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Clinton J. Devin, Mohamad Bydon, Mohammed Ali Alvi, Panagiotis Kerezoudis, Inamullah Khan, Ahilan Sivaganesan, Matthew J. McGirt, Kristin R. Archer, Kevin T. Foley, Praveen V. Mummaneni, Erica F. Bisson, John J. Knightly, Christopher I. Shaffrey and Anthony L. Asher

OBJECTIVE

Back pain and neck pain are two of the most common causes of work loss due to disability, which poses an economic burden on society. Due to recent changes in healthcare policies, patient-centered outcomes including return to work have been increasingly prioritized by physicians and hospitals to optimize healthcare delivery. In this study, the authors used a national spine registry to identify clinical factors associated with return to work at 3 months among patients undergoing a cervical spine surgery.

METHODS

The authors queried the Quality Outcomes Database registry for information collected from April 2013 through March 2017 for preoperatively employed patients undergoing cervical spine surgery for degenerative spine disease. Covariates included demographic, clinical, and operative variables, and baseline patient-reported outcomes. Multiple imputations were used for missing values and multivariable logistic regression analysis was used to identify factors associated with higher odds of returning to work. Bootstrap resampling (200 iterations) was used to assess the validity of the model. A nomogram was constructed using the results of the multivariable model.

RESULTS

A total of 4689 patients were analyzed, of whom 82.2% (n = 3854) returned to work at 3 months postoperatively. Among previously employed and working patients, 89.3% (n = 3443) returned to work compared to 52.3% (n = 411) among those who were employed but not working (e.g., were on a leave) at the time of surgery (p < 0.001). On multivariable logistic regression the authors found that patients who were less likely to return to work were older (age > 56–65 years: OR 0.69, 95% CI 0.57–0.85, p < 0.001; age > 65 years: OR 0.65, 95% CI 0.43–0.97, p = 0.02); were employed but not working (OR 0.24, 95% CI 0.20–0.29, p < 0.001); were employed part time (OR 0.56, 95% CI 0.42–0.76, p < 0.001); had a heavy-intensity (OR 0.42, 95% CI 0.32–0.54, p < 0.001) or medium-intensity (OR 0.59, 95% CI 0.46–0.76, p < 0.001) occupation compared to a sedentary occupation type; had workers’ compensation (OR 0.38, 95% CI 0.28–0.53, p < 0.001); had a higher Neck Disability Index score at baseline (OR 0.60, 95% CI 0.51–0.70, p = 0.017); were more likely to present with myelopathy (OR 0.52, 95% CI 0.42–0.63, p < 0.001); and had more levels fused (3–5 levels: OR 0.46, 95% CI 0.35–0.61, p < 0.001). Using the multivariable analysis, the authors then constructed a nomogram to predict return to work, which was found to have an area under the curve of 0.812 and good validity.

CONCLUSIONS

Return to work is a crucial outcome that is being increasingly prioritized for employed patients undergoing spine surgery. The results from this study could help surgeons identify at-risk patients so that preoperative expectations could be discussed more comprehensively.

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Scott L. Parker, Ahilan Sivaganesan, Silky Chotai, Matthew J. McGirt, Anthony L. Asher and Clinton J. Devin

OBJECTIVE

Hospital readmissions lead to a significant increase in the total cost of care in patients undergoing elective spine surgery. Understanding factors associated with an increased risk of postoperative readmission could facilitate a reduction in such occurrences. The aims of this study were to develop and validate a predictive model for 90-day hospital readmission following elective spine surgery.

METHODS

All patients undergoing elective spine surgery for degenerative disease were enrolled in a prospective longitudinal registry. All 90-day readmissions were prospectively recorded. For predictive modeling, all covariates were selected by choosing those variables that were significantly associated with readmission and by incorporating other relevant variables based on clinical intuition and the Akaike information criterion. Eighty percent of the sample was randomly selected for model development and 20% for model validation. Multiple logistic regression analysis was performed with Bayesian model averaging (BMA) to model the odds of 90-day readmission. Goodness of fit was assessed via the C-statistic, that is, the area under the receiver operating characteristic curve (AUC), using the training data set. Discrimination (predictive performance) was assessed using the C-statistic, as applied to the 20% validation data set.

