Related Experiment Video
Updated: Feb 2, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis
Clinton J Devin1, Mohamad Bydon2, Mohammed Ali Alvi2
11Department of Orthopedic Surgery and Neurological Surgery, Vanderbilt Spine Center, Vanderbilt University Medical Center, Nashville, Tennessee.
Abstract:
OBJECTIVEBack 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.METHODSThe 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.RESULTSA 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.CONCLUSIONSReturn 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.
Related Concept Videos
Predicting Reaction Outcomes
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Predicting Products: Substitution vs. Elimination
The following factors can influence the mechanisms competing against each other:

