Comparison of eight modern preoperative scoring systems for survival prediction in patients with extremity metastasis.

Autor: Lee TY; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.; Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan., Chen YA; Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan., Groot OQ; Department of Orthopaedic Surgery, University Medical Center Utrecht-Utrecht University, Utrecht, Netherlands.; Department of Orthopaedic Surgery, Massachusetts General Hospital-Harvard Medical School, Boston, USA., Yen HK; Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu, Taiwan.; Department of Medical Education, National Taiwan University Hospital, Hsin-Chu, Taiwan., Bindels BJJ; Department of Orthopaedic Surgery, University Medical Center Utrecht-Utrecht University, Utrecht, Netherlands., Pierik RJ; Department of Orthopaedic Surgery, University Medical Center Utrecht-Utrecht University, Utrecht, Netherlands.; Department of Orthopaedic Surgery, Massachusetts General Hospital-Harvard Medical School, Boston, USA., Hsieh HC; Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu, Taiwan., Karhade AV; Department of Orthopaedic Surgery, Massachusetts General Hospital-Harvard Medical School, Boston, USA., Tseng TE; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan., Lai YH; Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan., Yang JJ; Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan., Lee CC; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan., Hu MH; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan., Verlaan JJ; Department of Orthopaedic Surgery, University Medical Center Utrecht-Utrecht University, Utrecht, Netherlands., Schwab JH; Department of Orthopaedic Surgery, Massachusetts General Hospital-Harvard Medical School, Boston, USA., Yang RS; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan., Lin WH; Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Jazyk: angličtina
Zdroj: Cancer medicine [Cancer Med] 2023 Jul; Vol. 12 (13), pp. 14264-14281. Date of Electronic Publication: 2023 Jun 12.
DOI: 10.1002/cam4.6097
Abstrakt: Background: Survival is an important factor to consider when clinicians make treatment decisions for patients with skeletal metastasis. Several preoperative scoring systems (PSSs) have been developed to aid in survival prediction. Although we previously validated the Skeletal Oncology Research Group Machine-learning Algorithm (SORG-MLA) in Taiwanese patients of Han Chinese descent, the performance of other existing PSSs remains largely unknown outside their respective development cohorts. We aim to determine which PSS performs best in this unique population and provide a direct comparison between these models.
Methods: We retrospectively included 356 patients undergoing surgical treatment for extremity metastasis at a tertiary center in Taiwan to validate and compare eight PSSs. Discrimination (c-index), decision curve (DCA), calibration (ratio of observed:expected survivors), and overall performance (Brier score) analyses were conducted to evaluate these models' performance in our cohort.
Results: The discriminatory ability of all PSSs declined in our Taiwanese cohort compared with their Western validations. SORG-MLA is the only PSS that still demonstrated excellent discrimination (c-indexes>0.8) in our patients. SORG-MLA also brought the most net benefit across a wide range of risk probabilities on DCA with its 3-month and 12-month survival predictions.
Conclusions: Clinicians should consider potential ethnogeographic variations of a PSS's performance when applying it onto their specific patient populations. Further international validation studies are needed to ensure that existing PSSs are generalizable and can be integrated into the shared treatment decision-making process. As cancer treatment keeps advancing, researchers developing a new prediction model or refining an existing one could potentially improve their algorithm's performance by using data gathered from more recent patients that are reflective of the current state of cancer care.
(© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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