RESULTS

A total of 2803 consecutive patients were enrolled in the registry, and their data were analyzed for this study. Of this cohort, 227 (8.1%) patients were readmitted to the hospital (for any cause) within 90 days postoperatively. Variables significantly associated with an increased risk of readmission were as follows (OR [95% CI]): lumbar surgery 1.8 [1.1–2.8], government-issued insurance 2.0 [1.4–3.0], hypertension 2.1 [1.4–3.3], prior myocardial infarction 2.2 [1.2–3.8], diabetes 2.5 [1.7–3.7], and coagulation disorder 3.1 [1.6–5.8]. These variables, in addition to others determined a priori to be clinically relevant, comprised 32 inputs in the predictive model constructed using BMA. The AUC value for the training data set was 0.77 for model development and 0.76 for model validation.

CONCLUSIONS

Identification of high-risk patients is feasible with the novel predictive model presented herein. Appropriate allocation of resources to reduce the postoperative incidence of readmission may reduce the readmission rate and the associated health care costs.

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Matthew J. McGirt, Mohamad Bydon, Kristin R. Archer, Clinton J. Devin, Silky Chotai, Scott L. Parker, Hui Nian, Frank E. Harrell Jr., Theodore Speroff, Robert S. Dittus, Sharon E. Philips, Christopher I. Shaffrey, Kevin T. Foley and Anthony L. Asher

OBJECTIVE

Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifically, clinical registry data are progressively informing health care decision-making. In this analysis, the authors used data from a national prospective outcomes registry (the Quality Outcomes Database) to develop a predictive model for 12-month postoperative pain, disability, and quality of life (QOL) in patients undergoing elective lumbar spine surgery.

METHODS

Included in this analysis were 7618 patients who had completed 12 months of follow-up. The authors prospectively assessed baseline and 12-month patient-reported outcomes (PROs) via telephone interviews. The PROs assessed were those ascertained using the Oswestry Disability Index (ODI), EQ-5D, and numeric rating scale (NRS) for back pain (BP) and leg pain (LP). Variables analyzed for the predictive model included age, gender, body mass index, race, education level, history of prior surgery, smoking status, comorbid conditions, American Society of Anesthesiologists (ASA) score, symptom duration, indication for surgery, number of levels surgically treated, history of fusion surgery, surgical approach, receipt of workers’ compensation, liability insurance, insurance status, and ambulatory ability. To create a predictive model, each 12-month PRO was treated as an ordinal dependent variable and a separate proportional-odds ordinal logistic regression model was fitted for each PRO.

RESULTS

There was a significant improvement in all PROs (p < 0.0001) at 12 months following lumbar spine surgery. The most important predictors of overall disability, QOL, and pain outcomes following lumbar spine surgery were employment status, baseline NRS-BP scores, psychological distress, baseline ODI scores, level of education, workers’ compensation status, symptom duration, race, baseline NRS-LP scores, ASA score, age, predominant symptom, smoking status, and insurance status. The prediction discrimination of the 4 separate novel predictive models was good, with a c-index of 0.69 for ODI, 0.69 for EQ-5D, 0.67 for NRS-BP, and 0.64 for NRS-LP (i.e., good concordance between predicted outcomes and observed outcomes).

CONCLUSIONS

This study found that preoperative patient-specific factors derived from a prospective national outcomes registry significantly influence PRO measures of treatment effectiveness at 12 months after lumbar surgery. Novel predictive models constructed with these data hold the potential to improve surgical effectiveness and the overall value of spine surgery by optimizing patient selection and identifying important modifiable factors before a surgery even takes place. Furthermore, these models can advance patient-focused care when used as shared decision-making tools during preoperative patient counseling.

