Predicting survival for metastatic spine disease: a comparison of nine scoring systems

Prognostic Factors after Surgical Treatment for Spinal Metastases

STUDY DESIGN

A retrospective multicenter case series was conducted.

PURPOSE

This study aimed to investigate survival and prognostic factors after surgery for a metastatic spinal tumor.

OVERVIEW OF LITERATURE

Prognostic factors after spinal metastasis surgery remain controversial.

METHODS

A retrospective multicenter study was conducted. The study participants included 345 patients who underwent surgery for spinal metastases from 2010 to 2020 at nine referral spine centers in Japan. Data for each patient were extracted from medical records. To identify the factors predicting survival prognosis after surgery, univariate analyses were performed using a Cox proportional hazards model.

RESULTS

The mean age was 65.9 years. Common primary tumors were lung (n=72), prostate (n=61), and breast (n=39), and 67.8% (n=234) presented with osteolytic lesions. The epidural spinal cord compression scale score 2 or 3 was recognized in 79.0% (n=271). Frankel grade A paralysis accounted for 1.4% (n=5), and 73.3% (n=253) were categorized as intermediate or high risk according to the new Katagiri score. The overall survival rates were -71.0% at 6 months, 57.4% at 12, and 43.3% at 24. In the univariate analysis, Frankel grade A (hazard ratio [HR], 3.59; 95% confidence interval [CI], 1.23-10.50; p<0.05), intermediate risk (HR, 3.34; 95% CI, 2.10-5.32; p<0.01), and high risk (HR, 7.77; 95% CI, 4.72-12.8; p<0.01) in the new Katagiri score were significantly associated with poor survival. On the contrary, postoperative chemotherapy (HR, 0.23; 95% CI, 0.15-0.36; p<0.01), radiation therapy (HR, 0.43; 95% CI, 0.26-0.70; p<0.01), and both adjuvant therapy (HR, 0.21; 95% CI, 0.14-0.32; p<0.01) were suggested to improve survival.

CONCLUSIONS

Surgical indications for patients with Frankel grade A or intermediate or high risk in the new Katagiri score should be carefully considered because of poor survival. Chemotherapy or radiation therapy should be considered after surgery for better survival.

Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases

STUDY DESIGN

A retrospective study.

PURPOSE

This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis.

OVERVIEW OF LITERATURE

This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care.

METHODS

A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery.

CONCLUSIONS

These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Predictive Modeling for Spinal Metastatic Disease

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.

Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?

BACKGROUND

Bone metastasis in advanced cancer is challenging because of pain, functional issues, and reduced life expectancy. Treatment planning is complex, with consideration of factors such as location, symptoms, and prognosis. Prognostic models help guide treatment choices, with Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) showing promise in predicting survival for initial spinal metastases and extremity metastases treated with surgery or radiotherapy. Improved therapies extend patient lifespans, increasing the risk of subsequent skeletal-related events (SREs). Patients experiencing subsequent SREs often suffer from disease progression, indicating a deteriorating condition. For these patients, a thorough evaluation, including accurate survival prediction, is essential to determine the most appropriate treatment and avoid aggressive surgical treatment for patients with a poor survival likelihood. Patients experiencing subsequent SREs often suffer from disease progression, indicating a deteriorating condition. However, some variables in the SORG prediction model, such as tumor histology, visceral metastasis, and previous systemic therapies, might remain consistent between initial and subsequent SREs. Given the prognostic difference between patients with and without a subsequent SRE, the efficacy of established prognostic models-originally designed for individuals with an initial SRE-in addressing a subsequent SRE remains uncertain. Therefore, it is crucial to verify the model's utility for subsequent SREs.

QUESTION/PURPOSE

We aimed to evaluate the reliability of the SORG-MLAs for survival prediction in patients undergoing surgery or radiotherapy for a subsequent SRE for whom both the initial and subsequent SREs occurred in the spine or extremities.

METHODS

We retrospectively included 738 patients who were 20 years or older who received surgery or radiotherapy for initial and subsequent SREs at a tertiary referral center and local hospital in Taiwan between 2010 and 2019. We excluded 74 patients whose initial SRE was in the spine and in whom the subsequent SRE occurred in the extremities and 37 patients whose initial SRE was in the extremities and the subsequent SRE was in the spine. The rationale was that different SORG-MLAs were exclusively designed for patients who had an initial spine metastasis and those who had an initial extremity metastasis, irrespective of whether they experienced metastatic events in other areas (for example, a patient experiencing an extremity SRE before his or her spinal SRE would also be regarded as a candidate for an initial spinal SRE). Because these patients were already validated in previous studies, we excluded them in case we overestimated our result. Five patients with malignant primary bone tumors and 38 patients in whom the metastasis's origin could not be identified were excluded, leaving 584 patients for analysis. The 584 included patients were categorized into two subgroups based on the location of initial and subsequent SREs: the spine group (68% [399]) and extremity group (32% [185]). No patients were lost to follow-up. Patient data at the time they presented with a subsequent SRE were collected, and survival predictions at this timepoint were calculated using the SORG-MLAs. Multiple imputation with the Missforest technique was conducted five times to impute the missing proportions of each predictor. The effectiveness of SORG-MLAs was gauged through several statistical measures, including discrimination (measured by the area under the receiver operating characteristic curve [AUC]), calibration, overall performance (Brier score), and decision curve analysis. Discrimination refers to the model's ability to differentiate between those with the event and those without the event. An AUC ranges from 0.5 to 1.0, with 0.5 indicating the worst discrimination and 1.0 indicating perfect discrimination. An AUC of 0.7 is considered clinically acceptable discrimination. Calibration is the comparison between the frequency of observed events and the predicted probabilities. In an ideal calibration, the observed and predicted survival rates should be congruent. The logarithm of observed-to-expected survival ratio [log(O:E)] offers insight into the model's overall calibration by considering the total number of observed (O) and expected (E) events. The Brier score measures the mean squared difference between the predicted probability of possible outcomes for each individual and the observed outcomes, ranging from 0 to 1, with 0 indicating perfect overall performance and 1 indicating the worst performance. Moreover, the prevalence of the outcome should be considered, so a null-model Brier score was also calculated by assigning a probability equal to the prevalence of the outcome (in this case, the actual survival rate) to each patient. The benefit of the prediction model is determined by comparing its Brier score with that of the null model. If a prediction model's Brier score is lower than the null model's Brier score, the prediction model is deemed as having good performance. A decision curve analysis was performed for models to evaluate the "net benefit," which weighs the true positive rate over the false positive rate against the "threshold probabilities," the ratio of risk over benefit after an intervention was derived based on a comprehensive clinical evaluation and a well-discussed shared-decision process. A good predictive model should yield a higher net benefit than default strategies (treating all patients and treating no patients) across a range of threshold probabilities.

RESULTS

For the spine group, the algorithms displayed acceptable AUC results (median AUCs of 0.69 to 0.72) for 42-day, 90-day, and 1-year survival predictions after treatment for a subsequent SRE. In contrast, the extremity group showed median AUCs ranging from 0.65 to 0.73 for the corresponding survival periods. All Brier scores were lower than those of their null model, indicating the SORG-MLAs' good overall performances for both cohorts. The SORG-MLAs yielded a net benefit for both cohorts; however, they overestimated 1-year survival probabilities in patients with a subsequent SRE in the spine, with a median log(O:E) of -0.60 (95% confidence interval -0.77 to -0.42).

CONCLUSION

The SORG-MLAs maintain satisfactory discriminatory capacity and offer considerable net benefits through decision curve analysis, indicating their continued viability as prediction tools in this clinical context. However, the algorithms overestimate 1-year survival rates for patients with a subsequent SRE of the spine, warranting consideration of specific patient groups. Clinicians and surgeons should exercise caution when using the SORG-MLAs for survival prediction in these patients and remain aware of potential mispredictions when tailoring treatment plans, with a preference for less invasive treatments. Ultimately, this study emphasizes the importance of enhancing prognostic algorithms and developing innovative tools for patients with subsequent SREs as the life expectancy in patients with bone metastases continues to improve and healthcare providers will encounter these patients more often in daily practice.

LEVEL OF EVIDENCE

Level III, prognostic study.

Stereotactic body radiation therapy for spinal metastases: A new standard of care

Advancements in systemic therapies for patients with metastatic cancer have improved overall survival and, hence, the number of patients living with spinal metastases. As a result, the need for more versatile and personalized treatments for spinal metastases to optimize long-term pain and local control has become increasingly important. Stereotactic body radiation therapy (SBRT) has been developed to meet this need by providing precise and conformal delivery of ablative high-dose-per-fraction radiation in few fractions while minimizing risk of toxicity. Additionally, advances in minimally invasive surgical techniques have also greatly improved care for patients with epidural disease and/or unstable spines, which may then be combined with SBRT for durable local control. In this review, we highlight the indications and controversies of SBRT along with new surgical techniques for the treatment of spinal metastases.

Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery

STUDY DESIGN

Retrospective cohort study.

PURPOSE

This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis.

OVERVIEW OF LITERATURE

Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals.

METHODS

This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score.

RESULTS

In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88).

CONCLUSIONS

Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

The Impact of the COVID-19 Pandemic on the Presentation of Patients With Spinal Metastases

STUDY DESIGN

Retrospective review.

OBJECTIVE

The aim was to determine if preoperative spinal instability neoplastic scores (SINSs) and Tokuhashi prognostication scores differed in patients receiving surgical care before and during the coronavirus disease-2019 (COVID-19) pandemic.

SUMMARY OF BACKGROUND DATA

The COVID-19 pandemic has caused delays in scheduling nonemergent surgeries. Delay in presentation and/or surgical treatment for oncology patients with metastatic spinal disease could result in progression of the disease, which can complicate surgical care and worsen patient outcomes.

MATERIALS AND METHODS

Retrospective review of electronic medical records between March 1, 2019 and March 1, 2021 at a tertiary medical center was performed to identify patients who underwent surgery for metastatic spine disease. Primary spinal tumors were excluded. Patients were separated into two groups base on their surgery date: before the COVID-19 pandemic (March 1, 2019-February 29, 2020) and during the COVID-19 pandemic (March 1, 2020-March 1, 2021). Primary outcomes included SINS and Tokuhashi scores. A variety of statistical tests were performed to compare the groups.

RESULTS

Fifty-two patients who underwent surgery before the COVID-19 pandemic were compared to 41 patients who underwent surgery during the COVID-19 pandemic. There was a significant difference between the before and during groups with respect to SINS (9.31±2.39 vs . 11.00±2.74, P =0.002) and Tokuhashi scores (9.27±2.35 vs . 7.88±2.85, P =0.012). Linear regression demonstrated time of surgery (before or during COVID-19 restrictions) was a significant predictor of SINS (β=1.55, 95% CI: 0.42-2.62, P =0.005) and Tokuhashi scores (β=-1.41, 95% CI: -2.49 to -0.34, P =0.010).

CONCLUSIONS

Patients with metastatic spinal disease who underwent surgery during the COVID-19 pandemic had higher SINS, lower Tokuhashi scores and similar Skeletal Oncology Research Group scores compared to patients who underwent surgery before the pandemic. This suggests the pandemic has impacted the instability of disease at presentation in patients with spinal metastases, but has not impacted surgical prognosis, as there were no differences in Skeletal Oncology Research Group scores and the difference in Tokuhashi scores is most likely not clinically significant.

Treatment strategies in vertebral metastasis. Need for multidisciplinary committees from the perspective of the surgeon. Narration of literatura

Improvements in cancer diagnosis and treatment have improved survival. Secondarily, the number of patients who present a vertebral metastasis and the number with some morbidity in relation to these metastases also increases. Vertebral fracture, root compression or spinal cord injury cause a deterioration of their quality of life. The objective in the treatment of the vertebral metastasis must be the control of pain, maintenance of neurological function and vertebral stability, bearing in mind that in most cases it will be a palliative treatment. The treatment of these complications needs a multidisciplinary approach, radiologists, interventional radiologists, oncologists and radiation therapists, spine surgeons, but also rehabilitation or pain units. Recent studies show that a multidisciplinary approach of these patients can improve quality of life and even prognosis. In the present article, a review and reading of the literature on the multidisciplinary management of these patients is carried out.

Critical evaluation of the literature on decision-making in spinal metastases

Decision-making in patients with vertebral metastases is highly complex. Different factors of the patient, their cancer disease and treatment options are involved in it. Treatment schemes and strategies have been modified with the evolution of knowledge and treatment of disseminated oncological disease. This paper analyzes the bibliography that has been used for decision-making in the last three decades, as well as the evolution to the schemes that we could consider contemporary.

Predictors of Survival After Emergent Surgical Decompression for Acutely Presenting Spinal Metastasis

BACKGROUND

Metastatic spinal tumors represent 90% of spinal masses and present variably with slow progression and/or rapid symptomatic worsening. Several prognostic scoring systems have been proposed. However, patients presenting acutely and requiring emergent surgery represent a unique subset of patients with different prognostic indicators.

METHODS

All cases of symptomatic spinal metastases requiring emergent surgery between 2010 and 2021 at our institution were retrospectively reviewed. Survival time from date of surgery to death or last follow-up was calculated. Patients were stratified on the basis of survival for more or less than 6 months after surgery. Multivariate logistic regression was used to develop a model predicting probability of mortality at 6 months.

RESULTS

Forty-four patients satisfied inclusion criteria. Mean age at presentation was 60.4 ± 11.8 years with a median survival time of 6.5 [1.9-19.5 interquartile range] months. On univariate analysis, higher Tokuhashi score, Karnofksy performance scale (KPS), and lower modified McCormick scale were significantly associated with 6-month survival (P = 0.018, P < 0.001, P = 0.002, respectively). Preoperative American Spinal Injury Association grade and Spine Instability Neoplastic Score scores were not associated with survival. Multivariate analysis found KPS significantly correlated with survival (0.91 odds ratio, 0.85-0.98, 95% confidence interval, P = 0.011) at 6 months and that a stepwise regression model derived from KPS and Tokuhashi score demonstrated the highest predictive accuracy for 6-month survival (area under the curve = 0.843, Akaike information criterion = 37.1, P = 0.0039).

CONCLUSIONS

KPS and Tokuhashi scores most strongly correlated with 6-month survival in patients presenting with acutely symptomatic spinal metastases. These findings underscore the importance of baseline functional status and overall tumor burden on survival and may be useful in preoperative evaluation and surgical decision making for acutely presenting spinal metastases.

[Translated article] Treatment strategies in vertebral metastasis. Need for multidisciplinary committees from the perspective of the surgeon. Narration of literature

Improvements in cancer diagnosis and treatment have improved survival. Secondarily, the number of patients who present a vertebral metastasis and the number with some morbidity in relation to these metastases also increase. Vertebral fracture, root compression or spinal cord injury cause a deterioration of their quality of life. The objective in the treatment of the vertebral metastasis must be the control of pain, maintenance of neurological function and vertebral stability, bearing in mind that in most cases it will be a palliative treatment. The treatment of these complications needs a multidisciplinary approach, radiologists, interventional radiologists, oncologists and radiation therapists, spine surgeons, but also rehabilitation or pain units. Recent studies show that a multidisciplinary approach of these patients can improve quality of life and even prognosis. In the present article, a review and reading of the literature on the multidisciplinary management of these patients is carried out.

[Translated article] Critical evaluation of the literature on decision-making in spinal metastases

Decision-making in patients with vertebral metastases is highly complex. Different factors of the patient, their cancer disease and treatment options are involved in it. Treatment schemes and strategies have been modified with the evolution of knowledge and treatment of disseminated oncological disease. This paper analyzes the bibliography that has been used for decision-making in the last three decades, as well as the evolution to the schemes that we could consider contemporary.

Sociodemographic Characteristics of Patients Undergoing Surgery for Metastatic Disease of the Spine

INTRODUCTION

Some patients, particularly those who are socioeconomically deprived, are diagnosed with primary and/or metastatic cancer only after presenting to the emergency department. Our objective was to determine sociodemographic characteristics of patients undergoing surgery for metastatic spine disease at our institution.

METHODS

This retrospective case series included patients 18 years and older who presented to the emergency department with metastatic spine disease requiring surgery. Demographics and survival data were collected. Sociodemographic characteristics were estimated using the Social Deprivation Index (SDI) and Area Deprivation Index (ADI) for the state of California. Univariate log-rank tests and Kaplan-Meier curves were used to assess differences in survival for predictors of interest.

RESULTS

Between 2015 and 2021, 64 patients underwent surgery for metastatic disease of the spine. The mean age was 61.0 ± 12.5 years, with 60.9% being male (n = 39). In this cohort, 89.1% of patients were non-Hispanic (n = 57), 71.9% were White (n = 46), and 62.5% were insured by Medicare/Medicaid (n = 40). The mean SDI and ADI were 61.5 ± 28.0 and 7.7 ± 2.2, respectively. 28.1% of patients (n = 18) were diagnosed with primary cancer for the first time while 39.1% of patients (n = 25) were diagnosed with metastatic cancer for the first time. During index hospitalization, 37.5% of patients (n = 24) received palliative care consult. The 3-month, 6-month, and all-time mortality rates were 26.7% (n = 17), 39.5% (n = 23), and 50% (n = 32), respectively, with 10.9% of patients (n = 7) dying during their admission. Payor plan was significant at 3 months ( P = 0.02), and palliative consultation was significant at 3 months ( P = 0.007) and 6 months ( P = 0.03). No notable association was observed with SDI and ADI in quantiles or as continuous variables.

