Discharge Disposition After Anterior Cervical Discectomy and Fusion

Socioeconomic Status Impacts Length of Stay and Nonhome Discharge Disposition After Posterior Cervical Decompression and Fusion

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

To examine how community-level economic disadvantage impacts short-term outcomes following posterior cervical decompression and fusion (PCDF) for cervical spondylotic myelopathy.

SUMMARY OF BACKGROUND DATA

The effects of socioeconomic factors, measured by the Distress Community Index (DCI), on postoperative outcomes after PCDF are underexplored. By understanding the impact of socioeconomic status (SES) on PCDF outcomes, disparities in care can be addressed.

MATERIALS AND METHODS

Retrospective review of 554 patients who underwent PCDF for cervical spondylotic myelopathy between 2017 and 2022. SES was assessed using DCI obtained from patient zip codes. Patients were stratified into quintiles from Prosperous to Distressed based on DCI. Bivariate analyses and multivariate regressions were performed to evaluate the associations between social determinants of health and surgical outcomes, including length of stay, home discharge, complications, and readmissions.

RESULTS

Patients living in at-risk/distressed communities were more likely to be Black (53.3%). Patients living in at-risk/distressed communities had the longest hospitalization (6.24 d vs. prosperous: 3.92, P =0.006). Significantly less at-risk/distressed patients were discharged home without additional services (37.3% vs. mid-tier: 52.5% vs. comfortable: 53.4% vs. prosperous: 56.4%, P <0.001). On multivariate analysis, residing in an at-risk/distressed community was independently associated with nonhome discharge [odds ratio (OR): 2.28, P =0.007] and longer length of stay (E:1.54, P =0.017).

CONCLUSIONS

Patients from socioeconomically disadvantaged communities experience longer hospitalizations and are more likely to be discharged to a rehabilitation or skilled nursing facility following PCDF. Social and economic barriers should be addressed as part of presurgical counseling and planning in elective spine surgery to mitigate these disparities and improve the quality and value of health care delivery, regardless of socioeconomic status.

Predictors of Nonhome Discharge After Cervical Disc Replacement

STUDY DESIGN

Retrospective review of a national database.

OBJECTIVE

The aim of this study was to identify the factors that increase the risk of nonhome discharge after CDR.

SUMMARY OF BACKGROUND DATA

As spine surgeons continue to balance increasing surgical volume, identifying variables associated with patient discharge destination can help expedite postoperative placement and reduce unnecessary length of stay. However, no prior study has identified the variables predictive of nonhome patient discharge after cervical disc replacement (CDR).

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent primary 1-level or 2-level CDR between 2011 and 2020. Multivariable Poisson regression with robust error variance was employed to identify the predictors for nonhome discharge destination following surgery.

RESULTS

A total of 7276 patients were included in this study, of which 94 (1.3%) patients were discharged to a nonhome destination. Multivariable regression revealed older age (OR: 1.076, P <0.001), Hispanic ethnicity (OR: 4.222, P =0.001), BMI (OR: 1.062, P =0.001), ASA class ≥3 (OR: 2.562, P =0.002), length of hospital stay (OR: 1.289, P <0.001), and prolonged operation time (OR: 1.007, P <0.001) as predictors of nonhome discharge after CDR. Outpatient surgery setting was found to be protective against nonhome discharge after CDR (OR: 0.243, P <0.001).

CONCLUSIONS

Age, Hispanic ethnicity, BMI, ASA class, prolonged hospital stay, and prolonged operation time are independent predictors of nonhome discharge after CDR. Outpatient surgery setting is protective against nonhome discharge. These findings can be utilized to preoperatively risk stratify expected discharge destination, anticipate patient discharge needs postoperatively, and expedite discharge in these patients to reduce health care costs associated with prolonged length of hospital stay.

LEVEL OF EVIDENCE

IV.

Late-week Multilevel Anterior Cervical Discectomy and Fusion Associated With Increased Length of Stay

STUDY DESIGN

Retrospective analysis of clinical data from a single institution.

OBJECTIVE

To assess the day of surgery during the week as a possible predictor of length of stay (LOS) following anterior cervical discectomy and fusion (ACDF).

SUMMARY OF BACKGROUND DATA

Surgeries later in the week may result in longer LOS and higher costs for joint arthroplasty, yet this is unclear following spine surgery. Procedures performed later in the week may lead to weekend admissions when there are limited services that may contribute to an extended LOS. We attempt to identify associations between day of surgery and LOS, readmission, and complications following single- and multilevel ACDF.