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Anthony L. Asher, Clinton J. Devin, Kristin R. Archer, Silky Chotai, Scott L. Parker, Mohamad Bydon, Hui Nian, Frank E. Harrell Jr., Theodore Speroff, Robert S. Dittus, Sharon E. Philips, Christopher I. Shaffrey, Kevin T. Foley and Matthew J. McGirt

OBJECTIVE

Current costs associated with spine care are unsustainable. Productivity loss and time away from work for patients who were once gainfully employed contributes greatly to the financial burden experienced by individuals and, more broadly, society. Therefore, it is vital to identify the factors associated with return to work (RTW) after lumbar spine surgery. In this analysis, the authors used data from a national prospective outcomes registry to create a predictive model of patients’ ability to RTW after undergoing lumbar spine surgery for degenerative spine disease.

METHODS

Data from 4694 patients who underwent elective spine surgery for degenerative lumbar disease, who had been employed preoperatively, and who had completed a 3-month follow-up evaluation, were entered into a prospective, multicenter registry. Patient-reported outcomes—Oswestry Disability Index (ODI), numeric rating scale (NRS) for back pain (BP) and leg pain (LP), and EQ-5D scores—were recorded at baseline and at 3 months postoperatively. The time to RTW was defined as the period between operation and date of returning to work. A multivariable Cox proportional hazards regression model, including an array of preoperative factors, was fitted for RTW. The model performance was measured using the concordance index (c-index).

RESULTS

Eighty-two percent of patients (n = 3855) returned to work within 3 months postoperatively. The risk-adjusted predictors of a lower likelihood of RTW were being preoperatively employed but not working at the time of presentation, manual labor as an occupation, worker’s compensation, liability insurance for disability, higher preoperative ODI score, higher preoperative NRS-BP score, and demographic factors such as female sex, African American race, history of diabetes, and higher American Society of Anesthesiologists score. The likelihood of a RTW within 3 months was higher in patients with higher education level than in those with less than high school–level education. The c-index of the model’s performance was 0.71.

CONCLUSIONS

This study presents a novel predictive model for the probability of returning to work after lumbar spine surgery. Spine care providers can use this model to educate patients and encourage them in shared decision-making regarding the RTW outcome. This evidence-based decision support will result in better communication between patients and clinicians and improve postoperative recovery expectations, which will ultimately increase the likelihood of a positive RTW trajectory.

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Anthony L. Asher, Silky Chotai, Clinton J. Devin, Theodore Speroff, Frank E. Harrell Jr., Hui Nian, Robert S. Dittus, Praveen V. Mummaneni, John J. Knightly, Steven D. Glassman, Mohamad Bydon, Kristin R. Archer, Kevin T. Foley and Matthew J. McGirt

OBJECTIVE

Prospective longitudinal outcomes registries are at the center of evidence-driven health care reform. Obtaining real-world outcomes data at 12 months can be costly and challenging. In the present study, the authors analyzed whether 3-month outcome measurements sufficiently represent 12-month outcomes for patients with degenerative lumbar disease undergoing surgery.

METHODS

Data from 3073 patients undergoing elective spine surgery for degenerative lumbar disease were entered into a prospective multicenter registry (N2QOD). Baseline, 3-month, and 12-month follow-up Oswestry Disability Index (ODI) scores were recorded. The absolute differences between actual 12- and 3-month ODI scores was evaluated. Additionally, the authors analyzed the absolute difference between actual 12-month ODI scores and a model-predicted 12-month ODI score (the model used patients' baseline characteristics and actual 3-month scores). The minimal clinically important difference (MCID) for ODI of 12.8 points and the substantial clinical benefit (SCB) for ODI of 18.8 points were used based on the previously published values. The concordance rate of achieving MCID and SCB for ODI at 3-and 12-months was computed.

