Predicting Occurrence of Spine Surgery Complications Using Big Data Modeling of an Administrative Claims Database

Ratliff, John K. MD; Balise, Ray PhD; Veeravagu, Anand MD; Cole, Tyler S. BS; Cheng, Ivan MD; Olshen, Richard A. PhD; Tian, Lu PhD

Journal of Bone & Joint Surgery - American Volume:
doi: 10.2106/JBJS.15.00301
Scientific Articles
Abstract

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.

Author Information

1Departments of Neurosurgery (J.K.R., A.V., and T.S.C.) and Orthopaedic Surgery (I.C.), and Health and Research Policy, Division of Biostatistics (R.B., R.A.O., and L.T.), Stanford University School of Medicine, Stanford, California

E-mail address for J.K. Ratliff: jratliff@stanford.edu

Article Outline

Postoperative metrics are increasingly important in determining standards of quality for physicians and hospitals. Little is known about what contributes to variation in health outcomes of spinal surgery. Understanding potential contributors to complication incidence is of great importance to the public1-6. Analysis of a prospective data set revealed that the occurrence of a major complication added more than $13,000 United States dollars (USD) to the cost of care in 20137.

Assessing the incidence of distinct complications in isolation is a poor way to identify predictors of operative outcome, as case complexity, patient comorbidities, presenting diagnoses, and a wide array of variables may influence the risk of complications8-12. Development of a model to assess the risk of perioperative complications of spine surgery would be valuable for modeling relative risk and in particular for counseling patients13. The paucity of detailed information on the impact of patient factors on occurrence of complications makes modeling relative risk difficult10,11,14,15.

The definition of what constitutes a complication of spine surgery is not clear in the literature4. The approach of our research group incorporated all adverse events, including all medical adverse events, occurring within the first 30 days after a surgical procedure, regardless of their causal relationship to the procedure performed16.

The Charlson and American Society of Anesthesiologists (ASA) classifications have been used in previous efforts at modeling complications after spine surgery7,17. Use of claims data has been widely reported in the literature18-26. Although there are substantial limitations to using administrative data, such databases typically include very large numbers of patients.

We used longitudinal prospective data obtained from the Truven Health Analytics MarketScan database of private insurance payers and developed a prediction model incorporating occurrence and type of complications for patients undergoing spine surgery. The purpose of this report is to present the results of this modeling effort and discuss opportunities for expanding the scope of these investigations.

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Materials and Methods

We performed a retrospective observational administrative database study of more than 270,000 patients who had undergone spine procedures in the United States from 2006 to 2010. We used claims data from the Truven Health Analytics MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits databases, which include data from >100 payers and use specific patient identifiers for longitudinal patient tracking of claims, billing, and payment history. The depth of the data set allows assessment of patient comorbidities and complications both before and after the index procedure. The MarketScan database also allows tracking of patient health-care expenditures for the entirety of their private insurance coverage.

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Definition of Patient Cohort

We developed our cohort of patients by querying the overall MarketScan database for patients with Common Procedural Terminology (CPT) descriptors for spine surgery procedures, as listed in Table I. We divided patients into 4 general diagnostic categories as shown in Table II. The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes designating patient comorbidities are presented in Table III. ICD-9-CM definitions for comorbidities and complication occurrence have been described previously27-29.

The ability to identify a comorbid condition, most commonly myocardial infarction, as either a comorbidity or a perioperative complication was facilitated by the MarketScan database. The longitudinal database allows retrospective assessment of diagnoses present prior to the admission for surgery. Complications were defined as the occurrence of new ICD-9-CM codes either during the admission for a given spinal procedure or during the patient’s postoperative follow-up claims history. The analysis was restricted to the 30 days immediately after the date of the spinal procedure. Definitions of complication are presented in Table IV.

