The Impact of Social Determinants of Health on Discharge Disposition Following One- and Two-Level Posterior Interbody Fusion

Background Current research is limited in exploring the impact of social determinants of health (SDOH) on the discharge location within elective spine surgery. Further understanding of the influence of SDOH on disposition is necessary to improve outcomes. This study explores how SDOH influence discharge disposition for patients undergoing one- or two-level posterior interbody fusion (TLIF/PLIF). Methods This was a retrospective propensity-matched cohort study. Patients who underwent TLIF/PLIF between 2017 and 2020 at a single academic medical center were identified. The chart review gathered demographics, perioperative characteristics, intra/post-operative complications, discharge disposition, and 90-day outcomes. Discharge dispositions included subacute nursing facility (SNF), home with self-care (HSC), home with health services (HHS), and acute rehab facility (ARF). Demographic, perioperative, and disposition outcomes were analyzed by chi-square analysis and one-way ANOVA based on gender, race, and income quartiles. Results Propensity score matching for significant demographic factors isolated 326 patients. The rate of discharge to SNF was higher in females compared to males (25.00% vs 10.56%; p=0.001). Men were discharged to home at a higher rate than women (75.4% vs 61.95%; p=0.010). LatinX patients had the highest rate of home discharge, followed by Asians, Caucasians, and African Americans (83.33% vs 70.31% vs 66.45% vs 65.90%; p<0.001). The post hoc Tukey test demonstrated statistically significant differences between Asians and all other races in the context of age and BMI. Additionally, patients discharged to SNF showed the highest Charlson comorbidity index (CCI) score, followed by those at ARF, HHS, and HSC (4.36 vs 4.05 vs 2.87 vs 2.37; p<0.001). The estimated median income for the cohort ranged from $52,000 to $250,001, with no significant differences in income seen across comparisons. Conclusion Discharge disposition following one- or two-level TLIF/PLIF shows significant association with gender and race. No association was seen when comparing discharge rates among zip code-level median income quartiles.

Assessment of a Private Payer Bundled Payment Model for Lumbar Decompression Surgery

INTRODUCTION

Although bundled payment models are well-established in Medicare-aged individuals, private insurers are now developing bundled payment plans. The role of these plans in spine surgery has not been evaluated. Our objective was to analyze the performance of a private insurance bundled payment program for lumbar decompression and microdiskectomy.

METHODS

A retrospective review was conducted of all lumbar decompressions in a private payer bundled payment model at a single institution from October 2018 to December 2020. 120-day episode of care cost data were collected and reported as net profit or loss regarding set target prices. A stepwise multivariable linear regression model was developed to measure the effect of patient and surgical factors on net surplus or deficit.

RESULTS

Overall, 151 of 468 (32.2%) resulted in a deficit. Older patients (58.6 vs. 50.9 years, P < 0.001) with diabetes (25.2% vs. 13.9%, P = 0.004), hypertension (38.4% vs. 28.4%, P = 0.038), heart disease (13.9% vs. 7.57%, P = 0.030), and hyperlipidemia (51.7% vs. 35.6%, P = 0.001) were more likely to experience a loss. Surgically, decompression of more levels (1.91 vs. 1.19, P < 0.001), posterior lumbar decompression (86.8% vs. 56.5%, P < 0.001), and performing surgery at a tertiary hospital (84.8% vs. 70.3%, P < 0.001) were more likely to result in loss. All readmissions resulted in a loss (4.64% vs. 0.0%, P < 0.001). On multivariable regression, microdiskectomy (β: $2,398, P = 0.012) and surgery in a specialty hospital (β: $1,729, P = 0.096) or ambulatory surgery center (β: $3,534, P = 0.055) were associated with cost savings. Increasing number of levels, longer length of stay, active smoking, and history of cancer, dementia, or congestive heart failure were all associated with degree of deficit.

CONCLUSIONS

Preoperatively optimizing comorbidities and using risk stratification to identify those patients who may safely undergo surgery at a facility other than an inpatient hospital may help increase cost savings in a bundled payment model of working-age and Medicare-age individuals.

Racial Disparities in Spine Surgery: A Systematic Review

STUDY DESIGN

Systematic Review.

OBJECTIVES

To synthesize previous studies evaluating racial disparities in spine surgery.

METHODS

We queried PubMed, Embase, Cochrane Library, and Web of Science for literature on racial disparities in spine surgery. Our review was constructed in accordance with Preferred Reporting Items and Meta-analyses guidelines and protocol. The main outcome measures were the occurrence of racial disparities in postoperative outcomes, mortality, surgical management, readmissions, and length of stay.

RESULTS

A total of 1753 publications were assessed. Twenty-two articles met inclusion criteria. Seventeen studies compared Whites (Ws) and African Americans (AAs) groups; 14 studies reported adverse outcomes for AAs. When compared with Ws, AA patients had higher odds of postoperative complications including mortality, cerebrospinal fluid leak, nervous system complications, bleeding, infection, in-hospital complications, adverse discharge disposition, and delay in diagnosis. Further, AAs were found to have increased odds of readmission and longer length of stay. Finally, AAs were found to have higher odds of nonoperative treatment for spinal cord injury, were more likely to undergo posterior approach in the treatment of cervical spondylotic myelopathy, and were less likely to receive cervical disk arthroplasty compared with Ws for similar indications.

CONCLUSIONS

This systematic review of spine literature found that when compared with W patients, AA patients had worse health outcomes. Further investigation of root causes of these racial disparities in spine surgery is warranted.

Sex Differences in Outcomes of Percutaneous Pulmonary Artery Thrombectomy in Patients With Pulmonary Embolism

BACKGROUND

The sex differences in use, safety outcomes, and health-care resource use of patients with pulmonary embolism (PE) undergoing percutaneous pulmonary artery thrombectomy are not well characterized.

RESEARCH QUESTION

What are the sex differences in outcomes for patients diagnosed with PE who undergo percutaneous pulmonary artery thrombectomy?

STUDY DESIGN AND METHODS

This retrospective cross-sectional study used national inpatient claims data to identify patients in the United States with a discharge diagnosis of PE who underwent percutaneous thrombectomy between January 2016 and December 2018. We evaluated the demographics, comorbidities, safety outcomes (in-hospital mortality), and health-care resource use (discharge to home, length of stay, and hospital charges) of patients with PE undergoing percutaneous thrombectomy.

