Outcomes from 871,441 Consecutive Surgical Procedures without Overlap or with Maximally Permissible Non-Concurrent Overlap.

OBJECTIVE: To isolate the impact of subsumed surgery (a shorter procedure completed entirely during overlapping non-critical portions of a longer antecedent procedure) on patient outcomes. SUMMARY BACKGROUND DATA: The American College of Surgeons recently recommended the elimination of "concurrent surgery" with overlap during a procedure's critical portions. Guidelines for non-concurrent overlap have been established, but the safety of subsumed surgery remains to be examined. METHODS: All consecutive procedures from 2013 to 2021 within a multihospital academic medical center were included (n=871,441). Simple logistic regression was performed to compare postoperative events between patients undergoing non-overlap surgery (n=533,032) and completely subsumed surgery (n=11,319). Thereafter, coarsened exact matching was used to match patients with non-overlap and subsumed surgery 1:1 on CPT code, 18 demographic features, baseline health characteristics, and procedural variables (n=7,146). Exact-matched cases were subsequently limited to pairs performed by the same surgeon (n=5,028). Primary outcomes included 30-day readmission, ED visits, and reoperations. RESULTS: Univariate analysis suggested that subsumed surgery had a higher 30-day risk of readmission (OR 1.55, P<0.0001), ED evaluation (OR 1.19, P<0.0001), and reoperation (OR 1.98, P<0.0001). When comparison was limited to the exact same procedure and patients were matched on demographics and health characteristics, there were no outcome differences between patients with subsumed surgery and non-overlapping surgery, even when limiting analyses to the same surgeon. CONCLUSIONS: Similar surgeries for similar patients result in similar outcomes whether there is completely subsumed or no overlap. Individual surgeons performing a specific procedure have no outcome differences with subsumed and non-overlapping cases.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Sidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Predicting post-surgical outcomes in idiopathic normal pressure hydrocephalus using clinically important changes from the cerebrospinal fluid tap test.

OBJECTIVE: Patients diagnosed with idiopathic Normal Pressure Hydrocephalus (iNPH) typically experience symptom improvements after undergoing a cerebrospinal fluid-tap test (CSF-TT), These improvements are recognized as indicative of potential improvements following surgical intervention. As gait disturbance is the most common iNPH symptom, gait improvements are of predominant interest. The purpose of this study was to examine if clinically important changes in gait and balance from CSF-TT predict meaningful changes following surgery. METHOD: The study involved analysis of data collected in a prospective observational study for 34 iNPH patients who underwent a CSF-TT and subsequent surgery. Linear regression, logistic regression and classification trees were used for predictive modelling comparing changes from CSF-TT with post-surgical changes in Tinetti, Timed Up and Go (TUG) and Berg Balance Scale (BBS) outcomes. RESULTS: Predictive models for minimal clinically important differences (MCIDs) from CSF-TT to surgery were significant for Tinetti (odds ratio = 1.42, p = 0.02) and BBS (odds ratio = 1.57, p < 0.01). Four items on Tinetti and two items on BBS were identified with a predictive accuracy of 79% and 76% respectively. BBS has the highest sensitivity (85%) and negative predictive value (77%). TUG had a 100% specificity and 100% positive predictive value. The predictive model using MCIDs for TUG was not significant (odds ratio = 1.13, p = 0.06). CONCLUSION: Clinically important changes from CSF-TT are useful in predicting post-surgical outcomes in iNPH patients. Tinetti and BBS, both have predictive value using MCID scores as cut off values, of which BBS is a stronger outcome measure for prediction.

Disparities in seizure outcomes revealed by large language models.

OBJECTIVE: Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different seizure outcomes. MATERIALS AND METHODS: We tested our LLM for differences and equivalences in prediction accuracy and confidence across demographic groups defined by race, ethnicity, sex, income, and health insurance, using manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for demographic outcome disparities, using univariable and multivariable analyses. RESULTS: We analyzed 84 675 clinic visits from 25 612 unique patients seen at our epilepsy center. We found little evidence of bias in the prediction accuracy or confidence of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, P ≤ .001), those with public insurance (OR 1.53, P ≤ .001), and those from lower-income zip codes (OR ≥1.22, P ≤ .007). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, P = .66). CONCLUSION: We found little evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings quantify the critical need to reduce disparities in the care of people with epilepsy.