DISCUSSION

In this study, 28.1% of patients were diagnosed with cancer for the first time. Three-month and 6-month mortality rates for patients undergoing surgery were 26.7% and 39.5%, respectively. Furthermore, mortality was markedly associated with palliative care consultation and insurance status, but not with SDI and ADI.

LEVEL OF EVIDENCE

Retrospective case series, Level III evidence.

Assessing the Performance of Prognostic Scores in Patients with Spinal Metastases from Lung Cancer Undergoing Non-surgical Treatment

STUDY DESIGN

Retrospective study.

PURPOSE

The purpose of this study was to see how well the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic Skeletal Oncology Research Group (SORG) algorithm, SORG nomogram, and New England Spinal Metastasis Score (NESMS) predicted 3-month, 6-month, and 1-year survival of non-surgical lung cancer spinal metastases.

OVERVIEW OF LITERATURE

There has been no study assessing the performance of prognostic scores for non-surgical lung cancer spinal metastases.

METHODS

Data analysis was carried out to identify the variables that had a significant impact on survival. For all patients with spinal metastasis from lung cancer who received non-surgical treatment, the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic SORG algorithm, SORG nomogram, and NESMS were calculated. The performance of the scoring systems was assessed by using receiver operating characteristic (ROC) curves at 3 months, 6 months, and 12 months. The predictive accuracy of the scoring systems was quantified using the area under the ROC curve (AUC).

RESULTS

A total of 127 patients are included in the present study. The median survival of the population study was 5.3 months (95% confidence interval [CI], 3.7-9.6 months). Low hemoglobin was associated with shorter survival (hazard ratio [HR], 1.49; 95% CI, 1.00-2.23; p =0.049), while targeted therapy after spinal metastasis was associated with longer survival (HR, 0.34; 95% CI, 0.21-0.51; p <0.001). In the multivariate analysis, targeted therapy was independently associated with longer survival (HR, 0.3; 95% CI, 0.17-0.5; p <0.001). The AUC of the time-dependent ROC curves for the above prognostic scores revealed all of them performed poorly (AUC <0.7).

CONCLUSIONS

The seven scoring systems investigated are ineffective at predicting survival in patients with spinal metastasis from lung cancer who are treated non-surgically.

Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists.

Risk Factors for Poor Outcome after Palliative Surgery for Metastatic Spinal Tumors

Palliative surgery is performed to improve the quality of life of patients with spinal metastases. However, it is sometimes difficult to achieve the expected results because the patient's condition, and risk factors related to poor outcomes have not been well elucidated. This study aimed to evaluate the functional outcomes and investigate the risk factors for poor outcomes after palliative surgery for spinal metastasis. We retrospectively reviewed the records of 117 consecutive patients who underwent palliative surgery for spinal metastases. Neurological and ambulatory statuses were evaluated pre- and post-operatively. Poor outcomes were defined as no improvement or deterioration in functional status or early mortality, and the related risk factors were analyzed using multivariate logistic regression analysis. The results showed neurological improvement in 48% and ambulatory improvement in 70% of the patients with preoperative impairment, whereas 18% of the patients showed poor outcomes. In the multivariate analysis, low hemoglobin levels and low revised Tokuhashi scores were identified as risk factors for poor outcomes. The present results suggest that anemia and low revised Tokuhashi scores are related not only to life expectancy but also to functional recovery after surgery. Treatment options should be carefully selected for the patients with these factors.

A Comparison of Prognostic Models to Facilitate Surgical Decision-Making for Patients With Spinal Metastatic Disease

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

Compare the performance of and provide cutoff values for commonly used prognostic models for spinal metastases, including Revised Tokuhashi, Tomita, Modified Bauer, New England Spinal Metastases Score (NESMS), and Skeletal Oncology Research Group model, at three- and six-month postoperative time points.

SUMMARY OF BACKGROUND DATA

Surgery may be recommended for patients with spinal metastases causing fracture, instability, pain, and/or neurological compromise. However, patients with less than three to six months of projected survival are less likely to benefit from surgery. Prognostic models have been developed to help determine prognosis and surgical candidacy. Yet, there is a lack of data directly comparing the performance of these models at clinically relevant time points or providing clinically applicable cutoff values for the models.

MATERIALS AND METHODS

Sixty-four patients undergoing surgery from 2015 to 2022 for spinal metastatic disease were identified. Revised Tokuhashi, Tomita, Modified Bauer, NESMS, and Skeletal Oncology Research Group were calculated for each patient. Model calibration and discrimination for predicting survival at three months, six months, and final follow-up were evaluated using the Brier score and Uno's C, respectively. Hazard ratios for survival were calculated for the models. The Contral and O'Quigley method was utilized to identify cutoff values for the models discriminating between survival and nonsurvival at three months, six months, and final follow-up.

RESULTS

Each of the models demonstrated similar performance in predicting survival at three months, six months, and final follow-up. Cutoff scores that best differentiated patients likely to survive beyond three months included the Revised Tokuhashi score=10, Tomita score=four, Modified Bauer score=three, and NESMS=one.

CONCLUSION

We found comparable efficacy among the models in predicting survival at clinically relevant time points. Cutoff values provided herein may assist surgeons and patients when deciding whether to pursue surgery for spinal metastatic disease.

LEVEL OF EVIDENCE

4.

Performance assessment and external validation of specific thresholds of total psoas muscle cross-sectional area as predictors of mortality in oncologic spine surgery for spinal metastases

PURPOSE

The purpose of this study was to assess the utility of low muscle mass (LMM) in predicting 90-day and 12-month mortality after spinal tumor surgery.

METHODS

We identified 115 patients operated on for spinal metastases between April 2012 and August 2022 who had available perioperative abdominal or lumbar spine CT scans and minimum 90-day follow-up. LMM was defined as a total psoas muscle cross-sectional area (TPA) at the L4 pedicle level less than 10.5 cm² for men and less than 7.2 cm² for women based on previously reported thresholds. A secondary analysis was performed by analyzing TPA as a continuous variable. The primary endpoint was 90-day mortality, and the secondary endpoint was 12-month mortality. Multivariate logistic regression analyses were performed.

RESULTS

The 90-day mortality was 19% for patients without and 42% for patients with LMM (p = 0.010). After multivariate analysis, LMM was not independently associated with increased odds of 90-day mortality (odds ratio 2.16 [95% confidence interval 0.62 to 7.50]; p = 0.223). The 12-month mortality was 45% for patients without and 71% for patients with LMM (p = 0.024). After multivariate analysis, LMM was not independently associated with increased odds of 12-month mortality (OR 1.64 [95% CI 0.46 to 5.86]; p = 0.442). The secondary analysis showed no independent association between TPA and 90-day or 12-month mortality.

CONCLUSION

Patients with LMM had higher rates of 90-day and 12-month mortality in our study, but this was not independent of other parameters such as performance status, hypoalbuminemia, or primary cancer type.

Conditional survival after surgery for metastatic tumors of the spine: does prognosis change over time?

PURPOSE

Conditional survival (CS) provides a dynamic prediction of patient survival by incorporating the time an individual has already survived given their disease specific characteristics. The objective of the current study was to estimate CS among patients after surgery for spinal cord compression or spinal instability, as well as stratify CS according to relevant patient- and disease-related characteristics.

METHODS

The clinical outcomes of 361 patients undergoing surgical management of metastatic spinal tumors were retrospectively analyzed. Stratification of this cohort according to disease and surgery-specific characteristics allowed for univariate and multivariate statistical analyses of our study population. Observed overall and conditional survival estimates were calculated by the Kaplan-Meier method.

RESULTS

12-month conditional survival in patients undergoing surgical management of metastatic spine tumors increased from 57% at baseline to 70% at 24 months following spine surgery. Overall survival (OS) was influenced by CCI grade, Katagiri tumor type, presence of lung metastasis, type of spine surgery, presence of postoperative systemic therapy and ambulatory status at follow-up. Analyses of OS and CS by prognostic strata were similar with exception of stratification by surgery type. Differences in survival between strata tend to converge over time. Unfavorable factors for OS appear to be less relevant after a period of 24 months following spine surgery.

CONCLUSION

Patients after surgery for metastatic tumors of the spine can expect a positive trend in conditional survival as survivorship increases. Even patients with a more severe disease can be encouraged with gains in conditional survival over time.

LEVEL OF EVIDENCE

Level IV (retrospective cohort study).

