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Study designA retrospective, case-control study.ObjectiveWe aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF).Summary of background dataThe average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility.MethodsThis is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020.ResultsA total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator.ConclusionWe report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

BackgroundC5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy.MethodsWe identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model.ResultsA total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age.ConclusionsWe report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.

PurposePosterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance.MethodsThis is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance with standard ML models and logistic regression (LR), ranking contribution of included variables to model performance.ResultsOf the included 6822 patients, 18.8% suffered a major complication or readmission. The ensemble model demonstrated slightly superior predictive performance compared to LR and standard ML models. The most important features to performance include sex, malignancy, pneumonia, stroke, and teaching hospital status. Seven of the ten most important features for the ensemble model were markedly less important for LR.ConclusionWe report an ensemble ML model for prediction of major complications and readmission after posterior cervical fusion with a modest risk prediction advantage compared to LR and benchmark ML models. Notably, the features most important to the ensemble are markedly different from those for LR, suggesting that advanced ML methods may identify novel prognostic factors for adverse outcomes after posterior cervical fusion.

Study Design: Retrospective chart review. Objective: The goal of this study is to examine the relationship between global alignment and proportion (GAP) score and postoperative orthoses with likelihood of developing proximal junctional kyphosis (PJK). Methods: Patients who underwent thoracic or lumbar fusions of ≥4 levels for adult spinal deformity (ASD) with 1-year post-operative alignment x-rays were included. Chart review was conducted to determine spinopelvic alignment parameters, PJK, and reoperation. Results: A total of 81 patients were included; baseline and 1-year postoperative alignment did not differ between patients with and without PJK. There was no PJK in 53.1%, 29.6% had PJK from 10-20°, and 17.3% had severe PJK over 20° (sPJK). At baseline, 80% of patients had severely disproportioned GAP, 13.75% moderate, 6.25% proportioned. GAP improved across the population, but improved GAP was not associated with sPJK. Greater correction of the upper instrumented vertebra to pelvic angle (UIV-PA) was associated with a larger PJK angle (PJKA) change (R = -0.28) as was the 1 year T1-upper instrumented vertebra (T1-UIV) angle (R = 0.30), both P <.05. GAP change was not correlated with PJKA change. Postoperative orthoses were used in 46% of patients and did not impact sPJK. Conclusions: There was no correlation between PJK and GAP or change in GAP. Greater correction of UIV-PA and larger postop T1-UIV was associated with greater PJKA change; suggesting that the greater alignment correction led to greater likelihood of failure. Postoperative orthoses had no impact on PJK. [ABSTRACT FROM AUTHOR]

Achieving fusion in spine surgery can be challenging because of patient factors such as smoking and diabetes. The consequences of pseudarthrosis can be severe, including pain, instability, and additional surgery. Autologous iliac crest bone graft is the historical standard for augmenting spine fusion, providing high rates of fusion throughout the cervical, thoracic, and lumbar spine. Harvest of autologous iliac crest bone can be associated with comorbidities and this has led to development of alternative biologic materials to enhance spine fusion. Substitutes include various forms of allograft products including decellularized allograft; demineralized bone matrix; synthetic materials including bioactive glass; and autologous and allograft mesenchymal stem cells. Bone morphogenetic proteins can be efficacious for fusion but have significant risks and are not suitable for all spine procedures. There is a wide variety of utilization of biologics for spine fusion that are influenced by spinal region, surgeon preference, surgical training, health system formulary, and cost.