Low Household Income Increases Hospital Length of Stay and Decreases Home Discharge Rates in Lumbar Fusion.

STUDY DESIGN: Retrospective Matched Cohort Study. OBJECTIVES: Low median household income (MHI) has been correlated with worsened surgical outcomes, but few studies have rigorously controlled for demographic and medical factors at the patient level. This study isolates the relationship between MHI and surgical outcomes in a lumbar fusion cohort using coarsened exact matching. METHODS: Patients undergoing single-level, posterior lumbar fusion at a single institution were consecutively enrolled and retrospectively analyzed (n = 4263). Zip code was cross-referenced to census data to derive MHI. Univariate regression correlated MHI to outcomes. Patients with low MHI were matched to those with high MHI based on demographic and medical factors. Outcomes evaluated included complications, length of stay, discharge disposition, 30- and 90 day readmissions, emergency department (ED) visits, reoperations, and mortality. RESULTS: By univariate analysis, MHI was significantly associated with 30- and 90 day readmission, ED visits, reoperation, and non-home discharge, but not mortality. After exact matching (n = 270), low-income patients had higher odds of non-home discharge (OR = 2.5, P = .016) and higher length of stay (mean 100.2 vs 92.6, P = .02). There were no differences in surgical complications, ED visits, readmissions, or reoperations between matched groups. CONCLUSIONS: Low MHI was significantly associated with adverse short-term outcomes from lumbar fusion. A matched analysis controlling for confounding variables uncovered longer lengths of stay and higher rates of discharge to post-acute care (vs home) in lower MHI patients. Socioeconomic disparities affect health beyond access to care, worsen surgical outcomes, and impose costs on healthcare systems. Targeted interventions must be implemented to mitigate these disparities.

Association Between Race and Short-Term Outcomes Across 3988 Consecutive Single-Level Spinal Fusions.

BACKGROUND AND OBJECTIVES: Race has implications for access to medical care. However, the impact of race, after access to care has been attained, remains poorly understood. The objective of this study was to isolate the relationship between race and short-term outcomes across patients undergoing a single, common neurosurgical procedure. METHODS: In this retrospective cohort study, 3988 consecutive patients undergoing single-level, posterior-only open lumbar fusion at a single, multihospital, academic medical center were enrolled over a 6-year period. Among them, 3406 patients self-identified as White, and 582 patients self-identified as Black. Outcome disparities between all White patients vs all Black patients were estimated using logistic regression. Subsequently, coarsened exact matching controlled for outcome-mitigating factors; White and Black patients were exact-matched 1:1 on key demographic and health characteristics (matched n = 1018). Primary outcomes included 30-day and 90-day hospital readmissions, emergency department (ED) visits, reoperations, mortality, discharge disposition, and intraoperative complication. RESULTS: Before matching, Black patients experienced increased rate of nonhome discharge, readmissions, ED visits, and reoperations (all P < .001). After exact matching, Black patients were less likely to be discharged to home (odds ratio [OR] 2.68, P < .001) and had higher risk of 30-day and 90-day readmissions (OR 2.24, P < .001; OR 1.91, P < .001; respectively) and ED visits (OR 1.79, P = .017; OR 2.09, P < .001). Black patients did not experience greater risk of intraoperative complication (unintentional durotomy). CONCLUSION: Between otherwise homogenous spinal fusion cohorts, Black patients experienced unfavorable short-term outcomes. These disparities were not explained by differences in intraoperative complications. Further investigation must characterize and mitigate institutional and societal factors that contribute to outcome disparities.

Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study.

OBJECTIVE: Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy. METHODS: Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach. RESULTS: In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. SIGNIFICANCE: Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

Racial disparities in short-term spinal fusion outcomes across 4263 consecutive patients.