RESULTS

Among 1,128,904 patients with a diagnosis of PE between 2016 and 2018, 5,160 patients (0.5%) underwent percutaneous pulmonary artery thrombectomy. When compared with male patients, female patients showed higher procedural bleeding (16.9% vs 11.2%; P < .05), required more blood transfusions (11.9% vs 5.7%; P < .05), and experienced more vascular complications (5.0% vs 1.5%; P < .05). Women experienced higher in-hospital mortality (16.9% vs 9.3%; adjusted OR, 1.9; 95% CI, 1.2-3.0; P = .003) when compared with men. Although length of stay and hospital charges were similar to those of men, women were less likely to be discharged home after surviving hospitalization (47.9% vs 60.3%; adjusted OR, 0.7; 95% CI, 0.50-0.99; P = .04).

INTERPRETATION

In this large nationwide cohort, women with PE who underwent percutaneous thrombectomy showed higher morbidity and in-hospital mortality compared with men.

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.

Preoperative Treatment of Severe Diabetes Mellitus and Hypertension Mitigates Healthcare Disparities and Prevents Adverse Postoperative Discharge to a Nursing Home

OBJECTIVE

To evaluate whether patients of Black race are at higher risk of losing the ability to live independently after surgery, and if a higher prevalence of severe diabetes mellitus and hypertension are contributing.

SUMMARY BACKGROUND DATA

It is unclear whether a patient's race predicts adverse discharge to a nursing home after surgery, and if preexisting diseases are contributing.

METHODS

368,360 adults undergoing surgery between 2007 and 2020 across two academic healthcare networks in New England were included. Patients of self-identified Black or White race were compared. The primary outcome was postoperative discharge to a nursing facility. Mediation analysis was used to examine the impact of preexisting severe diabetes mellitus and hypertension on the primary association.

RESULTS

10.3% (38,010/368,360) of patients were Black and 26,434 (7.2%) patients lost the ability to live independently after surgery. Black patients were at increased risk of postoperative discharge to a nursing facility (adjusted absolute risk difference [ARDadj] 1.9%;95%CI 1.6-2.2%;P<0.001). A higher prevalence of preexisting severe diabetes mellitus and hypertension in Black patients mediated 30.2% and 15.6% of this association. Preoperative medication-based treatment adherent to guidelines in patients with severe diabetes mellitus or hypertension mitigated the primary association (p-for-interaction<0.001). The same pattern of effect mitigation by pharmacotherapy was observed for the endpoint 30-day readmission.

CONCLUSIONS

Black race was associated with postoperative discharge to a nursing facility compared to White race. Optimized preoperative assessment and treatment of diabetes and hypertension improves surgical outcomes and provides an opportunity to the surgeon to help eliminate healthcare disparities.

Impact of race on nonroutine discharge, length of stay, and postoperative complications after surgery for spinal metastases

OBJECTIVE

Previous studies have suggested the possibility of racial disparities in surgical outcomes for patients undergoing spine surgery, although this has not been thoroughly investigated in those with spinal metastases. Given the increasing prevalence of spinal metastases requiring intervention, knowledge about potential discrepancies in outcomes would benefit overall patient care. The objective in the present study was to investigate whether race was an independent predictor of postoperative complications, nonroutine discharge, and prolonged length of stay (LOS) after surgery for spinal metastasis.

METHODS

The authors retrospectively examined patients at a single comprehensive cancer center who had undergone surgery for spinal metastasis between April 2013 and April 2020. Demographic information, primary pathology, preoperative clinical characteristics, and operative outcomes were collected. Factors achieving p values < 0.15 on univariate regression were entered into a stepwise multivariable logistic regression to generate predictive models. Nonroutine discharge was defined as a nonhome discharge destination and prolonged LOS was defined as LOS greater than the 75th percentile for the entire cohort.

RESULTS

Three hundred twenty-eight patients who had undergone 348 operations were included: 240 (69.0%) White and 108 (31.0%) Black. On univariable analysis, cohorts significantly differed in age (p = 0.02), marital status (p < 0.001), insurance status (p = 0.03), income quartile (p = 0.02), primary tumor type (p = 0.04), and preoperative Karnofsky Performance Scale (KPS) score (p < 0.001). On multivariable analysis, race was an independent predictor for nonroutine discharge: Black patients had significantly higher odds of nonroutine discharge than White patients (adjusted odds ratio [AOR] 2.24, 95% confidence interval [CI] 1.28-3.92, p = 0.005). Older age (AOR 1.06 per year, 95% CI 1.03-1.09, p < 0.001), preoperative KPS score ≤ 70 (AOR 3.30, 95% CI 1.93-5.65, p < 0.001), preoperative Frankel grade A-C (AOR 3.48, 95% CI 1.17-10.3, p = 0.02), insurance status (p = 0.005), being unmarried (AOR 0.58, 95% CI 0.35-0.97, p = 0.04), number of levels (AOR 1.17 per level, 95% CI 1.05-1.31, p = 0.004), and thoracic involvement (AOR 1.71, 95% CI 1.02-2.88, p = 0.04) were also predictive of nonroutine discharge. However, race was not independently predictive of postoperative complications or prolonged LOS. Higher Charlson Comorbidity Index (AOR 1.22 per point, 95% CI 1.04-1.43, p = 0.01), low preoperative KPS score (AOR 1.84, 95% CI 1.16-2.92, p = 0.01), and number of levels (AOR 1.15 per level, 95% CI 1.05-1.27, p = 0.004) were predictive of complications, while insurance status (p = 0.05), income quartile (p = 0.01), low preoperative KPS score (AOR 1.64, 95% CI 1.03-2.72, p = 0.05), and number of levels (AOR 1.16 per level, 95% CI 1.05-1.30, p = 0.004) were predictive of prolonged LOS.

CONCLUSIONS

Race, insurance status, age, baseline functional status, and marital status were all independently associated with nonroutine discharge. This suggests that a combination of socioeconomic factors and functional status, rather than medical comorbidities, may best predict postdischarge disposition in patients treated for spinal metastases. Further investigation in a prospective cohort is merited.

Racial Disparities in Outcomes After Spine Surgery: A Systematic Review and Meta-Analysis

OBJECTIVE

Racial disparities are a major issue in health care but the overall extent of the issue in spinal surgery outcomes is unclear. We conducted a systematic review/meta-analysis of disparities in outcomes among patients belonging to different racial groups who had undergone surgery for degenerative spine disease.

METHODS

We searched Ovid MEDLINE, Scopus, Cochrane Review Database, and ClinicalTrials.gov from inception to January 20, 2021 for relevant articles assessing outcomes after spine surgery stratified by race. We included studies that compared outcomes after spine surgery for degenerative disease among different racial groups.

RESULTS

We found 30 studies that met our inclusion criteria (28 articles and 2 published abstracts). We included data from 20 cohort studies in our meta-analysis (3,501,830 patients), which were assessed to have a high risk of observation/selection bias. Black patients had a 55% higher risk of dying after spine surgery compared with white patients (relative risk [RR], 1.55, 95% confidence interval [CI], 1.28-1.87; I² = 70%). Similarly, black patients had a longer length of stay (mean difference, 0.93 days; 95% CI, 0.75-1.10; I² = 73%), and higher risk of nonhome discharge (RR, 1.63; 95% CI, 1.47-1.81; I² = 89%), and 30-day readmission (RR, 1.45; 95% CI, 1.03-2.04; I² = 96%). No significant difference was noted in the pooled analyses for complication or reoperation rates.

CONCLUSIONS

Black patients have a significantly higher risk of unfavorable outcomes after spine surgery compared with white patients. Further work in understanding the reasons for these disparities will help develop strategies to narrow the gap among the racial groups.

Patient- and hospital-related risk factors for non-routine discharge after lumbar decompression and fusion for spondylolisthesis

OBJECTIVE

In various spinal surgeries, non-routine discharges have been associated with inferior outcomes. However, there exists a paucity of data regarding the relationship between non-routine discharge and quality of care among patients with spondylolisthesis. The aim of this study was to identify independent predictors for non-routine discharge following spinal decompression and fusion for lumbar spondylolisthesis.

METHODS

A retrospective cohort study was performed using the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database from 2010 through 2016. Adult patients (≥18 years old) who underwent spinal decompression and fusion for lumbar spondylolisthesis were identified using ICD-9-CM diagnosis and CPT procedural coding systems. The study population was divided into two cohorts based on discharge disposition: routine (RD) and non-routine discharge (NRD). Patient demographics, comorbidities, adverse events, LOS, reoperation, and readmission were assessed. A multivariate logistic regression model was used to identify the independent predictors of non-home discharge and 30-day unplanned readmission.

RESULTS

A total of 5252 patients were identified, of which 4316 (82.2%) had a RD and 936 (18.8%) had a NRD. The NRD cohort tended to be older (p < 0.001) and have a higher BMI (p < 0.001). Patients who experienced a NRD had a longer LOS (NRD: 4.7 ± 3.7 days vs RD: 3.1 ± 2.0 days, p < 0.001), a higher proportion of adverse events (p < 0.001), higher rates of reoperation (p = 0.005) and unplanned 30-day readmission rates (p < 0.001). On multivariate regression analysis, age [OR: 1.08, 95% CI (1.06-1.10), p < 0.001], female sex [OR: 2.01, 95% (1.51-2.69), p < 0.001], non-Hispanic Black race/ethnicity [OR: 2.10, 95% CI (1.36-3.24), p = 0.001], BMI [OR: 1.03, 95% CI (1.01-1.05), p = 0.007], dependent functional status [OR: 3.33, 95% CI (1.59 - 6.99), p = 0.001], malnourishment [OR: 2.14, 95% CI (1.27-3.62), p = 0.005], and LOS [OR: 1.26, 95% CI (1.18-1.33), p < 0.001] were all independent predictors for NRD. However, NRD did not independently predict an unplanned 30-day readmission on multivariate analysis.

CONCLUSION

In our study we found that on univariate analysis NRD was associated with increased adverse events, length of stay and 30-day unplanned readmission. When controlling for patient- and hospital-related factors, we found that female sex, non-Hispanic Black race, BMI, dependent functional status, malnourishment and longer LOS were independently associated with NRD. However, NRD did not independently predict an unplanned 30-day readmission.

Risk Factors and Clinical Outcomes of Nonhome Discharge in Patients With Acute Decompensated Heart Failure: An Observational Study

Background

No clinical studies have focused on the factors associated with discharge destination in patients with acute decompensated heart failure.

Methods and Results

Of 4056 consecutive patients hospitalized for acute decompensated heart failure in the KCHF (Kyoto Congestive Heart Failure) registry, we analyzed 3460 patients hospitalized from their homes and discharged alive. There were 3009 and 451 patients who were discharged to home and nonhome, respectively. We investigated the factors associated with nonhome discharge and compared the outcomes between home discharge and nonhome discharge. Factors independently and positively associated with nonhome discharge were age ≥80 years (odds ratio [OR],1.76; 95% CI,1.28–2.42), body mass index ≤22 kg/m² (OR,1.49; 95% CI,1.12–1.97), poor medication adherence (OR, 2.08; 95% CI,1.49–2.88), worsening heart failure (OR, 2.02; 95% CI, 1.46–2.82), stroke during hospitalization (OR, 3.74; 95% CI, 1.75–8.00), functional decline (OR, 12.24; 95% CI, 8.74–17.14), and length of hospital stay >16 days (OR, 4.14; 95% CI, 3.01–5.69), while those negatively associated were diabetes mellitus (OR, 0.69; 95% CI, 0.51–0.94), cohabitants (OR, 0.62; 95% CI, 0.46–0.85), and ambulatory state before admission (OR, 0.25; 95% CI, 0.18–0.36). The cumulative 1‐year incidence of all‐cause death was significantly higher in the nonhome discharge group than in the home discharge group. The nonhome discharge group compared with the nonhome discharge group was associated with a higher adjusted risk for all‐cause death (hazard ratio, 1.66; P<0.001).

Conclusions

The discharge destination of patients with acute decompensated heart failure is influenced by factors such as prehospital social background, age, body mass index, low self‐care ability, events during hospitalization (worsening heart failure, stroke, etc), functional decline, and length of hospital stay; moreover, the prognosis of nonhome discharge patients is worse than that of home discharge patients.

Registration Information

clinicaltrials.gov. Identifier: NCT02334891.

Application of Cooperative Game Theory Principles to Interpret Machine Learning Models of Nonhome Discharge Following Spine Surgery

STUDY DESIGN

Retrospective analysis of prospectively acquired data.

OBJECTIVE

The aim of this study was to identify interaction effects that modulate nonhome discharge (NHD) risk by applying coalitional game theory principles to interpret machine learning models and understand variable interaction effects underlying NHD risk.

