Optimization of a Tool for Predicting Postoperative Clinical Evolution After Lumbar Surgery

Surgery Clinical Trial

— 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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05166018
• Other study ID #: DeepSurgeryMH_01
• Status: Completed
• Phase: N/A
• Start date: June 15, 2021
• Est. completion date: December 30, 2022

Study information

• Verified date: February 2023
• Source: Cortexx Medical Intelligence
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 119
• Est. completion date: December 30, 2022
• Est. primary completion date: June 30, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Spinal Diseases
• Spinal Fusion
• Spine Degeneration
• Spine Disease
• Surgery

Intervention

Diagnostic Test:
• SuMO Patient
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.

Locations

Country	Name	City	State
France	Polyclinique Jean Villar	Bruges	Nouvelle Aquitaine
France	Clinique Geoffroy Saint-Hilaire	Paris

Sponsors (4)

Lead Sponsor	Collaborator
Cortexx Medical Intelligence	Elsan, Malakoff-Humanis, Ramsay Générale de Santé

Country where clinical trial is conducted

France, 

References & Publications (1)

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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).	14 months
Secondary	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	14 months

External Links

•   Mobile application for collecting patient's data