Shahrestani, Shane

Study Design Retrospective cohort study using a nationally representative inpatient database. Objective To determine the incidence, timing, and predictors of new-onset psychiatric disorders following lumbar fusion surgery, and to propose a data-driven risk stratification model for early identification and intervention. Methods We queried the Nationwide Readmissions Database (2016-2019) to identify adults undergoing elective inpatient lumbar fusion. Patients with preexisting psychiatric diagnoses were excluded. Outcomes included new-onset depression, anxiety, suicidal ideation, and homicidal ideation within 12 months postoperatively. Predictors were assessed using odds ratios. Timing of onset was evaluated with density plots. Results Among 839,576 patients, 12,011 developed depression, 11,828 anxiety, 916 suicidal ideation, and 63 homicidal ideation. Most diagnoses occurred within 90 days postoperatively, with peak incidence around 3 weeks. Opioid use during the index admission significantly increased odds of suicidal ideation (OR 2.13, P = 0.001), anxiety (OR 1.26, P = 0.002), and showed a trend toward depression (OR 1.14, P = 0.087). Medicaid insurance was the strongest predictor of suicidal (OR 3.69) and homicidal ideation (OR 6.41). Female sex and younger age were associated with anxiety and depression; older age was protective against ideation. Conclusions New-onset psychiatric disorders are a frequent and early complication following lumbar fusion. Several modifiable and demographic factors - including perioperative opioid use and socioeconomic status - significantly predict risk. These findings underscore the need for structured mental health screening within the first 3 months post-surgery. Implementing a predictive risk stratification tool could enable earlier psychiatric referral, reduce long-term morbidity, and improve both recovery trajectories and overall quality of life.

Artificial intelligence (AI) is reshaping neurosurgery, offering unprecedented opportunities to enhance diagnostics, personalize treatment, and predict outcomes. At the heart of this transformation is the ability to effectively harness big data (BD) within the electronic medical record. Understanding these data structures is essential for making sense of the vast volumes of information generated in modern neurosurgical practice. Equally important are the machine learning (ML) models driving these advancements. From supervised learning and convolutional neural networks to generative AI, these tools are already making a mark in areas such as brain tumor segmentation and spine surgery outcome predictions. Their versatility highlights the potential of ML to complement clinical expertise and streamline decision-making in neurosurgery. However, adopting BD and ML also brings ethical challenges that cannot be ignored. Bias in algorithms threatens to reinforce health disparities, whereas concerns about data privacy demand vigilance in handling sensitive patient information. In addition, the question of liability looms large as ML increasingly influences clinical decisions. The aim of the study was to provide a roadmap for neurosurgeons navigating the evolving intersection of BD, ML, and ethical responsibility in the AI era.

Summary of Background Data: Although pseudoangina is most commonly caused by cervical disc herniation, several cases have been described where thoracic herniation produced symptoms of pseudoangina. If thoracic herniation can produce angina-like pain, then it is important to consider whether pathology of the thoracolumbar spine, in general, can trigger false pain syndromes distinct from pseudoangina. Objective: We seek to provide the most comprehensive study regarding the diagnosis and treatment of spinal conditions causing false pain syndromes. Study Design: Systematic review of the current literature using PRISMA 2020 recommendations. Methods: We queried the literature and systematically selected relevant studies according to PRISMA guidelines. Results: Across 22 selected studies, the sample size was 30 patients, and a total of 26 met the criteria for statistical analysis. Seven (26.9%) of these patients presented with a chief complaint of pseudoangina resulting from thoracic disc herniation. 73.1% (19/26) of patients exhibited pain mimicking visceral origin. Overall, 13/19 (68.4%) patients exhibited thoracic spine disease only and 4/19 (21.1%) patients were affected at lumbar levels only, while 2 (10.5%) patients exhibited thoracolumbar herniation. Presentations included abdominal pain (11/19) mimicking appendicitis or pancreatitis, flank pain mimicking renal colic (8/19), and 2 cases of scrotal pain/orchalgia. Symptom durations ranged from acute (<24 h) to 7 years. Treatments were reported for 18/19 patients and all treated patients reported alleviated pain. Seven out of 18 patients were managed conservatively while 11/18 were treated surgically. Misdiagnosis resulted in unnecessary surgery (pancreaticojejunostomy) or other invasive procedures. Conclusions: In spinal disorders manifesting with atypical pain syndromes, delay in proper diagnosis and unnecessary treatments can, unfortunately, cause prolonged patient suffering and increased cost of health care. As a result, some have proposed that spinal screening should be incorporated into clinical examinations involving false pain syndromes.