SUMMARY OF BACKGROUND DATA

NHD may predispose patients to adverse outcomes during their care. Previous studies identified potential factors implicated in NHD; however, it is unclear how interaction effects between these factors contribute to overall NHD risk.

METHODS

Of the 11,150 reviewed cases involving procedures for degenerative spine conditions, 1764 cases (15.8%) involved NHD. Gradient boosting classifiers were used to construct predictive models for NHD for each patient. Shapley values, which assign a unique distribution of the total NHD risk to each model variable using an optimal cost-sharing rule, quantified feature importance and examined interaction effects between variables.

RESULTS

Models constructed from features identified by Shapley values were highly predictive of patient-level NHD risk (mean C-statistic = 0.91). Supervised clustering identified distinct patient subgroups with variable NHD risk and their shared characteristics. Focused interaction analysis of surgical invasiveness, age, and comorbidity burden suggested age as a worse risk factor than comorbidity burden due to stronger positive interaction effects. Additionally, negative interaction effects were found between age and low blood loss, indicating that intraoperative hemostasis may be critical for reducing NHD risk in the elderly.

CONCLUSION

This strategy provides novel insights into feature interactions that contribute to NHD risk after spine surgery. Patients with positively interacting risk factors may require special attention during their hospitalization to control NHD risk.Level of Evidence: 3.

Discharge Disposition and Clinical Outcomes After Spine Surgery

OBJECTIVE

Spinal decompression with or without fusion is one of the most commonly performed procedures in spine surgery. However, there is limited evidence on the effect of discharge environment on outcomes after surgery. The purpose of this study is to identify the effects of discharge disposition setting on clinical outcomes after spine surgery.

METHODS

Patients who underwent lumbar decompression, lumbar decompression and fusion, or posterior cervical decompression and fusion surgery were retrospectively identified. All clinical and demographic data were obtained from electronic health records. Surgical outcomes included wound complications, revision surgery, "30-day" readmission (0-30 d), and "90-day" readmission (31-90 d). Discharge disposition was stratified into home/self-care, acute inpatient rehabilitation, and subacute rehabilitation. Patient-reported outcome measures including VAS Back, VAS Leg, VAS Neck, VAS Arm, PCS-12 and MCS-12, ODI, and NDI were compared between patient discharge disposition settings using the Mann-Whitney U test. Pearson's chi-square analysis was used to assess for differences in wound complications, revision surgery, 30-day readmission, or 90-day readmission rates. Multivariate logistic regression incorporating age, sex, body mass index (BMI), Charlson Comorbidity Index (CCI), and discharge disposition was used to determine independent predictors of wound complications.

RESULTS

A total of 637 patients were included in the study. A significant difference (P = 0.03) was found in wound complication based on discharge disposition, with subacute disposition having the highest proportion of wound complications (6.1%) and home disposition having the lowest (1.5%). There were no significant differences in the rates of revision surgery, 30-day readmission, or 90-day readmission between groups. Subacute rehabilitation (odds ratio: 3.67, P = 0.047) and CCI (odds ratio 1.49, P = 0.01) were independent predictors of wound complications. Significant improvement in PROMs was seen across all postacute discharge dispositions. Baseline (P = 0.02) and postoperative (P = 0.02) ODI were significantly higher among patients discharged to an acute facility (49.4 and 32.0, respectively) compared to home (42.2 and 20.0) or subacute (47.4 and 28.4) environments.

CONCLUSION

Subacute rehabilitation disposition and CCI are independent predictors of wound complications after spinal decompression surgery. Patients undergoing spine surgery have similar readmission and revision rates and experience similar clinical improvement across all postacute discharge dispositions.

The influence of sagittal spinopelvic alignment on patient discharge disposition following minimally invasive lumbar interbody fusion

Background

The aim of this study was to investigate the changes to spinopelvic sagittal alignment following minimally invasive (MIS) lumbar interbody fusion, and the influence of such changes on postoperative discharge disposition.

Methods

The Michigan Spine Surgery Improvement Collaborative was queried for all patients who underwent transforaminal lumbar interbody fusion (TLIF)or lateral lumbar interbody fusion (LLIF) procedures for degenerative spine disease. Several spinopelvic sagittal alignment parameters were measured, including sagittal vertical axis (SVA), lumbar lordosis, pelvic tilt, pelvic incidence, and pelvic incidence-lumbar lordosis mismatch. Primary outcome measure-discharge to a rehabilitation facility-was expressed as adjusted odds ratio (ORadj) following a multivariable logistical regression.

Results

Of the 83 patients in the study population, 11 (13.2%) were discharged to a rehabilitation facility. Preoperative SVA was equivalent. Postoperative SVA increased to 8.0 cm in the discharge-to-rehabilitation division versus a decrease to 3.6 cm in the discharge-to-home division (P<0.001). The odds of discharge to a rehabilitation facility increased by 25% for every 1-cm increase in postoperative sagittal balance (OR_adj =1.27, P=0.014). The strongest predictor of discharge to rehabilitation was increasing decade of life (OR_adj =3.13, P=0.201).

Conclusions

Correction of sagittal balance is associated with greater odds of discharge to home. These findings, coupled with the recognized implications of admission to a rehabilitation facility, will emphasize the importance of spine surgeons accounting for SVA into their surgical planning of MIS lumbar interbody fusions.

Comparative Demographics and Outcomes of Minimally Invasive Transforaminal Lumbar Interbody Fusion in Chinese, Malays, and Indians

STUDY DESIGN

This study carried out a retrospective review of prospectively collected registry data.

OBJECTIVE

This study aimed to determine whether (1) utilization rates; (2) demographics and preoperative statuses; and (3) clinical outcomes differ among Chinese, Malays, and Indians undergoing minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF).

SUMMARY OF BACKGROUND DATA

There is a marked racial disparity in spine surgery outcomes between white and African American patients. Comparative studies of ethnicity have mostly been carried out in American populations, with an underrepresentation of Asian ethnic groups. It is unclear whether these disparities exist among Chinese, Malays, and Indians.

METHODS

A prospectively maintained registry was reviewed for 753 patients who underwent primary MIS-TLIF for degenerative spondylolisthesis between 2006 and 2013. The cohort was stratified by race. Comparisons of demographics, functional outcomes, and patient satisfaction were performed preoperatively and 1 month, 3 months, 6 months, and 2 years postoperatively.