MATERIALS AND METHODS

Patients at a single institution undergoing ACDF by 7 primary surgeons in both orthopedic and neurosurgery spine departments between 2015 and 2019 were retrospectively reviewed. Patients were stratified by surgery day at either the beginning (Monday/Tuesday) or end (Thursday/Friday) of the week and by single- or multilevel ACDF. Surgery for trauma, infections, adjacent level disease, or revision were excluded. Patient demographics, Charlson Comorbidity Index (CCI), LOS, postoperative complications, and readmission rates were assessed.

RESULTS

Six hundred fifty-two patients underwent ACDF. For single-level ACDF, 222 were reviewed, with 112 having surgery at the beginning and 110 at the end of the week. For multilevel ACDF, 431 were reviewed, with 192 having surgery at the beginning and 239 at the end of the week. No differences in pre- or postoperative variables were determined for single-level ACDF. Despite no differences in pre-operative variables, CCI, operative duration, or number of levels, late-week multilevel ACDF had longer average LOS (2.8±3.0 days) compared to early-week surgery (2.0±2.0 days) ( P =0.018).

CONCLUSIONS

Late-week multilevel ACDF was associated with an increased LOS, as it may prove beneficial to surgical planning. This conflicts with previous reports that day of week was not associated with LOS following ACDF.

LEVEL OF EVIDENCE

III.

Risk analysis index predicts mortality and non-home discharge following posterior lumbar interbody fusion: a nationwide inpatient sample analysis of 429,380 patients (2019-2020)

PURPOSE

Frailty is an independent risk factor for adverse postoperative outcomes following spine surgery. The ability of the Risk Analysis Index (RAI) to predict adverse outcomes following posterior lumbar interbody fusion (PLIF) has not been studied extensively and may improve preoperative risk stratification.

METHODS

Patients undergoing PLIF were queried from Nationwide Inpatient Sample (NIS) (2019-2020). The relationship between RAI-measured preoperative frailty and primary outcomes (mortality, non-home discharge (NHD)) and secondary outcomes (extended length of stay (eLOS), complication rates) was assessed via multivariate analyses. The discriminatory accuracy of the RAI for primary outcomes was measured in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

A total of 429,380 PLIF patients (mean age = 61y) were identified, with frailty cohorts stratified by standard RAI convention: 0-20 "robust" (R)(38.3%), 21-30 "normal" (N)(54.3%), 31-40 "frail" (F)(6.1%) and 41+ "very frail" (VF)(1.3%). The incidence of primary and secondary outcomes increased as frailty thresholds increased: mortality (R 0.1%, N 0.1%, F 0.4%, VF 1.3%; p < 0.001), NHD (R 6.5%, N 18.1%, F 36.9%, VF 42.0%; p < 0.001), eLOS (R 18.0%, N 21.9%, F 31.6%, VF 43.8%; p < 0.001) and complication rates (R 6.6%, N 8.8%, F 11.1%, VF 12.2%; p < 0.001). The RAI demonstrated acceptable discrimination for NHD (C-statistic: 0.706) and mortality (C-statistic: 0.676) in AUROC curve analysis.

CONCLUSION

Increasing RAI-measured frailty is significantly associated with increased NHD, eLOS, complication rates, and mortality following PLIF. The RAI demonstrates acceptable discrimination for predicting NHD and mortality, and may be used to improve frailty-based risk assessment for spine surgeons.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction.

SUMMARY OF BACKGROUND DATA

Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications.

METHODS

Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions.

RESULTS

A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination.

CONCLUSIONS

Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

Indication as a predictor for outcomes in anterior cervical discectomy and fusion: The impact of myelopathy on disposition

BACKGROUND

While the indication for Anterior Cervical Discectomy and Fusion (ACDF) may influence the expected postoperative course, there is limited data comparing how length of stay (LOS) and disposition for patients with myelopathy differ from those with radiculopathy. This study aimed to compare LOS and discharge disposition, in patients undergoing ACDF for cervical radiculopathy versus those for myelopathy.

METHODS

A retrospective review of all adult ACDF cases between 2013 and 2019 was conducted analyzing sex, age, race, comorbidities, level of surgery, myelopathy measures when applicable, complications, dysphagia, hospital LOS, and discharge disposition.

RESULTS

A total of 157 patients were included in the study with 73 patients undergoing an ACDF for radiculopathy and 84 for myelopathy. Univariate analysis determined older age (p < 0.01), male sex (p = 0.03), presence of CKD (p < 0.01) or COPD (p = 0.01), surgery at C3/4 level (p = 0.01), and indication (p < 0.01) as predictors for a discharge to either acute rehabilitation or a skilled nursing facility rather than to home. Multivariate logistic regression demonstrated age and indication as the only independent predictors of disposition, with home disposition being more likely with decreased age (OR 0.92, 95 % CI 0.86-0.98) and radiculopathy as the diagnosis (OR 6.72, 95 % CI 1.22- 37.02).