OBJECTIVE: Race plays a salient role in access to surgical care. However, few investigations have assessed the impact of race within surgical populations after care has been delivered. The objective of this study was to employ an exact matching protocol to a homogenous population of spine surgery patients in order to isolate the relationships between race and short-term postoperative outcomes. METHODS: In total, 4263 consecutive patients who underwent single-level, posterior-only lumbar fusion at a single multihospital academic medical center were retrospectively enrolled. Of these patients, 3406 patients self-identified as White and 857 patients self-identified as non-White. Outcomes were initially compared across all patients via logistic regression. Subsequently, White patients and non-White patients were exactly matched on the basis of key demographic and health characteristics (1520 matched patients). Outcome disparities were evaluated between the exact-matched cohorts. Primary outcomes were readmissions, emergency department (ED) visits, reoperations, mortality, intraoperative complications, and discharge disposition. RESULTS: Before matching, non-White patients were less likely to be discharged home and more likely to be readmitted, evaluated in the ED, and undergo reoperation. After matching, non-White patients experienced higher rates of nonhome discharge, readmissions, and ED visits. Non-White patients did not have more surgical complications either before or after matching. CONCLUSIONS: Between otherwise similar cohorts of spinal fusion cases, non-White patients experienced unfavorable discharge disposition and higher risk of multiple adverse postoperative outcomes. However, these findings were not accounted for by differences in surgical complications, suggesting that structural factors underlie the observed disparities.

Quantifying a novel three-dimensional marker of scoliosis.

BACKGROUND: Scoliosis causes abnormal spinal curvature and torsional rotation of the vertebrae and has implications for human suffering and societal cost. In differential geometry, Writhe describes three-dimensional curvature. Differential geometric quantities can inform better diagnostic metrics of scoliotic deformity. This evaluation could help physicians and researchers study scoliosis and determine treatments. METHODS: Eight adult lumbar spine CT scans were analyzed in custom MATLAB programs to estimate Writhe and Cobb angle. Five patients exhibited scoliotic curvature, and three controls were asymptomatic. Vertebral centroids in three-dimensional space were determined, and Writhe was approximated. A T-test determined whether the affected spines had greater Writhe than the controls. Cohen's D test was used to determine effect size. RESULTS: Writhe of scoliotic spines (5.4E-4 ± 2.7E-4) was significantly higher than non-scoliotic spines (8.2E-5 ± 1.1E-4; p = 0.008). CONCLUSION: Writhe, a measure of curvature derived from 3D imaging, is significantly greater in scoliotic than in non-scoliotic spines. Future directions must include more subjects and examine writhe as a marker of scoliosis severity, progression, and response to treatment.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

OBJECTIVE: Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. METHODS: We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. RESULTS: We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. SIGNIFICANCE: iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Disparities in seizure outcomes revealed by large language models.

Objective: Large-language models (LLMs) in healthcare have the potential to propagate existing biases or introduce new ones. For people with epilepsy, social determinants of health are associated with disparities in access to care, but their impact on seizure outcomes among those with access to specialty care remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to test the hypothesis that different demographic groups have different seizure outcomes. Methods: First, we tested our LLM for intrinsic bias in the form of differential performance in demographic groups by race, ethnicity, sex, income, and health insurance in manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for outcome disparities in the same demographic groups, using univariable and multivariable analyses. Results: We analyzed 84,675 clinic visits from 25,612 patients seen at our epilepsy center 2005-2022. We found no differences in the accuracy, or positive or negative class balance of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, p = 3×10-8), those with public insurance (OR 1.53, p = 2×10-13), and those from lower-income zip codes (OR ≥ 1.22, p ≤ 6.6×10-3). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, p = 0.66). Significance: We found no evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings highlight the critical need to reduce disparities in the care of people with epilepsy.

The Impact of Neighborhood Socioeconomic Disadvantage on Operative Outcomes after Single-Level Lumbar Fusion.

