Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation

Abstract

BACKGROUND Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. OBJECTIVE To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. METHODS Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. RESULTS Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.

Treatment Strategy for Metastatic Spinal Tumors: A Narrative Review

“…In their study, the nomogram was found intuitive and demonstrated a comparable level of performance. Then, in 2019, the SORG used machine-learning algorithms to develop a novel prognostic model for metastatic spinal disease [28], which was externally validated in subsequent studies [29,30].…”

Lymphopenia predicts 30-day morbidity and mortality following spinal metastasis surgery

“…The usefulness of PLR has been demonstrated for predicting survival within 6 and 12 months, treatment failure, and readmissions in patients with spinal metastasis [25] . Also, in the study conducted by Karhade et al [24] , a poor prognosis indicated by TLC seems to be related to postoperative complications (i.e., short-term outcomes), while a poor prognosis indicated by other inflammatory biomarkers seems to be related to tumor progression (i.e., medium-and long-term outcomes).…”

A Review on the Use of Artificial Intelligence in Spinal Diseases

“…Neural network techniques have also been applied to develop predictive algorithms for postoperative complications following anterior cervical discectomy and fusion [ 56 ], and to evaluate clinically relevant improvement in leg pain, back pain and functional disability after lumbar disc herniation (LDH) surgery [ 59 ], and to automatically quantify muscle fat infiltration following whiplash injury [ 62 ]. In addition, ANNs have been shown to accurately predict survival, discharge and hospital readmission rates following spinal metastasis surgery [ 57 , 60 , 61 ], to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion [ 63 ], and to predict prolonged opioid prescription after surgery for LDH [ 64 ]. Last but not least, ANNs have been used to predict the survival rate following a spinopelvic chondrosarcoma diagnosis [ 65 ] and to predict the occurrence of four types of major complications, namely cardiac complications, wound complications, venous thromboembolism, and mortality in the patients undergoing spine fusion, and it has achieved better results than the commonly used clinical scoring methods [ 16 ].…”

“…It is worth mentioned that preoperative prediction of opioid use could improve the risk stratification, shared decision-making, and patient counseling before LDH surgery [ 84 ]. In addition, Karhade et al [ 61 ] developed a machine learning tool to automatically predict 90-day and 1-year mortality in spinal metastatic disease ( https://sorg-apps.shinyapps.io/spinemetssurvival/ ) [ 85 ]. Meanwhile, the same group also developed a machine learning algorithm for predicting discharge disposition after elective inpatient surgery for lumbar disc disease [ 55 ], the model is available at https://sorg-apps.shinyapps.io/discdisposition/ [ 86 ].…”