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BackgroundAdults undergoing spine surgery often have underlying osteoporosis, which may be a risk factor for postoperative complications. Although these associations have been described, osteoporosis remains profoundly underdiagnosed and undertreated in the spine surgery population. A thorough, comprehensive systematic review summarizing the relationships between bone mineral density (BMD) and specific complications of lumbar fusion surgery could be a valuable resource for raising awareness and supporting clinical practice changes.MethodsPubMed, Embase, and Web of Science databases were searched for original clinical research articles reporting on BMD, or surrogate measure, as a predictor of complications in adults undergoing elective lumbar fusion for degenerative disease or deformity. Endpoints included cage subsidence, screw loosening, pseudarthrosis, vertebral fracture, junctional complications, and reoperation.ResultsA total of 71 studies comprising 12,278 patients were included. Overall, considerable heterogeneity in study populations, methods of bone health assessment, and definition and evaluation of clinical endpoints precluded meta-analysis. Nevertheless, low BMD was associated with higher rates of implant failures like cage subsidence and screw loosening, which were often diagnosed with concomitant pseudarthrosis. Osteoporosis was also a significant risk factor for proximal junctional kyphosis, particularly due to fracture. Many studies found surgical site-specific BMD to best predict focal complications. Functional outcomes were inconsistently addressed.ConclusionsOur findings suggest osteoporosis is a significant risk factor for mechanical complications of lumbar fusion. These results emphasize the importance of preoperative osteoporosis screening, which allows for medical and surgical optimization of high-risk patients. This review also highlights current practical challenges facing bone health evaluation in patients undergoing elective surgery. Future prospective studies using standardized methods are necessary to strengthen existing evidence, identify optimal predictive thresholds, and establish specialty-specific practice guidelines. In the meantime, an awareness of the surgical implications of osteoporosis and utility of preoperative screening can provide for more informed, effective patient care.

BACKGROUND: Despite the pivotal role of clinical trials in advancing orthopaedic oncology knowledge and treatment strategies, the persistent issues of trial discontinuation and nonpublication are significant problems. This study conducted an analysis examining clinical trial discontinuation rates, associations between intervention types and discontinuation/nonpublication, and the role of funding, enrollment size, and their implications for trial success and completion. METHODS: This study, conducted on May 1, 2023, utilized a cross-sectional design to comprehensively analyze phase 3 and 4 randomized controlled trials within the realm of orthopaedic oncology. We specifically incorporated Phase 3 and 4 trials as they are designed to evaluate prolonged outcomes in human subjects and are more likely to reach publication. Study characteristics of interest included the intervention utilized in the clinical trial, presence of funding, whether the trial was published, completed, and trial enrollment size. The investigation involved an examination of ClinicalTrials.gov, a prominent online repository of clinical trial data managed by the National Library of Medicine of the USA. Descriptive statistics and multivariate logistic regressions were used to determine statistical significance. RESULTS: Among the cohort of 130 trials, 19.2% were prematurely discontinued. Completion rates varied based on intervention type; 111 pharmaceutical trials demonstrated a completion rate of 83.8%, whereas 19 non-pharmaceutical trials exhibited a completion rate of 8.0% (P

IntroductionThe purpose of this study is to provide a state-of-the-art review regarding risk factors for perioperative complications in adult spinal deformity (ASD) surgery. The review includes levels of evidence for risk factors associated with complications in ASD surgery.MethodsUsing the PubMed database, we searched for complications, risk factors, and adult spinal deformity. The included publications were assessed for level of evidence as described in clinical practice guidelines published by the North American Spine Society, with summary statements generated for each risk factor (Bono et al. in Spine J 9:1046-1051, 2009).ResultsFrailty had good evidence (Grade A) as a risk for complications in ASD patients. Fair evidence (Grade B) was assigned for bone quality, smoking, hyperglycemia and diabetes, nutritional status, immunosuppression/steroid use, cardiovascular disease, pulmonary disease, and renal disease. Indeterminate evidence (Grade I) was assigned for pre-operative cognitive function, mental health, social support, and opioid utilization.ConclusionsIdentification of risk factors for perioperative complications in ASD surgery is a priority for empowering informed choices for patients and surgeons and managing patient expectations. Risk factors with grade A and B evidence should be identified prior to elective surgery and modified to reduce the risk of perioperative complications.

