Complications and Risk Factors in En Bloc Resection of Spinal Tumors: A Retrospective Analysis on 298 Patients Treated in a Single Institution.
Stefano BandieraLuigi Emanuele NoliCristiana GriffoniGiovanni TosiniElisa CarrettaStefano PasiniEleonora PesceAlfio Damiano RuinatoGiovanni Barbanti BrodanoGiuseppe TedescoMarco GirolamiSilvia TerziRiccardo GhermandiGisberto EvangelistiValerio PipolaAlessandro GasbarriniPublished in: Current oncology (Toronto, Ont.) (2022)
En bloc resection consists in the surgical removal of a vertebral tumor in a single piece with a sufficient margin, to improve survival and reduce recurrence rate. This procedure is technically demanding and correlates with a high complication rate. The purpose of this study is to investigate the risk factors for complications in en bloc resection and evaluate if benefits overcome the risks in term of overall survival. We retrospectively analyzed prospectively collected data of patients treated with en bloc resection between 1980 and 2021. Complications were classified according to SAVES-V2. Overall Survival was estimated using Kaplan-Meier method. A total of 149 patients out of 298 (50%) suffered from at least one complication. Moreover, 220 adverse events were collected (67 intraoperative, 82 early post-operative, 71 late post-operative), 54% of these were classified as grade 3 (in a severity scale from 1 to 6). Ten years overall survival was 67% (95% CI 59-74). The occurrence of relapses was associated to an increased risk of mortality with OR 3.4 (95% CI 2.1-5.5), while complications did not affect the overall survival. Despite a high complication rate, en bloc resection allows for a better control of disease and should be performed in selected patients by specialized surgeons.
Keyphrases
- risk factors
- end stage renal disease
- free survival
- ejection fraction
- newly diagnosed
- chronic kidney disease
- risk assessment
- prognostic factors
- type diabetes
- palliative care
- spinal cord
- preterm infants
- cardiovascular events
- cardiovascular disease
- minimally invasive
- patient reported outcomes
- big data
- patients undergoing
- machine learning
- postmenopausal women
- coronary artery disease