Development of Validated Computer-Based Preoperative Predictive Model for Proximal Junction Failure (PJF) or Clinically Significant PJK with 86% Accuracy based on 510 ASD Patients with 2-year Follow-up
Year: 2016
Type: article
Study design: Observational Study
Abstract: Introduction Proximal Junction Failure (PJF) and Proximal Junction Kyphosis (PJK) are significant complications. It remains unclear what are the specific drivers behind the development of either. This study attempts to develop a preoperative predictive model to identify patients at risk to develop PJF or PJK. Material and Methods Inclusion criteria: age ≥18, adult spinal deformity (ASD), ≥4 levels fused. Variables included in the model were: demographics, primary/revision, use of 3-column osteotomy, UIV/LIV levels and anchor (screw, hooks), number of levels fused, and baseline sagittal radiographs (PT, PI, PI-LL, TK, and SVA). PJF was defined as requiring revision for PJK and PJK was defined as an increase from baseline of PJK > 20° and with deterioration by at least 1 SRS-Schwab sagittal modifier grade from 6wks postop. An ensemble of decision trees were constructed using the C5.0 algorithm with 5 different bootstrapped models, and internally validated via a 70:30 data split for training and testing. Accuracy and the area under a receiver operator characteristic curve (AUC) were calculated. Final model utilized 13 preoperative variables. Results 510 patients were included, with 357 for model training and 153 as testing targets (PJF: 37, PJK: 102). The overall model accuracy was 86.3% with an AUC of 0.89 indicating a good model fit. The 6 strongest (importance ≥0.95) predictors were (% target): age (>64yrs, 41.4%), PI-LL (>48.7deg, 35.6%), UIV (T10-L3, 35.1%), SVA (>13.5cm, 32.5%), LIV (sacroiliac, 31.6%), and UIV Type (screws, 29.8%). If a patient met these criteria, they had a 66.7% chance of developing PJF or PJK with deterioration of sagittal alignment. Conclusion A successful model (86% accuracy, 0.89 AUC) was built predicting either PJF or clinically significant PJK. This model can set the groundwork for preoperative point of care decision making, risk stratification, and need for prophylactic strategies for patients undergoing ASD surgery.
Source: Global Spine Journal
Authors: Justin Scheer, Justin Smith, Frank Schwab, Virginie Lafage, Robert Hart, R. Shay Bess, Breton Line, Bassel Diebo, Themistocles Protopsaltis, Amit Jain, Tamir Ailon, Douglas Burton, Eric Klineberg, Christopher Ames, International Spine Study Group
Institutions: University of California San Diego, University of Virginia Hospital, University of Virginia Medical Center, University of Virginia, Hospital for Special Surgery, Oregon Health & Science University, NYU Langone Health, New York University Langone Orthopedic Hospital, Johns Hopkins University, Johns Hopkins Hospital, University of Kansas Medical Center, University of California Davis Medical Center, University of California, Davis, University of California, San Francisco
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W2562221019