Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning-derived body composition analysis

Assessing a revised-risk analysis index for morbidity and mortality after spine surgery for metastatic spinal tumors

BACKGROUND

Risk Analysis Index (RAI) has been increasingly used to assess surgical frailty in various procedures, but its effectiveness in predicting mortality or in-patient hospital outcomes for spine surgery in metastatic disease remains unclear. The aim of this study was to compare the predictive values of the revised RAI (RAI-rev), the modified frailty index-5 (mFI-5), and advanced age for extended length of stay, 30-day readmission, complications, and mortality among patients undergoing spine surgery for metastatic spinal tumors.

METHODS

A retrospective cohort study was performed using the 2012-2022 ACS NSQIP database to identify adult patients who underwent spinal surgery for metastatic spinal pathologies. Using receiver operating characteristic (ROC) and multivariable analyses, we compared the discriminative thresholds and independent associations of RAI-rev, mFI-5, and greater patient age with extended length of stay (LOS), 30-day complications, hospital readmission, and mortality.

RESULTS

A total of 1,796 patients were identified, of which 1,116 (62.1%) were male and 1,008 (70.7%) were non-Hispanic White. RAI-rev identified 1,291 (71.9%) frail and 208 (11.6%) very frail patients, while mFI-5 identified 272 (15.1%) frail and 49 (2.7%) very frail patients. In the ROC analysis for extended LOS, both RAI-rev and mFI-5 showed modest predictive capabilities with area under the curve (AUC) values of 0.5477 and 0.5329, respectively, and no significant difference in their predictive abilities (p = 0.446). When compared to age, RAI-rev demonstrated superior prediction (p = 0.015). With respect to predicting 30-day readmission, no significant difference was observed between RAI-rev and mFI-5 (AUC 0.5394 l respectively, p = 0.354). However, RAI-rev outperformed age (p = 0.001). When assessing the risk of 30-day complications, RAI-rev significantly outperformed mFI-5 (AUC: 0.6016 and 0.5542 respectively, p = 0.022) but not age. Notably, RAI-rev demonstrated superior ability for predicting 30-day mortality compared to mFI-5 and age (AUC: 0.6541, 0.5652, and 0.5515 respectively, p < 0.001). Multivariate analysis revealed RAI-rev as a significant predictor of extended LOS [aOR: 1.96, 95% CI: 1.13-3.38, p = 0.016] and 30-day mortality [aOR: 5.27, 95% CI: 1.73-16.06, p = 0.003] for very frail patients. Similarly, the RAI-rev significantly predicted 30-day complications for frail [aOR: 2.63, 95% CI: 1.21-5.72, p = 0.015] and very frail [aOR: 3.69, 95% CI: 1.60-8.51, p = 0.002] patients. However, the RAI did not significantly predict 30-day readmission [Very Frail aOR: 1.52, 95% CI: 0.75-3.07, p = 0.245; Frail aOR: 1.46, 95% CI: 0.79-2.68, p = 0.225].

CONCLUSION

Our study demonstrates the utility of RAI-rev in predicting morbidity and mortality in patients undergoing spine surgery for metastatic spinal pathologies. Particularly, the superiority that RAI-rev has in predicting 30-day mortality may have significant implications in multidisciplinary decision making.

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Impact of perioperative hemoglobin-related parameters on clinical outcomes in patients with spinal metastases: identifying key markers for blood management

PURPOSE

Patients with spinal metastases undergoing surgical treatment face challenges related to preoperative anemia, intraoperative blood loss, and frailty, emphasizing the significance of perioperative blood management. This retrospective analysis aimed to assess the correlation between hemoglobin-related parameters and outcomes, identifying key markers to aid in blood management.

METHODS

A retrospective review was performed to identify patients who underwent surgical treatment for spinal metastases. Hb-related parameters, including baseline Hb, postoperative nadir Hb, predischarge Hb, postoperative nadir Hb drift, and predischarge Hb drift (both in absolute values and percentages) were subjected to univariate and multivariate analyses. These analyses were conducted in conjunction with other established variables to identify independent markers predicting patient outcomes. The outcomes of interest were postoperative short-term (6-week) mortality, long-term (1-year) mortality, and postoperative 30-day morbidity.

