Study Design. Prospective observational study. Objective. To determine how patient comorbidities and perioperative complications after spinal surgery affect the health care costs to society. Summary of Background Data. Despite efforts to reduce adverse events related to spinal surgery, complications are common and significantly increased by patient comorbidities. Methods. Patients who underwent spinal surgery at a tertiary academic center during a 6-month period (May 2008 to December 2008) were prospectively followed. All demographic data, comorbidities, procedural information, and complications to 30-day follow-up were recorded. Diagnosis-Related Group codes and Current Procedural Terminology codes were captured for each patient. Direct costs were estimated from a societal perspective, using 2008 Medicare rates of reimbursement. A multivariable analysis was performed to assess the impact of specific patient comorbidities and complications on total health care costs. Results. A total of 226 cases were analyzed. The mean cost of care for cases with complications was greater than that for cases without complications ($13,518.35 [95% confidence interval (CI), $9378.80–$17,657.90]; P < 0.0001). These results were consistent across degenerative, traumatic, and tumor/infection preoperative diagnoses. Cases with major complications were more costly than those with minor complications ($13,714.88 [CI, $6353.02–$21,076.74]; P = 0.0001). Systemic malignancy and preoperative neurological comorbidity were each associated with an increase in the cost of care ($7919 [CI, $2073–$15,225]; P = 0.006] and $5508 [CI, $814–$11,198; P = 0.02]), respectively, when compared with a baseline cost of care derived from all cases in the database. The cost of care was increased by pulmonary complications ($7233 [CI, $3982.53–$11,152.88]; P < 0.0001), instrumentation malposition ($6968 [CI, $1705.90–$14,277.16]; P = 0.0062), new neurological deficit ($4537 [CI, $863.95–$9274.30]; P = 0.013), and by wound infection ($4067 [CI, $1682.79–$6872.39]; P = 0.0004), after adjustment for covariates. Conclusion. Both minor and major complications were found to increase the cost of care in a prospective assessment of spine surgery complications. Specific patient comorbidities and perioperative complications are associated with significant increases in the total cost of care to society.

OBJECTIVE: The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort.METHODS: The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery.RESULTS: The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60–0.74] in RAT, 0.669 [95% CI 0.60–0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48–0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently produced complication predictions that underestimated complication occurrence: 3.4% in the low-risk group (observed 12.6%), 5.9% in the medium-risk group (observed 34.5%), and 12.5% in the high-risk group (observed 38.8%). The RAT was more accurate than the ACS NSQIP calculator (p = 0.0018).CONCLUSIONS: While the RAT and ACS NSQIP calculator were both able to identify patients more likely to experience complications following spine surgery, both have substantial room for improvement. Risk stratification is feasible in spine surgery procedures; currently used measures have low accuracy.

Rates of adverse events following spine surgery vary widely by patient-, diagnosis-, and procedure-related factors. It is critical to understand the expected rates of complications and to be able to implement targeted efforts at limiting these events. To develop and evaluate a set of predictive models for common adverse events after spine surgery. A retrospective cohort study. We extracted 345,510 patients from the Truven MarketScan (MKS) and MarketScan Medicaid Databases and 760,724 patients from the Centers for Medicare and Medicaid Services (CMS) Medicare database who underwent spine surgeries between 2009 and 2013. Overall adverse event (AE) occurrence and types of AE occurrence during the 30-day postoperative follow-up. We applied a least absolute shrinkage and selection operator regularization method and a logistic regression approach for predicting the risks of an overall AE and the top six most commonly observed AEs. Predictors included patient demographics, location of the spine procedure, comorbidities, type of surgery performed, and preoperative diagnosis. The median ages of MKS and CMS patients were 49 years and 69, respectively. The most frequent individual AE was a cardiac dysfunction in CMS (10.6%) patients and a pulmonary complication (4.7%) in MKS. The area under the curve (AUC) of a prediction model for an overall AE was 0.7. Among the six individual prediction models, the model for predicting the risk of a pulmonary complication showed the greatest accuracy (AUC 0.76), and the range of AUC for these six models was 0.7 and 0.76. Medicaid status was one of the most important factors in predicting the occurrences of AEs; Medicaid recipients had increased odds of AEs by 20%-60% compared with non-Medicaid patients (odds ratios 1.28-1.6; p<10-10). Logistic regression showed higher AUCs than least absolute shrinkage and selection operator across these different models. We present a set of predictive models for AEs following spine surgery that account for patient-, diagnosis-, and procedure-related factors which can contribute to patient-counseling, accurate risk adjustment, and accurate quality metrics.

