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

Cortexx Medical Intelligence

Status: Completed

Conditions: Surgery; Spine Disease; Spinal Fusion; Spine Degeneration

Treatments: Diagnostic Test: SuMO Patient

Study type: Interventional

Funder types: Other; Industry

Identifiers

NCT05166018

DeepSurgeryMH_01

Details and patient eligibility

About

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.

Enrollment: 119 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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119 participants in 1 patient group

SuMO Patient

Experimental group

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.

Treatment:

Diagnostic Test: SuMO Patient