Surgical treatment of metastatic spine disease: an update on national trends and clinical outcomes from 2010 to 2014

Outcomes in Patients with Spinal Metastases Managed with Surgical Intervention

BACKGROUND

Spinal metastases are a significant cause of morbidity in patients with advanced cancer, and management often requires surgical intervention. Although prior studies have identified factors that influence outcomes with surgery, the ability of these factors to predict outcomes remains unclear in the era of contemporary therapies, and there is a need to better identify patients who are likely to benefit from surgery.

METHODS

We performed a single-center, retrospective analysis to evaluate risk factors for poor outcomes in patients with spinal metastases treated with surgery. The primary outcome was mortality at 180 days.

RESULTS

A total of 128 patients were identified. Age ≥ 65 years at surgery (p = 0.0316), presence of extraspinal metastases (p = 0.0110), and ECOG performance scores >1 (p = 0.0397) were associated with mortality at 180 days on multivariate analysis. These factors and BMI ≤ 30 mg/kg2 (p = 0.0008) were also associated with worse overall survival.

CONCLUSIONS

Age > 65, extraspinal metastases, and performance status scores >1 are factors associated with mortality at 180 days in patients with spinal metastases treated with surgery. Patients with these factors and BMI ≤ 30 mg/kg2 had worse overall survival. Our results support multidisciplinary discussions regarding the benefits and risks associated with surgery in patients with these risk factors.

Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques

BACKGROUND CONTEXT

Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies.

PURPOSE

The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease.

STUDY DESIGN/SETTING

An observational cohort study.

PATIENT SAMPLE

Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort.

OUTCOME MEASURES

The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more.

METHODS

Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance.

RESULTS

The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/.

CONCLUSIONS

The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery.

Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease

BACKGROUND CONTEXT

Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited.

PURPOSE

The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease.

STUDY DESIGN/SETTING

A prospective cohort study.

PATIENT SAMPLE

A total of 1043 cancer patients with spinal metastatic disease were included.

OUTCOME MEASURES

The main outcome was severe psychological distress.

METHODS

The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set.

RESULTS

Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788-0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768-0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770-0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756-0.916; Accuracy: 0.783).

CONCLUSIONS

Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress.

Hardware Failure in Spinal Tumor Surgery: A Hallmark of Longer Survival?

Objective: Instrumentation failure in spine tumor surgery is a common reason for revision operation. Increases in patient survival demand a better understanding of the hardware longevity. The study objective was to investigate risk factors for instrumentation failure requiring revision surgery in patients with spinal tumors.Methods: A retrospective cohort from a single tertiary care specialty hospital from January 2005 to January 2021, for patients with spinal primary or metastatic tumors who underwent surgical intervention with instrumentation. Demographic and treatment data were collected and analyzed. Kaplan-Meier analysis was performed for overall survival, and separate univariate and multivariate regression analysis was performed.Results: Three hundred fifty-one patients underwent surgical intervention for spinal tumor, of which 23 experienced instrumentation failure requiring revision surgery (6.6%). Multivariate regression analysis identified pelvic fixation (odds ratio [OR], 10.9), spinal metastasis invasiveness index (OR, 1.11), and survival of greater than 5 years (OR, 3.6) as significant risk factors for hardware failure. One- and 5-year survival rates were 57% and 8%, respectively.Conclusion: Instrumentation failure after spinal tumor surgery is a common reason for revision surgery. Our study suggests that the use of pelvic fixation, invasiveness of the surgery, and survival greater than 5 years are independent risk factors for instrumentation failure.

National trends in the surgical management of metastatic lung cancer to the spine using the national inpatient sample database from 2005 to 2014

