Explainable Machine Learning Approach to Prediction of Prolonged Intesive Care Unit Stay in Adult Spinal Deformity Patients: Machine Learning Outperforms Logistic Regression.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Prolonged ICU stay is a driver of higher costs and inferior outcomes in Adult Spinal Deformity (ASD) patients. Machine learning (ML) models have recently been seen as a viable method of predicting pre-operative risk but are often 'black boxes' that do not fully explain the decision-making process. This study aims to demonstrate ML can achieve similar or greater predictive power as traditional statistical methods and follows traditional clinical decision-making processes. METHODS: Five ML models (Decision Tree, Random Forest, Support Vector Classifier, GradBoost, and a CNN) were trained on data collected from a large urban academic center to predict whether prolonged ICU stay would be required post-operatively. 535 patients who underwent posterior fusion or combined fusion for treatment of ASD were included in each model with a 70-20-10 train-test-validation split. Further analysis was performed using Shapley Additive Explanation (SHAP) values to provide insight into each model's decision-making process. RESULTS: The model's Area Under the Receiver Operating Curve (AUROC) ranged from 0.67 to 0.83. The Random Forest model achieved the highest score. The model considered length of surgery, complications, and estimated blood loss to be the greatest predictors of prolonged ICU stay based on SHAP values. CONCLUSIONS: We developed a ML model that was able to predict whether prolonged ICU stay was required in ASD patients. Further SHAP analysis demonstrated our model aligned with traditional clinical thinking. Thus, ML models have strong potential to assist with risk stratification and more effective and cost-efficient care.

Scoping Review: Anesthesiologist Involvement in Alternative Payment Models, Value Measurement, and Nonclinical Capabilities for Success in the United States of America.

The US healthcare sector is undergoing significant payment reforms, leading to the emergence of Alternative Payment Models (APMs) aimed at improving clinical outcomes and patient experiences while reducing costs. This scoping review provides an overview of the involvement of anesthesiologists in APMs as found in published literature. It specifically aims to categorize and understand the breadth and depth of their participation, revolving around 3 main axes or "Aims": (1) shaping APMs through design and implementation, (2) gauging the value and quality of care provided by anesthesiologists within these models, and (3) enhancing nonclinical abilities of anesthesiologists for promoting more value in care. To map out the existing literature, a comprehensive search of relevant electronic databases was conducted, yielding a total of 2173 articles, of which 24 met the inclusion criteria, comprising 21 prospective or retrospective cohort studies, 2 surveys, and 1 case-control cohort study. Eleven publications (45%) discussed value-based, bundled, or episode-based payments, whereas the rest discussed non-payment-based models, such as Enhanced Recovery After Surgery (7 articles, 29%), Perioperative Surgical Home (4 articles, 17%), or other models (3 articles, 13%).The review identified key themes related to each aim. The most prominent themes for aim 1 included protocol standardization (16 articles, 67%), design and implementation leadership (8 articles, 33%), multidisciplinary collaboration (7 articles, 29%), and role expansion (5 articles, 21%). For aim 2, the common themes were Process-Based & Patient-Centric Metrics (1 article, 4%), Shared Accountability (3 articles, 13%), and Time-Driven Activity-Based Costing (TDABC) (3 articles, 13%). Furthermore, we identified a wide range of quality metrics, spanning 8 domains that were used in these studies to evaluate anesthesiologists' performance. For aim 3, the main extracted themes included Education on Healthcare Transformation and Policies (3 articles, 13%), Exploring Collaborative Leadership Skills (5 articles, 21%), and Embracing Advanced Analytics and Data Transparency (4 articles, 17%).Findings revealed the pivotal role of anesthesiologists in the design, implementation, and refinement of these emerging delivery and payment models. Our results highlight that while payment models are shifting toward value, patient-centered metrics have yet to be widely accepted for use in measuring quality and affecting payment for anesthesiologists. Gaps remain in understanding how anesthesiologists assess their direct impact and strategies for enhancing the sustainability of anesthesia practices. This review underscores the need for future research contributing to the successful adaptation of clinical practices in this new era of healthcare delivery.

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.

Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review.

