Degenerative lumbar spondylolisthesis (DLS) is a common condition with many available treatment options. The Degenerative Spondylolisthesis Instability Classification (DSIC) scheme, based on a systematic review of best available evidence, was proposed by Simmonds et al. in 2015. This classification scheme proposes that the stability of the patient's pathology be determined by a surgeon based on quantitative and qualitative clinical and radiographic parameters. The purpose of the study is to utilise machine learning to classify DLS patients according to the DSIC scheme, offering a novel approach in which an objectively consistent system is employed.

The patient data was collected by CSORN between 2015 and 2018 and included 224 DLS surgery cases. The data was cleaned by two methods, firstly, by deleting all patient entries with missing data, and secondly, by imputing the missing data using a maximum likelihood function. Five machine learning algorithms were used: logistic regression, boosted trees, random forests, support vector machines, and decision trees. The models were built using Python-based libraries and trained and tested using sklearn and pandas librairies. The algorithms were trained and tested using the two data sets (deletion and imputation cleaning methods). The matplotlib library was used to graph the ROC curves, including the area under the curve.

The machine learning models were all able to predict the DSIC grade. Of all the models, the support vector machine model performed best, achieving an area under the curve score of 0.82. This model achieved an accuracy of 63% and an F1 score of 0.58. Between the two data cleaning methods, the imputation method was better, achieving higher areas under the curve than the deletion method. The accuracy, recall, precision, and F1 scores were similar for both data cleaning methods.

The machine learning models were able to effectively predict physician decision making and score patients based on the DSIC scheme. The support vector machine model was able to achieve an area under the curve of 0.82 in comparison to physician classification. Since the data set was relatively small, the results could be improved with training on a larger data set. The use of machine learning models in DLS classification could prove to be an efficient approach to reduce human bias and error. Further efforts are necessary to test the inter- and intra-observer reliability of the DSIC scheme, as well as to determine if the surgeons using the scheme are following DLS treatment recommendations.

Resident involvement in the operating room is a vital component of their medical education. Conflicting and limited research exists regarding the effects of surgical resident participation on spine surgery patient outcomes. Our objective was to determine the effect of resident involvement on surgery duration, length of hospital stay and 30-day post-operative complication rates.

This study was a multicenter retrospective analysis of the prospectively collected American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database. All anterior cervical or posterior lumbar fusion surgery patients were identified. Patients who had missing trainee involvement information, surgery for cancer, preoperative infection or dirty wound classification, spine fractures, traumatic spinal cord injury, intradural surgery, thoracic surgery and emergency surgery were excluded. Propensity score for risk of any complication was calculated to account for baseline characteristic differences between the attending alone and trainee present group. Multivariate logistic regression was used to investigate the impact of resident involvement on surgery duration, length of hospital stay and 30 day post-operative complication rates.

1441 patients met the inclusion criteria: 1142 patients had surgeries with an attending physician alone and 299 patients had surgeries with trainee involvement. After adjusting using the calculated propensity score, the multivariate analysis demonstrated that there was no significant difference in any complication rates between surgeries involving trainees compared to surgeries with attending surgeons alone. Surgery times were found to be significantly longer for surgeries involving trainees. To further explore this relationship, separate analyses were performed for tertile of predicted surgery duration, cervical or lumbar surgery, instrumentation, inpatient or outpatient surgery. The effect of trainee involvement on increasing surgery time remained significant for medium predicted surgery duration, longer predicted surgery duration, cervical surgery, lumbar surgery, lumbar fusion surgery and inpatient surgery. There were no significant differences reported for any other factors.

After adjusting for confounding, we demonstrated in a national database that resident involvement in surgeries did not increase complication rates, length of hospital stay or surgical duration of more routine surgical cases. We found that resident involvement in surgical cases that were generally more complexed resulted in increased surgery time. Further study is required to determine the relationship between surgery complexity and the effect of resident involvement on surgery duration.