Spinal tumours: recommendations of the Polish Society of Spine Surgery, the Polish Society of Oncology, the Polish Society of Neurosurgeons, the Polish Society of Oncologic Surgery, the Polish Society of Oncologic Radiotherapy, and the Polish Society of Orthopaedics and Traumatology

PURPOSE

The purpose of these recommendations is to spread the available evidence for evaluating and managing spinal tumours among clinicians who encounter such entities.

METHODS

The recommendations were developed by members of the Development Recommendations Group representing seven stakeholder scientific societies and organizations of specialists involved in various forms of care for patients with spinal tumours in Poland. The recommendations are based on data yielded from systematic reviews of the literature identified through electronic database searches. The strength of the recommendations was graded according to the North American Spine Society's grades of recommendation for summaries or reviews of studies.

RESULTS

The recommendation group developed 89 level A-C recommendations and a supplementary list of institutions able to manage primary malignant spinal tumours, namely, spinal sarcomas, at the expert level. This list, further called an appendix, helps clinicians who encounter spinal tumours refer patients with suspected spinal sarcoma or chordoma for pathological diagnosis, surgery and radiosurgery. The list constitutes a basis of the network of expertise for the management of primary malignant spinal tumours and should be understood as a communication network of specialists involved in the care of primary spinal malignancies.

CONCLUSION

The developed recommendations together with the national network of expertise should optimize the management of patients with spinal tumours, especially rare malignancies, and optimize their referral and allocation within the Polish national health service system.

Evaluation of different scoring systems for spinal metastases based on a Chinese cohort

INTRODUCTIONS

The spine is one of the most common sites of metastasis for malignancies. This study aimed to compare the predictive performance of seven commonly used prognostic scoring systems for surgically treated spine metastases. It is expected to assist surgeons in selecting appropriate scoring systems to support clinical decision-making and better inform patients.

METHODS

We performed a retrospective study involving 268 surgically treated patients with spine metastases between 2017 and 2020 at a single regional oncology center in China. The revised Tokuhashi, Tomita, modified Bauer, revised Katagiri, van der Linden, Skeletal Oncology Research Group (SORG) nomogram, and SORG machine-learning (ML) scoring systems were externally validated. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate sensitivity and specificity at different postoperative time points. The actual survival time was compared with the reference survival time provided in the original publication.

RESULTS

In the present study, the median survival was 16.6 months. The SORG ML scoring system demonstrated the highest accuracy in predicting 90-day (AUC: 0.743) and 1-year survival (AUC: 0.787). The revised Katagiri demonstrated the highest accuracy (AUC: 0.761) in predicting 180-day survival. The revised Katagiri demonstrated the highest accuracy (AUC: 0.779) in predicting 2-year survival. Based on this series, the actual life expectancy was underestimated compared with the original reference survival time.

CONCLUSIONS

None of the scoring systems can perform optimally at all time points and for all pathology types, and the reference survival times provided in the original study need to be updated. A cautious awareness of the underestimation by these models is of paramount importance in relation to current patients.

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

BACKGROUND

Many scoring systems that predict overall patient survival are based on clinical parameters and primary tumor type. To date, no consensus exists regarding which scoring system has the greatest predictive survival accuracy, especially when applied to specific primary tumors. Additionally, such scores usually fail to include modern treatment modalities, which influence patient survival. This study aimed to evaluate both the overall predictive accuracy of such scoring systems and the predictive accuracy based on the primary tumor.

METHODS

A retrospective review on spinal metastasis patients who were aged more than 18 years and underwent surgical treatment was conducted between October 2008 and August 2018. Patients were scored based on data before the time of surgery. A survival probability was calculated for each patient using the given scoring systems. The predictive ability of each scoring system was assessed using receiver operating characteristic analysis at postoperative time points; area under the curve was then calculated to quantify predictive accuracy.

RESULTS

A total of 186 patients were included in this analysis: 101 (54.3%) were men and the mean age was 57.1 years. Primary tumors were lung in 37 (20%), breast in 26 (14%), prostate in 20 (10.8%), hematologic malignancy in 18 (9.7%), thyroid in 10 (5.4%), gastrointestinal tumor in 25 (13.4%), and others in 40 (21.5%). The primary tumor was unidentified in 10 patients (5.3%). The overall survival was 201 days. For survival prediction, the Skeletal Oncology Research Group (SORG) nomogram showed the highest performance when compared to other prognosis scores in all tumor metastasis but a lower performance to predict survival with lung cancer. The revised Katagiri score demonstrated acceptable performance to predict death for breast cancer metastasis. The Tomita and revised Tokuhashi scores revealed acceptable performance in lung cancer metastasis. The New England Spinal Metastasis Score showed acceptable performance for predicting death in prostate cancer metastasis. SORG nomogram demonstrated acceptable performance for predicting death in hematologic malignancy metastasis at all time points.

CONCLUSIONS

The results of this study demonstrated inconsistent predictive performance among the prediction models for the specific primary tumor types. The SORG nomogram revealed the highest predictive performance when compared to previous survival prediction models.

The predictive ability of routinely collected laboratory markers for surgically treated spinal metastases: a retrospective single institution study

PURPOSE

We aimed to identify effective routinely collected laboratory biomarkers for predicting postoperative outcomes in surgically treated spinal metastases and attempted to establish an effective prediction model.

METHODS

This study included 268 patients with spinal metastases surgically treated at a single institution. We evaluated patient laboratory biomarkers to determine trends to predict survival. The markers included white blood cell (WBC) count, platelet count, neutrophil count, lymphocyte count, hemoglobin, albumin, alkaline phosphatase, creatinine, total bilirubin, calcium, international normalized ratio (INR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR). A nomogram based on laboratory markers was established to predict postoperative 90-day and 1-year survival. The discrimination and calibration were validated using concordance index (C-index), area under curves (AUC) from receiver operating characteristic curves, and calibration curves. Another 47 patients were used as a validation group to test the accuracy of the nomogram. The prediction accuracy of the nomogram was compared to Tomita, revised Tokuhashi, modified Bauer, and Skeletal Oncology Research Group machine-learning (SORG ML).

RESULTS

WBC, lymphocyte count, albumin, and creatinine were shown to be the independent prognostic factors. The four predictive laboratory markers and primary tumor, were incorporated into the nomogram to predict the 90-day and 1-year survival probability. The nomogram performed good with a C-index of 0.706 (0.702-0.710). For predicting 90-day survival, the AUC in the training group and the validation group was 0.740 (0.660-0.819) and 0.795 (0.568-1.000), respectively. For predicting 1-year survival, the AUC in the training group and the validation group was 0.765 (0.709-0.822) and 0.712 (0.547-0.877), respectively. Our nomogram seems to have better predictive accuracy than Tomita, revised Tokuhashi, and modified Bauer, alongside comparable prediction ability to SORG ML.

CONCLUSIONS

Our study confirmed that routinely collected laboratory markers are closely associated with the prognosis of spinal metastases. A nomogram based on primary tumor, WBC, lymphocyte count, albumin, and creatinine, could accurately predict postoperative survival for patients with spinal metastases.

Predicting survival of patients with spinal metastatic disease using PathFx 3.0 - A validation study of 668 patients in Sweden

Introduction

PathFx is a computer-based prediction model for estimating survival of patients with bone metastasis. The model has been validated in several studies, but this is the first validation using exclusively patients with spinal metastases.

Research question

Is PathFx 3.0 a tool useful for predicting survival for patients with spinal metastatic disease?

Material and methods

668 patients (67% male, median age 67 years) presenting with spinal metastases at two university hospitals in Sweden 1991-2014 were included. Of those, the majority (82%, n ​= ​551) underwent surgery. Data on all patients was analyzed with PathFx version 3.0, generating a probability of survival at 1, 3, 6, 12, 18 and 24 months. The predictions were compared to real survival data and the precision in estimation was evaluated with Receiver-Operating Characteristic curve (ROC) analysis where the Area Under Curve (AUC) was calculated. Brier score and decision curve analyses were also assessed.

Results

The AUC for 1-, 3-, 6- and 12 months survival predictions were 0.64 (95% CI 0.5-0.71), 0.71 (95% CI 0.67-0.75), 0.70 (95% CI 0.66-0.77) and 0.74 (95% CI 0.70-0.78). For 18- and 24 months survival the AUC were 0.74 (95% CI 0.69-0.78) and 0.76 (95% CI 0.72-0.81). The Brier scores were all 0.23 or lower depending on the estimated survival time.

Discussion and conclusion

PathFx 3.0 is a reasonably reliable tool for predicting survival in patients with spinal metastatic disease. As the PathFx computer model can be updated to reflect advancements in oncology, we suggest this type of model, rather than rigid point-based scoring systems, to be used for estimating survival in patients with metastatic spinal disease in the future.