OBJECTIVE: The objective of this study was to investigate the perioperative management and outcomes of patients with a prior history of successful transplantation undergoing spine surgery. METHODS: We searched Medline, Embase, and Cochrane Central Register of Controlled Trials for matching reports in July 2021. We included case reports, cohort studies, and retrospective analyses, including terms for various transplant types and an exhaustive list of key words for various forms of spine surgery. RESULTS: We included 45 studies consisting of 34 case reports (published 1982-2021), 3 cohort analyses (published 2005-2006), and 8 retrospective analyses (published 2006-2020). The total number of patients included in the case reports, cohort studies, and retrospective analysis was 35, 48, and 9695, respectively. The mean 1-year mortality rate from retrospective analyses was 4.6% ± 1.93%, while the prevalence of perioperative complications was 24%. Cohort studies demonstrated an 8.5% ± 12.03% 30-day readmission rate. The most common procedure performed was laminectomy (38.9%) among the case reports. Mortality after spine surgery was noted for 4 of 35 case report patients (11.4%). CONCLUSIONS: This is the first systematic scoping review examining the population of transplant patients with subsequent unrelated spine surgery. There is significant heterogeneity in the outcomes of post-transplant spine surgery patients. Given the inherent complexity of managing this group and elevated mortality and complications compared to the general spine surgery population, further investigation into their clinical care is warranted.

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.

Seasonal Effects on Postoperative Complications After Spinal Surgery: A National Database Analysis.

OBJECTIVE: To investigate the role of seasonality on postoperative complications after spinal surgery. METHODS: Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2018. Current Procedural Terminology codes were used to identify the following procedures: posterior cervical decompression and fusion, cervical laminoplasty, posterior lumbar fusion, lumbar laminectomy, and spinal deformity surgery. The database was queried for deep vein thrombosis (DVT), pulmonary embolism, pneumonia, sepsis, septic shock, Clostridium difficile infection, stroke, cardiac arrest, myocardial infarction, urinary tract infection (UTI), and early unplanned hospital readmission (readmission). Warm season was defined as April-September, whereas cold season was defined as October-March. Statistical analysis included computing overall complication rates and comparison between seasons using univariate analysis and multivariable logistic regression. RESULTS: A total of 208,291 individuals underwent spinal surgery from 2011 to 2018. There was a statistically significant increase in UTI (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.07-1.26; P = 0.0002) and readmission (OR, 1.06; 95% CI, 1.02-1.11, P = 0.007) in the warm season compared with the cold season. An investigation into the July effect showed increases in DVT (OR, 1.24; 95% CI, 1.03-1.48; P = 0.020) and thromboembolic events (OR 1.17; 95% CI, 1.01-1.35; P = 0.032) in July-September compared with the preceding 3 months. CONCLUSIONS: The results showed a higher incidence of UTI and readmission among spine surgery patients in the warm season and a higher incidence of DVT and thromboembolic events from July to September. In both cases, the effect of seasonality is statistically significant, but the absolute difference is small and may not suggest policy changes.

The Impact of Obstructive Sleep Apnea on Clinical, Perioperative, and Cost Outcomes in Patients Who Underwent Posterior Cervical Decompression and Fusion: A Single-Center Retrospective Analysis From 2008 to 2016.

BACKGROUND: Obstructive sleep apnea (OSA) is a pervasive problem that can result in diminished neurocognitive performance, increased risk of all-cause mortality, and significant cardiovascular disease. While previous studies have examined risk factors that influence outcomes following cervical fusion procedures, to our knowledge, no study has examined the cost or outcome profiles for posterior cervical decompression and fusion (PCDF) procedures in patients with OSA. METHODS: All cases at a single institution between 2008 and 2016 involving a PCDF were included. The primary outcome was prolonged extubation, defined as an extubation that took place outside of the operating room. Secondary outcomes included admission to the intensive care unit (ICU), complications, extended hospitalization, nonhome discharge, readmission within 30 and 90 days, emergency room visit within 30 and 90 days, and higher total costs. RESULTS: We reviewed 1191 PCDF cases, of which 93 patients (7.81%) had a history of OSA. At the univariate level, patients with OSA had higher rates of ICU admissions (33.3% vs 16.8%, P < 0.0001), total complications (29.0% vs 19.0%, P = 0.0202), and respiratory complications (12.9% vs 6.6%, P = 0.0217). Multivariate regression analyses revealed no difference in the odds of a prolonged extubation (P = 0.4773) and showed that history of OSA was not predictive of higher costs. However, a significant difference was observed in the odds of having an ICU admission (P = 0.0046). CONCLUSION: While patients with sleep apnea may be more likely to be admitted to the ICU postoperatively, OSA status a lone is not a risk factor for poor primary and secondary clinical outcomes following posterior cervical fusion procedures. CLINICAL RELEVANCE: Various deformities of the cervical spine can exert extraluminal forces that partially collapse or obstruct the airway, thereby predisposing patients to OSA; however, no study has examined the cost or outcome profiles for PCDF procedures in patients with OSA. Therefore, this investigation highlights the ways in which OSA influences the risks, outcomes, and costs following PCDF using medical data from an institutional registry.

Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale.

BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization. OBJECTIVE: To develop a machine learning algorithm aimed at predicting extended LOS after cervical spine surgery on a national level and elucidate drivers of prediction. METHODS: Electronic medical records from a large, urban academic medical center were retrospectively examined to identify patients who underwent cervical spine fusion surgeries between 2008 and 2019 for machine learning algorithm development and in-sample validation. The National Inpatient Sample database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for out-of-sample validation of algorithm performance. Gradient-boosted trees predicted LOS and efficacy was assessed using the area under the receiver operating characteristic curve (AUROC). Shapley values were calculated to characterize preoperative risk factors for extended LOS and explain algorithm predictions. RESULTS: Gradient-boosted trees accurately predicted extended LOS across cohorts, achieving an AUROC of 0.87 (SD = 0.01) on the single-center validation set and an AUROC of 0.84 (SD = 0.00) on the nationwide National Inpatient Sample data set. Anterior approach only, elective admission status, age, and total number of Elixhauser comorbidities were important predictors that affected the likelihood of prolonged LOS. CONCLUSION: Machine learning algorithms accurately predict extended LOS across single-center and national patient cohorts and characterize key preoperative drivers of increased LOS after cervical spine surgery.

Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review Protocol.

Spine surgery patients with a history of organ transplantation are a complex population due to their unique anesthetic considerations, immunologic profiles, drug interactions, and potential organ dysfunction. It is common for these patients to develop neck/back pain and pathology that warrants surgical intervention. However, there is a relative dearth of literature examining their outcomes and clinical considerations. The purpose of this protocol is to investigate their clinical outcomes following spine surgery and medical management. We perform a systematic literature search using PRISMA-ScR guidelines to include case reports, cohort studies, and retrospective analyses. The search terms include kidney, liver, heart, pancreas, lung, and bone marrow for transplants of interest and contain an extensive list of terms covering spine surgery. The search is conducted using Medline, Embase, and the Cochrane Central Register of Controlled Trials. A thorough examination of titles and abstracts is performed followed by data extraction. The data points include patient demographics, past medical history, spine procedural information, and clinical outcomes. This systematic review will aid clinicians in identifying demographics, medical management, and clinical outcomes for spine surgery patients with a previous organ transplant. These findings will highlight the gaps in the knowledge of this complex population and stimulate further research.

Robust Prediction of Non-home Discharge After Thoracolumbar Spine Surgery With Ensemble Machine Learning and Validation on a Nationwide Cohort.

BACKGROUND: Delays in postoperative referrals to rehabilitation or skilled nursing facilities contribute toward extended hospital stays. Facilitating more efficient referrals through accurate preoperative prediction algorithms has the potential to reduce unnecessary economic burden and minimize risk of hospital-acquired complications. We develop a robust machine learning algorithm to predict non-home discharge after thoracolumbar spine surgery that generalizes to unseen populations and identifies markers for prediction. METHODS: Retrospective electronic health records were obtained from our single-center data warehouse (SCDW) to identify patients undergoing thoracolumbar spine surgeries between 2008 and 2019 for algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify thoracolumbar surgeries between 2009 and 2017 for out-of-sample validation. Ensemble decision trees were constructed for prediction and area under the receiver operating characteristic curve (AUROC) was used to assess performance. Shapley additive explanations values were derived to identify drivers of non-home discharge for interpretation of algorithm predictions. RESULTS: A total of 5224 cases of thoracolumbar spine surgeries were isolated from the SCDW and 492,312 cases were identified from NIS. The model achieved an AUROC of 0.81 (standard deviation [SD] = 0.01) on the SCDW test set and 0.77 (SD = 0.01) on the nationwide NIS data set, thereby demonstrating robust prediction of non-home discharge across all diverse patient cohorts. Age, total Elixhauser comorbidities, Medicare insurance, weighted Elixhauser score, and female sex were among the most important predictors of non-home discharge. CONCLUSIONS: Machine learning algorithms reliably predict non-home discharge after thoracolumbar spine surgery across single-center and national cohorts and identify preoperative features of importance that elucidate algorithm decision-making.