PurposeTo identify independent risk factors, including the Risk Assessment and Prediction Tool (RAPT) score, associated with extended length of stay (eLOS) and non-home discharge following elective multi-level instrumented spine fusion operations for diagnosis of adult spinal deformity (ASD) and lumbar degenerative pathology.MethodsAdults who underwent multi-level ([Formula: see text] segments) instrumented spine fusions for ASD and lumbar degenerative pathology at a single institution (2016-2021) were reviewed. Presence of a pre-operative RAPT score was used as an inclusion criterion. Excluded were patients who underwent non-elective operations, revisions, operations for trauma, malignancy, and/or infections. Outcomes were eLOS (> 7 days) and discharge location (home vs. non-home). Predictor variables included demographics, comorbidities, operative information, Surgical Invasiveness Index (SII), and RAPT score. Fisher's exact test was used for univariate analysis, and significant variables were implemented in multivariate binary logistic regression, with generation of 95% percent confidence intervals (CI), odds ratios (OR), and p-values.ResultsIncluded for analysis were 355 patients. Post-operatively, 36.6% (n = 130) had eLOS and 53.2% (n = 189) had a non-home discharge. Risk factors significant for a non-home discharge were older age (> 70 years), SII > 36, pre-op RAPT  20, RAPT

Study designA retrospective study at a single academic institution.ObjectiveThe purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator.Summary of background dataA total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record.MethodsPatients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days).ResultsOf 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R2 =0.16), the predictions of poisson regression ( R2 =0.29) and LASSO ( R2 =0.29) models were significantly more correlated with observed LOS ( P =0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP ( P =0.135).ConclusionThe predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

PurposeTo develop a model for factors predictive of Post-Acute Care Facility (PACF) discharge in adult patients undergoing elective multi-level (≥ 3 segments) lumbar/thoracolumbar spinal instrumented fusions.MethodsThe State Inpatient Databases acquired from the Healthcare Cost and Utilization Project from 2005 to 2013 were queried for adult patients who underwent elective multi-level thoracolumbar fusions for spinal deformity. Outcome variables were classified as discharge to home or PACF. Predictive variables included demographic, pre-operative, and operative factors. Univariate and multivariate logistic regression analyses informed development of a logistic regression-based predictive model using seven selected variables. Performance metrics included area under the curve (AUC), sensitivity, and specificity.ResultsIncluded for analysis were 8866 patients. The logistic model including significant variables from multivariate analysis yielded an AUC of 0.75. Stepwise logistic regression was used to simplify the model and assess number of variables needed to reach peak AUC, which included seven selected predictors (insurance, interspaces fused, gender, age, surgical region, CCI, and revision surgery) and had an AUC of 0.74. Model cut-off for predictive PACF discharge was 0.41, yielding a sensitivity of 75% and specificity of 59%.ConclusionsThe seven variables associated significantly with PACF discharge (age > 60, female gender, non-private insurance, primary operations, instrumented fusion involving 8+ interspaces, thoracolumbar region, and higher CCI scores) may aid in identification of adults at risk for discharge to a PACF following elective multi-level lumbar/thoracolumbar spinal fusions for spinal deformity. This may in turn inform discharge planning and expectation management.

Study designNarrative Summary Review for Navigation & Robotics Focus Issue.ObjectiveTo discuss the challenges and complications of S2-Alar-Iliac (S2AI) spinopelvic fixation using freehand techniques, and to introduce the utility of navigation & robotics to enhance patient safety.MethodsThis study involved search of literature using the PubMed database, including retrospective clinical studies, anatomic reports, and surgical reports. The intention was to find literature that discussed complications regarding screw malfunction from manual S2AI placement, anatomical complexity of the sacroiliac joint, and outcomes of S2AI procedures conducted with robotic guidance systems.ResultsThe sacroiliac joint presents numerous complexities that can lead to challenges in free-hand placement of the S2-alar-iliac screw. Anatomic considerations of the S2AI screw involve close proximity to vital neurovascular structures, including: superior gluteal vessels, external iliac vessels, pudendal vessels, superior gluteal nerves, sciatic nerve, sympathetic chain ganglia, and pudendal nerves. The complications associated with manual S2AI screw installation include screw misplacement, breach of cortical bone, and injury to neurovascular structures. Robotic techniques for establishing S2AI screws involve greater accuracy of screw placement and reduced complications.ConclusionsAccurate placement of S2AI screws is compromised by variation in pelvic anatomy and by a pathway that traverses dense cortical bone of the sacroiliac joint. Accurate placement of S2AI screws is important for patient safety regarding neurovascular structures, and for effective, stable fixation across the SI joint. Robotic navigation of S2AI fixation offers significant utility in improving the accuracy of screw placement and patient safety.