RESULTS

The 3-month ODI scores differed from 12-month scores by an absolute difference of 11.9 ± 10.8, and predictive modeling estimations of 12-month ODI scores differed from actual 12-month scores by a mean (± SD) of 10.7 ± 9.0 points (p = 0.001). Sixty-four percent of patients (n = 1982) achieved an MCID for ODI at 3 months in comparison with 67% of patients (n = 2088) by 12 months; 51% (n = 1731) and 61% (n = 1860) of patients achieved SCB for ODI at 3 months and 12 months, respectively. Almost 20% of patients had ODI scores that varied at least 20 points (the point span of an ODI functional category) between actual 3- and 12-month values. In the aggregate analysis of achieving MCID, 77% of patients were concordant and 23% were discordant in achieving or not achieving MCID at 3 and 12 months. The discordance rates of achieving or not achieving MCID for ODI were in the range of 19% to 27% for all diagnoses and treatments (decompression with and without fusion). The positive and negative predictive value of 3-months ODI to predict 12-month ODI was 86% and 60% for MCID and 82% and 67% for SCB.

CONCLUSIONS

Based on their findings, the authors conclude the following: 1) Predictive methods for functional outcome based on early patient experience (i.e., baseline and/or 3-month data) should be used to help evaluate the effectiveness of procedures in patient populations, rather than serving as a proxy for long-term individual patient experience. 2) Prospective longitudinal registries need to span at least 12 months to determine the effectiveness of spine care at the individual patient and practitioner level.

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Silky Chotai, Scott L. Parker, Ahilan Sivaganesan, J. Alex Sielatycki, Anthony L. Asher, Matthew J. McGirt and Clinton J. Devin

OBJECT

There is a paradigm shift toward rewarding providers for quality rather than volume. Complications appear to occur at a fairly consistent frequency in large aggregate data sets. Understanding how complications affect long-term patient-reported outcomes (PROs) following degenerative lumbar surgery is vital. The authors hypothesized that 90-day complications would adversely affect long-term PROs.

METHODS

Nine hundred six consecutive patients undergoing elective surgery for degenerative lumbar disease over a period of 4 years were enrolled into a prospective longitudinal registry. The following PROs were recorded at baseline and 12-month follow-up: Oswestry Disability Index (ODI) score, numeric rating scales for back and leg pain, quality of life (EQ-5D scores), general physical and mental health (SF-12 Physical Component Summary [PCS] and Mental Component Summary [MCS] scores) and responses to the North American Spine Society (NASS) satisfaction questionnaire. Previously published minimum clinically important difference (MCID) threshold were used to define meaningful improvement. Complications were divided into major (surgicalsite infection, hardware failure, new neurological deficit, pulmonary embolism, hematoma and myocardial infarction) and minor (urinary tract infection, pneumonia, and deep venous thrombosis).

RESULTS

Complications developed within 90 days of surgery in 13% (118) of the patients (major in 12% [108] and minor in 8% [68]). The mean improvement in ODI scores, EQ-5D scores, SF-12 PCS scores, and satisfaction at 3 months after surgery was significantly less in the patients with complications than in those who did not have major complications (ODI: 13.5 ± 21.2 vs 21.7 ± 19, < 0.0001; EQ-5D: 0.17 ± 0.25 vs 0.23 ± 0.23, p = 0.04; SF-12 PCS: 8.6 ± 13.3 vs 13.0 ± 11.9, 0.001; and satisfaction: 76% vs 90%, p = 0.002). At 12 months after surgery, the patients with major complications had higher ODI scores than those without complications (29.1 ± 17.7 vs 25.3 ± 18.3, p = 0.02). However, there was no difference in the change scores in ODI and absolute scores across all other PROs between the 2 groups. In multivariable linear regression analysis, after controlling for an array of preoperative variables, the occurrence of a major complication was not associated with worsening ODI scores 12 months after surgery. There was no difference in the percentage of patients achieving the MCID for disability (66% vs 64%), back pain (55% vs 56%), leg pain (62% vs 59%), or quality of life (19% vs 14%) or in patient satisfaction rates (82% vs 80%) between those without and with major complications.