CONCLUSIONS

Myelopathic patients, as compared to those with radiculopathy at presentation, had significantly longer LOS, increased dysphagia, and were more often discharged to a facility. Understanding these two distinct populations as separate entities will streamline the pre and post-surgical care as the current DRG codes and ICD 10 PCS do not differentiate the expected post-operative course in patients undergoing ACDF for myelopathy versus radiculopathy.

Public health insurance, frailty, and lack of home support predict rehab discharge following elective anterior cervical discectomy and fusion

BACKGROUND CONTEXT

Anterior cervical discectomy and fusion (ACDF) is a commonly-performed and generally well-tolerated procedure used to treat cervical disc herniation. Rarely, patients require discharge to inpatient rehab, leading to inconvenience for the patient and increased healthcare expenditure for the medical system.

PURPOSE

The objective of this study was to create an accurate and practical predictive model for, as well as delineate associated factors with, rehab discharge following elective ACDF.

STUDY DESIGN

This was a retrospective, single-center, cohort study.

PATIENT SAMPLE

Patients who underwent ACDF between 2012 and 2022 were included. Those with confounding diagnoses or who underwent concurrent, staged, or nonelective procedures were excluded.

OUTCOME MEASURES

Primary outcomes for this study included measurements of accuracy for predicting rehab discharge. Secondary outcomes included associations of variables with rehab discharge.

METHODS

Current Procedural Terminology codes identified patients. Charts were reviewed to obtain additional demographic and clinical characteristics on which an initial univariate analysis was performed. Two logistic regression and two machine learning models were trained and evaluated on the data using cross-validation. A multimodel logistic regression was implemented to analyze independent variable associations with rehab discharge.

RESULTS

A total of 466 patients were included in the study. The logistic regression model with minimum corrected Akaike information criterion score performed best overall, with the highest values for area under the receiver operating characteristic curve (0.83), Youden's J statistic (0.71), balanced accuracy (85.7%), sensitivity (90.3%), and positive predictive value (38.5%). Rehab discharge was associated with a modified frailty index of 2 (p=.007), lack of home support (p=.002), and having Medicare or Medicaid insurance (p=.007) after correction for multiple hypotheses.

CONCLUSIONS

Nonmedical social determinants of health, such as having public insurance or a lack of support at home, may play a role in rehab discharge following elective ACDF. In combination with the modified frailty index and other variables, these factors can be used to predict rehab discharge with high accuracy, improving the patient experience and reducing healthcare costs.

Price Transparency for Cervical Spinal Fusion Among High-Performing Spine Centers in the United States

BACKGROUND AND OBJECTIVES

As of January 1, 2021, all US hospitals are required by the Hospital Price Transparency Final Rule (HPTFR) to publish standard charges for all items and services, yet the state of price transparency for cervical spinal fusion is unknown. Here, we assess the nationwide price transparency landscape for cervical spinal fusion among high-performing spine centers in the United States.

METHODS

In this cross-sectional economic evaluation, we queried publicly available price transparency websites of 332 "high-performing" spine centers, as defined by the US News and World Report. We extracted variables including gross charges for cervical spinal fusion, payor options, price reporting methodology, and prices relevant to consumers including listed cash prices and minimum and maximum negotiated charges.

RESULTS

While nearly all 332 high-performing spine surgery centers (99.4%) had an online cost estimation tool, the HPTFR compliance rate was only 8.4%. Gross charges for cervical spinal fusion were accessible for 68.1% of hospitals, discounted cash prices for 46.4% of hospitals, and minimum and maximum charges for 10.8% of hospitals. There were large IQRs for gross charges ($48 491.98-$99 293.37), discounted cash prices ($26 952.25-$66 806.63), minimum charges ($10 766.11-$21 248.36), and maximum charges ($39 280.49-$89 035.35). There was geographic variability in the gross charges of cervical spinal fusion among high-performing spine centers within and between states. There was a significant association between "excellent" discharge to home status and lower mean gross charges.

CONCLUSION

Although online cost reporting has drastically increased since implementation of the HPTFR, data reported for cervical spinal fusion remain inadequate and difficult to interpret by both providers and patients.

Developing a machine learning algorithm to predict the probability of aseptic loosening of the glenoid component after anatomical total shoulder arthroplasty: protocol for a retrospective, multicentre study

INTRODUCTION

Despite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing emphasis is placed on the prevention of glenoid component failure. Previous studies have successfully predicted range of motion, patient-reported outcomes and short-term complications after ATSA using machine learning methods, but an accurate predictive model for (glenoid component) revision is currently lacking. This study aims to use a large international database to accurately predict aseptic loosening of the glenoid component after ATSA using machine learning algorithms.