OBJECTIVE: The authors performed a population-based analysis of national trends, costs, and outcomes associated with cervical spondylotic myelopathy (CSM) in the United States. They assessed postoperative complications, resource utilization, and predictors of costs, in this surgically treated CSM population. METHODS: MarketScan data (2006–2010) were used to retrospectively analyze the complications and costs of different spine surgeries for CSM. The authors determined outcomes following anterior cervical discectomy and fusion (ACDF), posterior fusion, combined anterior/posterior fusion, and laminoplasty procedures. RESULTS: The authors identified 35,962 CSM patients, comprising 5154 elderly (age ≥ 65 years) patients (mean 72.2 years, 54.9% male) and 30,808 nonelderly patients (mean 51.1 years, 49.3% male). They found an overall complication rate of 15.6% after ACDF, 29.2% after posterior fusion, 41.1% after combined anterior and posterior fusion, and 22.4% after laminoplasty. Following ACDF and posterior fusion, a significantly higher risk of complication was seen in the elderly compared with the nonelderly (reference group). The fusion level and comorbidity-adjusted ORs with 95% CIs for these groups were 1.54 (1.40–1.68) and 1.25 (1.06–1.46), respectively. In contrast, the elderly population had lower 30-day readmission rates in all 4 surgical cohorts (ACDF, 2.6%; posterior fusion, 5.3%; anterior/posterior fusion, 3.4%; and laminoplasty, 3.6%). The fusion level and comorbidity-adjusted odds ratios for 30-day readmissions for ACDF, posterior fusion, combined anterior and posterior fusion, and laminoplasty were 0.54 (0.44–0.68), 0.32 (0.24–0.44), 0.17 (0.08–0.38), and 0.39 (0.18–0.85), respectively. CONCLUSIONS: The authors' analysis of the MarketScan database suggests a higher complication rate in the surgical treatment of CSM than previous national estimates. They found that elderly age (≥ 65 years) significantly increased complication risk following ACDF and posterior fusion. Elderly patients were less likely to experience a readmission within 30 days of surgery. Postoperative complication occurrence, and 30-day readmission were significant drivers of total cost within 90 days of the index surgical procedure.

Background: Postoperative metrics are increasingly important in determining standards of quality for physicians and hospitals. Although complications following spinal surgery have been described, procedural and patient variables have yet to be incorporated into a predictive model of adverse-event occurrence. We sought to develop a predictive model of complication occurrence after spine surgery. Methods: We used longitudinal prospective data from a national claims database and developed a predictive model incorporating complication type and frequency of occurrence following spine surgery procedures. We structured our model to assess the impact of features such as preoperative diagnosis, patient comorbidities, location in the spine, anterior versus posterior approach, whether fusion had been performed, whether instrumentation had been used, number of levels, and use of bone morphogenetic protein (BMP). We assessed a variety of adverse events. Prediction models were built using logistic regression with additive main effects and logistic regression with main effects as well as all 2 and 3-factor interactions. Least absolute shrinkage and selection operator (LASSO) regularization was used to select features. Competing approaches included boosted additive trees and the classification and regression trees (CART) algorithm. The final prediction performance was evaluated by estimating the area under a receiver operating characteristic curve (AUC) as predictions were applied to independent validation data and compared with the Charlson comorbidity score. Results: The model was developed from 279,135 records of patients with a minimum duration of follow-up of 30 days. Preliminary assessment showed an adverse-event rate of 13.95%, well within norms reported in the literature. We used the first 80% of the records for training (to predict adverse events) and the remaining 20% of the records for validation. There was remarkable similarity among methods, with an AUC of 0.70 for predicting the occurrence of adverse events. The AUC using the Charlson comorbidity score was 0.61. The described model was more accurate than Charlson scoring (p < 0.01). Conclusions: We present a modeling effort based on administrative claims data that predicts the occurrence of complications after spine surgery. Clinical Relevance: We believe that the development of a predictive modeling tool illustrating the risk of complication occurrence after spine surgery will aid in patient counseling and improve the accuracy of risk modeling strategies.