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

Extracranial-Intracranial Bypass for Moyamoya Disease: The Influence of Racial and Socioeconomic Disparities on Outcomes - A National Inpatient Sample Analysis

BACKGROUND

Extracranial-intracranial (EC-IC) bypass is an established therapeutic option for Moyamoya disease (MMD). However, little is known about the effects of racial and ethnic disparities on outcomes. This study assessed trends in EC-IC bypass outcomes among MMD patients stratified by race and ethnicity.

METHODS

Utilizing the US National Inpatient Sample, we identified MMD patients undergoing EC-IC bypass between 2002 and 2020. Demographic and hospital-level data were collected. Multivariable analysis was conducted to identify independent factors associated with outcomes. Trend analysis was performed using piecewise joinpoint regression.

RESULTS

Out of 14,062 patients with MMD, 1771 underwent EC-IC bypass. Of these, 60.59% were White, 17.56% were Black, 12.36% were Asians, 8.47% were Hispanic, and 1.02% were Native Americans. Nonhome discharge was noted in 21.7% of cases, with a 6.7% death and 3.8% postoperative neurologic complications rates. EC-IC bypass was more commonly performed in Native Americans (23.38%) and Asians (17.76%). Hispanics had the longest mean length of stay (8.4 days) and lower odds of nonhome discharge compared to Whites (odds ratio: 0.64; 95% confidence interval: 0.40-1.03; P = 0.04). Patients with Medicaid, private insurance, self-payers, and insurance paid by other governments had lower odds of nonhome discharge than those with Medicare.

CONCLUSION

This study highlights racial and socioeconomic disparities in EC-IC bypass for patients with MMD. Despite these disparities, we did not find any significant difference in the quality of care. Addressing these disparities is essential for optimizing MMD outcomes.

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 and Ethnic Disparities in Treatment of Critical Limb Ischemia: A National Perspective

Background Recent guidelines have emphasized the use of medical management, early diagnosis, and a multidisciplinary team to effectively treat patients with critical limb ischemia (CLI). Previous literature briefly highlighted the current racial disparities in its intervention. Herein, we analyze the trend over a 14-year time period to investigate whether the disparities gap in CLI management is closing. Methods and Results The National Inpatient Sample was queried between 2005 and 2018 for hospitalizations involving CLI. Nontraumatic amputations and revascularization were identified. Utilization trends of these procedures were compared between races (White, Black, Hispanic, Asian and Pacific Islander, Native American, and Other). Multivariable regression assessed differences in race regarding procedure usage. There were 6 904 562 admissions involving CLI in the 14-year study period. The rate of admissions in White patients who received any revascularization decreased by 0.23% (P<0.001) and decreased by 0.25% (P=0.025) for Asian and Pacific Islander patients. Among all patients, the annual rate of admission in White patients who received any amputation increased by 0.21% (P<0.001), increased by 0.19% (P=0.001) for Hispanic patients, and increased by 0.19% (P=0.012) for the Other race patients. Admissions involving Black, Hispanic, Asian and Pacific Islander, or Other race patients had higher odds of receiving any revascularization compared with White patients. All races had higher odds of receiving major amputation compared with White patients. Conclusions Our analysis highlights disparities in CLI treatment in our nationally representative sample. Non-White patients are more likely to receive invasive treatments, including major amputations and revascularization for CLI, compared with White patients.

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.

Endoscopic and Nonendoscopic Approaches to Single-Level Lumbar Spine Decompression: Propensity Score-Matched Comparative Analysis and Frailty-Driven Predictive Model

