Fine-tuning the Predictive Model for Proximal Junctional Failure in Surgically Treated Patients with Adult Spinal Deformity

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Abstract

Study Design. Multicenter retrospective study. Objective. To validate and improve the predictive model for proximal junctional failure (PJF) with or without the bone mineral density (BMD) score. Summary of Background Data. PJF is a serious complication of surgery for adult spinal deformity (ASD). A predictive model for PJF was recently reported that has good accuracy, but does not include BMD, a known PJF risk factor, as a variable. Methods. We included 145 surgically treated ASD patients who were older than 50 at the time of surgery and had been followed up for at least 2 years. Variables included age, sex, body mass index (BMI), fusion level, upper and lower instrumented vertebral (UIV and LIV) level, primary or revision surgery, pedicle subtraction osteotomy (PSO), Schwab-SRS type, and BMD. PJF was defined as a ≥ 20° increase from baseline (immediately postoperative) of the proximal junctional angle with concomitant deterioration of at least 1 SRS-Schwab sagittal modifier grade, or any proximal junctional kyphosis requiring revision. Decision-making trees were constructed using the C5.0 algorithm with 10 different bootstrapped models, and validated by a 7:3 data split for training and testing; 112 patients were categorized as training and 33 as testing samples. Results. PJF incidence was 20% in the training samples. Univariate analyses showed that BMD, BMI, pelvic tilt (PT), UIV level, and LIV level were PJF risk factors. Our predictive model was 100% accurate in the testing samples with an AUC of 1.0, indicating excellent fit. The best predictors were (strongest to weakest): PT, BMD, LIV level (pelvis), UIV level (lower thoracic), PSO, global alignment, BMI, pelvic incidence minus lumbar lordosis, and age. Conclusion. A successful model was developed for predicting PJF that included BMD. Our model could inform physicians about patients with a high risk of developing PJF in the perioperative period. Level of Evidence: 4.

Original languageEnglish
Pages (from-to)767-773
Number of pages7
JournalSpine
Volume43
Issue number11
DOIs
Publication statusPublished - 2018 Jun 1

Fingerprint

Bone Density
Body Mass Index
Osteotomy
Pelvic Bones
Lordosis
Decision Trees
Kyphosis
Perioperative Period
Incidence
Pelvis
Reoperation
Multicenter Studies
Area Under Curve
Decision Making
Thorax
Retrospective Studies
Physicians

Keywords

  • adult spinal deformity
  • ASD
  • complication
  • decision-making tree
  • paralysis
  • PJF
  • PJK
  • predictive model
  • proximal junctional failure
  • proximal junctional kyphosis
  • revision

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Clinical Neurology

Cite this

@article{b8c699b008194273a7c5531145490c84,
title = "Fine-tuning the Predictive Model for Proximal Junctional Failure in Surgically Treated Patients with Adult Spinal Deformity",
abstract = "Study Design. Multicenter retrospective study. Objective. To validate and improve the predictive model for proximal junctional failure (PJF) with or without the bone mineral density (BMD) score. Summary of Background Data. PJF is a serious complication of surgery for adult spinal deformity (ASD). A predictive model for PJF was recently reported that has good accuracy, but does not include BMD, a known PJF risk factor, as a variable. Methods. We included 145 surgically treated ASD patients who were older than 50 at the time of surgery and had been followed up for at least 2 years. Variables included age, sex, body mass index (BMI), fusion level, upper and lower instrumented vertebral (UIV and LIV) level, primary or revision surgery, pedicle subtraction osteotomy (PSO), Schwab-SRS type, and BMD. PJF was defined as a ≥ 20° increase from baseline (immediately postoperative) of the proximal junctional angle with concomitant deterioration of at least 1 SRS-Schwab sagittal modifier grade, or any proximal junctional kyphosis requiring revision. Decision-making trees were constructed using the C5.0 algorithm with 10 different bootstrapped models, and validated by a 7:3 data split for training and testing; 112 patients were categorized as training and 33 as testing samples. Results. PJF incidence was 20{\%} in the training samples. Univariate analyses showed that BMD, BMI, pelvic tilt (PT), UIV level, and LIV level were PJF risk factors. Our predictive model was 100{\%} accurate in the testing samples with an AUC of 1.0, indicating excellent fit. The best predictors were (strongest to weakest): PT, BMD, LIV level (pelvis), UIV level (lower thoracic), PSO, global alignment, BMI, pelvic incidence minus lumbar lordosis, and age. Conclusion. A successful model was developed for predicting PJF that included BMD. Our model could inform physicians about patients with a high risk of developing PJF in the perioperative period. Level of Evidence: 4.",
keywords = "adult spinal deformity, ASD, complication, decision-making tree, paralysis, PJF, PJK, predictive model, proximal junctional failure, proximal junctional kyphosis, revision",
author = "Mitsuru Yagi and Nobuyuki Fujita and Eijiro Okada and Osahiko Tsuji and Narihito Nagoshi and Takashi Asazuma and Ken Ishii and Masaya Nakamura and Morio Matsumoto and Koota Watanabe",
year = "2018",
month = "6",
day = "1",
doi = "10.1097/BRS.0000000000002415",
language = "English",
volume = "43",
pages = "767--773",
journal = "Spine",
issn = "0362-2436",
publisher = "Lippincott Williams and Wilkins",
number = "11",

