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The patient demographics, radiographic index and surgical invasiveness for mechanical failure (PRISM) model established for adult spinal deformity surgery
- Source :
- Scientific Reports, Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group UK, 2020.
-
Abstract
- 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.
- Subjects :
- Male
Reoperation
medicine.medical_specialty
Multivariate analysis
lcsh:Medicine
Logistic regression
Risk Assessment
Article
Cohort Studies
03 medical and health sciences
0302 clinical medicine
Medicine
Humans
Treatment Failure
Risk factor
lcsh:Science
Demography
Mechanical Phenomena
Retrospective Studies
030222 orthopedics
Multidisciplinary
Models, Statistical
business.industry
lcsh:R
Retrospective cohort study
Middle Aged
Surgery
Radiography
Risk factors
Scoliosis
Outcomes research
Cohort
lcsh:Q
Female
business
Risk assessment
030217 neurology & neurosurgery
Cohort study
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....bad8997ad36bb69fd641f4fe8b492dc6