RESULTS

Compared with population statistics, there was an overrepresentation of Chinese (6.6%) and an underrepresentation of Malays (5.0%) and Indians (3.5%) who underwent MIS-TLIF. Malays and Indians were younger and had higher body mass index at the time of surgery compared with Chinese. After adjusting for age, sex, and body mass index, Malays had significantly worse back pain and Indians had poorer Short-Form 36 Physical Component Summary compared with Chinese preoperatively. Chinese also had a better preoperative Oswestry Disability Index compared with the other races. Although significant differences remained at 1 month, there was no difference in outcomes up to 2 years postoperatively, except for a lower Physical Component Summary in Indians compared with Chinese at 2 years. The rate of minimal clinically important difference attainment, satisfaction, and expectation fulfillment was also comparable. At 2 years, 87.0% of Chinese, 76.9% of Malays, and 91.7% of Indians were satisfied.

CONCLUSION

The variations in demographics, preoperative statuses, and postoperative outcomes between races should be considered when interpreting outcome studies of lumbar spine surgery in Asian populations.

LEVEL OF EVIDENCE

Level III-nonrandomized cohort study.

Development and validation of a novel scoring tool for predicting facility discharge after elective posterior lumbar fusion

BACKGROUND CONTEXT

Discharge to acute/intermediate care facilities is a common occurrence after posterior lumbar fusion and can be associated with increased costs and complications after these procedures. This is particularly relevant with the growing popularity of bundled payment plans, creating a need to identify patients at greatest risk.

PURPOSE

To develop and validate a risk-stratification tool to identify patients at greatest risk for facility discharge after open posterior lumbar fusion.

STUDY DESIGN

Retrospective cohort study.

PATIENT SAMPLE

Patients were queried using separate databases from the institution of study and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) for all patients undergoing open lumbar fusion between 2011 and 2018.

OUTCOME MEASURES

Discharge to intermediate care and/or rehabilitation facilities.

METHODS

Using an 80:20 training and testing NSQIP data split, collected preoperative demographic and operative variables were used in a multivariate logistic regression to identify potential risk factors for postoperative facility discharge, retaining those with a p value <.05. A nomogram was generated to develop a scoring system from this model, with probability cutoffs determined for facility discharge. This model was subsequently validated within the NSQIP database, in addition to external validation at the institution of study. Overall model performance and calibration was assessed using the Brier score and calibration plots, respectively.

RESULTS

A total of 11,486 patients (10,453 NSQIP, 1,033 local cohort) were deemed eligible for study, of which 16.1% were discharged to facilities (16.7% NSQIP, 9.6% local cohort). Utilizing training data, age (p<.001), body mass index (p<.001), female sex (p<.001), diabetes (p=.043), peripheral vascular disease (p=.001), cancer (p=.010), revision surgery (p<.001), number of levels fused (p<.001), and spondylolisthesis (p=.049) were identified as significant risk factors for facility discharge. The area under the receiver operating characteristic curve (AUC) indicated a strong predictive model (AUC=0.750), with similar predictive ability in the testing (AUC=0.757) and local data sets (AUC=0.773). Using this tool, patients identified as low- and high-risk had a 7.94% and 33.28% incidence of facility discharge in the testing data set, while rates of 4.44% and 16.33% were observed at the institution of study.

CONCLUSIONS

Using preoperative variables as predictors, this scoring system demonstrated high efficiency in risk-stratifying patients with an approximate four to fivefold difference in rates of facility discharge after posterior lumbar fusion. This tool may help inform medical decision-making and guide reimbursement under bundled-care repayment plans.

Machine Learning for Predictive Modeling of 90-day Readmission, Major Medical Complication, and Discharge to a Facility in Patients Undergoing Long Segment Posterior Lumbar Spine Fusion

STUDY DESIGN

Retrospective case control study.

OBJECTIVE

To develop predictive models for postoperative outcomes after long segment lumbar posterior spine fusion (LSLPSF).

SUMMARY OF BACKGROUND DATA

Surgery for adult spinal deformity is effective for treating spine-related disability; however, it has high complication and readmission rates.

METHODS

Patients who underwent LSLPSF (three or more levels) were identified in State Inpatient Database. Data was queried for discharge-to-facility (DTF), 90-day readmission, and 90-day major medical complications, and demographic, comorbid, and surgical data. Data was partitioned into training and testing sets. Multivariate logistic regression, random forest, and elastic net regression were performed on the training set. Models were applied to the testing set to generate AUCs. AUCs between models were compared using the method by DeLong et al. RESULTS.: 37,852 patients were analyzed. The DTF, 90-day readmission, and 90-day major medical complication rates were 35.4%, 19.0%, and 13.0% respectively. For DTF, the logistic regression AUC was 0.77 versus 0.75 for random forest and 0.76 for elastic net (P < 0.05 for all comparisons). For 90-day readmission, the logistic regression AUC was 0.65, versus 0.63 for both random forest and elastic net (P < 0.05 for all comparisons). For 90-day major medical complications, the logistic regression AUC was 0.70, versus 0.69 for random forest and 0.68 for elastic net (P < 0.05 for all comparisons).

CONCLUSION

This study created comprehensive models to predict discharge to facility, 90-day readmissions, and 90-day major medical complications after LSLPSF. This information can be used to guide decision making between the surgeon and patient, as well as inform value-based payment models.

LEVEL OF EVIDENCE

3.

Drivers for nonhome discharge in a consecutive series of 1502 patients undergoing 1- or 2-level lumbar fusion

OBJECTIVE

Unexpected nonhome discharge causes additional costs in the current reimbursement models, especially to the payor. Nonhome discharge is also related to longer length of hospital stay and therefore higher healthcare costs to society. With increasing demand for spine surgery, it is important to minimize costs by streamlining discharges and reducing length of hospital stay. Identifying factors associated with nonhome discharge can be useful for early intervention for discharge planning. The authors aimed to identify the drivers of nonhome discharge in patients undergoing 1- or 2-level instrumented lumbar fusion.

METHODS

The electronic medical records from a single-center hospital administrative database were analyzed for consecutive patients who underwent 1- to 2-level instrumented lumbar fusion for degenerative lumbar conditions during the period from 2016 to 2018. Discharge disposition was determined as home or nonhome. A logistic regression analysis was used to determine associations between nonhome discharge and age, sex, body mass index (BMI), race, American Society of Anesthesiologists grade, smoking status, marital status, insurance type, residence in an underserved zip code, and operative factors.