Optimal management of metastatic lung cancer to the spine (MLCS) incorporates a multidisciplinary approach. With improvements in lung cancer screening andnonsurgical treatment, the role for surgerymay be affected. The objective of this study is to assess trends in the surgical management of MLCS using the National Inpatient Sample (NIS) database. The NIS was queried for patients with MLCS who underwent surgery from 2005 to 2014. The frequencies of spinal decompression alone, spinal stabilization with or without (+/-) decompression, and vertebral augmentation were calculated. Statistical analysis was performed to analyze the effect of patient characteristics on outcomes. The most common procedure performed was vertebral augmentation (10719, 44.3%), followed by spinal stabilization +/- decompression (8634, 35.7%) and then decompression alone (4824, 20.0%). The total number of surgeries remained stable, while the rate of spinal stabilizations increased throughout the study period (p < 0.001). Invasive procedures such as stabilization and decompression were associated with greater costs, length of stay,complications and mortality. Increasingcomorbidity was associated with increased odds of complication, especially in patients undergoing more invasive procedures. In patients with lowpre-operative comorbidity, the type of procedure did not influence the odds of complication. Graded increases in length of stay, cost and mortality were seen with increasing complication rate.The rate of spinal stabilizations increased, which may be due to either increased early detection of disease facilitating use of outpatient vertebral augmentation procedures and/or the recognition that surgical decompression and stabilization are necessary for optimal outcome in the setting of MLCS with neurological deficit.

Epidemiologic Study of Operative Treatment for Spinal Metastasis in Thailand : A Review of National Healthcare Data from 2005 to 2014

Objective

To study the factors relating to operative treatment for spinal metastasis in Thailand during 2005-2014 and to determine the hospital costs, mortality rate, and incidence of perioperative complication.

Methods

Inpatient reimbursement data from 2005 to 2014 was reviewed from three national healthcare organizations, including the National Health Security Office, the Social Security Office, and the Comptroller General's Department. The search criteria were secondary malignant neoplasm of bone and bone marrow patients (International Classification of Diseases 10th revision, Thai modification codes [ICD 10-TM], C79.5 and C79.8) who underwent spinal surgical treatment (ICD 9th revision, clinical modification procedure with extension codes [ICD 9-CM], 03.0, 03.4, 03.09, and 81.0) during 2005-2014. Epidemiology, comorbidity, and perioperative complication were analyzed.

Results

During the study period, the number of spinal metastasis patients who underwent operative treatment was significantly increased from 0.30 to 0.59 per 100000 (p<0.001). More males (56.14%) underwent surgical treatment for spinal metastasis than females. The most common age group was 45-64 (55.1%). The most common primary tumor sites were the unknown origin, lung, breast, prostate, and hepatocellular/bile duct. Interestingly, the proportion of hepatocellular/bile duct, breast, and lung cancer was significantly increased (p<0.001). The number of patients who had comorbidity or in-hospital complication significantly increased over time (p<0.01); however, the in-hospital mortality rate decreased.

Conclusion

During the last decade, operative treatment for spinal metastasis increased in Thailand. The overall in-hospital complication rate increased; however, the in-hospital mortality rate decreased.

Current trends in incidence, characteristics, and surgical management of metastatic breast cancer to the spine: A National Inpatient Sample analysis from 2005 to 2014

Management of metastatic breast cancer to the spine (MBCS) incorporates a multimodal approach. Improvement in screening and nonsurgical therapies may alter the trends in surgical management of MBCS. The objective of this study is to assess trends in surgical management of MBCS and short-term outcomes based on the National Inpatient Sample (NIS) database. The NIS database was queried for patients with MBCS who underwent surgery from 2005 to 2014. The weighted frequencies of spinal decompression alone, spinal stabilization +/- decompression, and vertebral augmentation were calculated. Multivariate analysis was performed to analyze the effect of patient characteristics on outcomes stratified by procedure. The most common procedure performed was vertebral augmentation (11,114, 53.4%), followed by stabilization +/- decompression (6,906, 33.2%) and then decompression alone (3,312, 13.4%). The total population-adjusted rate of surgical management for MBCS remained stable, while the rate of spinal stabilization increased (P < 0.001) and vertebral augmentation decreased (p < 0.003). The risk of complication increased with spinal stabilization and decompression compared to vertebral augmentation procedures in those with fewer comorbidities. This relative increase in risk abated in patients with higher numbers of pre-operative comorbidities. Any single complication was associated with increases in length of stay, cost, and mortality. The rate of in-hospital interventions remained stable over the study period. Stratified by procedure, the rate of stabilizations increased with a concomitant decrease in vertebral augmentations, which suggests that patients who require hospitalization for MBCS are becoming more likely to represent advanced cases that are not amenable to palliative vertebral augmentation procedures.