Seasonal Effects on Surgical Site Infections Following Spine Surgery.

BACKGROUND: Studies investigating seasonality as a risk factor for surgical site infections (SSIs) after spine surgery show mixed results. This study used national data to analyze seasonal effects on spine surgery SSIs. METHODS: National Surgical Quality Improvement Program data (2011-2018) were queried for posterior cervical fusions (PCFs), cervical laminoplasties, posterior lumbar fusions (PLFs), lumbar laminectomies, and deformity surgeries. Patients aged >89 and procedures for tumors, fractures, infections, and nonelective indications were excluded. Patients were divided into warm (admitted April-September) and cold (admitted October-March) seasonal groups. End points were SSIs and reoperations for wound débridement/drainage. Stratified analyses were performed by surgery type and pre-versus postdischarge infections. RESULTS: Overall (N = 208,291), SSIs were more likely in the warm season (odds ratio [OR] 1.15, 95% confidence interval [CI] 1.08-1.23, P < 0.0001) and for PCFs (OR 1.40, 95% CI 1.08-1.80, P = 0.011), PLFs (OR 1.15, 95% CI 1.04-1.28, P = 0.006), and lumbar laminectomies (OR 1.13, 95% CI 1.03-1.25, P = 0.014). Postdischarge infections were also more likely in the warm season overall (OR 1.15, 95% CI 1.07-1.23, P < 0.0001) and for PCFs (OR 1.32, 95% CI 1.01-1.73, P = 0.041), PLFs (OR 1.14, 95% CI 1.03-1.27, P = 0.014), and lumbar laminectomies (OR 1.15, CI 1.04-1.27, P = 0.007). In-hospital infections were more likely during the warm season only for PCFs (OR 2.54, 95% CI 1.06-6.10, P = 0.037). Reoperations for infection were more likely during the warm season for PLFs (OR 1.29, 95% CI 1.08-1.54, P = 0.005). CONCLUSIONS: PCF, PLF, and lumbar laminectomy performed during the warm season had significantly higher odds of SSI, especially postdischarge SSIs. Reoperation rates for wound management were significantly increased during the warm season for PLFs. Identifying seasonal causes merits further investigation and may influence surgeon scheduling and expectations.

Clinical Trials in Spinal Tumors: A Two-Decade Review.

OBJECTIVE: Clinical trials are essential for assessing the advancements in spine tumor therapeutics. The purpose of the present study was to characterize the trends in clinical trials for primary and metastatic tumor treatment during the past 2 decades. METHODS: The ClinicalTrials.gov database was queried using the search term "spine" for all interventional studies from 1999 to 2020 with the categories of "cancer," "neoplasm," "tumor," and/or "metastasis." The tumor type, phase data, enrollment numbers, and home institution country were recorded. The sponsor was categorized as an academic institution, industry, government, or other and the intervention type as procedure, drug, device, radiation therapy, or other. The frequency of each category and the cumulative frequency during the 20-year period were calculated. RESULTS: A total of 106 registered trials for spine tumors were listed. All, except for 2, that had begun before 2008 had been completed. An enrollment of 51-100 participants (29.8%) was the most common, and most were phase II studies (54.4%). Most of the studies had examined metastatic tumors (58.5%), and the number of new trials annually had increased 3.4-fold from 2009 to 2020. Most of the studies had been conducted in the United States (56.4%). The most common intervention strategy was radiation therapy (32.1%), although from 2010 to 2020, procedural studies had become the most frequent (2.4/year). Most of the studies had been sponsored by academic institutions (63.2%), which during the 20-year period had sponsored 3.2-fold more studies compared with the industry partners. CONCLUSIONS: The number of clinical trials for spine tumor therapies has rapidly increased during the past 15 years, owing to studies at U.S. academic medical institutions investigating radiosurgery for the treatment of metastases. Targeted therapies for tumor subtypes and sequelae have updated international best practices.