RESULTS

A total of 1502 patients were included. The majority (81%) were discharged home. Factors associated with a nonhome discharge were older age, higher BMI, living in an underserved zip code, not being married, being on government insurance, and having more levels fused. Patients discharged to a nonhome facility had longer lengths of hospital stay (5.6 vs 3.0 days, p < 0.001) and significantly increased hospital costs ($21,204 vs $17,518, p < 0.001).

CONCLUSIONS

Increased age, greater BMI, residence in an underserved zip code, not being married, and government insurance are drivers for discharge to a nonhome facility after a 1- to 2-level instrumented lumbar fusion. Early identification and intervention for these patients, even before admission, may decrease the length of hospital stay and medical costs.

Black Race as a Social Determinant of Health and Outcomes After Lumbar Spinal Fusion Surgery: A Multistate Analysis, 2007 to 2014

STUDY DESIGN

A retrospective analysis of patient hospitalization and discharge records.

OBJECTIVE

To examine the association between race and inpatient postoperative complications following lumbar spinal fusion surgery.

SUMMARY OF BACKGROUND DATA

Racial disparities in healthcare have been demonstrated across a range of surgical procedures. Previous research has identified race as a social determinant of health that impacts outcomes after lumbar spinal fusion surgery. However, these studies are limited in that they are outdated, contain data from a single institution, analyze small limited samples, and report limited outcomes. Our study aims to expand and update the literature examining the association between race and inpatient postoperative complications following lumbar spine surgery.

METHODS

We analyzed 267,976 patient discharge records for inpatient lumbar spine surgery using data from the Healthcare Cost and Utilization Project's State Inpatient Databases for California, Florida, New York, Maryland, and Kentucky from 2007 through 2014. We used unadjusted bivariate analysis, adjusted multivariable, and stratified analysis to compare patient demographics, present-on-admission comorbidities, hospital characteristics, and complications by categories of race/ethnicity.

RESULTS

Black patients were 8% and 14% more likely than white patients to experience spine surgery specific complications (adjusted odds ratios [aOR]: 1.08, 95% confidence interval [CI]: 1.03-1.13) and general postoperative complications (aOR: 1.14, 95% CI: 1.07-1.20), respectively. Black patients, compared with white patients, also had increased adjusted odds of 30-day readmissions (aOR: 1.13, 95% CI: 1.07-1.20), 90-day readmissions (aOR: 1.07, 95% CI: 1.02-1.13), longer length of stay (LOS) (adjusted Incidence Rate Ratio: 1.15, 95% CI: 1.14-1.16), and higher total charges (adjusted Incidence Rate Ratio: 1.08, 95% CI: 1.07-1.09).

CONCLUSION

Our findings demonstrate that black patients, as compared with white patients, are more likely to have postoperative complications, longer postoperative lengths of stay, higher total hospital charges, and increased odds of 30- and 90-day readmissions following lumbar spinal fusion surgery.

LEVEL OF EVIDENCE

4.

Racial and Ethnic Disparities in Pregnancy-Related Acute Kidney Injury

BACKGROUND

Pregnancy-related AKI (PR-AKI) is increasing in the United States. PR-AKI is associated with adverse maternal outcomes. Disparities in racial/ethnic differences in PR-AKI by race have not been studied.

METHODS

This was a retrospective cohort study using the National Inpatient Sample (NIS) from 2005 to 2015. We identified patients who were admitted for a pregnancy-related diagnosis using the Neomat variable provided by the NIS database that indicates the presence of a maternal or neonatal diagnosis code or procedure code. PR-AKI was identified using ICD codes. Survey logistic regression was used for multivariable analysis adjusting for age, medical comorbidities, socioeconomic factors, and hospital/admission factors.

RESULTS

From 48,316,430 maternal hospitalizations, 34,001 (0.07%) were complicated by PR-AKI. Hospitalizations for PR-AKI increased from 3.5/10,000 hospitalizations in 2005 to 11.8/10,000 hospitalizations in 2015 with the largest increase seen in patients aged ≥35 and black patients. PR-AKI was associated with higher odds of miscarriage (adjusted odds ratio [aOR], 1.64; 95% CI, 1.34 to 2.07) and mortality (aOR, 1.53; 95% CI, 1.25 to 1.88). After adjustment for age, medical comorbidities, and socioeconomic factors, blacks were more likely than whites to develop PR-AKI (aOR, 1.17; 95% CI, 1.04 to 1.33). On subgroup analyses in hospitalizations of patients with PR-AKI, blacks and Hispanics were more likely to have preeclampsia/eclampsia compared with whites (aOR, 1.29; 95% CI, 1.01 to 1.65; and aOR, 1.69; 95% CI, 1.23 to 2.31, respectively). Increased odds of mortality in PR-AKI compared with whites were only seen in black patients (aOR, 1.61; 95% CI, 1.02 to 2.55).

CONCLUSIONS

The incidence of PR-AKI has increased and the largest increase was seen in older patients and black patients. PR-AKI is associated with miscarriages, adverse discharge from hospital, and mortality. Black and Hispanic patients with PR-AKI were more likely to have adverse outcomes than white patients. Further research is needed to identify factors contributing to these discrepancies.

The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population

BACKGROUND

As the use of bundled care payment models has become widespread in neurosurgery, there is a distinct need for improved preoperative predictive tools to identify patients who will not benefit from prolonged hospitalization, thus facilitating earlier discharge to rehabilitation or nursing facilities.

OBJECTIVE

To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition.

METHODS

Patients undergoing elective posterior lumbar fusion from June 2016 to February 2017 were prospectively enrolled. RAPT scores and discharge outcomes were recorded for patients aged 50 yr or more (n = 432). Logistic regression analysis was used to assess the ability of RAPT score to predict discharge disposition. Multivariate regression was performed in a backwards stepwise logistic fashion to create a binomial model.

RESULTS

Escalating RAPT score predicts disposition to home (P &lt; .0001). Every unit increase in RAPT score increases the chance of home disposition by 55.8% and 38.6% than rehab and skilled nursing facility, respectively. Further, RAPT score was significant in predicting length of stay (P = .0239), total surgical cost (P = .0007), and 30-d readmission (P &lt; .0001). Amongst RAPT score subcomponents, walk, gait, and postoperative care availability were all predictive of disposition location (P &lt; .0001) for both models. In a generalized multiple logistic regression model, the 3 top predictive factors for disposition were the RAPT score, length of stay, and age (P &lt; .0001, P &lt; .0001 and P = .0001, respectively).