SORG algorithm to predict 3- and 12-month survival in metastatic spinal disease: a cross-sectional population-based retrospective study

PURPOSE

In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.

METHODS

We recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi original and revised scores.

RESULTS

The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90 days and 0.85 for 1 year. The SORG nomogram showed a predictive ability at 90 days and 1 year with AUCs of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi original score which achieved AUCs of 0.70 and 0.69 and the Tokuhashi revised score which had AUCs of 0.65 and 0.71 for 3 months and 1 year respectively.

CONCLUSION

The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Surgical Efficacy and Prognosis of 54 Cases of Spinal Metastases from Breast Cancer

OBJECTIVE

To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on 1 year postoperative survival.

METHODS

A retrospective study was conducted of 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorder-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, 1-way tests were performed using the log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, and modified Katagiri score were examined by plotting the subject operating characteristic curve and calculating the area under the curve. The area under the curve was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG Web version for 1-year postoperative survival in patients with spinal metastases from breast cancer.

RESULTS

The average age was 51.3 ± 8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 mL. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken fixation. The mean survival was 32.2 months, including a 6-month survival of 90.7%, a 1-year survival of 77.8%, and a 2-year survival of 60.3%. Univariate analysis showed that preoperation with neurologic deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG Web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer.

CONCLUSIONS

Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival after surgery. Patients with hormone-insensitive type, with brain metastasis, have a poor prognosis, and SORG Web version can predict patients' 1-year survival more accurately.

Comparison of frailty metrics and the Charlson Comorbidity Index for predicting adverse outcomes in patients undergoing surgery for spine metastases

OBJECTIVE

Frailty-the state defined by decreased physiological reserve and increased vulnerability to physiological stress-is exceedingly common in oncology patients. Given the palliative nature of spine metastasis surgery, it is imperative that patients be healthy enough to tolerate the physical insult of surgery. In the present study, the authors compared the association of two frailty metrics and the widely used Charlson Comorbidity Index (CCI) with postoperative morbidity in spine metastasis patients.

METHODS

A retrospective cohort of patients who underwent operations for spinal metastases at a comprehensive cancer center were identified. Data on patient demographic characteristics, disease state, medical comorbidities, operative details, and postoperative outcomes were collected. Frailty was measured with the modified 5-item frailty index (mFI-5) and metastatic spinal tumor frailty index (MSTFI). Outcomes of interest were length of stay (LOS) greater than the 75th percentile of the cohort, nonroutine discharge, and the occurrence of ≥ 1 postoperative complication.

RESULTS

In total, 322 patients were included (mean age 59.5 ± 12 years; 56.9% of patients were male). The mean ± SD LOS was 11.2 ± 9.9 days, 44.5% of patients had nonroutine discharge, and 24.0% experienced ≥ 1 postoperative complication. On multivariable analysis, increased frailty on mFI-5 and MSTFI was independently predictive of all three outcomes: prolonged LOS (OR 1.67 per point, 95% CI 1.06-2.63, p = 0.03; and OR 1.63 per point, 95% CI 1.29-2.05, p < 0.01, respectively), nonroutine discharge (OR 2.65 per point, 95% CI 1.74-4.04, p < 0.01; and OR 1.69 per point, 95% CI 1.36-2.11, p < 0.01), and ≥ 1 complication (OR 1.95 per point, 95% CI 1.23-3.09, p = 0.01; and OR 1.41 per point, 95% CI 1.12-1.77, p < 0.01). CCI was found to be independently predictive of only the occurrence of ≥ 1 postoperative complication (OR 1.45 per point, 95% CI 1.22-1.72, p < 0.01).

CONCLUSIONS

Frailty measured with either mFI-5 or MSTFI scores was a more robust independent predictor of adverse postoperative outcomes than the more widely used CCI. Both mFI-5 and MSTFI were significantly associated with prolonged LOS, higher complication rates, and nonroutine discharge. Further investigation in a prospective multicenter cohort is merited.

Body composition predictors of mortality on computed tomography in patients with spinal metastases undergoing surgical treatment

BACKGROUND CONTEXT

Although survival of patients with spinal metastases has improved over the last decades due to advances in multi-modal therapy, there are currently no reliable predictors of mortality. Body composition measurements obtained using computed tomography (CT) have been recently proposed as biomarkers for survival in patients with and without cancer. Patients with cancer routinely undergo CT for staging or surveillance of therapy. Body composition assessed using opportunistic CTs might be used to determine survival in patients with spinal metastases.

PURPOSE

The purpose of this study was to determine the value of body composition measures obtained on opportunistic abdomen CTs to predict 90-day and 1-year mortality in patients with spinal metastases undergoing surgery. We hypothesized that low muscle and abdominal fat mass were positive predictors of mortality.

STUDY DESIGN

Retrospective study at a single tertiary care center in the United States.

PATIENT SAMPLE

This retrospective study included 196 patients between 2001 and 2016 that were 18 years of age or older, underwent surgical treatment for spinal metastases, and had a preoperative CT of the abdomen within three months prior to surgery.

OUTCOME MEASURES

Ninety-day and 1-year mortality by any cause.

METHODS

Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and paraspinous and abdominal skeletal muscle were performed on CT images at the level of L4 using an in-house automated algorithm. Sarcopenia was determined by total muscle CSA (cm²) divided by height squared (m²) with cutoff values of <52.4 cm²/m² for men and <38.5 cm²/m² for women. Bivariate and multivariate Cox proportional-hazard analyses were used to determine the associations between body compositions and 90-day and 1-year mortality.

RESULTS

The median age was 62 years (interquartile range=53-70). The mortality rate for 90-day was 24% and 1-year 54%. The presence of sarcopenia was associated with an increased 1-year mortality rate of 66% compared with a 1-year mortality rate of 41% in patients without sarcopenia (hazard ratio, 1.68; 95% confidence interval, 1.08-2.61; p=.02) after adjusting for various clinical factors including primary tumor type, ECOG performance status, additional metastases, neurology status, and systemic therapy. Additional analysis showed an association between sarcopenia and increased 1-year mortality when controlling for the prognostic modified Bauer score (HR, 1.58; 95%CI, 1.04-2.40; p=.03). Abdominal fat CSAs or muscle attenuation were not independently associated with mortality.

CONCLUSIONS

The presence of sarcopenia is associated with an increased risk of 1-year mortality for patients surgically treated for spinal metastases. Sarcopenia retained an independent association with mortality when controlling for the prognostic modified Bauer score. This implies that body composition measurements such as sarcopenia could serve as novel biomarkers for prediction of mortality and may supplement other existing prognostic tools to improve shared decision making for patients with spinal metastases that are contemplating surgical treatment.

Treatment Outcomes in Spinal Metastatic Disease With Indeterminate Stability

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The purpose of this study was to compare outcomes between different treatment modalities for metastatic disease with indeterminate instability (Spinal Instability Neoplastic Score [SINS] 7-12).

METHODS

We retrospectively reviewed neurologically intact patients treated for spinal metastatic disease with a SINS of 7 to 12. The cohort was stratified by treatment approach: external beam radiation therapy alone (EBRT), surgery + EBRT (S+E), and cement augmentation + EBRT (K+E). Kaplan-Meier analysis was used to assess differences in length of survival (LOS) and ability to ambulate at time of death. Multivariate analysis was performed to assess adjusted LOS and ability to ambulate at time of death.

RESULTS

The cohort included 211 patients, S+E (n = 57), EBRT (n = 128), and K+E (n = 27). In the S+E group, the median LOS was 430 days, which was statistically longer than the median LOS for the EBRT group (121 days) and the K+E group (169 days). In the S+E group, 52 patients (91.2%) and in the K+E group 24 patients (92.3%) retained the ability to ambulate at their time of death compared to 99 patients (77.3%) of the EBRT patients (P = .01). The overall rate of revision treatment at the spinal level initially treated was 17.5%, S+E (15.8%), EBRT (20.3%), and K+E (7.7%).

CONCLUSIONS

The length of survival, ability to maintain ambulatory ability, and revision treatment rates were all improved following surgical management and radiation therapy compared to radiation therapy alone. The authors' conclusion from these results are that patients with indeterminate spinal instability should be discussed in a multidisciplinary setting for the need of spinal stabilization in addition to radiation therapy.

Decreased psoas muscle area is a prognosticator for 90-day and 1-year survival in patients undergoing surgical treatment for spinal metastasis

BACKGROUND AND AIMS

Survival estimation for patients with spinal metastasis is crucial to treatment decisions. Psoas muscle area (PMA), a surrogate for total muscle mass, has been proposed as a useful survival prognosticator. However, few studies have validated the predictive value of decreased PMA in an Asian cohort or its predictive value after controlling for existing preoperative scoring systems (PSSs). In this study, we aim to answer: (1) Is PMA associated with survival in Han Chinese patients with spinal metastasis? (2) Is PMA a good prognosticator according to concordance index (c-index) and decision curve analysis (DCA) after controlling for six existing and commonly used PSSs?