METHODS AND ANALYSIS

For this multicentre, retrospective study, individual patient data will be compiled from previously published studies reporting revision of ATSA. A systematic literature search will be performed in Medline (PubMed) identifying all studies reporting outcomes of ATSA. Authors will be contacted and invited to participate in the Machine Learning Consortium by sharing their anonymised databases. All databases reporting revisions after ATSA will be included, and individual patients with a follow-up less than 2 years or a fracture as the indication for ATSA will be excluded. First, features (predictive variables) will be identified using a random forest feature selection. The resulting features from the compiled database will be used to train various machine learning algorithms (stochastic gradient boosting, random forest, support vector machine, neural network and elastic-net penalised logistic regression). The developed and validated algorithms will be evaluated across discrimination (c-statistic), calibration, the Brier score and the decision curve analysis. The best-performing algorithm will be used to create an open-access online prediction tool.

ETHICS AND DISSEMINATION

Data will be collected adhering to the WHO regulation on data sharing. An Institutional Review Board review is not applicable. The study results will be published in a peer-reviewed journal.

An 11-Year-Long Analysis of the Risks Associated With Age in Patients Undergoing Anterior Cervical Discectomy and Fusion in a Large, Urban Academic Hospital

STUDY DESIGN

A retrospective database study of patients at an urban academic medical center undergoing an Anterior Cervical Discectomy and Fusion (ACDF) surgery between 2008 and 2019.

OBJECTIVE

ACDF is one of the most common spinal procedures. Old age has been found to be a common risk factor for postoperative complications across a plethora of spine procedures. Little is known about how this risk changes among elderly cohorts such as the difference between elderly (60+) and octogenarian (80+) patients. This study seeks to analyze the disparate rates of complications following elective ACDF between patients aged 60-69 or 70-79 and 80+ at an urban academic medical center.

METHODS

We identified patients who had undergone ACDF procedures using CPT codes 22,551, 22,552, and 22,554. Emergent procedures were excluded, and patients were subdivided on the basis of age. Then each cohort was propensity matched for univariate and univariate logistic regression analysis.

RESULTS

The propensity matching resulted in 25 pairs in both the 70-79 and 80+ y.o. cohort comparison and 60-69 and 80+ y.o. cohort comparison. None of the cohorts differed significantly in demographic variables. Differences between elderly cohorts were less pronounced: the 80+ y.o. cohort experienced only significantly higher total direct cost (P = .03) compared to the 70-79 y.o. cohort and significantly longer operative time (P = .04) compared to the 60-69 y.o. cohort.

CONCLUSIONS

Octogenarian patients do not face much riskier outcomes following elective ACDF procedures than do younger elderly patients. Age alone should not be used to screen patients for ACDF.

Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach

BACKGROUND CONTEXT

The number of elective spinal fusion procedures performed each year continues to grow, making risk factors for post-operative complications following this procedure increasingly clinically relevant. Nonhome discharge (NHD) is of particular interest due to its associations with increased costs of care and rates of complications. Notably, increased age has been found to influence rates of NHD.

PURPOSE

To identify aged-adjusted risk factors for nonhome discharge following elective lumbar fusion through the utilization of Machine Learning-generated predictions within stratified age groupings.

STUDY DESIGN

Retrospective Database Study.

PATIENT SAMPLE

The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database years 2008 to 2018.

OUTCOME MEASURES

Postoperative discharge destination.

METHODS

ACS-NSQIP was queried to identify adult patients undergoing elective lumbar spinal fusion from 2008 to 2018. Patients were then stratified into the following age ranges: 30 to 44 years, 45 to 64 years, and ≥65 years. These groups were then analyzed by eight ML algorithms, each tasked with predicting post-operative discharge destination.

RESULTS

Prediction of NHD was performed with average AUCs of 0.591, 0.681, and 0.693 for those aged 30 to 44, 45 to 64, and ≥65 years respectively. In patients aged 30 to 44, operative time (p<.001), African American/Black race (p=.003), female sex (p=.002), ASA class three designation (p=.002), and preoperative hematocrit (p=.002) were predictive of NHD. In ages 45 to 64, predictive variables included operative time, age, preoperative hematocrit, ASA class two or class three designation, insulin-dependent diabetes, female sex, BMI, and African American/Black race all with p<.001. In patients ≥65 years, operative time, adult spinal deformity, BMI, insulin-dependent diabetes, female sex, ASA class four designation, inpatient status, age, African American/Black race, and preoperative hematocrit were predictive of NHD with p<.001. Several variables were distinguished as predictive for only one age group including ASA Class two designation in ages 45 to 64 and adult spinal deformity, ASA class four designation, and inpatient status for patients ≥65 years.

