Ignite Creation Date:
2024-05-11 @ 8:31 AM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06408896
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-05-10
First Post:
2024-05-07
Brief Title:
Development and Internal Validation of Predicting Models of Idiopathic Scoliosis Natural History and Treatment Outcomes Through the Use of Artificial Intelligence in a Large Clinical Database
Sponsor:
Istituto Scientifico Italiano Colonna Vertebrale
Organization:
Istituto Scientifico Italiano Colonna Vertebrale
Study Overview
Official Title:
Development and Internal Validation of Predicting Models of Idiopathic Scoliosis Natural History and Treatment Outcomes Through the Use of Artificial Intelligence in a Large Clinical Database
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PREPARE
Brief Summary:
Scoliosis is a three-dimensional deformity of the spine In its most common form about 70 of cases the causes are unknown therefore it is called idiopathic scoliosis In most cases it is discovered after 10 years of age and is defined diagnostically as a curve of at least 10 measured on a standing x-ray using the Cobb method
If scoliosis exceeds the critical threshold of 30 Cobb at the end of growth there is a progressively greater risk of health and social problems in adult life For this reason the main aim of the treatment is to complete the growth period with a curve less than 30 and good sagittal balance or at least well below 50 which represents the surgical threshold
Growth is a factor favouring the evolution of deformities therefore patients are followed until the end of growth This is why therapy can last many years from the discovery of the presence of a deformity until bone maturation is achieved
The early identification of parameters predictive of the outcome of the therapy to direct the least possible aggressiveness towards the result necessary for the patients future integrated with the evaluation of its effectiveness monitoring is one of the most important objectives in this field to minimize the burden of treatment in a particular phase of growth such as adolescent development as well as to identify the subjects most at risk of worsening in adulthood
The systematic collection of clinical data during the therapeutic process offers the possibility through advanced analysis models applied retrospectively to identify predisposing factors and protective factors When the data available is sufficiently large it is possible to obtain predictive equations that assist clinicians in therapeutic choices and help patients understand the risks and benefits of available therapies New technologies such as artificial intelligence techniques offer new and interesting ways of estimating risks and calculating the benefits and safety of some therapeutic choices compared to others
This study aims to develop and internally validate data-driven stratification and prediction models to predict multiple end-of-care outcome measures that include curve magnitude measured in Cobb degrees measures determining the sagittal balance and measures of quality of life and function measured through self-completion questionnaires
If scoliosis exceeds the critical threshold of 30 Cobb at the end of growth there is a progressively greater risk of health and social problems in adult life For this reason the main aim of the treatment is to complete the growth period with a curve less than 30 and good sagittal balance or at least well below 50 which represents the surgical threshold
Growth is a factor favouring the evolution of deformities therefore patients are followed until the end of growth This is why therapy can last many years from the discovery of the presence of a deformity until bone maturation is achieved
The early identification of parameters predictive of the outcome of the therapy to direct the least possible aggressiveness towards the result necessary for the patients future integrated with the evaluation of its effectiveness monitoring is one of the most important objectives in this field to minimize the burden of treatment in a particular phase of growth such as adolescent development as well as to identify the subjects most at risk of worsening in adulthood
The systematic collection of clinical data during the therapeutic process offers the possibility through advanced analysis models applied retrospectively to identify predisposing factors and protective factors When the data available is sufficiently large it is possible to obtain predictive equations that assist clinicians in therapeutic choices and help patients understand the risks and benefits of available therapies New technologies such as artificial intelligence techniques offer new and interesting ways of estimating risks and calculating the benefits and safety of some therapeutic choices compared to others
This study aims to develop and internally validate data-driven stratification and prediction models to predict multiple end-of-care outcome measures that include curve magnitude measured in Cobb degrees measures determining the sagittal balance and measures of quality of life and function measured through self-completion questionnaires
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None