Prognostic model development for risk of curve progression in adolescent idiopathic scoliosis: a prospective cohort study of 127 patients
View abstract on PubMed
Summary
This summary is machine-generated.This study developed a survival model to predict adolescent idiopathic scoliosis curve progression. Key predictors include Risser stage, Cobb angle, and patient-reported spinal appearance, aiding in early intervention for scoliosis.
Area Of Science
- Orthopedics
- Biostatistics
- Pediatric Spine Surgery
Background
- Adolescent idiopathic scoliosis (AIS) is a common spinal deformity.
- Predicting curve progression in AIS is crucial for timely intervention.
- Existing prognostic models may not fully capture disease heterogeneity.
Purpose Of The Study
- To develop and internally validate a prognostic survival model for adolescent idiopathic scoliosis curve progression.
- Identify baseline variables that predict significant curve worsening.
- Enhance clinical decision-making for AIS management.
Main Methods
- Longitudinal prognostic cohort analysis of 135 patients (ages 9-17) with AIS.
- Cox proportional hazards regression (CoxPH) and machine learning models were used for development and validation.
- Prognostic outcome defined as >6° Cobb angle progression before skeletal maturity.
Main Results
- The final CoxPH model (n=127) demonstrated acceptable discriminative ability (concordance=0.79).
- Significant predictors of progression included Risser stage 0, larger Cobb angle, and higher patient-reported Spinal Appearance Questionnaire (pSAQ) scores.
- Treatment exposure was also a significant factor (HR 3.1, P=0.001).
Conclusions
- A prognostic model incorporating Risser stage, Cobb angle, and pSAQ effectively predicts >6° curve progression in AIS.
- Patient-reported outcomes via pSAQ may offer significant clinical value for prognosis.
- The model provides a robust tool for identifying at-risk adolescents.
Related Concept Videos
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