INTRODUCTION: The relationship between socioeconomic status and neurosurgical outcomes has been investigated with respect to insurance status or median household income, but few studies have considered more comprehensive measures of socioeconomic status. This study examines the relationship between Area Deprivation Index (ADI), a comprehensive measure of neighborhood socioeconomic disadvantage, and short-term postoperative outcomes after lumbar fusion surgery. METHODS: 1861 adult patients undergoing single-level, posterior-only lumbar fusion at a single, multihospital academic medical center were retrospectively enrolled. An ADI matching protocol was used to identify each patient's 9-digit zip code and the zip code-associated ADI data. Primary outcomes included 30- and 90-day readmission, emergency department visits, reoperation, and surgical complication. Coarsened exact matching was used to match patients on key demographic and baseline characteristics known to independently affect neurosurgical outcomes. Odds ratios (ORs) were computed to compare patients in the top 10% of ADI versus lowest 40% of ADI. RESULTS: After matching (n = 212), patients in the highest 10% of ADI (compared to the lowest 40% of ADI) had significantly increased odds of 30- and 90-day readmission (OR = 5.00, P < 0.001 and OR = 4.50, P < 0.001), ED visits (OR = 3.00, P = 0.027 and OR = 2.88, P = 0.007), and reoperation (OR = 4.50, P = 0.039 and OR = 5.50, P = 0.013). There was no significant association with surgical complication (OR = 0.50, P = 0.63). CONCLUSIONS: Among otherwise similar patients, neighborhood socioeconomic disadvantage (measured by ADI) was associated with worse short-term outcomes after single-level, posterior-only lumbar fusion. There was no significant association between ADI and surgical complications, suggesting that perioperative complications do not explain the socioeconomic disparities in outcomes.

Generalization of finetuned transformer language models to new clinical contexts.

Objective: We have previously developed a natural language processing pipeline using clinical notes written by epilepsy specialists to extract seizure freedom, seizure frequency text, and date of last seizure text for patients with epilepsy. It is important to understand how our methods generalize to new care contexts. Materials and methods: We evaluated our pipeline on unseen notes from nonepilepsy-specialist neurologists and non-neurologists without any additional algorithm training. We tested the pipeline out-of-institution using epilepsy specialist notes from an outside medical center with only minor preprocessing adaptations. We examined reasons for discrepancies in performance in new contexts by measuring physical and semantic similarities between documents. Results: Our ability to classify patient seizure freedom decreased by at least 0.12 agreement when moving from epilepsy specialists to nonspecialists or other institutions. On notes from our institution, textual overlap between the extracted outcomes and the gold standard annotations attained from manual chart review decreased by at least 0.11 F1 when an answer existed but did not change when no answer existed; here our models generalized on notes from the outside institution, losing at most 0.02 agreement. We analyzed textual differences and found that syntactic and semantic differences in both clinically relevant sentences and surrounding contexts significantly influenced model performance. Discussion and conclusion: Model generalization performance decreased on notes from nonspecialists; out-of-institution generalization on epilepsy specialist notes required small changes to preprocessing but was especially good for seizure frequency text and date of last seizure text, opening opportunities for multicenter collaborations using these outcomes.

Quantifying interictal intracranial EEG to predict focal epilepsy.

Introduction: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods: We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results: The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions: Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.

Relationship Between Comorbidity Burden and Short-Term Outcomes Across 4680 Consecutive Spinal Fusions.