CONCLUSIONS

Major complications within 90 days following lumbar spine surgery have significant impact on the short-term PROs. Patients with complications, however, do eventually achieve clinically meaningful outcomes and report satisfaction equivalent to those without major complications. This information allows a physician to counsel patients on the fact that a complication creates frustration, cost, and inconvenience; however, it does not appear to adversely affect clinically meaningful long-term outcomes and satisfaction.

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Scott L. Parker, Matthew J. McGirt, Kimon Bekelis, Christopher M. Holland, Jason Davies, Clinton J. Devin, Tyler Atkins, Jack Knightly, Rachel Groman, Irene Zyung and Anthony L. Asher

Meaningful quality measurement and public reporting have the potential to facilitate targeted outcome improvement, practice-based learning, shared decision making, and effective resource utilization. Recent developments in national quality reporting programs, such as the Centers for Medicare & Medicaid Services Qualified Clinical Data Registry (QCDR) reporting option, have enhanced the ability of specialty groups to develop relevant quality measures of the care they deliver. QCDRs will complete the collection and submission of Physician Quality Reporting System (PQRS) quality measures data on behalf of individual eligible professionals. The National Neurosurgery Quality and Outcomes Database (N2QOD) offers 21 non-PQRS measures, initially focused on spine procedures, which are the first specialty-specific measures for neurosurgery. Securing QCDR status for N2QOD is a tremendously important accomplishment for our specialty. This program will ensure that data collected through our registries and used for PQRS is meaningful for neurosurgeons, related spine care practitioners, their patients, and other stakeholders. The 2015 N2QOD QCDR is further evidence of neurosurgery’s commitment to substantively advancing the health care quality paradigm. The following manuscript outlines the measures now approved for use in the 2015 N2QOD QCDR. Measure specifications (measure type and descriptions, related measures, if any, as well as relevant National Quality Strategy domain[s]) along with rationale are provided for each measure.

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Scott L. Parker, Anthony L. Asher, Saniya S. Godil, Clinton J. Devin and Matthew J. McGirt

OBJECT

The health care landscape is rapidly shifting to incentivize quality of care rather than quantity of care. Quality and outcomes registry platforms lie at the center of all emerging evidence-driven reform models and will be used to inform decision makers in health care delivery. Obtaining real-world registry outcomes data from patients 12 months after spine surgery remains a challenge. The authors set out to determine whether 3-month patient-reported outcomes accurately predict 12-month outcomes and, hence, whether 3-month measurement systems suffice to identify effective versus noneffective spine care.

METHODS

All patients undergoing lumbar spine surgery for degenerative disease at a single medical institution over a 2-year period were enrolled in a prospective longitudinal registry. Patient-reported outcome instruments (numeric rating scale [NRS], Oswestry Disability Index [ODI], 12-Item Short Form Health Survey [SF-12], EQ-5D, and the Zung Self-Rating Depression Scale) were recorded prospectively at baseline and at 3 months and 12 months after surgery. Linear regression was performed to determine the independent association of 3- and 12-month outcome. Receiver operating characteristic (ROC) curve analysis was performed to determine whether improvement in general health state (EQ-5D) and disability (ODI) at 3 months accurately predicted improvement and achievement of minimum clinical important difference (MCID) at 12 months.

