文章基本信息

标题：The patient demographics, radiographic index and surgical invasiveness for mechanical failure (PRISM) model established for adult spinal deformity surgery
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作者：Mitsuru Yagi ; Naobumi Hosogane ; Nobuyuki Fujita 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-10
DOI：10.1038/s41598-020-66353-7
出版社：Springer Nature
摘要：Mechanical failure (MF) following adult spinal deformity (ASD) surgery is a severe complication and often requires revision surgery. Predicting a patient’s risk of MF is difficult, despite several potential risk factors that have been reported. The purpose of this study was to establish risk stratification model for predicting the MF based on demographic, and radiographic data. This is a multicenter retrospective review of the risk stratification for MF and included 321 surgically treated ASD patients (55 ± 19 yr, female: 91%). The analyzed variables were recorded for at least 2 yr and included age, gender, BMI, BMD, smoking status, frailty, fusion level, revision surgery, PSO, LIF, previous surgery, spinal alignment, GAP score, Schwab-SRS type, and rod materials. Multivariate logistic regression analyses were performed to identify the independent risk factors for MF. Each risk factor was assigned a value based on its regression coefficient, and the values of all risk factors were summed to obtain the PRISM score (range 0–12). We used an 8:2 ratio to split the data into a training and a testing cohort to establish and validate the model. MF developed in 41% (n = 104) of the training subjects. Multivariate analysis revealed that BMI, BMD, PT, and frailty were independent risk factors for MF (BMI: OR 1.7 [1.0–2.9], BMD: OR 3.8 [1.9–7.7], PT: OR 2.6 [1.8–3.9], frailty: OR 1.9 [1.1–3.2]). The MF rate increased with and correlated well with the risk grade as shown by ROC curve (AUC of 0.81 [95% CI 0.76–0.86]). The discriminative ability of the score in the testing cohort was also good (AUC of 0.86 ([95% CI 0.77–0.95]). We successfully developed an MF-predicting model from individual baseline parameters. This model can predict a patient’s risk of MF and will help surgeons adjust treatment strategies to mitigate the risk of MF.