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Analysis

We developed a prediction rule based on inpatient, outpatient, pharmacy, physician payment, and hospital payment data sets. Data were analyzed using R (https://http://www.R-project.org) and SAS (SAS Institute) software. We structured our modeling to assess preoperative diagnosis, 11 classes of patient comorbidities, location in the spine (cervical or thoracolumbar), anterior or posterior surgical approach, fusion or no fusion, instrumentation or no instrumentation, single-level or multiple-level surgery, and use or no use of bone morphogenetic protein (BMP). Patient age was defined as the age at the time of the procedure in question. We assessed for the occurrence of any of the 14 separate categories of adverse events (Table IV). The primary outcome of the assessment was the occurrence of complications within 30 days after an index surgical procedure.

The variables of interest were grouped into 5 general categories: (1) surgical, which consisted of location (cervical compared with thoracolumbar), approach (anterior compared with posterior), use of instrumentation, single compared with multiple-level surgery, and use of BMP; (2) disease (degenerative disease, infection, neoplasm, or trauma); (3) comorbidities (11 categories as shown in Table III); (4) demographics (age and sex); and (5) complications occurring within 30 days after the spinal procedure (occurrence of any complication, number of complications, length of hospital stay, total hospitalization costs, need for readmission, and need for a reoperation).

Analysis focused on using demographics, comorbidities, presenting diagnosis, and procedure characteristics to predict (1) the binary occurrence of complications within 30 days after a given operative procedure and (2) the specific type of complications within 30 days after a given operative procedure represented by a vector of multiple potential outcomes.

The total data bank was divided into 2 parts: training and validation data sets. In the training stage, the analysis first predicted the binary outcome of the occurrence of any complications after surgery. The features used in the prediction were extracted from the 2 data sets and included surgical characteristics, preoperative diagnoses, patient comorbidities present prior to the surgery, and demographic information. Continuous age was divided into 7 categories. For the purpose of statistical modeling of the risk of complication occurrence, we split the overall patient cohort into anterior cervical, posterior cervical, anterior thoracolumbar, and posterior thoracolumbar procedure groups. For each subgroup, we first performed the multivariate logistic regression analysis with the aforementioned features as the independent variables and the occurrence of any complication as the binary outcome variable based on 80% of the patients for training. The data on the remaining 20% of the patients were reserved for testing. Because of the large sample size, the results were not sensitive to the choice of the training or test sample.

A more comprehensive logistic regression model also was developed to predict the binary outcome of adverse-event occurrence with the aforementioned features and their up-to-3-way interactions. This model—least absolute shrinkage and selection operator (LASSO) regularization—was employed to produce a parsimonious prediction model (see Appendix)27,28.

In addition to the standard logistic regression model, we developed alternative methods to see if further improvement in model accuracy was feasible. Two other data-mining tools were employed in parallel: classification and regression trees (CART) algorithms for constructing a binary classification tree and a boosting algorithm for a nonparametric classification rule with a binary tree as the base learners7,21. After completing the testing stage, the fitted prediction model was applied to the validation set and assessed for accuracy for predicting (1) the binary occurrence of complications and (2) the specific type of complication. Each prediction was compared with its observed outcome; the quality of model prediction was summarized by the area under a receiver operating characteristic curve (AUC) with the 95% confidence interval (CI). The predictive performance was also compared with that based on the simple Charlson comorbidity score7.

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Results

The demographics of the patient cohorts are presented in Tables V, VI, and VII. Briefly, the assay of the overall MarketScan database identified 279,135 patients who had undergone spine surgery procedures, identified by their having had one or more of the CPT codes listed in Table I during a given hospital admission. Figure 1 provides a graphic representation of patient age.

Initial analyses revealed that use of BMP was associated with the incidence of adverse events after cervical spine surgery (odds ratio [OR] = 1.34, 95% CI = 1.2 to 1.5, p < 0.0001).

