The influence of modifiable risk factors on short-term postoperative outcomes following cervical spine surgery: A retrospective propensity score matched analysis

Preoperative Optimization Strategies in Elective Spine Surgery

» Although spine surgery is effective in reducing pain and improving functional status, it is associated with unacceptably high rates of complications, thus necessitating comprehensive preoperative patient optimization.» Numerous risk factors that can impact long-term surgical outcomes have been identified, including malnutrition, cardiovascular disease, osteoporosis, substance use, and more.» Preoperative screening and personalized, evidence-based interventions to manage medical comorbidities and optimize medications can enhance clinical outcomes and improve patient satisfaction following spine surgery.» Multidisciplinary team-based approaches, such as enhanced recovery after surgery protocols and multidisciplinary conferences, can further facilitate coordinated care from across specialties and reduce overall hospital length of stay.

Developing Mixed-Effects Models to Compare the Predictive Ability of Various Comorbidity Indices in a Contemporary Cohort of Patients Undergoing Lumbar Fusion

BACKGROUND AND OBJECTIVE

As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof.

METHODS

Patients who underwent spinal fusion were queried using data from the Nationwide Readmissions Database for the years 2016 through 2019. Using comorbidity indices as predictor variables, receiver operating characteristic curves were developed for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 30-day readmission.

RESULTS

A total of 750 183 patients were included. Nonroutine discharges occurred in 161 077 (21.5%) patients. The adjusted all-payer cost for the procedure was $37 616.97 ± $27 408.86 (top quartile: $45 409.20), and the length of stay was 4.1 ± 4.4 days (top quartile: 8.1 days). By comparing receiver operating characteristics of various models, it was found that models using Frailty + Elixhauser Comorbidity Index (ECI) as the primary predictor performed better than other models with statistically significant P -values on post hoc testing. However, for prediction of mortality, the model using Frailty + ECI was not better than the model using ECI alone ( P = .23), and for prediction of all-payer cost, the ECI model outperformed the models using frailty alone ( P < .0001) and the model using Frailty + ECI ( P < .0001).

CONCLUSION

This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.

Influence of Body Mass Index on Surgical and Patient Outcomes for Cervical Spine Surgery

STUDY DESIGN

Secondary analysis of prospectively collected registry.

OBJECTIVE

We aim to investigate the effects of body mass index (BMI) on postsurgical cervical spine surgery outcomes and identify a potential substratification of obesity with worse outcomes.

SUMMARY OF BACKGROUND DATA

The impact of BMI on cervical spine surgery is unknown, with controversial outcomes for patients high and low BMI.

METHODS

The cervical spine Quality Outcomes Database was queried for a total of 10,381 patients who underwent single-stage cervical spine surgery. Patients were substratified into 6 groups based on BMI. Surgical outcomes, complications, hospitalization outcomes, and patient-reported outcomes for each cohort, including modified Japanese Orthopedic Association Score, Numeric Rating Scale arm pain, Numeric Rating Scale neck pain, Neck Disability Index, and EuroQol Health Survey, were assessed. Univariate analysis was performed for 3- and 12-month follow-up after surgical intervention.

RESULTS

Obese patients (class I, II, and III) requiring spine surgery were statistically younger than nonobese patients and had higher rates of diabetes compared with normal BMI patients. The surgical length was found to be longer for overweight and all classes of obese patients ( P < 0.01). Class III obese patients had higher odds of postoperative complications. Patients with class II and III obesity had lower odds of achieving optimal modified Japanese Orthopedic Association Score at 3 months [OR = 0.8 (0.67-0.94), P < 0.01, OR = 0.68 (0.56-0.82), P < 0.001, respectively] and 12 months [OR = 0.82 (0.68-0.98), P = 0.03, OR = 0.79 (0.64-0.98), P = 0.03, respectively].

CONCLUSIONS

This study investigates the relationship between substratified BMI and postoperative outcomes of cervical spine surgery. Class II and III obese patients have substantially greater risk factors and poor outcomes postoperatively. In addition, low BMI also presents unique challenges for patients. Further research is needed for comprehensive analysis on outcomes of cervical spine surgery after correcting BMI.

Developing Mixed-effects Models to Optimize Prediction of Postoperative Outcomes in a Modern Sample of Over 450,000 Patients Undergoing Elective Cervical Spine Fusion Surgery

STUDY DESIGN

A retrospective cohort.

OBJECTIVE

We utilize big data and modeling techniques to create optimized comorbidity indices for predicting postoperative outcomes following cervical spine fusion surgery.

SUMMARY OF BACKGROUND DATA

Cervical spine decompression and fusion surgery are commonly used to treat degenerative cervical spine pathologies. However, there is a paucity of high-quality data defining the optimal comorbidity indices specifically in patients undergoing cervical spine fusion surgery.

METHODS

Using data from 2016 to 2019, we queried the Nationwide Readmissions Database (NRD) to identify individuals who had received cervical spine fusion surgery. The Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining indicator was used to assess frailty. To measure the level of comorbidity, Elixhauser Comorbidity Index (ECI) scores were queried. Receiver operating characteristic curves were developed utilizing comorbidity indices as predictor variables for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 1-year readmission.

