Optimization of a Tool for Predicting Postoperative Clinical Evolution After Lumbar Surgery (DeepSurgery)
Primary Purpose
Spine Disease, Spinal Fusion, Surgery
Status
Completed
Phase
Not Applicable
Locations
France
Study Type
Interventional
Intervention
SuMO Patient
Sponsored by
About this trial
This is an interventional diagnostic trial for Spine Disease
Eligibility Criteria
Inclusion Criteria:
- Major patient
- Eligible for lumbar decompression surgery, instrumented or not
- Social insured
- Having given consent
- Eligible for the acts described in Protocole
Exclusion Criteria:
- Minor
- Pregnant or breastfeeding woman
- Safeguard measure or guardianship
- Arthrodesis on more than 2 levels
- Interventions linked to a traumatic or infectious context are excluded
Sites / Locations
- Polyclinique Jean Villar
- Clinique Geoffroy Saint-Hilaire
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
SuMO Patient
Arm Description
92 data will be collected during the patient care episode. Among the 92 criteria, 63 are pre-operative, 29 are post-operative in order to provide an evolutionary prediction during the management of the patient. Post-operative follow-up criteria making it possible to establish the scalability or non-scalability of the quality of life after the surgical procedure. The results will be compared to the prediction proposed by the machine learning algorithm.
Outcomes
Primary Outcome Measures
Optimization of a tool for predicting the postoperative clinical course after lumbar surgery
Establishment and prospective evaluation of a predictive tool with the area under the receiver operating characteristic (AUROC) metric >= 80% Sensitivity >= 90% Specificity >= 60% in the capacity of providing for each back operated patient a clinical predictive status: green patient (success) orange (treatment failure), red patient (complication).
Secondary Outcome Measures
Collection of optimized data in the patient operative long terms care
Implementation, optimization and evaluation of a digital tool for collecting patient data on the episode of care
Outcome (unit) - Result expected assessment time connection means preoperatively (second/connection) - 300s time 'use and navigation (second) - 1800s number of connections made by the patient preoperatively (number) - 5 number of connections / day before operation (number) - 1 number of use (number) - 15 number of drops / connection (Ratio%) - <20% number of lost view (no connection> 20 days) (Ratio%) - <10% evaluation of average using time post-operative (second/connections) - 300 Time of use and navigation (second) - 1800 number of connections made by the patient in post -operative (number) - 5 number of connections / day after operation (number)- 1 number of uses (number) - 15 number of withdrawals (Ratio%) - <20% number of lost to follow-up (no connection> 20 days) (Ratio%) - <10% number of documents analyzed / patient (number) - 10
Full Information
NCT ID
NCT05166018
First Posted
November 16, 2021
Last Updated
February 9, 2023
Sponsor
Cortexx Medical Intelligence
Collaborators
Ramsay Générale de Santé, Elsan, Malakoff-Humanis
1. Study Identification
Unique Protocol Identification Number
NCT05166018
Brief Title
Optimization of a Tool for Predicting Postoperative Clinical Evolution After Lumbar Surgery
Acronym
DeepSurgery
Official Title
Optimization of a Tool for Predicting Postoperative Clinical Evolution After Lumbar Surgery Multicenter Longitudinal Prospective Study on a National Cohort Clinical Evolution After Lumbar Surgery
Study Type
Interventional
2. Study Status
Record Verification Date
February 2023
Overall Recruitment Status
Completed
Study Start Date
June 15, 2021 (Actual)
Primary Completion Date
June 30, 2022 (Actual)
Study Completion Date
December 30, 2022 (Actual)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Cortexx Medical Intelligence
Collaborators
Ramsay Générale de Santé, Elsan, Malakoff-Humanis
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes
5. Study Description
Brief Summary
The objective of the study is the establishment, optimization and prospective evaluation of a digital predictive platform capable of providing for each lumbar spine operated patient a clinical predictive status: Patient green (success) orange (treatment failure ), red patient (complication) in order to optimize his medical care up to 6 months.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Spine Disease, Spinal Fusion, Surgery, Spine Degeneration
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
The patient will be required to complete a guided digital questionnaire at each follow-up assessment.