CONCLUSION

Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition.

Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms

OBJECTIVE

Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.

METHODS

Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women's Hospital (2013-2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.

RESULTS

Overall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of -0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.

CONCLUSIONS

This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.

Reporting Methodology of Neurosurgical Studies Utilizing the American College of Surgeons-National Surgical Quality Improvement Program Database: A Systematic Review and Critical Appraisal

BACKGROUND

Use of large databases such as the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) has become increasingly common in neurosurgical research.

OBJECTIVE

To perform a critical appraisal and evaluation of the methodological reporting for studies in neurosurgical literature that utilize the ACS-NSQIP database.

METHODS

We queried Ovid MEDLINE, EMBASE, and PubMed databases for all neurosurgical studies utilizing the ACS-NSQIP. We assessed each study according to number of criteria fulfilled with respect to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement, REporting of studies Conducted using Observational Routinely-collected Health Data (RECORD) Statement, and Journal of American Medical Association–Surgical Section (JAMA-Surgery) Checklist. A separate analysis was conducted among papers published in core and noncore journals in neurosurgery according to Bradford's law.

RESULTS

A total of 117 studies were included. Median (interquartile range [IQR]) scores for number of fulfilled criteria for STROBE Statement, RECORD Statement, and JAMA-Surgery Checklist were 20 (IQR:19-21), 9 (IQR:8-9), and 6 (IQR:5-6), respectively. For STROBE Statement, RECORD Statement, and JAMA-Surgery Checklist, item 9 (potential sources of bias), item 13 (supplemental information), and item 9 (missing data/sensitivity analysis) had the highest number of studies with no fulfillment among all studies (56, 68, 50%), respectively. When comparing core journals vs noncore journals, no significant difference was found (STROBE, P = .94; RECORD, P = .24; JAMA-Surgery checklist, P = .60).

CONCLUSION

While we observed an overall satisfactory reporting of methodology, most studies lacked mention of potential sources of bias, data cleaning methods, supplemental information, and external validity. Given the pervasive role of national databases and registries for research and health care policy, the surgical community needs to ensure the credibility and quality of such studies that ultimately aim to improve the value of surgical care delivery to patients.

Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry

OBJECTIVE

Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.

METHODS

The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.

RESULTS

A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.

CONCLUSIONS

In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

Evaluation of the Risk Assessment and Prediction Tool for Postoperative Disposition Needs After Cervical Spine Surgery

BACKGROUND

Bundled care payment models are becoming more prevalent in neurosurgery. Such systems place the cost of postsurgical facilities in the hands of the discharging health system. Opportunity exists to leverage prediction tools for discharge disposition by identifying patients who will not benefit from prolonged hospitalization and facilitating discharge to post-acute care facilities.

OBJECTIVE

To validate the use of the Risk Assessment and Predictive Tool (RAPT) along with other clinical variables to predict discharge disposition in a cervical spine surgery population.

METHODS

Patients undergoing cervical spine surgery at our institution from June 2016 to February 2017 and over 50 yr old had demographic, surgical, and RAPT variables collected. Multivariable regression analyzed each variable's ability to predict discharge disposition. Backward selection was used to create a binomial model to predict discharge disposition.

RESULTS

A total of 263 patients were included in the study. Lower RAPT score, RAPT walk subcomponent, older age, and a posterior approach predicted discharge to a post-acute care facility compared to home. Lower RAPT also predicted an increased risk of readmission. RAPT score combined with age increased the predictive capability of discharge disposition to home vs skilled nursing facility or acute rehabilitation compared to RAPT alone (P < .001).

CONCLUSION

RAPT score combined with age is a useful tool in the cervical spine surgery population to predict postdischarge needs. This tool may be used to start early discharge planning in patients who are predicted to require post-acute care facilities. Such strategies may reduce postoperative utilization of inpatient resources.

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis

PURPOSE

We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.

METHODS

The National Surgical Quality Improvement Program (NSQIP) database was used to select patients that underwent surgical treatment for degenerative spondylolisthesis between 2009 and 2016. Our primary outcome measure was non-home discharge which was defined as any discharge not to home for which we grouped together all non-home discharge destinations including rehabilitation facility, skilled nursing facility, and unskilled nursing facility. We used Akaike information criterion to select the most appropriate model based on the outcomes of the stepwise backward logistic regression. Four machine learning algorithms were developed to predict discharge placement and were assessed by discrimination, calibration, and overall performance.

RESULTS

Nine thousand three hundred and thirty-eight patients were included. Median age was 63 (interquartile range [IQR] 54-71), and 63% (n = 5,887) were female. The non-home discharge rate was 18.6%. Our models included age, sex, diabetes, elective surgery, BMI, procedure, number of levels, ASA class, preoperative white blood cell count, and preoperative creatinine. The Bayes point machine was considered the best model based on discrimination (AUC = 0.753), calibration (slope = 1.111; intercept = - 0.002), and overall model performance (Brier score = 0.132).

CONCLUSION

This study has shown that it is possible to create a predictive machine learning algorithm with both good accuracy and calibration to predict discharge placement. Using our methodology, this type of model can be developed for many other conditions and (elective) treatments. These slides can be retrieved under Electronic Supplementary Material.

Indicators for Nonroutine Discharge Following Cervical Deformity-Corrective Surgery: Radiographic, Surgical, and Patient-Related Factors

BACKGROUND

Nonroutine discharge, including discharge to inpatient rehab and skilled nursing facilities, is associated with increased cost-of-care. Given the rising prevalence of cervical deformity (CD)-corrective surgery and the necessity of value-based healthcare, it is important to identify indicators for nonroutine discharge.

OBJECTIVE

To identify factors associated with nonroutine discharge after CD-corrective surgery using a statistical learning algorithm.

METHODS

A retrospective review of patients ≥18 yr with discharge and baseline (BL) radiographic data. Conditional inference decision trees identified factors associated with nonroutine discharge and cut-off points at which factors were significantly associated with discharge status. A conditional variable importance table used nonreplacement sampling set of 10 000 conditional inference trees to identify influential patient/surgical factors. The binary logistic regression indicated odds of nonroutine discharge for patients with influential factors at significant cut-off points.

