Improving Sarcoma Outcomes: Target Trial Emulation to Compare the Impact of Unplanned and Planned Resections on the Outcome.
Timothy T A F ObergfellKim N NydeggerPhilip HeesenGeorg SchellingBeata Bode-LesniewskaGabriela StuderMario F Scaglioninull nullPublished in: Cancers (2024)
This study follows the Target Trial Emulation (TTE) framework to assess the impact of unplanned resections (UEs) and planned resections (PEs) of sarcomas on local recurrence-free survival (LRFS), metastasis-free survival (MFS), cancer-specific survival (CSS), and overall survival (OS). Sarcomas, malignant tumors with mesenchymal differentiation, present a significant clinical challenge due to their rarity, complexity, and the frequent occurrence of UEs, which complicates effective management. Our analysis utilized real-world-time data from the Swiss Sarcoma Network, encompassing 429 patients, to compare the impact of UEs and PEs, adjusting for known prognostic factors through a multivariable Cox regression model and propensity score weighting. Our findings reveal a significantly higher risk of local recurrence for UEs and a short-term follow-up period that showed no marked differences in MFS, CSS, and OS between the UE and PE groups, underlining the importance of optimal initial surgical management. Furthermore, tumor grade was validated as a critical prognostic factor, influencing outcomes irrespective of surgical strategy. This study illuminates the need for improved referral systems to specialized sarcoma networks to prevent UEs and advocates for the integration of TTE in sarcoma research to enhance clinical guidelines and decision-making in sarcoma care. Future research should focus on the prospective validations of these findings and the exploration of integrated care models to reduce the incidence of UEs and improve patient outcomes.
Keyphrases
- free survival
- prognostic factors
- healthcare
- palliative care
- risk assessment
- stem cells
- end stage renal disease
- study protocol
- squamous cell carcinoma
- phase iii
- chronic kidney disease
- bone marrow
- single cell
- risk factors
- big data
- randomized controlled trial
- young adults
- liver metastases
- electronic health record
- machine learning
- gene expression
- peritoneal dialysis
- genome wide
- artificial intelligence