The Impact of American Society of Anesthesiologists Status on Cost of Care and Length of Stay Following Posterior Cervical Decompression and Fusion.

BACKGROUND: Increased posterior cervical decompression and fusion (PCDF) procedures over the past decade have raised the prospect of bundled payment plans. The American Society of Anesthesiologists (ASA) Physical Status Classification system may enable accurate estimation of health care costs, length of stay (LOS), and other postoperative outcomes in patients undergoing PCDF. METHODS: Low (I and II) versus high (III and IV) ASA class was used to evaluate 971 patients who underwent PCDF between 2008 and 2016 at a single institution. Demographics were compared using univariate analysis. Cost of care, LOS, and postoperative complications were compared using multivariable logistic and linear regression, controlling for sex, age, length of surgery, and number of segments fused. RESULTS: The high ASA class cohort was older (mean age 62 years vs. 55 years, P < 0.0001) and had higher Elixhauser comorbidity index scores (P < 0.0001). ASA class was independently associated with longer LOS (2.1 days, 95% confidence interval [CI] 1.3-2.9, P < 0.0001) and higher cost ($2936, 95% CI $1457-$4415, P < 0.0001). Patients with high ASA class were more likely to have a nonhome discharge (3.9, 95% CI 2.8-5.6, P < 0.0001), delayed extubation (3.2, 95% CI 1.4-7.3, P = 0.006), intensive care unit stay (2.4, 95% CI 1.5 3.7, P = 0.0001), in-hospital complications (1.5, 95% CI 1.0-2.2, P = 0.03), and 30-day (3.2, 95% CI 1.5-6.8, P = 0.003) and 90-day (3.2, 95% CI 1.8-5.7, P = 0.0001) readmission. CONCLUSIONS: High ASA class is strongly associated with increased costs, LOS, and adverse outcomes following PCDF and could be useful for preoperative prediction of these outcomes.

Comparison of Cost and Perioperative Outcome Profiles for Primary and Revision Posterior Cervical Fusion Procedures.

STUDY DESIGN: Retrospective analysis. OBJECTIVE: To compare perioperative outcomes and hospitalization costs between patients undergoing primary or revision posterior cervical discectomy and fusion (PCDF). SUMMARY OF BACKGROUND DATA: While prior studies found differences in outcomes between primary and revision anterior cervical discectomy and fusion (ACDF), risk, and outcome profiles for posterior cervical revision procedures have not yet been elucidated. METHODS: Institutional records were queried for cases involving isolated PCDF procedures to evaluate preoperative characteristics and outcomes for patients undergoing primary versus revision PCDF between 2008 and 2016. The primary outcome was perioperative complications, while perioperative and resource utilization measures such as hospitalization length, required stay in the intensive care unit (ICU), direct hospitalization costs, and 30-day emergency department (ED) admissions were explored as secondary outcomes. RESULTS: One thousand one hundred twenty four patients underwent PCDF, with 218 (19.4%) undergoing a revision procedure. Patients undergoing revision procedures were younger (53.0 vs. 60.5 yrs), but had higher Elixhauser scores compared with the non-revision cohort. Revision cases tended to involve fewer spinal segments (3.6 vs. 4.1 segments) and shorter surgical durations (179.3 vs. 206.3 min), without significant differences in estimated blood loss. There were no significant differences in the overall complication rates (P = 0.20), however, the primary cohort had greater rates of required ICU stays (P = 0.0005) and non-home discharges (P = 0.0003). The revision cohort did experience significantly increased odds of 30-day ED admission (P = 0.04) and had higher direct hospitalization (P = 0.03) and surgical (P < 0.0001) costs. CONCLUSION: Complication rates, including incidental durotomy, were similar between primary and revision PCDF cohorts. Although prior surgery status did not predict complication risk, comorbidity burden did. Nevertheless, patients undergoing revision procedures had decreased risk of required ICU stay but greater risk of 30-day ED admission and higher direct hospitalization and surgical costs.Level of Evidence: 3.

Palliative Care Consultation for Hospitalized Patients with Primary and Secondary Brain Tumors at a Single Academic Center.