RESULTS

A total of 593 patients undergoing elective lumbar surgery were included in the study. There was a significant correlation between 3-month and 12-month EQ-5D (r = 0.71; p < 0.0001) and ODI (r = 0.70; p < 0.0001); however, the authors observed a sizable discrepancy in achievement of a clinically significant improvement (MCID) threshold at 3 versus 12 months on an individual patient level. For postoperative disability (ODI), 11.5% of patients who achieved an MCID threshold at 3 months dropped below this threshold at 12 months; 10.5% of patients who did not meet the MCID threshold at 3 months continued to improve and ultimately surpassed the MCID threshold at 12 months. For ODI, achieving MCID at 3 months accurately predicted 12-month MCID with only 62.6% specificity and 86.8% sensitivity. For postoperative health utility (EQ-5D), 8.5% of patients lost an MCID threshold improvement from 3 months to 12 months, while 4.0% gained the MCID threshold between 3 and 12 months postoperatively. For EQ-5D (quality-adjusted life years), achieving MCID at 3 months accurately predicted 12-month MCID with only 87.7% specificity and 87.2% sensitivity.

CONCLUSIONS

In a prospective registry, patient-reported measures of treatment effectiveness obtained at 3 months correlated with 12-month measures overall in aggregate, but did not reliably predict 12-month outcome at the patient level. Many patients who do not benefit from surgery by 3 months do so by 12 months, and, conversely, many patients reporting meaningful improvement by 3 months report loss of benefit at 12 months. Prospective longitudinal spine outcomes registries need to span at least 12 months to identify effective versus noneffective patient care.

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Matthew J. McGirt, Ahilan Sivaganesan, Anthony L. Asher and Clinton J. Devin

OBJECT

Lumbar spine surgery has been demonstrated to be efficacious for many degenerative spine conditions. However, there is wide variability in outcome after spine surgery at the individual patient level. All stakeholders in spine care will benefit from identification of the unique patient or disease subgroups that are least likely to benefit from surgery, are prone to costly complications, and have increased health care utilization. There remains a large demand for individual patient-level predictive analytics to guide decision support to optimize outcomes at the patient and population levels.

METHODS

One thousand eight hundred three consecutive patients undergoing spine surgery for various degenerative lumbar diagnoses were prospectively enrolled and followed for 1 year. A comprehensive patient interview and health assessment was performed at baseline and at 3 and 12 months after surgery. All predictive covariates were selected a priori. Eighty percent of the sample was randomly selected for model development, and 20% for model validation. Linear regression was performed with Bayesian model averaging to model 12-month ODI (Oswestry Disability Index). Logistic regression with Bayesian model averaging was used to model likelihood of complications, 30-day readmission, need for inpatient rehabilitation, and return to work. Goodness-of-fit was assessed via R2 for 12-month ODI and via the c-statistic, area under the receiver operating characteristic curve (AUC), for the categorical endpoints. Discrimination (predictive performance) was assessed, using R2 for the ODI model and the c-statistic for the categorical endpoint models. Calibration was assessed using a plot of predicted versus observed values for the ODI model and the Hosmer-Lemeshow test for the categorical endpoint models.

RESULTS

On average, all patient-reported outcomes (PROs) were improved after surgery (ODI baseline vs 12 month: 50.4 vs 29.5%, p < 0.001). Complications occurred in 121 patients (6.6%), 108 (5.9%) were readmitted within 30 days of surgery, 188 (10.3%) required discharge to inpatient rehabilitation, 1630 (88.9%) returned to work, and 449 (24.5%) experienced an unplanned outcome (no improvement in ODI, a complication, or readmission). There were 45 unique baseline variable inputs, derived from 39 clinical variables and 38 questionnaire items (ODI, SF-12, MSPQ, VAS-BP, VAS-LP, VAS-NP), included in each model. For prediction of 12-month ODI, R2 was 0.51 for development and 0.47 for the validation study. For prediction of a complication, readmission, inpatient rehabilitation, and return to work, AUC values ranged 0.72-0.84 for development and 0.79-0.84 for validation study.

CONCLUSIONS

A novel prediction model utilizing both clinical data and patient interview inputs explained the majority of variation in outcome observed after lumbar spine surgery and reliably predicted 12-month improvement in physical disability, return to work, major complications, readmission, and need for inpatient rehabilitation for individual patients. Application of these models may allow clinicians to offer spine surgery specifically to those who are most likely to benefit and least likely to incur complications and excess costs.