METHODS

This study included 180 adult (≥18 years old) Taiwanese patients with a mean age of 58.3 years (range: 22-85) undergoing surgical treatment for spinal metastasis. A patient's PMA was classified into decreased, medium, and large if it fell into the lower (0-33%), middle (33-67%), and upper (67-100%) 1/3 in the study cohort, respectively. We used logistic and cox proportional-hazard regressions to assess whether PMA was associated with 90-day, 1-year, and overall survival. The model performance before and after addition of PMA to six commonly used PSSs, including Tomita score, original Tokuhashi score, revised Tokuhashi score, modified Bauer score, New England Spinal Metastasis Score, and Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs), was compared by c-index and DCA to determine if PMA was a useful survival prognosticator.

RESULTS

Patients with a larger PMA is associated with better 90-day, but not 1-year, survival. The model performance of 90-day survival prediction improved after PMA was incorporated into all PSSs except SORG-MLAs. PMA barely improved the discriminatory ability (c-index, 0.74; 95% confidence interval [CI], 0.67-0.82 vs. c-index, 0.74; 95% CI, 0.66-0.81) and provided little gain of clinical net benefit on DCA for SORG-MLAs' 90-day survival prediction.

CONCLUSIONS

PMA is a prognosticator for 90-day survival and improves the discriminatory ability of earlier-proposed PSSs in our Asian cohort. However, incorporating PMA into more modern PSSs such as SORG-MLAs did not significantly improve its prediction performance.

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

•

Prediction of survival is crucial for guiding patient-tailored treatment.

•

Radiomics can be described as the next era of possibilities in precision medicine.

•

Radiomics model had an inferior performance with no added predictive power to the clinical predictive model.

Study design

Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute.

Objective

This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM).

Background data

Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression.

Methods

A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.

A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient.

Results

The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.

PROFILE OF PATIENTS WITH SPINE TUMOR OPERATED IN A SOUTH AMERICAN REFERENCE SERVICE. EPIDEMIOLOGICAL STUDY

ABSTRACT Objective: The objective was to conduct an analytical epidemiological study to understand the profile, treatment, and outcome of patients with spinal tumors in a Brazilian Quaternary Hospital of the SUS. Methods: A retrospective cohort analysis of data from the last five years was performed. It was described qualitative characteristics evaluated by absolute and relative frequencies and quantitative characteristics by sintetized measures. Associations between characteristics were verified using chi-square tests or exact tests. Results: 92 patients met the eligibility criteria. The mean age was 56.1 years (±14.7), with 48 men (52.2%) and 44 women (47.8%). The types of tumors organized in the three proposed groups had 19 multiple myelomas (20.7%), 62 metastases (67.3%), and 11 other tumors (12%). The neurological status measured through the ASIA score was A: 5.4%, B: 22.8%, C: 26.1%, D: 35.9%, E: 9.8%. Karnofsky was prevalent in the 50-70 range with 65.2%. The total hospitalization period had a mean of 22.8±18 days, preoperatively 11.9±9.2 days, and postoperatively 10.9±14 days. Karnofsky presented lower values according to the worst ASIA (p < 0.001). A total of 12 patients (13%) died during hospitalization. The total and postoperative length of stay was longer in patients who died (p = 0.002 and p < 0.001). Conclusions: This study provides epidemiological data that allow an understanding of the profile of patients with spinal tumors in the Brazilian Public Health System. The severity of the patients is higher when compared to most of the series cases in the literature. The patients with longer hospitalization stay died. Level of evidence IV; Case series.

Prospective comparison of the accuracy of the New England Spinal Metastasis Score (NESMS) to legacy scoring systems in prognosticating outcomes following treatment of spinal metastases

BACKGROUND CONTEXT

We developed the New England Spinal Metastasis Score (NESMS) as a simple, informative, scoring scheme that could be applied to both operative and non-operative patients. The performance of the NESMS to other legacy scoring systems has not previously been compared using appropriately powered, prospectively collected, longitudinal data.

PURPOSE

To compare the predictive capacity of the NESMS to the Tokuhashi, Tomita and Spinal Instability Neoplastic Score (SINS) in a prospective cohort, where all scores were assigned at the time of baseline enrollment.

PATIENT SAMPLE

We enrolled 202 patients with spinal metastases who met inclusion criteria between 2017-2019.

OUTCOME MEASURES

One-year survival (primary); 3-month mortality and ambulatory function at 3- and 6-months were considered secondarily.

METHODS

All prognostic scores were assigned based on enrollment data, which was also assigned as time-zero. Patients were followed until death or survival at 365 days after enrollment. Survival was assessed using Kaplan-Meier curves and score performance was determined via logistic regression testing and observed to expected plots. The discriminative capacity (c-statistic) of the scoring measures were compared via the z-score.

RESULTS

When comparing the discriminative capacity of the predictive scores, the NESMS had the highest c-statistic (0.79), followed by the Tomita (0.69), the Tokuhashi (0.67) and the SINS (0.54). The discriminative capacity of the NESMS was significantly greater (p-value range: 0.02 to <0.001) than any of the other predictive tools. The NESMS was also able to inform independent ambulatory function at 3- and 6-months, a function that was only uniformly replicated by the Tokuhashi score.

CONCLUSIONS

The results of this prospective validation study indicate that the NESMS was able to differentiate survival to a significantly higher degree than the Tokuhashi, Tomita and SINS. We believe that these findings endorse the utilization of the NESMS as a prognostic tool capable of informing care for patients with spinal metastases.

Quantified CIN Score From Cell-free DNA as a Novel Noninvasive Predictor of Survival in Patients With Spinal Metastasis

Purpose: Most currently available scores for survival prediction of patients with bone metastasis lack accuracy. In this study, we present a novel quantified CIN (Chromosome Instability) score modeled from cfDNA copy number variation (CNV) for survival prediction. Experimental Design: Plasma samples collected from 67 patients with bone metastases from 11 different cancer types between November 2015 and May 2016 were sent through low-coverage whole genome sequencing followed by CIN computation to make a correlation analysis between the CIN score and survival prognosis. The results were validated in an independent cohort of 213 patients. Results: During the median follow-up period of 598 (95% CI 364-832) days until December 25, 2018, 124 (44.3%) of the total 280 patients died. Analysis of the discovery dataset showed that CIN score = 12 was the optimal CIN cutoff. Validation dataset showed that CIN was elevated (score ≥12) in 87 (40.8%) patients, including 5 (5.75%) with head and neck cancer, 11 (12.6%) with liver and gallbladder cancer, 11 (12.6%) with cancer from unidentified sites, 21 (24.1%) with lung cancer, 7 (8.05%) with breast cancer, 4 (4.60%) with thyroid cancer, 6 (6.90%) with colorectal cancer, 4 (4.60%) with kidney cancer, 2 (2.30%) with prostate cancer, and 16 (18.4%) with other types of cancer. Further analysis showed that patients with elevated CIN were associated with worse survival (p < 0.001). For patients with low Tokuhashi score (≤8) who had predictive survival of less than 6 months, the CIN score was able to distinguish patients with a median overall survival (OS) of 443 days (95% CI 301-585) from those with a median OS of 258 days (95% CI 184-332). Conclusion: CNV examination in bone metastatic cancer from cfDNA is superior to the traditional predictive model in that it provides a noninvasive and objective method of monitoring the survival of patients with spine metastasis.

Comparison of three predictive scoring systems for morbidity in oncological spine surgery

Estimating complications in oncological spine surgery is challenging. The objective of this study was to compare the accuracy of three scoring systems for predicting perioperative morbidity after surgery for spinal metastases. One-hundred and five patients who underwent surgery between 2013 and 2019 were included in this study. All patients had scores retrospectively calculated using the New England Spinal Metastasis Score (NESMS), Metastatic Spinal Tumor Frailty Index (MSTFI), and Anzuategui scoring systems. The main outcome measure was development of a medical complication (minor or major) within 30 days of surgery. The predictive ability for each system was assessed using receiver operating characteristic analysis and calculations of the area under the curve (AUC). The average age for all patients was 61 years and 61/105 patients (58.1%) were male. The most common primary tumor origins were hematologic (23.8%), prostate (16.2%), breast (14.3%), and lung (13.3%). The overall 30-day complication rate was 36.2% and the rate of major complications was 21.9%. Among all patients who underwent oncological spine surgery, the NESMS score had the highest AUC for 30-day overall (AUC 0.64; 95% CI, 0.53 - 0.75) and major morbidity (AUC 0.68; 95% CI, 0.54- 0.81) in our population. However, the accuracy did not meet the threshold for clinical utility. Future prospective validation of these systems in other populations is encouraged.