CONCLUSIONS

Application of ML algorithms to the ACS-NSQIP dataset identified a number of highly predictive and age-adjusted variables for NHD. As age is a risk factor for NHD following spinal fusion, our findings may be useful in both guiding perioperative decision-making and recognizing unique predictors of NHD among specific age groups.

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion

STUDY DESIGN

A retrospective cohort study from a multisite academic medical center.

OBJECTIVE

To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF).

SUMMARY OF BACKGROUND DATA

Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment. Recent data has shown marked hospital resource utilization after ACDF surgery, including readmissions, and ED visits. The burden of postoperative health care use presents a potential application of machine learning techniques, which may be capable of accurately identifying at-risk patients using patient-specific predictors.

METHODS

Patients 18-88 years old who underwent ACDF from 2011 to 2021 at a multisite academic center and had preoperative lab values within 3 months of surgery were included. Outcomes analyzed included 90-day readmissions, postoperative length of stay, and nonhome discharge. Four machine learning models-Extreme Gradient Boosted Trees, Balanced Random Forest, Elastic-Net Penalized Logistic Regression, and a Neural Network-were trained and evaluated through the Area Under the Curve estimates. Feature importance scores were computed for the highest-performing model per outcome through model-specific metrics.

RESULTS

A total of 1026 cases were included in the analysis cohort. All machine learning models were predictive for outcomes of interest, with the Random Forest algorithm consistently demonstrating the strongest average area under the curve performance, with a peak performance of 0.84 for nonhome discharge. Important features varied per outcome, though age, body mass index, American Society of Anesthesiologists classification >2, and medical comorbidities were highly weighted in the studied outcomes.

CONCLUSIONS

Machine learning models were successfully applied and predictive of postoperative health utilization after ACDF. Deployment of these tools can assist clinicians in determining high-risk patients.

LEVEL OF EVIDENCE

III.

Weekend Admission Increases Risk of Readmissions Following Elective Cervical Spinal Fusion

Objective: The “weekend effect” occurs when patients cared for during weekends versus weekdays experience worse outcomes. But reasons for this effect are unclear, especially amongst patients undergoing elective cervical spinal fusion (ECSF). Our aim was to analyze whether index weekend admission affects 30- and 90-day readmission rates post-ECSF.Methods: All ECSF patients > 18 years were retrospectively identified from the 2016–2018 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD), using unique patient linkage codes and International Classification of Diseases, Tenth Revision codes. Patient demographics, comorbidities, and outcomes were analyzed. Univariate logistic regression analyzed primary outcomes of 30- and 90-day readmission rates in weekday or weekend groups. Multivariate regression determined the impact of complications on readmission rates.Results: Compared to the weekday group (n = 125,590), the weekend group (n = 1,026) held a higher percentage of Medicare/Medicaid insurance, incurred higher costs, had longer length of stay, and fewer routine home discharge (all p < 0.001). There was no difference in comorbidity burden between weekend versus weekday admissions, as measured by the Elixhauser Comorbidity Index (p = 0.527). Weekend admissions had higher 30-day (4.30% vs. 7.60%, p < 0.001) and 90-day (7.80% vs. 16.10%, p < 0.001) readmission rates, even after adjusting for sex, age, insurance status, and comorbidities. All-cause complication rates were higher for weekend admissions (8.62% vs. 12.7%, p < 0.001), specifically deep vein thrombosis, infection, neurological conditions, and pulmonary embolism.Conclusion: Index weekend admission increases 30- and 90-day readmission rates after ECSF. In patients undergoing ECSF on weekends, postoperative care for patients at risk for specific complications will allow for improved outcomes and health care utilization.

Disparities in Elective Spine Surgery for Medicaid Beneficiaries: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

We sought to synthesize the literature investigating the disparities that Medicaid patients sustain with regards to 2 types of elective spine surgery, lumbar fusion (LF) and anterior cervical discectomy and fusion (ACDF).

METHODS

Our review was constructed in accordance with Preferred Reporting Items and Meta-analyses (PRISMA) guidelines and protocol. We systematically searched PubMed, Embase, Scopus, CINAHL, and Web of Science databases. We included studies comparing Medicaid beneficiaries to other payer categories with regards to rates of LF and ACDF, costs/reimbursement, and health outcomes.

