Abstract
Background context
The possibility and likelihood of a postoperative medical complication after spine
surgery undoubtedly play a major role in the decision making of the surgeon and patient
alike. Although prior study has determined relative risk and odds ratio values to
quantify risk factors, these values may be difficult to translate to the patient during
counseling of surgical options. Ideally, a model that predicts absolute risk of medical
complication, rather than relative risk or odds ratio values, would greatly enhance
the discussion of safety of spine surgery. To date, there is no risk stratification
model that specifically predicts the risk of medical complication.
Purpose
The purpose of this study was to create and validate a predictive model for the risk
of medical complication during and after spine surgery.
Study design/setting
Statistical analysis using a prospective surgical spine registry that recorded extensive
demographic, surgical, and complication data. Outcomes examined are medical complications
that were specifically defined a priori. This analysis is a continuation of statistical
analysis of our previously published report.
Methods
Using a prospectively collected surgical registry of more than 1,476 patients with
extensive demographic, comorbidity, surgical, and complication detail recorded for
2 years after surgery, we previously identified several risk factor for medical complications.
Using the beta coefficients from those log binomial regression analyses, we created
a model to predict the occurrence of medical complication after spine surgery. We
split our data into two subsets for internal and cross-validation of our model. We
created two predictive models: one predicting the occurrence of any medical complication
and the other predicting the occurrence of a major medical complication.
Results
The final predictive model for any medical complications had a receiver operator curve
characteristic of 0.76, considered to be a fair measure. The final predictive model
for any major medical complications had receiver operator curve characteristic of
0.81, considered to be a good measure. The final model has been uploaded for use on
SpineSage.com.
Conclusion
We present a validated model for predicting medical complications after spine surgery.
The value in this model is that it gives the user an absolute percent likelihood of
complication after spine surgery based on the patient's comorbidity profile and invasiveness
of surgery. Patients are far more likely to understand an absolute percentage, rather
than relative risk and confidence interval values. A model such as this is of paramount
importance in counseling patients and enhancing the safety of spine surgery. In addition,
a tool such as this can be of great use particularly as health care trends toward
pay-for-performance, quality metrics, and risk adjustment. To facilitate the use of
this model, we have created a website (SpineSage.com) where users can enter in patient data to determine likelihood of medical complications
after spine surgery.
Keywords
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Article info
Publication history
Published online: November 14, 2013
Accepted:
October 21,
2013
Received:
February 1,
2013
Footnotes
FDA device/drug status: Not applicable.
Author disclosures: MJL: Consulting: Stryker Spine-for work not related to the manuscript (C), AO Spine-Faculty (B); Endowments: Synthesis Spine (F, Paid directly to the institution); Fellowship Support: AOSpine (F, paid directly to the institution). AMC: Endowments: Synthes Spine (F, Paid directly to the institution). DH: Endowments: Synthes Spine as the Entity (F). JRC: Consulting: Synthes USA (B); Speaking and/or Teaching Arrangements: AO Spine (B), Synthes (B); Board of Directors: AOSpine North America (B per year), AOSpine Foundation (B); Endowments: Hans Joerg Wyss Foundation (Total endowment I, Paid directly to the institution); Grants: Medtronic (Fellowship support), Alseres Pharmaceuticals (Research support); Fellowship Support: AOSpine North America (Fellowship E, Paid directly to the institution).
The disclosure key can be found on the Table of Contents and at www.TheSpineJournalOnline.com.
Supported in part by the Spine End-Results Research Fund at the University of Washington Medical Center through a gift from Synthes Spine (Paoli, PA).
Identification
Copyright
© 2014 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