10-Year Trends in the Surgical Management of Patients with Spinal Metastases: A Scoping Review

BACKGROUND

Spinal metastases are present in approximately 20% of patients with cancer, giving a risk for neurologic dysfunction and instability. In already frail patients, surgeons strive to improve quality of life. Our goal was to review a 10-year trend in the surgical management of spinal metastases.

METHODS

A scoping review was performed systematically using PubMed to assess trends in surgical treatment for spinal metastases. The search terms used were: metastas∗, "neoplasm metastasis"[Mesh], "Spine"[Mesh], spine, spinal, "vertebral column," "vertebral body," laser, robot, radiofrequency, screws, fixation, "separation surgery," corpectomy, vertebrectomy, spondylectomy, vertebroplasty, kyphoplasty, surgery, "open surgery," "mini open surgery," "minimally invasive surgery," endoscopy, thoracoscopy, corpectom∗, vertebrectom∗, spondylectom∗, "en bloc," and MIS. The variables of interest were neurologic improvement, tumor recurrence, reoperation, and overall survival.

RESULTS

A total of 2132 articles were found within the primary query. Fifty-six studies were selected for final review. The results were organized into main surgical practices: decompression, mechanical stabilization, and pain management. For separation surgery, clinical outcomes were overall 1-year survival, 40.7%-78.4%; recurrence rate, 4.3%-22%; reoperation, 5%; and complications, 5.4%-14%. For corpectomy, clinical outcomes were overall 1-year survival, 30%-92%; reoperation, 1.1%-50%; and recurrence rate, of 1.1%-28%. Complications and reoperations with spinal instrumentation were 0%-13.6% and 0%-15%, respectively. Cement augmentation achieved pain reduction rates of 56%-100%, neurologic improvement/stability 84%-100%, and complication rates 6%-56%. Laser achieved local tumor control rate of 71%-82% at 1 year follow-up, reoperation rate of 15%-31%, and complication rate of 5%-26%.

CONCLUSIONS

Minimally invasive techniques for decompression and stabilization seem to be the preferred method to surgically treat metastatic spine disease, with good outcomes. More research with high level of evidence is required to support the long-term outcomes of these approaches.

International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort

BACKGROUND CONTEXT

Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and one-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population.

STUDY DESIGN/SETTING

Retrospective study at a single tertiary care center in Taiwan PATIENT SAMPLE: Four hundred and twenty-seven patients who underwent surgery for metastatic spine disease from November 1, 2010 to December 31, 2018 OUTCOME MEASURES: 90-day and one-year mortality METHODS: The baseline characteristics of our validation cohort were compared with those of the previously published developmental and external validation cohorts. Discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in this cohort.

RESULTS

Ninety-day and one-year mortality rates were 110 of 427 (26%) and 256 of 427 (60%), respectively. The external validation cohort and the developmental cohort differed in body mass index (BMI), preoperative performance status, American Spinal Injury Association impairment scale, primary tumor histology and in several laboratory measurements. The SORG ML algorithm for 90-day and 1-year mortality demonstrated a high level of discriminative ability (c-statistics of 0.73 [95% confidence interval [CI], 0.67-0.78] and 0.74 [95% CI, 0.69-0.79]), overall performance, and had a positive net benefit throughout the range of threshold probabilities in decision curve analysis. The algorithm for 1-year mortality had a calibration intercept of 0.08, representing a good calibration. However, the 90-day mortality algorithm underestimated mortality for the lowest predicted probabilities, with an overall intercept of 0.81.

CONCLUSIONS

The SORG algorithms for predicting 90-day and 1-year mortality in patients with spinal metastatic disease generally performed well on international external validation in a predominately Taiwanese population. However, 90-day mortality was underestimated in this group. Whether this inconsistency was due to different primary tumor characteristics, body mass index, selection bias or other factors remains unclear, and may be better understood with further validative works that utilize international and/or diverse populations.

Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis

BACKGROUND CONTEXT

Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling.

PURPOSE

To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis.

STUDY DESIGN/SETTING

Retrospective, cohort study PATIENT SAMPLE: Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES: Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS: This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis.

RESULTS

Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients.

CONCLUSIONS

With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.

Prognostic scores for survival as decisional support for surgery in spinal metastases: a performance assessment systematic review

PURPOSE

To review the evidence on the relative prognostic performance of the available prognostic scores for survival in spinal metastatic surgery in order to provide a recommendation for use in clinical practice.

METHODS

A systematic review of comparative external validation studies assessing the performance of prognostic scores for survival in independent cohorts was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies were identified through Medline and Embase until May 2021. Studies were included when they compared at least four survival scoring systems in surgical or mixed cohorts across all primary tumor types. Predictive performance was assessed based on discrimination and calibration for 3-month, 1-year and overall survival, and generalizability was assessed based on the characteristics of the development cohort and external validation cohorts. Risk of bias and concern regarding applicability were assessed based on the 'Prediction model study Risk Of Bias Assessment Tool' (PROBAST).

RESULTS

Twelve studies fulfilled the inclusion criteria and covered 17 scoring systems across 5.130 patients. Several scores suffer from suboptimal development and validation. The SORG Nomogram, developed in a large surgical cohort, showed good discrimination on 3-month and 1-year survival, good calibration and was superior in direct comparison with low risk of bias and low concern regarding applicability. Machine learning algorithms are promising as they perform equally well in direct comparison. Tokuhashi, Tomita and other traditional risk scores showed suboptimal performance.

CONCLUSION

The SORG Nomogram and machine learning algorithms outline superior performance in survival prediction for surgery in spinal metastases. Further improvement by comparative validation in large multicenter, prospective cohorts can still be obtained. Given the heterogeneity of spinal metastases, superior methodology of development and validation is key in improving future machine learning systems.

Interhospital transfer status for spinal metastasis patients in the United States is associated with more severe clinical presentations and higher rates of inpatient complications

OBJECTIVE

In patients with metastatic spinal disease (MSD), interhospital transfer can potentially impact clinical outcomes as the possible benefits of transferring a patient to a higher level of care must be weighed against the negative effects associated with potential delays in treatment. While the association of clinical outcomes and transfer status has been examined in other specialties, the relationship between transfer status, complications, and risk of mortality in patients with MSD has yet to be explored. The purpose of this study was to examine the impact of transfer status on in-hospital mortality and clinical outcomes in patients diagnosed with MSD.

METHODS

The National (Nationwide) Inpatient Sample (NIS) database was retrospectively queried for adult patients diagnosed with vertebral pathological fracture and/or spinal cord compression in the setting of metastatic disease between 2012 and 2014. Demographics, baseline characteristics (e.g., metastatic spinal cord compression [MSCC] and paralysis), comorbidities, type of intervention, and relevant patient outcomes were controlled in a multivariable logistic regression model to analyze the association of transfer status with patient outcomes.

RESULTS

Within the 10,360 patients meeting the inclusion and exclusion criteria, higher rates of MSCC (50.2% vs 35.9%, p < 0.001) and paralysis (17.3% vs 8.4%, p < 0.001) were observed in patients transferred between hospitals compared to those directly admitted. In univariable analysis, a higher percentage of transferred patients underwent surgical intervention (p < 0.001) when compared with directly admitted patients. After controlling for significant covariates and surgical intervention, transferred patients were more likely to develop in-hospital complications (OR 1.34, 95% CI 1.18-1.52, p < 0.001), experience prolonged length of stay (OR 1.33, 95% CI 1.16-1.52, p < 0.001), and have a discharge disposition other than home (OR 1.70, 95% CI 1.46-1.98, p < 0.001), with no significant difference in inpatient mortality rates.

CONCLUSIONS

Patients with MSD who were transferred between hospitals demonstrated more severe clinical presentations and higher rates of inpatient complications compared to directly admitted patients, despite demonstrating no difference in in-hospital mortality rates.

Surgical management of spinal metastases involving the cervicothoracic junction: results of a multicenter, European observational study

OBJECTIVE

Surgical management of spinal metastases at the cervicothoracic junction (CTJ) is highly complex and relies on case-based decision-making. The aim of this multicentric study was to describe surgical procedures for metastases at the CTJ and provide guidance for clinical and surgical management.

METHODS

Patients eligible for this study were those with metastases at the CTJ (C7-T2) who had been consecutively treated in 2005-2019 at 7 academic institutions across Europe. The Spine Instability Neoplastic Score, neurological function, clinical status, medical history, and surgical data for each patient were retrospectively assessed. Patients were divided into four surgical groups: 1) posterior decompression only, 2) posterior decompression and fusion, 3) anterior corpectomy and fusion, and 4) anterior corpectomy and 360° fusion. Endpoints were complications, surgical revision rate, and survival.