RESULTS

A total of 573 articles were assessed. Twenty-five articles were included in the analysis. We found that the literature is consistent with regards to Medicaid disparities. Medicaid was strongly associated with decreased access to LF and ACDF, lower reimbursement rates, and worse health outcomes (such as higher rates of readmission and emergency department utilization) compared to other insurance categories.

CONCLUSIONS

In adult patients undergoing elective spine surgery, Medicaid insurance is associated with wide disparities with regards to access to care and health outcomes. Efforts should focus on identifying causes and interventions for such disparities in this vulnerable population.

Impact of Race/Ethnicity on Hospital Resource Utilization After Elective Anterior Cervical Decompression and Fusion for Degenerative Myelopathy

INTRODUCTION

With the advent of bundled care payments for spine surgery, there is increasing scrutiny on the costs and resource utilization associated with surgical care. The purpose of this study was to compare (1) the total cost of the hospital episode of care and (2) discharge destination between White, Black, and Hispanic patients receiving elective anterior cervical decompression and fusion for degenerative cervical myelopathy (DCM) in Medicare patients.

METHODS

The 2019 Medicare Provider Analysis and Review Limited Data Set and the 2019 Impact File were used for this project. Multivariate models were created for total cost and discharge destination, controlling for confounders found on univariate analysis. We then performed a subanalysis for differences in specific cost-center charges.

RESULTS

There were 11,506 White (85.4%), 1,707 Black (12.7%), and 261 Hispanic (1.9%) patients identified. There were 6,447 males (47.8%) and 7,027 females (52.2%). Most patients were between 65 to 74 years of age (n = 7,101, 52.7%). The mean cost of the hospital episode was $20,919 ± 11,848. Most patients were discharged home (n = 11,584, 86.0%). Race/ethnicity was independently associated with an increased cost of care (Black: $783, Hispanic: $1,566, P = 0.001) and an increased likelihood of nonhome discharge (Black: adjusted odds ratio: 1.990, P < 0.001, Hispanic: adjusted odds ratio: 1.822, P < 0.001) compared with White patients. Compared with White patients, Black patients were charged more for accommodations ($1808), less for supplies (-$1780), and less for operating room (-$1072), whereas Hispanic patients were charged more ($3556, $7923, and $5162, respectively, P < 0.05).

CONCLUSION

Black and Hispanic race/ethnicity were found to be independently associated with an increased cost of care and risk for nonhome discharge after elective anterior cervical decompression and fusion for DCM compared with White patients. The largest drivers of this disparity appear to be accommodation, medical/surgical supply, and operating room-related charges. Further analysis of these racial disparities should be performed to improve value and equity of spine care for DCM.

Simple and actionable preoperative prediction of postoperative healthcare needs of single-level lumbar fusion patients

OBJECTIVE

Preoperative prediction of a patient's postoperative healthcare utilization is challenging, and limited guidance currently exists. The objective of the present study was to assess the capability of individual risk-related patient characteristics, which are available preoperatively, that may predict discharge disposition prior to lumbar fusion.

METHODS

In total, 1066 consecutive patients who underwent single-level, posterior-only lumbar fusion at a university health system were enrolled. Patients were prospectively asked 4 nondemographic questions from the Risk Assessment and Prediction Tool during preoperative office visits to evaluate key risk-related characteristics: baseline walking ability, use of a gait assistive device, reliance on community supports (e.g., Meals on Wheels), and availability of a postoperative home caretaker. The primary outcome was discharge disposition (home vs skilled nursing facility/acute rehabilitation). Logistic regression was performed to analyze the ability of each risk-related characteristic to predict likelihood of home discharge.

RESULTS

Regression analysis demonstrated that improved baseline walking ability (OR 3.17), ambulation without a gait assistive device (OR 3.13), and availability of a postoperative home caretaker (OR 1.99) each significantly predicted an increased likelihood of home discharge (all p < 0.0001). However, reliance on community supports did not significantly predict discharge disposition (p = 0.94).

CONCLUSIONS

Patient mobility and the availability of a postoperative caretaker, when determined preoperatively, strongly predict a patient's healthcare utilization in the setting of single-level, posterior lumbar fusion. These findings may help surgeons to streamline preoperative clinic workflow and support the patients at highest risk in a targeted fashion.

Choose Wisely: Surgical Selection of Candidates for Outpatient Anterior Cervical Surgery Based on Early Complications Among Inpatients

BACKGROUND

Anterior cervical discectomy and fusion (ACDF) and cervical disc arthroplasty (CDA) are attractive targets for transition to the outpatient setting. We assessed the prevalence of rapid responses and major complications in the inpatient setting following 1 or 2-level ACDFs and CDAs. We evaluated factors that may place patients at greater risk for a rapid response or a postoperative complication.