Introduction: Studies addressing palliative care delivery in neuro-oncology are limited. Objectives: To compare inpatients with brain tumors who received palliative care (through referral or trigger) with those receiving usual care. Design: Retrospective cohort study. Setting/Subjects: Inpatients with primary or secondary brain tumors who did or did not receive palliative care at a U.S. medical center. Measurements: Sociodemographic, clinical, and utilization characteristics were compared. Results: Of 1669 brain tumor patients, 386 (23.1%) received palliative care [nontrigger: 246 (14.7%); trigger: 140 (8.4%)] and 1283 (76.9%) received usual care. Nontrigger patients were oldest (mean age 65.0 years; trigger: 61.1 years; usual care: 55.5 years; p < 0.001); sickest at baseline (mean Elixhauser comorbidity index 3.76; trigger: 3.49; usual care: 1.84; p < 0.001); and had highest in-hospital death [34 (13.8%), trigger: 10 (7.1%), usual care: 7 (0.5%); p < 0.001] and hospice discharge [54 (22.0%), trigger: 18 (12.9%), usual care: 14 (1.1%); p < 0.001]. Conclusions: Trigger criteria may promote earlier palliative care referral, yet criteria tailored for neuro-oncology are undeveloped.

The Impact of Non-Elective Admission on Cost of Care and Length of Stay in Anterior Cervical Discectomy and Fusion: A Propensity-Matched Analysis.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To evaluate the impact of admission status on patient outcomes and healthcare costs in anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: Undergoing ACDF non-electively has been associated with higher patient comorbidity burdens. However, the impact of non-elective status on the total cost of hospital stay has yet to be quantified. METHODS: Patients undergoing ACDF at a single institution were placed into elective or non-elective cohorts. Propensity score-matching analysis in a 5:1 ratio controlling for insurance type and comorbidities was used to minimize selection bias. Demographics were compared by univariate analysis. Cost of care, length of stay (LOS), and clinical outcomes were compared between groups using multivariable linear and logistic regression with elective patients as reference cohort. All analyses controlled for sex, preoperative diagnosis, elixhauser comorbidity index (ECI), age, length of surgery, number of segments fused, and insurance type. RESULTS: Of 708 patients in the final ACDF cohort, 590 underwent an elective procedure and 118 underwent a non-elective procedure. The non-elective group was significantly younger (53.7 vs. 49.5 yr; P = 0.0007). Cohorts had similar proportions of private versus public health insurance, although elective had higher rates of commercial insurance (39.22% vs. 15.25%; P < 0.0001) and non-elective had higher rates of managed care (32.77% vs. 56.78%; P < 0.0001). Operation duration was significantly longer in non-elective patients (158 vs. 177 minutes; P = 0.01). Adjusted analysis also demonstrated that admission status independently affected cost (+$6877, 95% confidence interval [CI]: $4906-$8848; P < 0.0001) and LOS (+4.9 days, 95% CI: 3.9-6.0; P < 0.0001) for the non-elective cohort. The non-elective cohort was significantly more likely to return to the operating room (OR: 3.39; 95% CI: 1.37-8.36, P = 0.0008) and experience non-home discharge (OR: 10.95; 95% CI: 5.00-24.02, P < 0.0001). CONCLUSION: Patients undergoing ACDF non-electively had higher cost of care and longer LOS, as well as higher rates of postoperative adverse outcomes.Level of Evidence: 3.

Comparison of Surgical Outcomes of Microdiskectomy Procedures by Patient Admission Status.

OBJECTIVE: We sought to compare the cost and in-hospital outcomes following lumbar microdiskectomy procedures by admission type. METHODS: Patients undergoing lumbar microdiskectomy at a single institution from 2008 to 2016 following an elective admission (EL) were compared against those who were admitted from the emergency department (ED) or from elsewhere within or outside the hospital system (TR) for their perioperative outcomes and cost. Multivariable modeling controlled for age, sex, self-reported race, Elixhauser comorbidity score, payer type, number of segments, and procedure length. RESULTS: Of the 1249 patients included in this study, 1116 (89.4%) were admitted electively while 123 (9.8%) were admitted from the ED and 10 (0.8%) were transferred from other hospitals. EL patients had significantly lower comorbidity burdens (P < 0.0001). Univariate and multivariable analyses revealed that transfer admission patients experienced significantly longer hospitalizations (ED: +1.7 days; P < 0.0001; TR: +5.3 days; P < 0.0001) and higher direct costs (ED: $1889; P < 0.0001; TR: $7001; P < 0.0001) compared with EL patients. Despite these risks, ED and TR patients only had increased odds of nonhome discharge compared with EL patients (ED: 3.4; P = 0.002; TR: 7.9; P = 0.02). CONCLUSIONS: Patients admitted as transfers and from the ED had significantly increased hospitalization lengths of stay and direct costs compared with electively admitted patients.