OBJECTIVE: Preoperative management requires the identification and optimization of modifiable medical comorbidities, though few studies isolate comorbid status from related patient-level variables. This study evaluates Charlson Comorbidity Index (CCI)-an easily derived measure of aggregate medical comorbidity-to predict outcomes from spinal fusion surgery. Coarsened exact matching is employed to control for key patient characteristics and isolate CCI. METHODS: We retrospectively assessed 4680 consecutive patients undergoing single-level, posterior-only lumbar fusion at a single academic center. Logistic regression evaluated the univariate relationship between CCI and patient outcomes. Coarsened exact matching generated exact demographic matches between patients with high comorbid status (CCI >6) or no medical comorbidities (matched n = 524). Patients were matched 1:1 on factors associated with surgical outcomes, and outcomes were compared between matched cohorts. Primary outcomes included surgical complications, discharge status, 30- and 90-day risk of readmission, emergency department (ED) visits, reoperation, and mortality. RESULTS: Univariate regression of increasing CCI was significantly associated with non-home discharge, as well as 30- and 90-day readmission, ED visits, and mortality (all P < 0.05). Subsequent isolation of comorbidity between otherwise exact-matched cohorts found comorbid status did not affect readmissions, reoperations, or mortality; high CCI score was significantly associated with non-home discharge (OR = 2.50, P < 0.001) and 30-day (OR = 2.44, P = 0.02) and 90-day (OR = 2.29, P = 0.008) ED evaluation. CONCLUSIONS: Comorbidity, measured by CCI, did not increase the risk of readmission, reoperation, or mortality. Single-level, posterior lumbar fusions may be safe in appropriately selected patients regardless of comorbid status. Future studies should determine whether CCI can guide discharge planning and postoperative optimization.

Varying Degree of Overlap Following the Critical Steps of Lumbar Fusion and Short-term Outcomes.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: The present study analyzes the impact of end-overlap on short-term outcomes after single-level, posterior lumbar fusions. SUMMARY OF BACKGROUND DATA: Few studies have evaluated how "end-overlap" (i.e., surgical overlap after the critical elements of spinal procedures, such as during wound closure) influences surgical outcomes. METHODS: Retrospective analysis was performed on 3563 consecutive adult patients undergoing single-level, posterior-only lumbar fusion over a 6-year period at a multi-hospital university health system. Exclusion criteria included revision surgery, missing key health information, significantly elevated body mass index (>70), non-elective operations, non-general anesthesia, and unclean wounds. Outcomes included 30-day emergency department visit, readmission, reoperation, morbidity, and mortality. Univariate analysis was carried out on the sample population, then limited to patients with end-overlap. Subsequently, patients with the least end-overlap were exact-matched to patients with the most. Matching was performed based on key demographic variables-including sex and comorbid status-and attending surgeon, and then outcomes were compared between exact-matched cohorts. RESULTS: Among the entire sample population, no significant associations were found between the degree of end-overlap and short-term adverse events. Limited to cases with any end-overlap, increasing overlap was associated with increased 30-day emergency department visits ( P =0.049) but no other adverse outcomes. After controlling for confounding variables in the demographic-matched and demographic/surgeon-matched analyses, no differences in outcomes were observed between exact-matched cohorts. CONCLUSIONS: The degree of overlap after the critical steps of single-level lumbar fusion did not predict adverse short-term outcomes. This suggests that end-overlap is a safe practice within this surgical population.

iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices.

Background: Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. These tasks are still performed manually in many epilepsy centers. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. Methods: We created iEEG-recon, a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon, and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. Results: We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation. Discussion: iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.

Urine Drug Screening in a Telehealth Setting for the Treatment of Opioid Use Disorder.

Importance: Amid rapid and widespread adoption of telehealth-based opioid treatment (TBOT), there is an urgent need for rigorous studies exploring the feasibility and characteristics of urine drug screening (UDS). Objective: To investigate administration patterns and results of UDS to assess feasibility of UDS and patient outcomes in a TBOT setting. Design: This observational cohort study was conducted between January 1, 2021, and December 6, 2022, and included patients with opioid use disorder treated in Ophelia, a TBOT treatment platform in 14 states. Data analysis was performed from January to March 2023. Main Outcomes and Measures: Number and percentage of patients with UDS within 30, 90, and 180 days of intake, grouped by adherence to clinical protocols. Associations were assessed between baseline characteristics and UDS completion and opioid positivity in first 30 days using χ2 tests. Baseline and 180-day follow-up UDS results were compared using McNemar tests. Results: Among 3395 patients (mean [SD] age, 38.2 [9.3] years, mostly male [54.1%], non-Hispanic White [81.5%], urban-residing [80.3%], and cash-pay at intake [74.0%]), 2782 (83.3%) completed a UDS within 30 days (90.0% among protocol-adherent patients, 67.0% among protocol-nonadherent patients). A total of 2750 of 2817 (97.6%) patients retained more than 90 days completed 1 or more UDS, as did 2307 of 2314 (99.7%) patients retained more than 180 days. Younger patients, patients of a racial and ethnic minority group, those living in urban areas, and cash-pay patients were less likely to complete a UDS in the first 30 days. Buprenorphine positivity increased (from 96.9% to 98.4%, P = .004) and opioid positivity declined (from 7.9% to 3.3%, P < .001) over time. Conclusions and Relevance: In this cohort study of patients with opioid use disorder receiving buprenorphine in a remote care environment, UDS was highly feasible, though early UDS completion rates varied across demographic subgroups. The prevalence of unexpected UDS results was low and declined over time in treatment.