METHODS

This was an institutional review board-approved, retrospective cohort study of adults undergoing 1 or 2-level ACDF or CDA at 1 hospital over a 2-year period (2018 and 2019). Data on patient demographic characteristics, surgical procedures, and comorbidities were collected. Rapid response events were identified by hospital floor staff and involved acute changes in a patient's clinical condition. Complications were events that were life-threatening, required an intervention, or led to delayed hospital discharge.

RESULTS

In this study, 1,040 patients were included: 888 underwent ACDF and 152 underwent CDA. Thirty-six patients (3.5%) experienced a rapid response event; 22% occurred >24 hours after extubation. Patients having a rapid response event had a significantly higher risk of developing a complication (risk ratio, 10; p < 0.01) and had a significantly longer hospital stay. Twenty-four patients (2.3%) experienced acute complications; 71% occurred >6 hours after extubation. Patients with a complication were older and more likely to be current or former smokers, have chronic obstructive pulmonary disease, have asthma, and have an American Society of Anesthesiologists (ASA) score of >2. The length of the surgical procedure was significantly longer in patients who developed a complication. All patients who developed dysphagia had a surgical procedure involving C4-C5 or more cephalad. Patients with a rapid response event or complication were more commonly undergoing revision surgical procedures.

CONCLUSIONS

Rapid response and complications are uncommon following 1 or 2-level ACDFs or CDAs but portend a longer hospital stay and increased morbidity. Revision surgical procedures place patients at higher risk for rapid responses and complications. Additionally, older patients, patients with chronic obstructive pulmonary disease or asthma, patients who are current or former smokers, and patients who have an ASA score of ≥3 are at increased risk for postoperative complications.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above

Purpose

Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Methods

A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis.

Results

In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/

Conclusions

The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making.

Level of evidence

Prognostic Level II.

Impact of Discharge to Rehabilitation on Postdischarge Morbidity Following Multilevel Posterior Lumbar Fusion

STUDY DESIGN

This was a retrospective cohort study.

OBJECTIVE

The objective of this study was to compare 30-day postdischarge morbidity for 3-or-more level (multilevel) posterior lumbar fusion in patients who were discharged to home versus rehabilitation.

SUMMARY OF BACKGROUND DATA

Spine surgery has been increasingly performed in the elderly population, with many of these patients being discharged to rehabilitation and skilled nursing facilities. However, research evaluating the safety of nonhome discharge following spine surgery is limited.

MATERIALS AND METHODS

Patients who underwent multilevel posterior lumbar fusion from 2005 to 2018 were identified using the National Surgical Quality Improvement Program (NSQIP) database. Regression was utilized to compare primary outcomes between discharge disposition and to evaluate for predictors thereof.

RESULTS

We identified 5276 patients. Unadjusted analysis revealed that patients who were discharged to rehabilitation had greater postdischarge morbidity (5.6% vs. 2.6%). After adjusting for baseline differences, discharge to rehabilitation no longer predicted postdischarge morbidity [odds ratio (OR)=1.409, confidence interval: 0.918-2.161, P=0.117]. Multivariate analysis also revealed that age (P=0.026, OR=1.023), disseminated cancer (P=0.037, OR=6.699), and readmission (P<0.001, OR=28.889) independently predicted postdischarge morbidity.

CONCLUSIONS

Thirty days morbidity was statistically similar between patients who were discharged to home and rehabilitation. With appropriate patient selection, discharge to rehabilitation can potentially minimize 30-day postdischarge morbidity for more medically frail patients undergoing multilevel posterior lumbar fusion. These results are particularly important given an aging population, with a great portion of elderly patients who may benefit from postacute care facility discharge following spine surgery.

Insurance status as a mediator of clinical presentation, type of intervention, and short-term outcomes for patients with metastatic spine disease

BACKGROUND

It is well established that insurance status is a mediator of disease management, treatment course, and clinical outcomes in cancer patients. Our study assessed differences in clinical presentation, treatment course, mortality rates, and in-hospital complications for patients admitted to the hospital with late-stage cancer - specifically, metastatic spine disease (MSD), by insurance status.

METHODS

The United States National Inpatient Sample (NIS) database (2012-2014) was queried to identify patients with visceral metastases, metastatic spinal cord compression (MSCC) or pathological fracture of the spine in the setting of cancer. Clinical presentation, type of intervention, mortality rates, and in-hospital complications were compared amongst patients by insurance coverage (Medicare, Medicaid, commercial or unknown). Multivariable logistical regression and age sensitivity analyses were performed.

