Quality and safety improvement initiatives in complex spine surgery

Complications and Revisions After Spine Surgery in Patients With Skeletal Dysplasia: Have We Improved?

STUDY DESIGN

Retrospective case series.

OBJECTIVE

To report contemporary rates of complications and subsequent surgery after spinal surgery in patients with skeletal dysplasia.

METHODS

A case series of 25 consecutive patients who underwent spinal surgery between 2007 and 2017 were identified from a single institution's skeletal dysplasia registry. Patient demographics, medical history, surgical indication, complications, and subsequent surgeries (revisions, extension to adjacent levels, or for pathology at a non-contiguous level) were collected. Charlson comorbidity indices were calculated as a composite measure of overall health.

RESULTS

Achondroplasia was the most common skeletal dysplasia (76%) followed by spondyloepiphyseal dysplasia (20%); 1 patient had diastrophic dysplasia (4%). Average patient age was 53.2 ± 14.7 years and most patients were in excellent cardiovascular health (88% Charlson Comorbidity Index 0-4). Mean follow up after the index procedure was 57.4 ± 39.2 months (range). Indications for surgery were mostly for neurologic symptoms. The most commonly performed surgery was a multilevel thoracolumbar decompression without fusion (57%). Complications included durotomy (36%), neurologic complication (12%), and infection requiring irrigation and debridement (8%). Nine patients (36%) underwent a subsequent surgery. Three patients (12%) underwent a procedure at a non-contiguous anatomic zone, 3 (12%) underwent a revision of the previous surgery, and another 3 (12%) required extension of their previous decompression or fusion.

CONCLUSIONS

Surgical complication rates remain high after spine surgery in patients with skeletal dysplasia, likely attributable to inherent characteristics of the disease. Patients should be counseled on their risk for complication and subsequent surgery.

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F_(2,19) = 6.56, p < 0.01) and specificity (F_(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

Improving Complex Pediatric and Adult Spine Care While Embracing the Value Equation

INTRODUCTION

Value in health care is defined as the quotient of outcomes to cost. Both pediatric and adult spinal deformity surgeries are among the most expensive procedures offered today. With high variability in both outcomes and costs in spine surgery today, surgeons will be expected to consider long-term cost effectiveness when comparing treatment options.

METHODS

We summarize various methods by which value can be increased in complex spine surgery, both through the improvement of outcomes and the reduction of cost. These methods center around standardization, team-based and collaborative approaches, rigorous outcomes tracking through dashboards and registries, and continuous process improvement.

RESULTS

This manuscript reviews the expert opinion of leading spine specialists on the improvement of safety, quality and improvement of value of pediatric and adult spinal surgery.

CONCLUSION

Without surgeon leadership in this arena, suboptimal solutions may result from the isolated intervention of regulatory bodies or payer groups. The cooperative development of standardized, team-based approaches in complex spine surgery will lead to the high-quality, high-value care for patients.