Clinical trial NCT05993845: Modeling and Workflow Recognition for the Anterior Approach in Total Hip Arthroplasty
Official title: Modeling and Workflow Recognition for the Anterior Approach in Total Hip Arthroplasty
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hip Prosthesis
Keywords: Surgery Recording
Arthroplasty
Surgical Process Modelling
Lead sponsor: Balgrist University Hospital
Sponsor class: OTHER
Collaborators: name: Kantonsspital Baden
name: Kantonsspital Winterthur KSW
First posted: 2023-08-15
Enrollment: 60
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: We plan to record 60 THA surgeries in the operating room performed by different hip surgeons at Balgrist University Hospital, Kantonsspital Baden, or Kantonsspital Winterthur, for data collection. Patients are recruited if they fit the in-/exclusion criteria and get primary information from the surgeon as soon as the decision for surgery has been made in the outpatient clinic. The patients are handed a copy of the informed consent and can decide on participating until the follow-up consultation. The decision to perform the procedure is not linked to the study. The decision to participate does not affect the procedure itself.

Brief summary: The purpose of this study is to create a systematic and general description of the surgical process for the direct anterior approach (DAA) in total hip arthroplasty (THA). For this purpose a surgical process model with a labeled dataset of THA surgery videos will be segmented into the individual surgical steps and sub-steps using a systematic approach.

Detailed description: BACKGROUND AND PROJECT RATIONALE

In Total Hip Arthroplasty (THA), a degenerated hip joint is replaced with an artificial acetabular and femoral component. In clinical practice, several approaches to accessing bone anatomy are employed. The direct anterior approach (DAA) has been adopted widely in recent years due to smaller incisions, fewer complications, faster recovery, and improved patient outcomes. However, the reduced space and limited access may cause more complexity in the execution of surgical steps.

Primary objective

The first goal of this project is to create a systematic and general description of the surgical process for DAA in THA. For this purpose, surgical process models (SPMs) provide a basis to manage, organize, and optimize the surgical process. For this purpose, a labeled dataset of THA surgery videos will be segmented into individual surgical steps and sub-steps using a systematic approach. SPMs are a simplified network of surgical or surgery-related activities and their relationships. These models can be used to compare several interventions, surgeons, or whole Operating Room teams. In addition, the SPMs can be used as input for workflow management systems.

Eligibility criteria: Inclusion Criteria:

* Primary surgery for total hip arthroplasty (THA) at Balgrist University Hospital, Kantonsspital Baden and Kantonsspital Winterthur
* Complete radiological data (CT and Hip/Pelvis x-ray)
* Signed consent

Exclusion Criteria:

* Missing or incomplete consent
* Previous surgery of the hip at the site of the THA
* Fracture of the hip or pelvis at the site of the THA

Primary outcomes:
measure: labeled recordings of total hip arthroplasty
timeFrame: Up to 1 year
Secondary outcomes:
Other outcomes:
measure: automated deep learning-based approach for surgical workflow and activity recognition for total hip arthroplasty (THA)
timeFrame: Up to 1 year
Locations:
city: Baden
country: Switzerland
facility: Kantonsspital Baden
state: Aargau
city: Winterthur
country: Switzerland
facility: Kantonsspital Winterthur
state: Zurich
city: Zurich
country: Switzerland
facility: Balgrist University Hospital
Study officials:
affiliation: Balgrist University Hospital
name: Philipp Fürnstahl, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Balgrist University Hospital
name: Patrick Zingg, MD
role: STUDY_CHAIR
References:
citation: Carrillo F, Esfandiari H, Muller S, von Atzigen M, Massalimova A, Suter D, Laux CJ, Spirig JM, Farshad M, Furnstahl P. Surgical Process Modeling for Open Spinal Surgeries. Front Surg. 2022 Jan 25;8:776945. doi: 10.3389/fsurg.2021.776945. eCollection 2021.
citation: Charney M, Paxton EW, Stradiotto R, Lee JJ, Hinman AD, Sheth DS, Prentice HA. A Comparison of Risk of Dislocation and Cause-Specific Revision Between Direct Anterior and Posterior Approach Following Elective Cementless Total Hip Arthroplasty. J Arthroplasty. 2020 Jun;35(6):1651-1657. doi: 10.1016/j.arth.2020.01.033. Epub 2020 Jan 22.
citation: Haynes JA, Hopper RH Jr, Ho H, McDonald JF 3rd, Parks NL, Hamilton WG. Direct Anterior Approach for Primary Total Hip Arthroplasty Lowers the Risk of Dislocation Compared to the Posterior Approach: A Single Institution Experience. J Arthroplasty. 2022 Mar;37(3):495-500. doi: 10.1016/j.arth.2021.11.011. Epub 2021 Nov 10.
citation: Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O. Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc. 2009 Jan-Feb;16(1):72-80. doi: 10.1197/jamia.M2748. Epub 2008 Oct 24.
citation: Patel NN, Shah JA, Erens GA. Current Trends in Clinical Practice for the Direct Anterior Approach Total Hip Arthroplasty. J Arthroplasty. 2019 Sep;34(9):1987-1993.e3. doi: 10.1016/j.arth.2019.04.025. Epub 2019 Apr 18.
citation: Seibold, M., Hoch, A., Farshad, M., Navab, N., & Fürnstahl, P. (2022). Conditional generative data augmentation for clinical audio datasets. Medical Image Computing and Computer Assisted Intervention (MICCAI). Singapore.
citation: Zisimopoulos, O., Flouty, E., Luengo, I., Giataganas, P., Nehme, J., Chow, A., & Stoyanov, D. (2018). DeepPhase: Surgical Phase Recognition in CATARACTS Videos. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial, 11073, 265-275.