For predicting the postsurgery complications, prediction rules were built on the basis of simple logistic regression, LASSO logistic regression with interactions, CART, and boosting. The prediction performance of CART was inferior to those of the other 3 methods. The boosting procedure yielded individual predictions remarkably similar to those obtained with the logistic regression analyses (correlation coefficients of 75% with simple logistic regression and 81% with LASSO in the test set). Therefore, we will focus on the results from the logistic regression method (with and without LASSO).

The overall complication rate in the anterior cervical group was 10.55% in the test set. As a first analysis, we split the anterior cervical cohort into 3 risk groups—lowest 25%, highest 25%, and middle 50%—stratifying on the basis of the predicted incidence of complication occurrence from the model. We also examined the complication rate in the highest-5% risk group. In the anterior cervical cohort, the highest-25% risk group according to the simple logistic regression and LASSO analyses had a complication incidence of 19%, whereas the middle and lowest-risk groups had an incidence of approximately 8% and 6%, respectively. The complication rate in the highest-5% risk group was 39% according to both logistic regression and LASSO. The AUC was 0.662 (95% CI = 0.647 to 0.677) for predicting a complication with logistic regression and 0.660 (95% CI = 0.645 to 0.674) for predicting one with LASSO. The analysis revealed a higher overall incidence of complication occurrence (27.34%) in the posterior cervical patient cohort. The AUC for logistic regression and LASSO in the posterior cervical group was higher: 0.729 (95% CI = 0.705 to 0.753) and 0.728 (95% CI = 0.704 to 0.752), respectively. The results of the remaining logistic regression and LASSO analyses are shown in Table VIII, along with the results for the anterior and posterior thoracolumbar groups.

Overall, the results yielded by the simple logistic regression were similar to those demonstrated by the LASSO regularized counterpart model with interactions, but with a simpler prediction model. The overall AUC for predicting complication occurrence in the entire population was 0.695 (95% CI = 0.689 to 0.701) (Fig. 2). The formula and values used for calculating the adverse-event risk score are presented in Table IX.

When the Charlson comorbidity score was used to predict complication occurrence, the AUC was 0.609 (95% CI = 0.604 to 0.614) for the anterior cervical cohort, 0.616 (95% CI = 0.607 to 0.625) for the posterior cervical cohort, 0.614 (95% CI = 0.606 to 0.622) for the anterior thoracolumbar cohort, and 0.604 (95% CI = 0.601 to 0.608) for the posterior thoracolumbar cohort. The AUC for the entire cohort was 0.607 (95% CI = 0.604 to 0.610). All of the AUCs from the Charlson score were significantly less than their counterparts based on logistic regression (p < 0.01).

For predicting the specific type of complication that occurred, the analysis focused on the 44,507 occurrences of complications in the patient cohort. Predictive accuracy was poor for identifying which type of complication would occur.

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Discussion

Initially, the research group developed a working definition of “complications” after spine surgery using a survey of patients and surgeons4,16,30. A prospective analysis was then conducted with use of this patient-centered definition of perioperative complications. There was a clear trend toward an increased incidence of complications in patients who had undergone surgery for a neoplasm or infection. The complication incidence correlated with the surgical approach10,11,14,31,32. A higher number of patient comorbidities correlated with an increased risk of a major complication, a minor complication, and any complication. Some comorbidities independently predicted the occurrence of complications11,14.

An ICD-9-CM model of the prospectively developed comorbidity scoring approach was developed and applied to an administrative database. The predictive value of the score was compared with that of the Charlson measure. An increasing score correlated linearly with an increasing complication incidence, at least as well as the Charlson measure33.

Longitudinal data provide a more robust assessment of complication incidence than incident-only or admission-only data sets. An analysis of the MarketScan longitudinal database revealed that patient follow-up extending to 30 days substantially improved the sensitivity of our analyses in capturing adverse-event occurrence when compared with the sensitivity when adverse events were identified only as those occurring during a patient’s initial admission34. The overall increase in complication recognition was 35.8% with inclusion of longitudinal data, which provides one explanation for the poor accuracy of administrative reports that did not utilize longitudinal data for analysis10. Longitudinal data may be used to assess readmissions and reoperations and enable a more accurate assessment of perioperative adverse events27.

