Diabetes Is Predictive of Postoperative Outcomes and Readmission Following Posterior Lumbar Fusion.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The present study analyzes complication rates and episode-based costs for patients with and without diabetes mellitus (DM) following posterior lumbar fusion (PLF). METHODS: PLF cases at a single institution from 2008 to 2016 were queried (n = 3226), and demographic and perioperative data were analyzed. Patients with and without the diagnosis of DM were compared using chi-square, Student's t test, and multivariable regression modeling. RESULTS: Patients with diabetes were older (63.10 vs 56.48 years, P < .001) and possessed a greater number of preoperative comorbidities (47.84% of patients had Elixhauser Comorbidity Index >0 vs 42.24%, P < .001) than did patients without diabetes. When controlling for preexisting differences, diabetes remained a significant risk factor for prolonged length of stay (OR = 1.59, 95% CI 1.26-2.01, P < .001), intensive care unit stay (OR = 1.52, 95% CI 1.07-2.17, P = .021), nonhome discharge (OR = 1.86, 95% CI 1.46-2.37, P < .001), 30-day readmission (OR = 2.15, 95% CI 1.28-3.60, P = .004), 90-day readmission (OR = 1.65, 95% CI 1.05-2.59, P = .031), 30-day emergency room visit (OR = 2.15, 95% CI 1.27-3.63, P = .004), and 90-day emergency room visit (OR = 2.27, 95% CI 1.41-3.65, P < .001). Cost modeling controlling for overall comorbidity burden demonstrated that diabetes was associated with a $1709 increase in PLF costs (CI $344-$3074, P = .014). CONCLUSIONS: The present findings indicate a correlation between diabetes and a multitude of postoperative adverse outcomes and increased costs, thus illustrating the substantial medical and financial burdens of diabetes for PLF patients. Future studies should explore preventive measures that may mitigate these downstream effects.

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.

Making a match: trends in the application, interview, and ranking process for the neurological surgery residency programs.

OBJECTIVE: Neurosurgery is a highly competitive residency field with a match rate lower than that of other specialties. The aim of this study was to analyze trends associated with the residency match process from the applicants' and program directors' perspectives. METHODS: Between 2010 and 2020, the National Residency Matching Program (NRMP) Applicant and Program Director Surveys, the NRMP Charting Outcomes reports, and the Accreditation Council for Graduate Medical Education (ACGME) Data Resource Books were analyzed to identify the number of applicants interviewed and ranked in US programs, the applicants' ranking preferences, the program directors' preferential factors in offering interviews, and rank list order. Applicants were divided between US senior medical students and independent applicants. Each cohort was dichotomized for matched and unmatched applicants. RESULTS: Over the study period, 2935 applicants applied to neurosurgery residency, including 2135 US senior medical students and 800 independent applicants, with an overall match rate of 65%. Overall, matched applicants had a significantly higher number of publications (p < 0.05). Among US senior medical student applicants, the application-to-interview ratio more than doubled over the study period, yet the number of interview invitations received, interviews accepted, and programs ranked remained unchanged. In the US senior medical student cohort, the number of submitted applications, interview invitations, accepted interviews, and programs ranked did not significantly differ between matched and unmatched applicants. In both cohorts, applicants shifted ranking factors from a more academic focus in early years to more well-being in later years. Letters of recommendation and board scores were key factors for program directors while screening applicants for interviews and ranking. CONCLUSIONS: Neurosurgery residency continues to be a highly competitive field in medicine, with match rates of 65%. Recently, applicants have placed greater importance on ranking programs that value residents' well-being, as opposed to strictly academic factors. A data-driven understanding of factors important to applicants and program directors during the match process has the potential to improve resident candidate recruitment and overall resident-program fit, thereby improving well-being during residency, reducing the attrition rate, and overall enhancing the diversity of the neurosurgery resident workforce.

Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to develop explainable machine learning models to understand such interactions in a large cohort of patients treated with spine surgery. METHODS: Of 11,150 patients who underwent surgery for degenerative spine conditions at a single institution, 3,310 (29.7%) were characterized as having prolonged LOS. Machine learning models predicting LOS were built for each patient. Shapley additive explanation (SHAP) values were calculated for each patient model to quantify the importance of features and variable interaction effects. RESULTS: Models using features identified by SHAP values were highly predictive of prolonged LOS risk (mean C-statistic = 0.87). Feature importance analysis revealed that prolonged LOS risk is multifactorial. Non-elective admission produced elevated SHAP values, indicating a clear, strong risk of prolonged LOS. In contrast, intraoperative and sociodemographic factors displayed bidirectional influences on risk, suggesting potential protective effects with optimization of factors such as estimated blood loss, surgical duration, and comorbidity burden. CONCLUSIONS: Meticulous management of patients with high comorbidity burdens or Medicaid insurance who are admitted non-electively or spend clinically indicated time in the intensive care unit (ICU) during their hospitalization course may be warranted to reduce their risk of unanticipated prolonged LOS following spine surgery. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

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.

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.