En bloc resection for primary bone tumours and isolated metastasis are complex surgeries associated with a high rate of adverse events (AEs). The primary objective of this study was to explore the relationship between frailty/sarcopenia and major perioperative AEs following en bloc resection for primary bone tumours or isolated metastases of the spine. Secondary objectives were to report the prevalence and distribution of frailty and sarcopenia, and determine the relationship between these factors and length of stay (LOS), unplanned reoperation, and 1-year postoperative mortality in this population.

This is a retrospective study of prospectively collected data from a single quaternary care referral center consisting of patients undergoing an elective en bloc resection for a primary bone tumour or an isolated spinal metastasis between January 1st, 2009 and February 28th, 2020. Frailty was calculated with the modified frailty index (mFI) and spine tumour frailty index (STFI). Sarcopenia, determined by the total psoas area (TPA) vertebral body (VB) ratio (TPA/VB), was measured at L3 and L4. Regression analysis produced ORs, IRRs, and HRs that quantified the association between frailty/sarcopenia and major perioperative AEs, LOS, unplanned reoperation and 1-year postoperative mortality.

One hundred twelve patients met the inclusion criteria. Using the mFI, five patients (5%) were frail (mFI ³ 0.21), while the STFI identified 21 patients (19%) as frail (STFI ³ 2). The mean CT ratios were 1.45 (SD 0.05) and 1.81 (SD 0.06) at L3 and L4 respectively. Unadjusted analysis demonstrated that sarcopenia and frailty were not significant predictors of major perioperative AEs, LOS or unplanned reoperation. Sarcopenia defined by the CT L3 TPA/VB and CT L4 TPA/VB ratios significantly predicted 1-year mortality (HR of 0.32 per one unit increase, 95% CI 0.11-0.93, p=0.04 vs. HR of 0.28 per one unit increase, 95% CI 0.11-0.69, p=0.01) following unadjusted analysis. Frailty defined by an STFI score ≥ 2 predicted 1-year postoperative mortality (OR of 2.10, 95% CI 1.02-4.30, p=0.04).

The mFI was not predictive of any clinical outcome in patients undergoing en bloc resection for primary bone tumours or isolated metastases of the spine. Sarcopenia defined by the CT L3 TPA/VB and L4 TPA/VB and frailty assessed with the STFI predicted 1-year postoperative mortality on univariate analysis but not major perioperative AEs, LOS or reoperation. Further investigation with a larger cohort is needed to identify the optimal measure for assessing frailty and sarcopenia in this spine population.

Prolonged length of stay (LOS) is a significant contributor to the variation in surgical health care costs and resource utilization after elective spine surgery. The primary goal of this study was to identify patient, surgical and institutional variables that influence LOS. The secondary objective is to examine variability in institutional practices among participating centers.

This is a retrospective study of a prospectively multicentric followed cohort of patients enrolled in the CSORN between January 2015 and October 2020. A logistic regression model and bootstrapping method was used. A survey was sent to participating centers to assessed institutional level interventions in place to decrease LOS. Centers with LOS shorter than the median were compared to centers with LOS longer than the median.

A total of 3734 patients were included (979 discectomies, 1102 laminectomies, 1653 fusions). The median LOS for discectomy, laminectomy and fusion were respectively 0.0 day (IQR 1.0), 1.0 day (IQR 2.0) and 4.0 days (IQR 2.0). Laminectomy group had the largest variability (SD=4.4, Range 0-133 days). For discectomy, predictors of LOS longer than 0 days were having less leg pain, higher ODI, symptoms duration over 2 years, open procedure, and AE (p< 0.05). Predictors of longer LOS than median of 1 day for laminectomy were increasing age, living alone, higher ODI, open procedures, longer operative time, and AEs (p< 0.05). For posterior instrumented fusion, predictors of longer LOS than median of 4 days were older age, living alone, more comorbidities, less back pain, higher ODI, using narcotics, longer operative time, open procedures, and AEs (p< 0.05). Ten centers (53%) had either ERAS or a standardized protocol aimed at reducing LOS.

In this study stratifying individual patient and institutional level factors across Canada, several independent predictors were identified to enhance the understanding of LOS variability in common elective lumbar spine surgery. The current study provides an updated detailed analysis of the ongoing Canadian efforts in the implementation of multimodal ERAS care pathways. Future studies should explore multivariate analysis in institutional factors and the influence of preoperative patient education on LOS.

Frailty has been shown to be a risk predictor for peri-operative adverse events (AEs) in patients undergoing various type of spine surgery. However, its relationship with Patient Related Outcome Measures (PROMS) remains unknown. The primary objective of this study was to determine the impact of frailty on PROMS in patients undergoing surgery for thoraco-lumbar degenerative conditions. The secondary objective was to determine the association between frailty and baseline PROMS.

This is a retrospective study of a prospective cohort of patients >55 years old who underwent surgery between 2012 and 2018. Patient data and PROMS (EQ-5D, SF-12, ODI, back and leg pain NRS) were extracted from the Canadian Spine Outcomes and Research Network registry for a single academic centre. Frailty was retrospectively calculated using the modified frailty index (mFI) and patient were classified as frail, pre-frail and non-frail. Patient characteristics and outcomes were analyzed using ANOVA or Kruskal-Wallis test for continuous variables and Chi square or Fisher's exact test for proportions. A generalized estimating equations (GEEs) regression model was used to assess the association between patients' baseline frailty status and PROMs measures at three and 12 months.

293 patients were included with a mean age of 67 ± 7 years. Twenty-two percent of the patients (n= 65) were frail, 59 % (n=172) were pre-frail and 19% (n=56) were non-frail. At baseline, the three groups had similar PROMS, except for the PCS which was worse in the frail group (mean difference [95% CI], −4.9 [−8.6;-1.1], p= 0.0083). The improvement in the EQ-5D, PCS, MCS, ODI, back and leg pain NRS scores was not significantly different between the three groups (p> 0.05). The was no difference in the evolution of the PROMS at three and 12 months between the three groups (p> 0.05).

Although frailty is a known predictor of AEs, it does not predict worse PROMS after spine surgery in that population. At baseline, non-frail, pre-frail and frail patients have similar PROMS.

Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two- dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws' head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility.

In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%.

In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system.