}

TY - JOUR

T1 - Fine-tuning the Predictive Model for Proximal Junctional Failure in Surgically Treated Patients with Adult Spinal Deformity

AU - Yagi, Mitsuru

AU - Fujita, Nobuyuki

AU - Okada, Eijiro

AU - Tsuji, Osahiko

AU - Nagoshi, Narihito

AU - Asazuma, Takashi

AU - Ishii, Ken

AU - Nakamura, Masaya

AU - Matsumoto, Morio

AU - Watanabe, Koota

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Study Design. Multicenter retrospective study. Objective. To validate and improve the predictive model for proximal junctional failure (PJF) with or without the bone mineral density (BMD) score. Summary of Background Data. PJF is a serious complication of surgery for adult spinal deformity (ASD). A predictive model for PJF was recently reported that has good accuracy, but does not include BMD, a known PJF risk factor, as a variable. Methods. We included 145 surgically treated ASD patients who were older than 50 at the time of surgery and had been followed up for at least 2 years. Variables included age, sex, body mass index (BMI), fusion level, upper and lower instrumented vertebral (UIV and LIV) level, primary or revision surgery, pedicle subtraction osteotomy (PSO), Schwab-SRS type, and BMD. PJF was defined as a ≥ 20° increase from baseline (immediately postoperative) of the proximal junctional angle with concomitant deterioration of at least 1 SRS-Schwab sagittal modifier grade, or any proximal junctional kyphosis requiring revision. Decision-making trees were constructed using the C5.0 algorithm with 10 different bootstrapped models, and validated by a 7:3 data split for training and testing; 112 patients were categorized as training and 33 as testing samples. Results. PJF incidence was 20% in the training samples. Univariate analyses showed that BMD, BMI, pelvic tilt (PT), UIV level, and LIV level were PJF risk factors. Our predictive model was 100% accurate in the testing samples with an AUC of 1.0, indicating excellent fit. The best predictors were (strongest to weakest): PT, BMD, LIV level (pelvis), UIV level (lower thoracic), PSO, global alignment, BMI, pelvic incidence minus lumbar lordosis, and age. Conclusion. A successful model was developed for predicting PJF that included BMD. Our model could inform physicians about patients with a high risk of developing PJF in the perioperative period. Level of Evidence: 4.

AB - Study Design. Multicenter retrospective study. Objective. To validate and improve the predictive model for proximal junctional failure (PJF) with or without the bone mineral density (BMD) score. Summary of Background Data. PJF is a serious complication of surgery for adult spinal deformity (ASD). A predictive model for PJF was recently reported that has good accuracy, but does not include BMD, a known PJF risk factor, as a variable. Methods. We included 145 surgically treated ASD patients who were older than 50 at the time of surgery and had been followed up for at least 2 years. Variables included age, sex, body mass index (BMI), fusion level, upper and lower instrumented vertebral (UIV and LIV) level, primary or revision surgery, pedicle subtraction osteotomy (PSO), Schwab-SRS type, and BMD. PJF was defined as a ≥ 20° increase from baseline (immediately postoperative) of the proximal junctional angle with concomitant deterioration of at least 1 SRS-Schwab sagittal modifier grade, or any proximal junctional kyphosis requiring revision. Decision-making trees were constructed using the C5.0 algorithm with 10 different bootstrapped models, and validated by a 7:3 data split for training and testing; 112 patients were categorized as training and 33 as testing samples. Results. PJF incidence was 20% in the training samples. Univariate analyses showed that BMD, BMI, pelvic tilt (PT), UIV level, and LIV level were PJF risk factors. Our predictive model was 100% accurate in the testing samples with an AUC of 1.0, indicating excellent fit. The best predictors were (strongest to weakest): PT, BMD, LIV level (pelvis), UIV level (lower thoracic), PSO, global alignment, BMI, pelvic incidence minus lumbar lordosis, and age. Conclusion. A successful model was developed for predicting PJF that included BMD. Our model could inform physicians about patients with a high risk of developing PJF in the perioperative period. Level of Evidence: 4.

KW - adult spinal deformity

KW - ASD

KW - complication

KW - decision-making tree

KW - paralysis

KW - PJF

KW - PJK

KW - predictive model

KW - proximal junctional failure

KW - proximal junctional kyphosis

KW - revision

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U2 - 10.1097/BRS.0000000000002415

DO - 10.1097/BRS.0000000000002415

M3 - Article

VL - 43

SP - 767

EP - 773

JO - Spine

JF - Spine

SN - 0362-2436

IS - 11

ER -