Incidence and risk factors for skin cancer after heart transplantation: a systematic review and meta-analysis
Bibliographic record
Abstract
Studies have shown that patients who undergo heart transplantation (HTx) are at an increased risk for developing skin cancer. This condition can add physiological and psychological burden to patients. Therefore, assessing the incidence and identifying risk factors for skin cancer are crucial steps in its prevention. The purpose of this skin study is to systematically evaluate the incidence and risk factors of skin cancer in HTx. Two researchers independently conducted literature searches across 8 databases. The search covered publications from the establishment of the database through October 1, 2024. After screening title, abstract, and the full text, 34 eligible cohort studies were included. The studies were evaluated using the New castle-Ottawa Scale (NOS) for non-randomized studies, and papers selection followed PRISMA guidelines. The meta-analysis was conducted using the Stata 15.0 software. Among 34 cohort studies on HTx, the pooled incidence of skin cancer was 16% (95% CI: 14-19%). The incidences by type were 10% (95% CI: 8-12%) for squamous cell carcinoma and 8% (95% CI: 6-9%) for basal cell carcinoma. Regionally, the highest incidence was observed in the USA 22% (95% CI: 18-27%). Risk factors significantly associated with skin cancer included age (RR: 1.08, 95% CI: 1.04-1.11), male (RR: 1.53, 95% CI:1.11-2.12), white race (RR: 10.23, 95% CI: 7.32-14.30), smoking history (RR:1.26, 95% CI:1.05-1.51), prolonged sunlight exposure (≥ 2500 h) (RR:3.66, 95% CI: 2.11-6.36), pre-transplant cancer (RR: 1.61, 95% CI: 1.43-1.82), muromonab-CD3 (OKT3) (RR: 2.61, 95% CI: 2.11-3.24). The higher incidence of skin cancer observed in this study highlights the urgent need for follow-up care in heart transplant recipients. To address this, tailored skin cancer prevention strategies should be implemented, focusing on modifiable risk factors. Our findings provide a theoretical foundation to help healthcare professionals prevent and manage skin cancer in heart transplant patients.Patient or Public Contribution: YY, and HPY, were responsible for the conception and design of the study. YYS, FYL, and HPY, were responsible for the acquisition, analysis and interpretation of the data. All of the authors drafted the article or revised it critically for important intellectual content and provided final approval of the version to be submitted.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".