RESULTS

A total of 453,717 patients were eligible. Nonroutine discharges occurred in 93,961 (20.7%) patients. The mean adjusted all-payer cost for the procedure was $22,573.14±18,274.86 (top quartile: $26,775.80) and the mean length of stay was 2.7±4.4 days (top quartile: 4.7 d). There were 703 (0.15%) mortalities and 58,254 (12.8%) readmissions within 1 year postoperatively. Models using frailty+ECI as primary predictors consistently outperformed the ECI-only model with statistically significant P -values for most of the complications assessed. Cost and mortality were the only outcomes for which this was not the case, as frailty outperformed both ECI and frailty+ECI in cost ( P <0.0001 for all) and frailty+ECI performed as well as ECI alone in mortality ( P =0.10).

CONCLUSIONS

Our data suggest that frailty+ECI may most accurately predict clinical outcomes in patients receiving cervical spine fusion surgery. These models may be used to identify high-risk populations and patients who may necessitate greater resource utilization following elective cervical spinal fusion.

The Influence of Modifiable Risk Factors on Postoperative Outcomes in Patients Receiving Surgery for Resection for Acoustic Neuroma

Acoustic neuromas are the most common tumor of the cerebellopontine angle that are associated with a number of symptoms that negatively impact a patient's quality of life. While the mainstay of treatment for these benign tumors remains microsurgical resection, there is limited research exploring how certain modifiable risk factors (MRFs) may affect the perioperative course. The purpose of this study was to investigate how MRFs including malnutrition, obesity, dyslipidemia, uncontrolled hypertension, and smoking may affect postoperative rates of readmission and nonroutine discharges. We utilized the 2016 and 2017 Healthcare Cost and Utilization Project Nationwide Readmissions Database. MRFs were queried using appropriate International Classification of Diseases, Tenth Revision (ICD-10) coding for categories including malnutrition, obesity, dyslipidemia, smoking, alcohol, and hypertension. The statistical analysis was done using RStudio (Version 1.3.959). Chi-squared tests were done to evaluate differences between categorical variables. The Mann-Whitney U-testing was utilized to evaluate for statistically significant differences in continuous data. The "Epitools" package was used to develop logistic regression models for postoperative complications and post hoc receiver operating characteristic curves were developed. Pertaining to nonroutine discharge, predictive models using malnutrition outperformed all other MRFs as well as those with no MRFs (P < .05). In the case of readmission, models using malnutrition outperformed those of obesity and smoking (P < .05). Again, an increase in predictive power is seen in models using dyslipidemia when compared to obesity, smoking, or uncontrolled hypertension. Lastly, models using no MRFs outperformed those of obesity, smoking, and uncontrolled hypertension (P < .05). This is the first study of its kind to evaluate the role of MRFs in those undergoing surgical resection of their acoustic neuroma. We concluded that certain MRFs may play a role in complicating a patient's perioperative surgical course.

Put Down the Tin: Chewing Tobacco Use Is Associated With Worse Outcomes After Primary Lumbar Fusion

STUDY DESIGN

Retrospective study.

OBJECTIVE

This study evaluated the impact of chewing tobacco on both medical and spine-related complication rates after spinal lumbar fusions in comparison to both a control cohort and a smoking cohort.

SUMMARY OF BACKGROUND DATA

Smoking is a prevalent modifiable risk factor that has been demonstrated to be associated with increased complications after lumbar fusion. Although smoking rates have decreased in the United States, chewing tobacco use has not similarly reduced. Despite chewing tobacco delivering up to 4 times the dose of smoking, the impact of chewing tobacco is incompletely understood.

METHODS

A retrospective cohort study was conducted using the PearlDiver database. Patients who underwent lumbar spine fusion and used chewing tobacco were matched with a control cohort and a smoking cohort. Medical complications within 90 days after primary lumbar fusion were evaluated, including deep venous thrombosis, acute kidney injury, pulmonary embolism, transfusion, acute myocardial infarction, and inpatient readmission. Spine-related complications were evaluated at 2 years postoperatively, including pseudoarthrosis, incision and drainage (I&D), instrument failure, revision, and infection.

RESULTS

After primary lumbar fusion, the chewing tobacco cohort demonstrated significantly higher rates of pseudoarthrosis [odds ratio (OR): 1.41], revision (OR: 1.57), and any spine-related complication (OR: 1.32) compared with controls. The smoking cohort demonstrated significantly higher rates of pseudoarthrosis (OR: 1.88), I&D (OR: 1.27), instrument failure (OR: 1.39), revision (OR: 1.54), infection (OR: 1.34), and any spine-related complication (OR: 1.77) compared with controls. The chewing tobacco cohort demonstrated significantly lower rates of pseudoarthrosis (OR: 0.84), I&D (OR: 0.49), infection (OR: 0.70), and any spine-related complication (OR: 0.81) compared with the smoking cohort.

CONCLUSIONS

This study demonstrated that chewing tobacco is associated with higher rates of both spine-related and medical complications after primary lumbar fusion. However, chewing tobacco use is associated with less risk of complications compared with smoking.

LEVEL OF EVIDENCE

Level III.

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis

Purpose

This study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery.

Methods

The clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups.

Results

A total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05).

Conclusion

Several costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.