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

Not Applicable

Completed

• Conditions: Surgery; Spine Degeneration; Spinal Fusion; Spine Disease
• Interventions: Diagnostic Test: SuMO Patient

• Registration Number: NCT05166018
• Lead Sponsor: Cortexx Medical Intelligence

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-06-15
• Study First Submitted: 2021-11-16
• Study First Submitted QC: 2021-12-08
• Study First Posted: 2021-12-21
• Primary Completion: 2022-06-30
• Study Completion: 2022-12-30
• Status Verified: 2023-02
• Last Update Submitted: 2023-02-09
• Last Update Posted: 2023-02-10

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 119

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
SuMO Patient	SuMO Patient	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.

Primary Outcome Measures

Name	Time	Method
Optimization of a tool for predicting the postoperative clinical course after lumbar surgery	14 months	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

Name	Time	Method
Collection of optimized data in the patient operative long terms care	14 months	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

Trial Locations

Locations (2)

Clinique Geoffroy Saint-Hilaire; 🇫🇷 Paris, France

Polyclinique Jean Villar; 🇫🇷 Bruges, Nouvelle Aquitaine, France