Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool

Anand Veeravagu Departments of Neurosurgery,

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Amy Li Departments of Neurosurgery,

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Christian Swinney Departments of Neurosurgery,

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Lu Tian Biomedical Data Science, and

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Adrienne Moraff Departments of Neurosurgery,

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Tej D. Azad Departments of Neurosurgery,

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Ivan Cheng Orthopedic Surgery, Stanford University School of Medicine; and

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Todd Alamin Orthopedic Surgery, Stanford University School of Medicine; and

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Serena S. Hu Orthopedic Surgery, Stanford University School of Medicine; and

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Robert L. Anderson LogicThread Inc., Stanford, California

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Lawrence Shuer Departments of Neurosurgery,

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Atman Desai Departments of Neurosurgery,

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Jon Park Departments of Neurosurgery,

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Richard A. Olshen Biomedical Data Science, and

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John K. Ratliff Departments of Neurosurgery,

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OBJECTIVE

The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort.

METHODS

The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery.

RESULTS

The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60–0.74] in RAT, 0.669 [95% CI 0.60–0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48–0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently produced complication predictions that underestimated complication occurrence: 3.4% in the low-risk group (observed 12.6%), 5.9% in the medium-risk group (observed 34.5%), and 12.5% in the high-risk group (observed 38.8%). The RAT was more accurate than the ACS NSQIP calculator (p = 0.0018).

CONCLUSIONS

While the RAT and ACS NSQIP calculator were both able to identify patients more likely to experience complications following spine surgery, both have substantial room for improvement. Risk stratification is feasible in spine surgery procedures; currently used measures have low accuracy.

ABBREVIATIONS

ACS NSQIP = American College of Surgeons National Surgery Quality Improvement Program; AUC = area under the ROC curve; BMI = body mass index; BMP = bone morphogenetic protein; CCI = Charlson Comorbidity Index; RAT = Risk Assessment Tool; ROC = receiver operating characteristic.
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