Dataset Information

Proximal Junctional Kyphosis Prevention Strategies: A Video Technique Guide.

ABSTRACT: BACKGROUND:Proximal junctional kyphosis (PJK) is a well-recognized complication in patients undergoing posterior instrumented fusion procedures for adult spinal deformity. Strategies that reduce rates of PJK have the potential to improve the safety of these operations and decrease cost by eliminating the need for revision surgery. OBJECTIVE:To present a set of surgical techniques that can decrease rates of PJK in adults undergoing surgery for spinal deformity. METHODS:We summarize the use of vertebroplasty, transverse process hooks, terminal rod contouring, and ligament augmentation as means to reduce rates of PJK. RESULTS:We present PJK prevention strategies and a video technique guide that are safe, technically feasible, and add minimal operative time to these surgical procedures. When applied to appropriate high-risk patients, these techniques have the potential to dramatically reduce rates of PJK, which improves quality of life and decreases the cost associated with this treating adult spinal deformity. CONCLUSION:PJK prevention strategies represent a critical area for improvement in surgery for adult spinal deformity. We present a summary of techniques that are safe, feasible, and add minimal time to the overall procedure. These techniques warrant investigation in a thoughtful, prospective manner, but are supported by existing data and compelling biomechanical rationale. Our hope is that these strategies can be applied, particularly in high-risk patients, to help reduce rates of PJK.

SUBMITTER: Safaee MM

PROVIDER: S-EPMC6312084 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

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Proximal Junctional Kyphosis Prevention Strategies: A Video Technique Guide.

Safaee Michael M MM Osorio Joseph A JA Verma Kushagra K Bess Shay S Shaffrey Christopher I CI Smith Justin S JS Hart Robert R Deviren Vedat V Ames Christopher P CP

Operative neurosurgery (Hagerstown, Md.) 20171001 5

<h4>Background</h4>Proximal junctional kyphosis (PJK) is a well-recognized complication in patients undergoing posterior instrumented fusion procedures for adult spinal deformity. Strategies that reduce rates of PJK have the potential to improve the safety of these operations and decrease cost by eliminating the need for revision surgery.<h4>Objective</h4>To present a set of surgical techniques that can decrease rates of PJK in adults undergoing surgery for spinal deformity.<h4>Methods</h4>We su ...[more]

PMID: 28922883

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Project description:ObjectiveTo build a model for proximal junctional kyphosis (PJK) prognostication in Lenke 5 adolescent idiopathic scoliosis (AIS) patients undergoing long posterior instrumentation and fusion surgery by machine learning and analyze the risk factors for PJK.Materials and methodsIn total, 44 AIS patients (female/male: 34/10; PJK/non-PJK: 34/10) who met the inclusion criteria between January 2013 and December 2018 were retrospectively recruited from West China Hospital. Thirty-seven clinical and radiological features were acquired by two independent investigators. Univariate analyses between PJK and non-PJK groups were carried out. Twelve models were built by using four types of machine learning algorithms in conjunction with two oversampling methods [the synthetic minority technique (SMOTE) and random oversampling]. Area under the receiver operating characteristic curve (AUC) was used for model discrimination, and the clinical utility was evaluated by using F1 score and accuracy. The risk factors were simultaneously analyzed by a Cox regression and machine learning.ResultsStatistical differences between PJK and non-PJK groups were as follows: gender (p = 0.001), preoperative factors [thoracic kyphosis (p = 0.03), T1 slope angle (T1S, p = 0.078)], and postoperative factors [T1S (p = 0.097), proximal junctional angle (p = 0.003), upper instrumented vertebra (UIV) - UIV + 1 (p = 0.001)]. Random forest using SMOTE achieved the best prediction performance with AUC = 0.944, accuracy = 0.909, and F1 score = 0.667 on independent testing dataset. Cox model revealed that male gender and larger preoperative T1S were independent prognostic factors of PJK (odds ratio = 10.701 and 57.074, respectively). Gender was also at the first place in the importance ranking of the model with best performance.ConclusionThe random forest using SMOTE model has the great value for predicting the individual risk of developing PJK after long instrumentation and fusion surgery in Lenke 5 AIS patients. Moreover, the combination of the outcomes of a Cox model and the feature ranking extracted by machine learning is more valuable than any one alone, especially in the interpretation of risk factors.

Dataset Information

Proximal Junctional Kyphosis Prevention Strategies: A Video Technique Guide.

Publications

Proximal Junctional Kyphosis Prevention Strategies: A Video Technique Guide.

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