Development and preliminary validation of a PICC-related venous thrombosis risk prediction model for cancer patients: A systematic review and meta-analysis predominantly based on Chinese populations
Bibliographic record
Abstract
Objective To identify key risk factors for peripherally inserted central catheter -related deep venous thrombosis (PICC-RVT) in patients with cancer through a systematic review and meta-analysis, develop risk prediction model and validate its performance. Methods A systematic literature search was performed in Pubmed, EMBASE, Web of Science (core collection), Scopus, CINAHL and Ovid databases from the time of their inception to June 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) checklist. Meta-analysis was performed using RevMan 5.4 statistical software to identify independent risk factors for PICC-RVT in adult patients with cancer, then the effect of each independent risk factor was determined using β - formation conversion for developing the risk predictive model. Finally, we collected the clinical datal of 338 adult patients with cancer who underwent PICC catheterization from June 2024 to May 2025 to evaluate the predictive performance of the risk predictive model by drawing the receiver of curve (ROC). Results A total of 32 cohort studies involving 28,813 individuals (28 from China, 2 from UK, 1 each from US and Canada) were included. Twenty-one independent risk factors were identified through meta-analysis. The risk prediction model was developed using β - coefficient transformation: pooled odds ratios (ORs) from meta-analysis were converted to β coefficients through natural logarithmic transformation ( β = ln[OR]), then each β - coefficient was multiplied by 10 and rounded to one decimal place to create a point-based scoring system. The total risk score was calculated by summing individual factor scores. Preliminary external validation was conducted in 338 patients with cancer (15 thrombosis events, 4.4% incidence) from a single Chinese center, yielding an area under curve (AUC) of 0.792 (95% [confidence interval (CI)] 0.653–0.931). The model showed acceptable calibration (Hosmer–Lemeshow P = 0.082) but validation was underpowered (15 events vs. recommended 100+ events for precise performance estimation). Conclusions This study developed a PICC-RVT risk prediction model based primarily on Chinese patients with cancer. The model demonstrated moderate discrimination in preliminary single-center validation, but requires multi-center validation with adequate event numbers before clinical implementation. The model provides a framework for PICC-RVT risk stratification in Chinese and similar healthcare settings. Systematic review registration PROSPERO (CRD420250651190).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".