This questionnaire will be completed online by the patient in the Surgery Medical Outcomes (SuMO system) system developped by the Society Cortexx Medical Intelligence. The system access procedures and connection codes will be known to the patient by the investigating physician. Patients will, throughout the study, be automatically informed via the SUMO system of the availability of data to be completed. The security of patient data is guaranteed by encrypted and separate storage of medical data, in order to comply with applicable regulatory requirements.
Masking
None (Open Label)
Allocation
N/A
Enrollment
119 (Actual)
8. Arms, Groups, and Interventions
Arm Title
SuMO Patient
Arm Type
Experimental
Arm Description
92 data will be collected during the patient care episode. Among the 92 criteria, 63 are pre-operative, 29 are post-operative in order to provide an evolutionary prediction during the management of the patient.
Post-operative follow-up criteria making it possible to establish the scalability or non-scalability of the quality of life after the surgical procedure.
The results will be compared to the prediction proposed by the machine learning algorithm.
Intervention Type
Diagnostic Test
Intervention Name(s)
SuMO Patient
Intervention Description
The current study is interventional insofar as the patient is collecting all of his socio-medical information. The analysis of the data provided by the patient makes it possible to establish a long-term prognosis for the patient but does not in itself constitute a parallel medical approach.
SUMO allows the surgeon to transmit post-operative advice developed by the surgeons themselves.
Primary Outcome Measure Information:
Title
Optimization of a tool for predicting the postoperative clinical course after lumbar surgery
Description
Establishment and prospective evaluation of a predictive tool with the area under the receiver operating characteristic (AUROC) metric >= 80% Sensitivity >= 90% Specificity >= 60% in the capacity of providing for each back operated patient a clinical predictive status: green patient (success) orange (treatment failure), red patient (complication).
Time Frame
14 months
Secondary Outcome Measure Information:
Title
Collection of optimized data in the patient operative long terms care
Description
Implementation, optimization and evaluation of a digital tool for collecting patient data on the episode of care
Outcome (unit) - Result expected assessment time connection means preoperatively (second/connection) - 300s time 'use and navigation (second) - 1800s number of connections made by the patient preoperatively (number) - 5 number of connections / day before operation (number) - 1 number of use (number) - 15 number of drops / connection (Ratio%) - <20% number of lost view (no connection> 20 days) (Ratio%) - <10% evaluation of average using time post-operative (second/connections) - 300 Time of use and navigation (second) - 1800 number of connections made by the patient in post -operative (number) - 5 number of connections / day after operation (number)- 1 number of uses (number) - 15 number of withdrawals (Ratio%) - <20% number of lost to follow-up (no connection> 20 days) (Ratio%) - <10% number of documents analyzed / patient (number) - 10
Time Frame
14 months
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria:
Major patient
Eligible for lumbar decompression surgery, instrumented or not
Social insured
Having given consent
Eligible for the acts described in Protocole
Exclusion Criteria:
Minor
Pregnant or breastfeeding woman
Safeguard measure or guardianship
Arthrodesis on more than 2 levels
Interventions linked to a traumatic or infectious context are excluded
Facility Information:
Facility Name
Polyclinique Jean Villar
City
Bruges
State/Province
Nouvelle Aquitaine
ZIP/Postal Code
33520
Country
France
Facility Name
Clinique Geoffroy Saint-Hilaire
City
Paris
ZIP/Postal Code
75005
Country
France
12. IPD Sharing Statement
Plan to Share IPD
No
Citations:
PubMed Identifier
33207969
Citation
Andre A, Peyrou B, Carpentier A, Vignaux JJ. Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery. Global Spine J. 2022 Jun;12(5):894-908. doi: 10.1177/2192568220969373. Epub 2020 Nov 19.
Results Reference
result
Links:
URL
https://play.google.com/store/apps/details?id=com.sumoapp&hl=fr
Description
Mobile application for collecting patient's data
Available IPD and Supporting Information:
Available IPD/Information Type
Individual Participant Data Set
Available IPD/Information URL
https://sumo.doc.cortexxmi.com/
Available IPD/Information Identifier
SUMO
Learn more about this trial
Optimization of a Tool for Predicting Postoperative Clinical Evolution After Lumbar Surgery
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