Surgical Start Time Is Not Predictive of Microdiscectomy Outcomes.

STUDY DESIGN: Retrospective analysis of clinical data from a single institution. OBJECTIVE: The objective of this study was to assess the time of surgery as a possible predictor for outcomes, length of stay, and cost following microdiscectomy. SUMMARY OF BACKGROUND DATA: The volume of microdiscectomy procedures has increased year over year, heightening interest in surgical outcomes. Previous investigations have demonstrated an association between time of procedures and clinical outcomes in various surgeries, however, no study has evaluated its influence on microdiscectomy. METHODS: Demographic and outcome variables were collected from all patients that underwent a nonemergent microdiscectomy between 2008 and 2016. Patients were divided into 2 cohorts: those receiving surgery before 2 pm were assigned to the early group and those with procedures beginning after 2 pm were assigned to the late group. Outcomes and patient-level characteristics were compared using bivariate, multivariable logistic, and linear regression models. Adjusted length of stay and cost were coprimary outcomes. Secondary outcomes included operative complications, nonhome discharge, postoperative emergency department visits, or readmission rates. RESULTS: Of the 1261 consecutive patients who met the inclusion criteria, 792 were assigned to the late group and 469 were assigned to the early group. There were no significant differences in demographics or baseline characteristics between the 2 cohorts. In the unadjusted analysis, mean length of stay was 1.80 (SD=1.82) days for the early group and 2.00 (SD=1.70) days for the late group (P=0.054). Mean direct cost for the early cohort was $5088 (SD=$4212) and $4986 (SD=$2988) for the late cohort (P=0.65). There was no difference in adjusted length of stay or direct cost. No statistically significant differences were found in operative complications, nonhome discharge, postoperative emergency department visits, or readmission rates between the 2 cohorts. CONCLUSION: The study findings suggest that early compared with late surgery is not significantly predictive of surgical outcomes following microdiscectomy.

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.

Surgery for spinal deformity: non-elective admission status is associated with higher cost of care and longer length of stay.

INTRODUCTION: Surgery is commonly indicated for adult spinal deformity. Annual rates and costs of spinal deformity surgery have both increased over the past two decades. However, the impact of non-elective status on total cost of hospitalization and patient outcomes has not been quantified. OBJECTIVE: To evaluate the impact of admission status on patient outcomes and healthcare costs in spinal deformity surgery. METHODS: All patients who underwent spinal deformity surgery at a single institution between 2008 and 2016 were grouped by admission status: elective, emergency (ED), or transferred. Demographics were compared by univariate analysis. Cost of care and length of stay (LOS) were compared between admission statuses using multivariable linear regression with elective admissions as reference. Multivariate logistic regression was utilized to assess in-hospital complications, discharge destination, and readmission rates. RESULTS: There were 427 spinal deformity surgeries included in this study. Compared to elective patients, ED patients had higher Elixhauser Comorbidity Index scores (p < 0.0001), longer LOS (+ 10.9 days, 97.5% CI 6.1-15.6 days, p < 0.0001), and higher costs (+ $20,076, 97.5% CI $9,073-$31,080, p = 0.0008). Transferred patients had significantly higher Elixhauser scores (p = 0.0002), longer LOS (+ 8.8 days, 97.5% CI 3.0-14.7 days, p < 0.0001), and higher rates of non-home discharge (OR = 15.8, 97.5% CI 2.3-110.0, p = 0.001). CONCLUSION: Patients admitted from the ED undergoing spinal deformity surgery had significantly higher cost of care and longer LOS compared to elective patients. Transferred patients had significantly longer LOS and a higher rate of non-home discharge compared to elective patients.