Development and validation of a deep vein thrombosis risk nomogram for post-operative complications in prostate cancer patients
Bibliographic record
Abstract
BACKGROUND: Post-operative deep vein thrombosis (DVT) significantly compromises outcomes in prostate cancer (PCa) surgery patients. This study aimed to develop and validate a clinically applicable nomogram for individualized DVT risk stratification. METHODS: In this retrospective matched case-control study, 500 PCa patients (150 DVT, 350 non-DVT) undergoing surgery (2018-2023) were analyzed after rigorous DVT confirmation via duplex ultrasonography (92.2% adherence) and radiologist adjudication (κ = 0.86). To address the inflated DVT incidence due to case-control sampling, inverse probability weighting corrected sampling bias (weighted DVT incidence 12.3% versus true 12.1%), with post-weighting covariate balance confirmed by a standardized mean difference <0.08. Independent predictors were identified through multivariate logistic regression, with nomogram construction and validation (bootstrap optimism correction; temporal validation cohort n = 103). Decision curve analysis (DCA) evaluated clinical utility by quantifying net benefit across threshold probabilities (5%-80%). RESULTS: Age (OR 1.045 [95% CI 1.022-1.072] per year), surgery duration (OR 1.018/10 [95% CI 1.011-1.025 per min), preoperative D-dimer (OR 1.315 [95% CI 1.192-1.451] for every 0.1 mg/L), prostate-specific antigen density (PSAD; OR 4.805 [95% CI 2.761-8.365] per unit), and advanced tumour stage (T3-T4, OR 3.512 [95% CI 2.012-6.115]) were significant predictors. The nomogram demonstrated excellent discrimination (optimism-corrected area under the curve [AUC] = 0.942; temporal validation AUC=0.918) and calibration (slope = 0.94). Clinical thresholds: age ≥68.3 years, surgery ≥159.7 min, D-dimer ≥0.92 mg/L, PSAD ≥2.95 ng/mL/cm³. DCA revealed optimal clinical utility at 10%-60% risk thresholds, with a maximum net benefit (0.111) at 10% threshold probability, consistently outperforming default treatment strategies. CONCLUSIONS: This validated nomogram integrates five readily available clinical variables to precisely quantify DVT risk in PCa surgical patients. It enables personalized preoperative risk assessment, facilitating targeted prophylaxis to mitigate thromboembolic complications beyond guideline-compliant prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".