Background: Peripheral nerve injury (PNI) following revision total hip arthroplasty (rTHA) can be a devastating complication. This study assessed the frequency of and risk factors for postoperative PNI following rTHA. Methods: Patients who underwent rTHA from 2003 to 2015 were identified using the National Inpatient Sample (NIS). Demographics, medical history, surgical details, and complications were compared between patients who sustained a PNI and those who did not, to identify risk factors for the development of PNI after rTHA. Results: Overall, 112,310 patients who underwent rTHA were identified, 929 (0.83%) of whom sustained a PNI. Univariate analysis found that younger patients (p < 0.0001), females (p = 0.025), and those with a history of flexion contracture (0.65% vs. 0.22%, p = 0.005), hip dislocation (24.0% vs. 18.0%, p < 0.001), and spine conditions (4.8% vs. 2.7%, p < 0.001) had significantly higher rates of PNI. In-hospital complications associated with PNI included postoperative hematoma (2.6% vs. 1.2%, p < 0.0001), postoperative seroma (0.75% vs. 0.30%, p = 0.011), superficial wound dehiscence (0.65% vs. 0.23%, p = 0.008), and postoperative anemia (36.1% vs. 32.0%, p = 0.007). Multivariate analysis demonstrated that a history of pre-existing spine conditions (aOR: 1.7; 95%-CI: 1.3–2.4, p < 0.001), prior dislocation (aOR 1.5; 95%-CI: 1.3–1.7, p < 0.001), postoperative anemia (aOR 1.2; 95%-CI: 1.0–1.4, p = 0.01), and hematoma (aOR 2.1; 95%-CI: 1.4–3.2, p < 0.001) were associated with increased risk for PNI. Conclusions: Our findings align with the existing literature, affirming that sciatic nerve injury is the prevailing neuropathic complication after total hip arthroplasty (THA). Furthermore, we observed a 0.83% incidence of PNI following rTHA and identified pre-existing spine conditions, prior hip dislocation, postoperative anemia, or hematoma as risk factors. Orthopedic surgeons may use this information to guide their discussion of PNI following rTHA, especially in high-risk patients.

Background Context Several studies have shown that factors such as insurance type and patient income are associated with different readmission rates following certain orthopaedic procedures. The literature, however, remains sparse with regard to these demographic characteristics and their associations to perioperative lumbar spine fusion outcomes. Purpose The purpose of this study was to assess the associations between hospital type, insurance type, and patient median income to both 30-day complication and readmission rates following lumbar spine fusion. Patient Sample Patients who underwent primary lumbar spine fusion (n = 596,568) from 2010-2016 were queried from the National Readmissions Database (NRD). Outcome Measures Incidence of 30-day complication and readmission rates Methods All relevant diagnoses and procedures were identified using International Classification of Disease, 9th and 10th Edition (ICD-9, 10) codes. Hospital types were categorized as metropolitan non-teaching (n = 212,131), metropolitan teaching (n = 364,752), and rural (n = 19,685). Insurance types included: Medicare (n = 213,534), Medicaid (n = 78,520), private insurance (n = 196,648), and out-of-pocket (n = 45,025). Patient income was divided into the following quartiles: Q1 (n = 112,083), Q2 (n = 145,755), Q3 (n = 156,276), and Q4 (n = 147,289), wherein quartile 1 corresponded to lower income ranges and quartile 4 to higher ranges. Statistical analysis was conducted in R. Kruskal-Wallis tests with Dunn’s pairwise comparisons were performed to analyze differences in 30-day readmission and complication rates in patients who underwent lumbar spine fusion. Complications analyzed included infection, wound injury, hematoma, neurological injury, thromboembolic event, and hardware failure. Results 30-day readmission was significantly higher in metropolitan teaching hospitals compared to metropolitan non-teaching hospitals and rural hospitals ( P < .05). Patients from metropolitan teaching hospitals had significantly higher rates of infection ( P < .001), wound injury ( P < .001), hematoma ( P = .018), and hardware failure ( P < .002) compared to those treated at metropolitan non-teaching hospitals. Privately insured patients were significantly less likely to be readmitted at 30 days than those paying with Medicare or Medicaid ( P < .01). Patients with private insurance also experienced significantly lower rates of hematoma formation than Medicare beneficiaries and out-of-pocket payers ( P < .01), postoperative wound injury compared to Medicaid patients and out-of-pocket payers ( P < .005), and infection compared to all other groups ( P < .001). Patients in Quartile 4 experienced significantly greater rates of hematoma formation compared to those in Quartiles 1 and 2 and were more likely to experience a thromboembolic event compared to all other groups. Conclusion Patients undergoing lumbar spine fusion at metropolitan non-teaching hospitals and paying with private insurance had significantly lower 30-day readmission rates than their counterparts. Complications within 30 days following lumbar spine fusion were significantly higher in patients treated at metropolitan teaching hospitals and in Medicare and Medicaid beneficiaries. Aside from a few exceptions, however, patient income was generally not associated with differential complication rates.

