Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors

Abstract

Objective: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors.Methods: IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset.Results: A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n = 5,023, 99.3%) and tumors were most commonly found in the thoracic region (n = 1,941, 38.4%), followed by the lumbar (n = 1,781, 35.2%) and cervical (n = 1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%).Conclusion: Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.

Current Applications of Machine Learning for Spinal Cord Tumors

“…The proposed algorithm used a least absolute shrinkage and selection operator (LASSO) approach to classify discharge destination and 90-day readmission as binary outcomes based on input features, which included patient-specific and tumor-specific factors. Their integrated model outperformed other models, which employed only tumor-specific or patient-specific features, as it demonstrated superior discrimination and accuracy for non-home discharge and 90-day readmissions for patients undergoing surgery for intradural tumors [ 42 ]. Other non-machine learning calculators which provide risk stratification information for physicians have emerged.…”

Role of Posterior Longitudinal Ligament Complex in Spinal Deformity Secondary to Surgical Resection of the Intradural Tumor

“…The intradural tumor accounted for 30% of all primary vertebral body tumors. The main symptom of an intradural tumor is pain, headstand, and nocturnal seizures 1,2 . As microsurgery technology advances, the surgical treatment of intramedullary tumors has made great progress 3 which may improve patients' survival rates and better quality of life 4–6 .…”

“…The main symptom of an intradural tumor is pain, headstand, and nocturnal seizures. 1 , 2 As microsurgery technology advances, the surgical treatment of intramedullary tumors has made great progress 3 which may improve patients' survival rates and better quality of life. 4 , 5 , 6 However, despite the promotion of minimally invasive surgical techniques, spine deformity remains one of the serious postoperative complications of intradural tumor surgery.…”