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

NCT05166018 | Completed DMC

• 1 other identifier: DeepSurgeryMH_01
• Study Type: interventional
• Target: 119
• Locations: 1 country
• Sites: 2

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.

Conditions

Spine Disease; Spinal Fusion; Surgery; Spine Degeneration

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Collection of optimized data in the patient operative long terms care

  14 months

Study Arms (1)

SuMO Patient

EXPERIMENTAL

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.

Diagnostic Test: SuMO Patient

Interventions

SuMO Patient DIAGNOSTIC_TEST

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.

SuMO Patient

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Major patient
• Eligible for lumbar decompression surgery, instrumented or not
• Social insured
• Having given consent
• Eligible for the acts described in Protocole

You may not qualify if:

• 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

Sponsors & Collaborators

• Cortexx Medical Intelligence: lead
• Ramsay Générale de Santé: collaborator
• Elsan: collaborator
• Malakoff-Humanis: collaborator

Study Sites (2)

Polyclinique Jean Villar

Bruges, Nouvelle-Aquitaine, 33520, France

Location

Clinique Geoffroy Saint-Hilaire

Paris, 75005, France

Location

Related 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.

  PMID: 33207969 RESULT

Related Links

• Mobile application for collecting patient's data

MeSH Terms

Conditions

Spinal Diseases

Condition Hierarchy (Ancestors)

Bone Diseases; Musculoskeletal Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: November 16, 2021
• First Posted: December 21, 2021
• Study Start: June 15, 2021
• Primary Completion: June 30, 2022
• Study Completion: December 30, 2022
• Last Updated: February 10, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

FR (2)