BACKGROUND AND OBJECTIVE: As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof. METHODS: Patients who underwent spinal fusion were queried using data from the Nationwide Readmissions Database for the years 2016 through 2019. Using comorbidity indices as predictor variables, receiver operating characteristic curves were developed for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 30-day readmission. RESULTS: A total of 750 183 patients were included. Nonroutine discharges occurred in 161 077 (21.5%) patients. The adjusted all-payer cost for the procedure was $37 616.97 ± $27 408.86 (top quartile: $45 409.20), and the length of stay was 4.1 ± 4.4 days (top quartile: 8.1 days). By comparing receiver operating characteristics of various models, it was found that models using Frailty + Elixhauser Comorbidity Index (ECI) as the primary predictor performed better than other models with statistically significant P -values on post hoc testing. However, for prediction of mortality, the model using Frailty + ECI was not better than the model using ECI alone ( P = .23), and for prediction of all-payer cost, the ECI model outperformed the models using frailty alone ( P < .0001) and the model using Frailty + ECI ( P < .0001). CONCLUSION: This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.

OBJECTIVE Lateral lumbar interbody fusion including anterior-to-psoas oblique lumbar interbody fusion has conventionally relied on pedicle screw placement (PSP) for construct stabilization. Single-position surgery with lumbar interbody fusion in the lateral decubitus position with concomitant PSP has been associated with increased operative efficiency. What remains unclear is the accuracy of PSP with robotic guidance when compared with the more familiar prone patient positioning. The present study aimed to compare robot-assisted screw placement accuracy between patients with instrumentation placed in the prone and lateral positions. METHODS The authors identified all consecutive patients treated with interbody fusion and PSP in the prone or lateral position by a single surgeon between January 2019 and October 2022. All pedicle screws placed were analyzed using CT scans to determine appropriate positioning according to the Gertzbein-Robbins classification grading system (grade C or worse was considered as a radiographically significant breach). Multivariate logistic regression models were constructed to identify risk factors for the occurrence of a radiographically significant breach. RESULTS Eighty-nine consecutive patients (690 screws) were included, of whom 46 (477 screws) were treated in the prone position and 43 (213 screws) in the lateral decubitus position. There were fewer breaches in the prone (n = 13, 2.7%) than the lateral decubitus (n = 15, 7.0%) group (p = 0.012). Nine (1.9%) radiographically significant breaches occurred in the prone group compared with 10 (4.7%) in the lateral decubitus group (p = 0.019), for a prone versus lateral decubitus PSP accuracy rate of 98.1% versus 95.3%. There were no significant differences in BMI between prone versus lateral decubitus cohorts (30.1 vs 29.6) or patients with screw breach versus those without (31.2 vs 29.5). In multivariate models, the prone position was the only significant protective factor for screw accuracy; no other significant risk factors for screw breach were identified. CONCLUSIONS The present data suggest that pedicle screws placed with robotic assistance have higher placement accuracy in the prone position. Further studies will be needed to validate the accuracy of PSP in the lateral position as single-position surgery becomes more commonplace in the treatment of spinal disorders.

OBJECTIVE While single-position surgery (SPS) eliminates the need for patient repositioning, the placement of screws in the unconventional lateral position poses unique challenges related to asymmetry relative to the surgical table. Use of robotic guidance or intraoperative navigation can help to overcome this. The aim of this study was to compare the relative accuracies offered by these various navigation modalities for pedicle screws placed in lateral SPS. METHODS According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the PubMed/Medline, Embase, and Cochrane Library databases were queried for studies reporting pedicle screw placement accuracy using fluoroscopic, CT-navigated, O-arm, or robotic guidance in lateral SPS, and a systematic review and meta-analysis was performed. Included studies all compared evaluated screw placement accuracy in lateral SPS using a single navigation method. Quality assessment was performed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system; risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. The primary outcome, rate of pedicle screw breach, was analyzed using random-effects meta-analysis. RESULTS Eleven studies were included comprising 548 patients who underwent the placement of instrumentation with 2488 screws. For the fluoroscopic, CT-navigated, O-arm, and robotic guidance cohorts, there were 3, 2, 3, and 3 studies, respectively. Breach rates by modality were as follows: fluoroscopic guidance (6.6%), CT navigation (4.7%), O-arm (3.9%), and robotic guidance (3.9%). Random-effects meta-analysis showed a significant difference between studies, with an overall breach rate of 4.9% (95% CI 3.1%–7.5%; p < 0.001); however, testing for subgroup differences failed to show a significant difference between guidance modalities (QM = 0.69, df = 3; p = 0.88). Heterogeneity between studies was significant (I2 = 79.0%, τ2 = 0.41, χ2 = 47.65, df = 10; p < 0.001). CONCLUSIONS Robotic guidance of screws is noninferior to alternative guidance modalities in lateral SPS; however, additional prospective studies directly comparing different guidance types are merited.

OBJECTIVE Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis. METHODS The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features. RESULTS Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998. CONCLUSIONS Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.