RESULTS

A total of 48,560 MSD patients were identified. Patients with Medicaid coverage presented with significantly higher rates of MSCC (p < 0.001), paralysis (0.008), and visceral metastases (p < 0.001). Patients with commercial insurance were more likely to receive surgical intervention (OR 1.43; p < 0.001). Patients with Medicaid < 65 had higher rates of prolonged length of stay (PLOS) (OR 1.26; 95% CI, 1.01-1.55; p = 0.040) while both Medicare and Medicaid patients < 65 were more likely to have non-routine discharges. In-hospital mortality rates were significantly higher for patients with Medicaid (OR 2.66; 95% CI 1.20-5.89; p = 0.016) and commercial insurance (OR 1.58; 95% CI 1.09-2.27;p = 0.013) older than 65.

CONCLUSION

Given the differing severity in MSD presentation, mortality rates, and rates of PLOS by insurance status, our results identify disparities based on insurance coverage.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients

BACKGROUND

In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows.

METHODS

We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples.

RESULTS

Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/).

CONCLUSIONS

A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

A novel online calculator predicting short-term postoperative outcomes in patients with metastatic brain tumors

Purpose

Establishing predictors of hospital length of stay (LOS), discharge deposition, and total hospital charges is essential to providing high-quality, value-based care. Though previous research has investigated these outcomes for patients with metastatic brain tumors, there are currently no tools that synthesize such research findings and allow for prediction of these outcomes on a patient-by-patient basis. The present study sought to develop a prediction calculator that uses patient demographic and clinical information to predict extended hospital length of stay, non-routine discharge disposition, and high total hospital charges for patients with metastatic brain tumors.

Methods

Patients undergoing surgery for metastatic brain tumors at a single academic institution were analyzed (2017–2019). Multivariate logistic regression was used to identify independent predictors of extended LOS (> 7 days), non-routine discharge, and high total hospital charges (> $ 46,082.63). p < 0.05 was considered statistically significant. C-statistics and the Hosmer–Lemeshow test were used to assess model discrimination and calibration, respectively.

Results

A total of 235 patients were included in our analysis, with a mean age of 62.74 years. The majority of patients were female (52.3%) and Caucasian (76.6%). Our models predicting extended LOS, non-routine discharge, and high hospital charges had optimism-corrected c-statistics > 0.7, and all three models demonstrated adequate calibration (p > 0.05). The final models are available as an online calculator (https://neurooncsurgery.shinyapps.io/brain_mets_calculator/).

Conclusions

Our models predicting postoperative outcomes allow for individualized risk-estimation for patients following surgery for metastatic brain tumors. Our results may be useful in helping clinicians to provide resource-conscious, high-value care.

Prediction calculator for nonroutine discharge and length of stay after spine surgery

BACKGROUND CONTEXT

Following spine surgery, delays in referral to rehabilitation facilities leads to increased length of hospital stay (LOS), increases costs, more risk of hospital acquired complications, and decreased patient satisfaction.

PURPOSE

We sought to create a prediction calculator to determine the expected LOS after spine surgery and identify patients most likely to need postoperative nonhome discharge. The goal would be to facilitate earlier referral to rehabilitation and thereby ultimately shorten LOS, reduce costs, and improve patient satisfaction.

STUDY DESIGN

Retrospective.

PATIENT SAMPLE

We retrospectively reviewed all adult patients who underwent spine surgery for all indications between January and June 2018.

OUTCOME MEASURES

Length of stay and discharge disposition.

METHODS

Demographic variables, insurance status, baseline comorbidities, narcotic use, operative characteristics, as well as postoperative length of stay and discharge disposition data were collected. Univariable and multivariable analyses were performed to identify independent predictors of LOS and discharge disposition.

RESULTS

Two hundred fifty-seven patients were included. Mean age was 59 years, 46% were females, and 52% had private insurance vs 7% with Medicaid and 41% with Medicare. The most commonly performed procedure was lumbar fusion (31.9%). Mean LOS after surgery was 4.8 days and 18% had prolonged LOS >7 days. Age, insurance type, marriage status, and surgical procedure were significantly associated with LOS and discharge disposition. The final model had an area under the curve of 89% with good discrimination. A web based calculator was developed: https://jhuspine1.shinyapps.io/RehabLOS/ CONCLUSIONS: This study established a novel pilot calculator to identify those patients most likely to be discharged to rehabilitation facilities and to predict LOS after spine surgery. Our calculator had a high predictive accuracy of 89% compared to others in the literature. With validation this tool may ultimately facilitate streamlining of the postoperative period to shorten LOS, optimize resource utilization, and improve patient care.