21,22 Ultimately, these risk scores are limited in their individualized assessment of VTE risk among patients with urological cancer. Therefore, the present study developed a risk stratification model for in-hospital VTE in patients with urological cancer based on clinical data, laboratory parameters, and Caprini scores.

In patients with urological cancer, there are multiple risk factors for VTE, including tumor heterogeneity, differences in staging, treatment modalities, and individual patient characteristics.^33,34 The results of the current study showed that the D-dimer level, Hb level, PT, APTT, age, tumor stage Surgical grade, drug treatment, and Caprini score were significantly different between the VTE and non-VTE groups (p < 0.05) and were risk factors for VTE in patients with urological cancer. The mechanism of VTE in cancer is complex and involves the production of tumor-related procoagulants, PLT activation, various changes in the coagulation and fibrinolytic systems, and endothelial injury and dysfunction. These factors collectively contribute to a hypercoagulable state.^35,36

D-dimer is a soluble fibrin degradation product from the ordered breakdown of thrombi by the fibrinolytic system.³⁷ The detection of D-dimers is simple and highly sensitive; however, it lacks specificity.^38,39 D-dimer is an important indicator of the activation of the fibrinolytic system and hypercoagulable state; moreover, it is a valuable and reliable biomarker for the diagnosis of VTE.^40,41 Therefore, D-dimer levels play a significant role in stratifying the risk of VTE in hospitalized patients with urological cancer, greatly improving prediction accuracy.⁴² Additionally, D-dimer levels play an important role in anticoagulant therapy for VTE.⁴³

The PT and APTT are often used to monitor the coagulation function of the body because they reflect the activities of coagulation factors^44,45; a shortened PT or APTT indicates a hypercoagulable state.⁴⁶ The Hb level is a key index used to assess anemia, and Khorana et al.⁴⁷ reported that an Hb level of <100 g/L is significantly associated with anemia. In contrast, Folsom et al.⁴⁸ suggested that an elevated Hb level is associated with VTE, a common complication in patients with cancers. The relationship between Hb levels and VTE in patients with urological cancer is controversial and requires further study. In the present study, the PT and Hb levels of patients in the VTE group were significantly lower than those of patients in the non-VTE group (p < 0.001), and shortened PT, APTT, and decreased Hb levels were risk factors for VTE in patients with urological cancer.

Studies have shown that the risk of VTE is higher in patients with cancer receiving drug treatment (chemotherapy, immunotherapy, or targeted therapy) than in those not receiving treatment; such drug treatments can cause acute vascular wall injury, PLT activation, decreased anticoagulant use, and promote the occurrence of VTE.^49,50 They may also contribute to immune microthrombus formation.⁵¹ In addition, surgical grade was used to reflect surgical complexity in accordance with relevant regulations, whereby each hospital assigns grades based on operative risk, technical difficulty, and resource consumption.⁵² In this study, surgical grade was identified as a VTE-related risk factor. Tumor stage is already recognized as an important risk factor and may be associated with a heightened procoagulant state, increased tumor invasiveness, tumor cell infiltration, and the release of procoagulant factors, as well as an overall elevated inflammatory burden.⁵³ In our study, tumor stage was also shown to be associated with VTE. Advancing age was also associated with an increased risk, potentially reflecting age-related reductions in endogenous anticoagulant activity, enhanced procoagulant tendency, and structural changes in the venous system.⁵⁴ These findings are consistent with those of the present study.^52–56

Although the Caprini score covers numerous risk factors for VTE, it still lacks indicators of the characteristics of patients with urological cancer.^31,57,58 A study on urological cancer surgery revealed that the Caprini score could not fully distinguish the risk levels of VTE in different patients.⁵⁹ ROC curve analysis showed that the model achieved an AUC of 0.933 (95% CI: 0.909–0.957) in the training cohort, with corresponding AUCs of 0.900 (95% CI: 0.850–0.950) in the validation cohort and 0.857 (95% CI: 0.776–0.938) in the external validation cohort, demonstrating favorable discriminatory performance. DCA indicated that the model has practical clinical value and may support VTE risk management strategies. The calibration curve demonstrated good agreement between the predicted and observed values, and the Brier score further confirmed the predictive accuracy. Collectively, these findings indicate that the proposed risk stratification provides favorable predictive performance for patients with urological cancer.

This study has certain limitations. Owing to the multicenter, retrospective nature of the study, it may be subject to certain inherent biases, which we sought to reduce by using standardized electronic medical records, clearly defined inclusion criteria, and trained personnel for data extraction. To account for variations in staging systems and surgical complexity among the five types of urological cancers included, we incorporated the categorical variables “tumor stage” and “surgical grade” into our regression model. Nevertheless, most surgical procedures in our study cohort were minimally invasive, which might limit the generalizability of our findings. In addition, the number of procedures in certain surgical grade categories may have been insufficient, potentially affecting the robustness of the related estimates. Moreover, the heterogeneity of staging criteria across cancer types could have introduced additional bias. Further prospective, multicenter studies, along with a deeper exploration of the potential impact of factors such as cancer staging, disease burden, and surgical grade, would be valuable for validating and refining our model.

Conclusion

In summary, a novel VTE risk stratification model based on age, APTT, PT, Hb level, D-dimer level, drug treatment, tumor stage, surgical grade, and Caprini score was developed in our study to provide a personalized assessment tool for patients with urological cancer. The nomogram developed from the model can be integrated into hospital electronic medical record systems to provide a simple and intuitive reference to help reduce the incidence and mortality of VTE in patients with urological cancer. However, this model requires further validation through large-scale, preclinical, prospective, multicenter studies.

Supplemental Material

sj-doc-1-tam-10.1177_17588359261430135 – Supplemental material for Risk stratification of in-hospital venous thromboembolism for urological cancers: a multicenter retrospective study

Supplemental material, sj-doc-1-tam-10.1177_17588359261430135 for Risk stratification of in-hospital venous thromboembolism for urological cancers: a multicenter retrospective study by Zhaoyang Li, Tonghe Zhang, Guangbin Zhu, Haishan Shen, Minghao Zhang, Huayu Wang, Huitang Yang, Hailong Hu and Yankui Li in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

None.

Declarations

ORCID iD

Yankui Li

Supplemental material

Supplemental material for this article is available online.

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