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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05166018
Other study ID # DeepSurgeryMH_01
Secondary ID
Status Completed
Phase N/A
First received
Last updated
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
Health authority
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

Study Design


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
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