RESULTS

Of 138 patients (61 yr, 63% female) undergoing surgery for CD (8 ± 5 levels; 49% posterior approach, 16% anterior, and 35% combined), 29% experienced nonroutine discharge. BL cervical/upper-cervical malalignment showed the strongest relationship with nonroutine discharge: C1 slope ≥ 14°, C2 slope ≥ 57°, TS-CL ≥ 57°. Patient-related factors associated with nonroutine discharge included BL gait impairment, age ≥ 59 yr and apex of CD primary driver ≥ C7. The only surgical factor associated with nonroutine discharge was fusion ≥ 8 levels. There was no relationship between nonhome discharge and reoperation within 6 mo or 1 yr (both P > .05) of index procedure. Despite no differences in BL EQ-5D (P = .946), nonroutine discharge patients had inferior 1-yr postoperative EQ-5D scores (P = .044).

CONCLUSION

Severe preoperative cervical malalignment was strongly associated with nonroutine discharge following CD-corrective surgery. Age, deformity driver, and ≥ 8 level fusions were also associated with nonroutine discharge and should be taken into account to improve patient counseling and health care resource allocation.

Predictors of Unplanned Returns to the Operating Room within 30 Days in Neurosurgery: Insights from a National Surgical Registry

BACKGROUND

In the modern, increasingly pay-for-performance era, unplanned return to the operating room (ROR) is gaining attention as a surgical quality metric. However, large-scale data on the appropriateness and usefulness of this measure in neurosurgery are scarce.

OBJECTIVE

To provide a comprehensive description of all RORs after neurosurgical procedures in a national surgical registry and identify factors associated with ROR.

METHODS

We queried the American College of Surgeons National Surgical Quality Improvement Program multicenter database for patients undergoing neurosurgical procedures during 2012-2016. Multivariable logistic regression was conducted to identify factors associated with 30-day unplanned ROR after the 3 most common inpatient cranial and spinal operations: craniotomy for intra-axial neoplasm, convexity/falx meningioma, or skull base tumors; anterior cervical discectomy and fusion; and posterior lumbar decompression and posterior lumbar fusion.

RESULTS

A total of 193,459 cases were identified, of which 7067 (3.7%) had at least 1 unplanned ROR within 30 days after the index procedure (inpatient, 4.3%; outpatient, 1.5%). Overall, the most common reasons were wound complication/surgical site infection (0.7%), hematoma evacuation (0.6%), and repeat surgery (0.5%). On multivariable analysis, the relative amount of variation in reoperation risk was found to be 1%-24% for demographics, 1%-19% for comorbidities, 1%-6% for preoperative laboratory values, and 4%-58% for operative characteristics.

CONCLUSIONS

These findings may inform stakeholders on the optimal parameters that need to be taken into account when crafting, endorsing, and implementing quality metrics for neurosurgery that aim to assess surgical performance and reward or penalize hospitals and providers.

Improving Complex Pediatric and Adult Spine Care While Embracing the Value Equation

INTRODUCTION

Value in health care is defined as the quotient of outcomes to cost. Both pediatric and adult spinal deformity surgeries are among the most expensive procedures offered today. With high variability in both outcomes and costs in spine surgery today, surgeons will be expected to consider long-term cost effectiveness when comparing treatment options.

METHODS

We summarize various methods by which value can be increased in complex spine surgery, both through the improvement of outcomes and the reduction of cost. These methods center around standardization, team-based and collaborative approaches, rigorous outcomes tracking through dashboards and registries, and continuous process improvement.

RESULTS

This manuscript reviews the expert opinion of leading spine specialists on the improvement of safety, quality and improvement of value of pediatric and adult spinal surgery.

CONCLUSION

Without surgeon leadership in this arena, suboptimal solutions may result from the isolated intervention of regulatory bodies or payer groups. The cooperative development of standardized, team-based approaches in complex spine surgery will lead to the high-quality, high-value care for patients.

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

OBJECTIVE

If not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who underwent elective inpatient spine surgery for lumbar disc herniation or lumbar disc degeneration between 2011 and 2016. Four machine learning algorithms were developed to predict nonroutine discharge and the best algorithm was incorporated into an open-access web application.

RESULTS

The rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Predictive factors selected by random forest algorithms were age, sex, body mass index, fusion, level, functional status, extent and severity of comorbid disease (American Society of Anesthesiologists classification), diabetes, and preoperative hematocrit level. On evaluation in the testing set (n = 5273), the neural network had a c-statistic of 0.823, calibration slope of 0.935, calibration intercept of 0.026, and Brier score of 0.0713. On decision curve analysis, the algorithm showed greater net benefit for changing management over all threshold probabilities than changing management on the basis of the American Society of Anesthesiologists classification alone or for all patients or for no patients. The model can be found here: https://sorg-apps.shinyapps.io/discdisposition/.

CONCLUSIONS

Machine learning algorithms show promising results on internal validation for preoperative prediction of nonroutine discharges. If found to be externally valid, widespread use of these algorithms via the open-access web application by healthcare professionals may help preoperative risk stratification of patients undergoing elective surgery for lumbar degenerative disc disorders.

Association between preoperative activity level and functional outcome at 12 months following surgical decompression for lumbar spinal stenosis

OBJECTIVE

This study defines the association of preoperative physical activity level with functional outcomes at 3 and 12 months following surgical decompression for lumbar spinal stenosis.

METHODS

Data were collected as a prospective observational registry at a single institution from 2012 through 2015, and then analyzed with a retrospective cohort design. Patients who were able to participate in activities outside the home preoperatively were compared to patients who did not participate in such activities, with respect to 3-month and 12-month functional outcomes postintervention, adjusted for relevant confounders.

RESULTS

Ninety-nine patients were included. At baseline, sedentary/inactive patients (n = 55) reported greater back pain, lower quality of life, and higher disability than similarly treated patients who were active preoperatively. Both cohorts experienced significant improvement from baseline in back pain, leg pain, disability, and quality of life at both 3 and 12 months after lumbar decompression surgery. At 3 months postintervention, sedentary/inactive patients reported more leg pain and worse disability than patients who performed activities outside the home preoperatively. However, at 12 months postintervention, there were no statistically significant differences between the two cohorts in back pain, leg pain, quality of life, or disability. Multivariate analysis revealed that sedentary/inactive patients had improved disability and higher quality of life after surgery compared to baseline. Active patients experienced greater overall improvement in disability compared to inactive patients.

CONCLUSIONS

Sedentary/inactive patients have a more protracted recovery after lumbar decompression surgery for spinal stenosis, but at 12 months postintervention can expect to reach similar long-term outcomes as patients who are active/perform activities outside the home preoperatively.