Objective: The endoscopic spine surgery (ESS) approach is associated with high levels of patient satisfaction, shorter recovery time, and reduced complications. The present study reports multicenter, international data, comparing ESS and non-ESS approaches for singlelevel lumbar decompression, and proposes a frailty-driven predictive model for nonhome discharge (NHD) disposition.Methods: Cases of ESS and non-ESS lumbar spine decompression were queried from the American College of Surgeons National Surgical Quality Improvement Program database (2017–2020). Propensity score matching was performed on baseline characteristics frailty score (measured by risk analysis index [RAI] and modified frailty index-5 [mFI-5]). The primary outcome of interest was NHD disposition. A predictive model was built using logistic regression with RAI as the primary driver.Results: Single-level nonfusion spine lumbar decompression surgery was performed in 38,686 patients. Frailty, as measured by RAI, was a reliable predictor of NHD with excellent discriminatory accuracy in receiver operating characteristic (ROC) curve analysis: C-statistic: 0.80 (95% confidence interval [CI], 0.65–0.94) in ESS cohort, C-statistic: 0.75 (95% CI, 0.73–0.76) overall cohort. After propensity score matching, there was a reduction in total operative time (89 minutes vs. 103 minutes, p = 0.049) and hospital length of stay (LOS) (0.82 days vs. 1.37 days, p < 0.001) in patients treated endoscopically. In ROC curve analysis, the frailty-driven predictive model performed with excellent diagnostic accuracy for the primary outcome of NHD (C-statistic: 0.87; 95% CI, 0.85–0.88).Conclusion: After frailty-based propensity matching, ESS is associated with reduced operative time, shorter hospital LOS, and decreased NHD. The RAI frailty-driven model predicts NHD with excellent diagnostic accuracy and may be applied to preoperative decisionmaking with a user-friendly calculator: nsgyfrailtyoutcomeslab.shinyapps.io/lumbar_decompression_dischargedispo.

A novel online calculator to predict nonroutine discharge, length of stay, readmission, and reoperation in patients undergoing surgery for intramedullary spinal cord tumors

BACKGROUND CONTEXT

Intramedullary spinal cord tumors (IMSCTs) are rare tumors associated with significant morbidity and mortality. Surgical resection is often indicated for symptomatic lesions but may result in new neurological deficits and decrease quality of life. Identifying predictors of these adverse outcomes may help target interventions designed to reduce their occurrence. Nonetheless, most prior studies have employed population-level datasets with limited granularity.

PURPOSE

To determine independent predictors of nonroutine discharge, prolonged length of stay (LOS), and 30 day readmission and reoperation, and to deploy these results as a web-based calculator.

STUDY DESIGN

Retrospective cohort study PATIENT SAMPLE: A total of 235 patients who underwent resection of IMSCTs at a single comprehensive cancer center.

OUTCOME MEASURES

Nonroutine discharge, prolonged LOS, 30 day readmission, and 30 day reoperation METHODS: Patients who underwent surgery from June 2002 to May 2020 at a single tertiary center were included. Data was collected on patient demographics, clinical presentation, tumor histology, surgical procedures, and 30 day readmission and reoperation. Functional status was assessed using the Modified McCormick Scale (MMS) and queried preoperative neurological symptoms included weakness, urinary and bowel dysfunction, numbness, and back and radicular pain. Variables significant on univariable analysis at the α≤0.15 level were entered into a stepwise multivariable logistic regression model.

RESULTS

Of 235 included cases, 131 (56%) experienced a nonhome discharge and 68 (29%) experienced a prolonged LOS. Of 178 patients with ≥ 30 days of follow-up, 17 (9.6%) were readmitted within 30 days and 13 (7.4%) underwent reoperation. Wound dehiscence (29%) was the most common reason for readmission. Nonhome discharge was independently predicted by older age (OR=1.03/year; p<.01), thoracic location of the tumor (OR=2.36; p=.01), presenting with bowel dysfunction (OR=4.09; p=.03), and longer incision length (OR=1.44 per level; p=.03). Independent predictors of prolonged LOS included presenting with urinary incontinence (OR=2.65; p=.05) or a higher preoperative white blood cell count (OR=1.08 per 10³/μL); p=.01), while GTR predicted shorter LOS (OR=0.40; p=.02). Independent predictive factors for 30 day unplanned readmission included experiencing ≥1 complications during the first hospitalization (OR=6.13; p<.01) and having a poor (A-C) versus good (D-E) baseline neurological status on the ASIA impairment scale (OR=0.23; p=.03). The only independent predictor of unplanned 30 day reoperation was experiencing ≥1 inpatient complications during the index hospitalization (OR=6.92; p<.01). Receiver operating curves for the constructed models produced C-statistics of 0.67-0.77 and the models were deployed as freely available web-based calculators (https://jhuspine5.shinyapps.io/Intramedullary30day).

CONCLUSIONS

We found that neurological presentation, patient demographics, and incision length were important predictors of adverse perioperative outcomes in patients with IMSCTs. The calculators can be used by clinicians for risk stratification, preoperative counseling, and targeted interventions.