Markers of systemic inflammation, such as high-sensitivity C-reactive protein (hsCRP), have been associated with the occurrence of major adverse cardiovascular and cerebrovascular events (MACCE) in patients undergoing percutaneous coronary intervention (PCI). Whether this risk varies according to the presence of high bleeding risk (HBR) conditions is unclear. The aim of this study was to evaluate the impact of systemic inflammation, as measured by hsCRP levels and cardiovascular outcomes in patients stratified by HBR status following PCI. Consecutive patients undergoing PCI between 2012 and 2019 with baseline hsCRP levels were included. High hsCRP was defined as >3 mg/L, and HBR was defined per the Academic Research Consortium HBR criteria. The primary outcome was MACCE, including all-cause death, myocardial infarction, or stroke at 1 year. All bleeding was assessed as a secondary outcome. A total of 15,150 patients were included, and 40.4% (n = 6,125) qualified as HBR. The adjusted risk for MACCE was consistently higher in patients with high hsCRP in both HBR (adjusted HR [aHR]: 1.49; 95% CI: 1.18-1.87) and non-HBR (aHR: 1.87; 95% CI: 1.31-2.66) subgroups, with no interaction between HBR status and hsCRP level (P interaction = 0.26). Conversely, although bleeding risk was higher in the HBR cohort, hsCRP did not predict the occurrence of bleeding in either the HBR (aHR: 1.04; 95% CI: 0.82-1.31) or the non-HBR (aHR: 0.99; 95% CI: 0.71-1.39) subgroup (P interaction = 0.539). Elevated hsCRP at the time of PCI is associated with a higher risk for ischemic but not bleeding events, irrespective of HBR status. [Display omitted] [ABSTRACT FROM AUTHOR]

Postoperative recovery after adult spinal deformity (ASD) operations is arduous, fraught with complications, and often requires extended hospital stays. A need exists for a method to rapidly predict patients at risk for extended length of stay (eLOS) in the preoperative setting. To develop a machine learning model to preoperatively estimate the likelihood of eLOS following elective multi-level lumbar/thoracolumbar spinal instrumented fusions (≥3 segments) for ASD. Retrospectively from a state-level inpatient database hosted by the Health care cost and Utilization Project. Of 8,866 patients of age ≥50 with ASD undergoing elective lumbar or thoracolumbar multilevel instrumented fusions. The primary outcome was eLOS (>7 days). Predictive variables consisted of demographics, comorbidities, and operative information. Significant variables from univariate and multivariate analyses were used to develop a logistic regression-based predictive model that use six predictors. Model accuracy was assessed through area under the curve (AUC), sensitivity, and specificity. Of 8,866 patients met inclusion criteria. A saturated logistic model with all significant variables from multivariate analysis was developed (AUC=0.77), followed by generation of a simplified logistic model through stepwise logistic regression (AUC=0.76). Peak AUC was reached with inclusion of six selected predictors (combined anterior and posterior approach, surgery to both lumbar and thoracic regions, ≥8 level fusion, malnutrition, congestive heart failure, and academic institution). A cutoff of 0.18 for eLOS yielded a sensitivity of 77% and specificity of 68%. This predictive model can facilitate identification of adults at risk for eLOS following elective multilevel lumbar/thoracolumbar spinal instrumented fusions for ASD. With a fair diagnostic accuracy, the predictive calculator will ideally enable clinicians to improve preoperative planning, guide patient expectations, enable optimization of modifiable risk factors, facilitate appropriate discharge planning, stratify financial risk, and accurately identify patients who may represent high-cost outliers. Future prospective studies that validate this risk assessment tool on external datasets would be valuable. [ABSTRACT FROM AUTHOR]