A Randomized Controlled Trial to Assess the Impact of Enhanced Recovery After Surgery on Patients Undergoing Elective Spine Surgery.

OBJECTIVE: To conduct a prospective, randomized controlled trial (RCT) of an enhanced recovery after surgery (ERAS) protocol in an elective spine surgery population. BACKGROUND: Surgical outcomes such as length of stay (LOS), discharge disposition, and opioid utilization greatly contribute to patient satisfaction and societal healthcare costs. ERAS protocols are multimodal, patient-centered care pathways shown to reduce postoperative opioid use, reduced LOS, and improved ambulation; however, prospective ERAS data are limited in spine surgery. METHODS: This single-center, institutional review board-approved, prospective RCT-enrolled adult patients undergoing elective spine surgery between March 2019 and October 2020. Primary outcomes were perioperative and 1-month postoperative opioid use. Patients were randomized to ERAS (n=142) or standard-of-care (SOC; n=142) based on power analyses to detect a difference in postoperative opioid use. RESULTS: Opioid use during hospitalization and the first postoperative month was not significantly different between groups (ERAS 112.2 vs SOC 117.6 morphine milligram equivalent, P =0.76; ERAS 38.7% vs SOC 39.4%, P =1.00, respectively). However, patients randomized to ERAS were less likely to use opioids at 6 months postoperatively (ERAS 11.4% vs SOC 20.6%, P =0.046) and more likely to be discharged to home after surgery (ERAS 91.5% vs SOC 81.0%, P =0.015). CONCLUSION: Here, we present a novel ERAS prospective RCT in the elective spine surgery population. Although we do not detect a difference in the primary outcome of short-term opioid use, we observe significantly reduced opioid use at 6-month follow-up as well as an increased likelihood of home disposition after surgery in the ERAS group.

What does high value care for musculoskeletal conditions mean and how do you apply it in practice? A consensus statement from a research network of physiotherapists in New South Wales, Australia.

OBJECTIVES: To develop a physiotherapist-led consensus statement on the definition and provision of high-value care for people with musculoskeletal conditions. DESIGN: We performed a three-stage study using Research And Development/University of California Los Angeles Appropriateness Method methodology. We reviewed evidence about current definitions through a rapid literature review and then performed a survey and interviews with network members to gather consensus. Consensus was finalised in a face-to-face meeting. SETTING: Australian primary care. PARTICIPANTS: Registered physiotherapists who are members of a practice-based research network (n=31). RESULTS: The rapid review revealed two definitions, four domains of high value care and seven themes of high-quality care. Online survey responses (n=26) and interviews (n=9) generated two additional high-quality care themes, a definition of low-value care, and 21 statements on the application of high value care. Consensus was reached for three working definitions (high value, high-quality and low value care), a final model of four high value care domains (high-quality care, patient values, cost-effectiveness, reducing waste), nine high-quality care themes and 15 statements on application. CONCLUSION: High value care for musculoskeletal conditions delivers most value for the patient, and the clinical benefits outweigh the costs to the individual or system providing the care. High-quality care is evidence based, effective and safe care that is patient-centred, consistent, accountable, timely, equitable and allows easy interaction with healthcare providers and healthcare systems.