Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability.

BACKGROUND: With spinal surgery rates increasing in North America, models that are able to accurately predict which patients are at greater risk of developing complications are highly warranted. However, the previously published methods which have used large, multi-centre databases to develop their prediction models have relied on the receiver operator characteristics curve with the associated area under the curve (AUC) to assess their model's performance. Recently, it has been found that a precision-recall curve with the associated F1-score could provide a more realistic analysis for these models. PURPOSE: To develop a logistic regression (LR) model for the prediction of complications following posterior lumbar spine surgery and to then assess for any difference in performance of the model when using the AUC versus the F1-score. STUDY DESIGN: Retrospective review of a prospective cohort. PATIENT SAMPLE: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) registry was used. All patients that underwent posterior lumbar spine surgery between 2005 to 2016 with appropriate data were included. OUTCOME MEASURES: Both the AUC and F1-score were utilized to assess the prognostic performance of the prediction model. METHODS: In order to develop the LR model used to predict a complication during or following spine surgery, 19 variables were selected by three orthopedic spine surgeons from the NSQIP registry. Two datasets were developed for this analysis: (1) an imbalanced dataset, which was taken directly from the NSQIP registry, and (2) a down-sampled set. The purpose of the down-sampled set was to balance the data in order to evaluate whether balancing the data had an effect on model performance. The AUC and F1-score were applied to both of these datasets. RESULTS: Within the NSQIP database, 52,787 spine surgery cases were identified of which only 10% of these cases had complications during surgery. Applying the LR model showed a large difference between the AUC (0.69) and the F1 score (0.075) on the imbalanced dataset. However, no major differences existed between the AUC and F1-score when the data was balanced and the LR model was reapplied (0.69 and 0.62, AUC and F1-score, respectively). CONCLUSIONS: The F1-score detected a drastically lower performance for the prediction of complications when using the imbalanced data, but detected a performance similar to the AUC level when balancing techniques were utilized for the dataset. This difference is due to a low precision score when many false positive classifications are present, which is not identified when using the AUC value. This lowers the utility of the AUC score, as many of the datasets used in medicine are imbalanced. Therefore, we recommend using the F1-score on large, prospective databases when the data is imbalanced with a large amount of true negative classifications.

Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.

BACKGROUND CONTEXT: Traumatic spinal cord injury can have a dramatic effect on a patient's life. The degree of neurologic recovery greatly influences a patient's treatment and expected quality of life. This has resulted in the development of machine learning algorithms (MLA) that use acute demographic and neurologic information to prognosticate recovery. The van Middendorp et al. (2011) (vM) logistic regression (LR) model has been established as a reference model for the prediction of walking recovery following spinal cord injury as it has been validated within many different countries. However, an examination of the way in which these prediction models are evaluated is warranted. The area under the receiver operators curve (AUROC) has been consistently used when evaluating model performance, but it has been shown that AUROC overemphasizes the most common event resulting in an inaccurate assessment when the data are imbalanced. Furthermore, there is evidence that the use of more advanced MLA, such as an unsupervised k-means model, may show superior performance compared to LR as they can handle a larger number of features. PURPOSE: The first objective of the study was to assess the performance of both an unsupervised MLA and LR model with complete admission neurologic information against the vM and Hicks models. Second, a comparison between the accuracy of the AUROC and the F1-score will be made to determine which method is superior for the assessment of diagnostic performance of prediction models on large-scale datasets. STUDY DESIGN: Retrospective review of a prospective cohort study. PATIENT SAMPLE: The Rick Hansen Spinal Cord Injury Registry (RHSCIR) was used in this study. All patients enrolled between 2004 and 2017 with complete neurologic examination and Functional Independence Measure outcome data at ≥1 year follow-up or who could walk at discharge were included. The prognostic variables included age (dichotomized at ≥65 years old); American Spinal Injury Association Impairment Scale (AIS) grade; and individual motor, light touch, and pinprick score from L2 to S1. OUTCOME MEASURES: The Functional Independence Measure locomotor score was used to assess independent walking ability at discharge or 1-year follow-up. METHODS: An unsupervised MLA with k=2 was chosen in order to identify a "walk" cluster and a "not walk" cluster. Model performance was assessed through the development of a receiver operating characteristic curve with associated AUROC and a precision-recall curve with associated F1-score. The study and the RHSCIR are supported by funding from Health Canada, Western Economic Diversification Canada, and the Governments of Alberta, British Columbia, Manitoba, and Ontario. These funders had no role in the study or study reporting and the authors have no conflicts of interest to report. RESULTS: No clinically relevant differences were found between with the use of an unsupervised MLA with a greater amount of initial neurologic information compared to the established standards for any AIS classification. Although demonstrated for all separate AIS classifications, most notably, the AUROC for the vM (0.78) and Hicks models (0.76) were found to be superior to that of the new LR model (0.72); however, the vM and Hicks models had more than double the amount of false negative classifications compared to the LR. The F1-scores between these three models were also found to be different but with the vM and Hicks models being lower than the LR (0.85, 0.81, and 0.89, respectively). CONCLUSIONS: No clinically relevant differences were found between the use of an unsupervised MLA with complete admission neurologic information compared to the previously validated standards; however, when comparing the performance of the AUROC and F1-score, the AUROC showed inaccurate prognostic performance when there was an imbalance toward a greater amount of false negatives. Importantly, the F1-score did not succumb to this imbalance. As AUROC has been used as the standard when evaluating performance of prediction models, consideration as to whether this is the most appropriate method is warranted. Future work should focus on comparing AUROC and F1-scores with other previously validated models.

Impact of resident involvement on cervical and lumbar spine surgery outcomes.

BACKGROUND CONTEXT: 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. PURPOSE: To determine the effect of resident involvement on surgery duration, length of hospital stay and 30-day postoperative complication rates in common spinal surgery using the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) database. STUDY DESIGN: Multicenter retrospective cohort study. PATIENT SAMPLE: A total of 1,441 patients met the inclusion criteria: 1,142 patients had surgeries with an attending physician alone and 299 patients had surgeries with trainee involvement. All anterior cervical or posterior lumbar 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. OUTCOME MEASURES: The main outcomes of interest analyzed from the ACS-NSQIP database included surgical complications, medical complications, length of hospital stay, and surgery duration. METHODS: 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 postoperative complication rates. RESULTS: 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 tertiles of predicted surgery duration, cervical or lumbar surgery, fusion or nonfusion, and 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, fusion surgery, and inpatient surgery. There were no significant differences reported for any other factors. CONCLUSIONS: After adjusting for confounding, we demonstrated in a national database that resident involvement in surgeries did not increase complication rates. We demonstrated that surgeries with more complex features may lead to an increase in operative time when trainees are involved. Further study is required to determine how to efficiently integrate resident involvement in surgeries without affecting their medical education.