Browse

You are looking at 1 - 5 of 5 items for

  • By Author: McGirt, Matthew J. x
  • By Author: Sivaganesan, Ahilan x
Clear All
Free access

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.

Restricted access

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.

Free access

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.

Free access

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.