RESULTS

Among the 238 patients eligible for inclusion this study, 37 were included in group 1 (15%), 127 in group 2 (53%), 18 in group 3 (8%), and 56 in group 4 (24%). Mechanical pain was the predominant symptom (79%, 189 patients). Surgical complications occurred in 16% (group 1), 20% (group 2), 11% (group 3), and 18% (group 4). Of these, hardware failure (HwF) occurred in 18% and led to surgical revision in 7 of 8 cases. The overall complication rate was 34%. In-hospital mortality was 5%.

CONCLUSIONS

Posterior fusion and decompression was the most frequently used technique. Care should be taken to choose instrumentation techniques that offer the highest possible biomechanical load-bearing capacity to avoid HwF. Since the overall complication rate is high, the prevention of in-hospital complications seems crucial to reduce in-hospital mortality.

The "Spinal Metastasis Invasiveness Index": A Novel Scoring System to Assess Surgical Invasiveness

STUDY DESIGN

Retrospective review.

OBJECTIVE

The aim of this study was to develop a surgical invasiveness index for metastatic spine tumor surgery (MSTS) that can serve as a standardized tool in predicting intraoperative blood loss and surgical duration; for the purpose of ascertaining resource requirements and aiding in patient education.

SUMMARY OF BACKGROUND DATA

Magnitude of surgery is important in the metastatic spine disease (MSD) population since these patients have a continuing postoperative oncological process; a consideration that must be taken into account to maintain or improve quality of life. Surgical invasiveness indices have been established for general spine surgery, adult deformity, and cervical deformity, but not yet for spinal metastasis.

METHODS

Demographic, oncological, and procedural data were collected from consecutive patients that underwent MSTS. Binary logistic regression, using median values for surgical duration and intraoperative estimated blood loss (EBL), was used to determine statistical significance of variables to be included in the "spinal metastasis invasiveness index" (SMII). The corresponding weightage of each of these variables was agreed upon by experienced spine surgeons. Multivariable regression analysis was used to predict operative time and EBL while controlling for demographical, procedural, and oncological characteristics.

RESULTS

Two hundred and sixty-one MSD patients were included with a mean age of 59.7-years and near equal sex distribution. The SMII strongly predicted extended surgical duration (R2 = 0.28, P < 0.001) and high intraoperative blood loss (R2 = 0.18, P < 0.001). When compared to a previously established surgical invasiveness index, the SMII accounted for more variability in the outcomes. For every unit increase in score, there was a 42-mL increase in mean blood loss (P < 0.001) and 5-minute increase in mean operative time (P < 0.001).

CONCLUSION

Long surgical duration and high blood loss were strongly predicted by the newly developed SMII. The use of the SMII may aid in preoperative risk assessment with the goal of improving patient outcomes and quality of life.Level of Evidence: 4.

The Effect of Adding Biological Factors to the Decision-Making Process for Spinal Metastasis of Non-Small Cell Lung Cancer

Molecular target therapies have markedly improved the survival of non-small cell lung cancer (NSCLC) patients, especially those with epidermal growth factor receptor (EGFR) mutations. A positive EGFR mutation is even more critical when the chronicity of spinal metastasis is considered. However, most prognostic models that estimate the life expectancy of spinal metastasis patients do not include these biological factors. We retrospectively reviewed 85 consecutive NSCLC patients who underwent palliative surgical treatment for spinal metastases to evaluate the following: (1) the prognostic value of positive EGFR mutation and the chronicity of spinal metastasis, and (2) the clinical significance of adding these two factors to an existing prognostic model, namely the New England Spinal Metastasis Score (NESMS). Among 85 patients, 38 (44.7%) were EGFR mutation-positive. Spinal metastasis presented as the initial manifestation of malignancy in 58 (68.2%) patients. The multivariate Cox proportional hazard model showed that the chronicity of spinal metastasis (hazard ratio (HR) = 1.88, p = 0.015) and EGFR mutation positivity (HR = 2.10, p = 0.002) were significantly associated with postoperative survival. The Uno’s C-index and time-dependent AUC 6 months following surgery significantly increased when these factors were added to NESMS (p = 0.004 and p = 0.022, respectively). In conclusion, biological factors provide an additional prognostic value for NSCLC patients with spinal metastasis.

Sarcopenia as a Prognostic Factor for 90-Day and Overall Mortality in Patients Undergoing Spine Surgery for Metastatic Tumors: A Multicenter Retrospective Cohort Study

BACKGROUND

Novel methods in predicting survival in patients with spinal metastases may help guide clinical decision-making and stratify treatments regarding surgery vs palliative care.

OBJECTIVE

To evaluate whether the frailty/sarcopenia paradigm is predictive of survival and morbidity in patients undergoing surgery for spinal metastasis.

METHODS

A total of 271 patients from 4 tertiary care centers who had undergone surgery for spinal metastasis were identified. Frailty/sarcopenia was defined by psoas muscle size. Survival hazard ratios were calculated using multivariate analysis, with variables from demographic, functional, oncological, and surgical factors. Secondary outcomes included improvement of neurological function and postoperative morbidity.

RESULTS

Patients in the smallest psoas tertile had shorter overall survival compared to the middle and largest tertile. Psoas size (PS) predicted overall mortality more strongly than Tokuhashi score, Tomita score, and Karnofsky Performance Status (KPS). PS predicted 90-d mortality more strongly than Tokuhashi score, Tomita score, and KPS. Patients with a larger PS were more likely to have an improvement in deficit compared to the middle tertile. PS was not predictive of 30-d morbidity.

CONCLUSION

In patients undergoing surgery for spine metastases, PS as a surrogate for frailty/sarcopenia predicts 90-d and overall mortality, independent of demographic, functional, oncological, and surgical characteristics. The frailty/sarcopenia paradigm is a stronger predictor of survival at these time points than other standards. PS can be used in clinical decision-making to select which patients with metastatic spine tumors are appropriate surgical candidates.

Utility of prediction model score: a proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review

OBJECTIVE

The objective of this study was to evaluate the characteristics and performance of current prediction models in the fields of spine metastasis and degenerative spine disease to create a scoring system that allows direct comparison of the prediction models.

METHODS

A systematic search of PubMed and Embase was performed to identify relevant studies that included either the proposal of a prediction model or an external validation of a previously proposed prediction model with 1-year outcomes. Characteristics of the original study and discriminative performance of external validations were then assigned points based on thresholds from the overall cohort.

RESULTS

Nine prediction models were included in the spine metastasis category, while 6 prediction models were included in the degenerative spine category. After assigning the proposed utility of prediction model score to the spine metastasis prediction models, only 1 reached the grade of excellent, while 2 were graded as good, 3 as fair, and 3 as poor. Of the 6 included degenerative spine models, 1 reached the excellent grade, while 3 studies were graded as good, 1 as fair, and 1 as poor.

CONCLUSIONS

As interest in utilizing predictive analytics in spine surgery increases, there is a concomitant increase in the number of published prediction models that differ in methodology and performance. Prior to applying these models to patient care, these models must be evaluated. To begin addressing this issue, the authors proposed a grading system that compares these models based on various metrics related to their original design as well as internal and external validation. Ultimately, this may hopefully aid clinicians in determining the relative validity and usability of a given model.

Inpatient palliative care utilization for patients with brain metastases

Introduction

Given the high symptom burden and complex clinical decision making associated with a diagnosis of brain metastases (BM), specialty palliative care (PC) can meaningfully improve patient quality of life. However, no prior study has formally evaluated patient-specific factors associated with PC consultation among BM patients.

Methods

We examined the rates of PC consults in a cohort of 1303 patients with BM admitted to three tertiary medical centers from October 2015 to December 2018. Patient demographics, surgical status, 30-day readmission, and death data were collected via retrospective chart review. PC utilization was assessed by identifying encounters for which an inpatient consult to PC was placed. Statistical analyses were performed to compare characteristics and outcomes between patients who did and did not receive PC consults.

Results

We analyzed 1303 patients admitted to the hospital with BM. The average overall rate of inpatient PC consultation was 19.6%. Rates of PC utilization differed significantly by patient race (17.5% in White/Caucasian vs 26.0% in Black/African American patients, P = .0014). Patients who received surgery during their admission had significantly lower rates of PC consultation (3.9% vs 22.4%, P < .0001). Patients who either died during their admission or were discharged to hospice had significantly higher rates of PC than those who were discharged home or to rehabilitation (P < .0001).

Conclusions

In our dataset, PC consultation rates varied by patient demographic, surgical status, discharging service, and practice setting. Further work is needed to identify the specific barriers to optimally utilizing specialty PC in this population.