RESULTS

A total of 289 patients were included. Our study demonstrated that predischarge Hb (OR 0.62, 95% CI 0.44-0.88, P = 0.007) was an independent prognostic factor of short-term mortality, while baseline Hb (OR 0.76, 95% CI 0.66-0.88, P < 0.001) was identified as an independent prognostic factor of long-term mortality. Additionally, nadir Hb drift (OR 0.82, 95% CI 0.70-0.97, P = 0.023) was found to be an independent prognostic factor for postoperative 30-day morbidity.

CONCLUSIONS

This study demonstrated that predischarge Hb, baseline Hb, and nadir Hb drift are prognostic factors for outcomes. These findings provide a foundation for precise blood management strategies. It is crucial to consider Hb-related parameters appropriately, and prospective intervention studies addressing these markers should be conducted in the future.

Machine Learning in Spine Oncology: A Narrative Review

STUDY DESIGN

Narrative Review.

OBJECTIVE

Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology.

METHODS

This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies.

RESULTS

Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors.

CONCLUSION

Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.

Assessment of Spinal Metastases Surgery Risk Stratification Tools in Breast Cancer by Molecular Subtype

BACKGROUND

Breast cancer molecular features and modern therapies are not included in spine metastasis prediction algorithms.

OBJECTIVE

To examine molecular differences and the impact of postoperative systemic therapy to improve prognosis prediction for spinal metastases surgery and aid surgical decision making.

METHODS

This is a retrospective multi-institutional study of patients who underwent spine surgery for symptomatic breast cancer spine metastases from 2008 to 2021 at the Massachusetts General Hospital and Brigham and Women's Hospital. We studied overall survival, stratified by breast cancer molecular subtype, and calculated hazard ratios (HRs) adjusting for demographics, tumor characteristics, treatments, and laboratory values. We tested the performance of established models (Tokuhashi, Bauer, Skeletal Oncology Research Group, New England Spinal Metastases Score) to predict and compare all-cause.

RESULTS

A total of 98 patients surgically treated for breast cancer spine metastases were identified (100% female sex; median age, 56 years [IQR, 36-84 years]). The 1-year probabilities of survival for hormone receptor positive, hormone receptor positive/human epidermal growth factor receptor 2+, human epidermal growth factor receptor 2+, and triple-negative breast cancer were 63% (45 of 71), 83% (10 of 12), 0% (0 of 3), and 12% (1 of 8), respectively ( P < .001). Patients with triple-negative breast cancer had a higher proportion of visceral metastases, brain metastases, and poor physical activity at baseline. Postoperative chemotherapy and endocrine therapy were associated with prolonged survival. The Skeletal Oncology Research Group prognostic model had the highest discrimination (area under the receiver operating characteristic, 0.77 [95% CI, 0.73-0.81]). The performance of all prognostic scores improved when preoperative molecular data and postoperative systemic treatment plans was considered.

CONCLUSION

Spine metastases risk tools were able to predict prognosis at a significantly higher degree after accounting for molecular features which guide treatment response.

Fifty-year history of the evolution of spinal metastatic disease management

Spine metastases are a significant source of morbidity in oncology. Treatment of these spine metastases largely remains palliative, but advances over the past 50 years have improved the effectiveness of interventions for preserving functional status and obtaining local control while minimizing morbidity. While the field began with conventional external beam radiation as the primary treatment modality, a series of paradigm shifts and technological advances in the 2000s led to a change in treatment patterns. These advances allowed for an increased role of surgical decompression of neural elements, a shift in the stereotactic capabilities of radiation oncologists, and an improved understanding of the radiobiology of metastatic disease. The result was improved local control while minimizing treatment morbidity. These advances fit within the larger framework of metastatic spine tumor management known as the Neurologic, Oncologic, Mechanical, and Systemic disease decision framework. This dynamic framework takes into account the neurological function of the patient, the radiobiology of their tumor, their degree of mechanical instability, and their systemic disease control and treatment options to help determine appropriate interventions based on the individual patient. Herein, we describe the 50-year evolution of metastatic spine tumor management and the impact of various advances on the field.