The overall retrospective complication incidences captured in this assessment were comparable with those obtained with similar prospective patient assessments14,31-33,35. Our results revealed higher complication incidences than have been noted in other, single-admission or non-longitudinal databases8,36-42.

Initially, we did not plan to incorporate use of BMP as a distinct predictor of adverse events. However, we included it after initial analyses revealed that use of BMP was associated with the incidence of adverse events in cervical spine surgery (OR = 1.34, 95% CI = 1.2 to 1.5, p < 0.0001)27.

Our assessment of patients who had undergone spine surgery showed an adverse-event rate of 13.95%, well within norms found in the literature and superior to the rates found in studies that used non-longitudinal data7,23. We built prediction models using different methods, primarily logistic regression with additive main effects and logistic regression with main effects as well as all 2 and 3-factor interactions (for subgroups of proportions >0.3%). Correlations between predictive scores derived with the various methods were high; for example, the correlation between LASSO and boosted additive trees was 0.81.

The model was less effective overall at predicting the type of complication, with AUCs of 0.60 to 0.62 for all complication types other than pulmonary complications. The model was effective at predicting pulmonary complications, with an AUC of 0.72.

Hence, our approach allowed us to reliably predict complication occurrence for given patients on the basis of operative, diagnostic, and patient-specific factors. A description of the use of the score and derivation of adverse-event probability is included in the Appendix.

There are important limitations of our model. A substantial weakness is the patient ages within the MarketScan database. Although the elderly constitute one of the most rapidly growing groups of patients receiving spine surgery, a plot of patient ages in the MarketScan database shows nearly no Medicare-aged patients (Fig. 2). While the incidence of spine surgery procedures is growing, the majority of the growth is in Medicare-aged patients, primarily because of the effects of degenerative disease on the aging spine1-3.

Administrative database studies rely on the accuracy of coding of both index procedures and comorbidities; errors may lead to inappropriate overreporting or underreporting. As is the case in all retrospective data analyses, there is a risk that confounders that were not assessed biased the study results. Although we accounted for these differences using both multivariate logistic regression and propensity score analysis, retrospective data cannot replace a prospective trial with these covariates balanced at the study outset.

The definition of comorbidities chosen for this analysis is broad, incorporating smoking status, history of psychiatric disorders, alcohol abuse, and other factors not used by other authors28,29,43,44. This broad approach may incorporate elements of limited clinical impact. Further development of the measure, and validation across multiple databases, will hopefully limit this risk and ensure that the measure appropriately models patient experience.

Finally, the predictive ability of the approach, while superior to that of the commonly used Charlson comorbidity score, still has room for improvement. We took a broad approach to assessing as many different factors as could be reliably extracted from a robust longitudinal administrative data set. While the overall complication incidence based on the administrative data appears representative, our ability to predict complication occurrence in the testing set only reached an AUC of 0.70. We believe that this may be improved by incorporating larger data sets and probably by expanding our assessment to Medicare-aged patients. However, this predictive accuracy may also reflect the inherent weaknesses of administrative data.

In conclusion, we present a novel data modeling effort that predicts the occurrence of complications in spine surgery on the basis of administrative data, with less accurate prediction of complication type. 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.

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Appendix Cited Here...

A description of the LASSO regularization and an example of adverse-event risk score calculation are available with the online version of this article as a data supplement at jbjs.org.

Investigation performed at Stanford University School of Medicine, Stanford, California

Disclosure: This research was supported by a research grant from the Orthopaedic Research and Education Foundation. Additional support for secondary aspects of the study was obtained from the Walsh Foundation, the Stanford Medical Scholars Program, the Stanford Society of Physician Scholars, and a medical student scholarship provided by the Council of State Neurosurgical Societies. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article.

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