H. Koller, W. Hitzl, M. C. Marks, P. O. Newton
August 2019, Volume 28, Issue 9, pp 1987 - 1997 Original Article Read Full Article 10.1007/s00586-019-06000-6
First Online: 24 June 2019
Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC
A multicenter observational prospective analysis was conducted to determine predictors of SLCC in AIS patients that had posterior STF. Curve types included major thoracic curves (Lenke 1, 3–4).The primary outcome variable was to establish prediction models, and a postoperative lumbar curve (LC) ≤ 20° was defined as the target variable. Multivariate logistic regression models were established to study the relationship between selected variables and a LC ≤ 20° versus a LC > 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up.
410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (P = 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (P
An accurate prediction model for postoperative SLCC was established based on a large analysis of prospective STF cases. These models can support prediction and understanding of postoperative SLCC aiding in surgical decision making when contemplating a selective thoracic fusion.
These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]
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