Florent Bernard-de-Villeneuve1, Kayahan Kayikci1, Elliot Sappey-Marinier2, Timothy Lording3, Cécile Batailler1, Elvire Servien1,4, Sébastien Lustig1,5. 1. FIFA Medical Center of Excellence, Orthopaedics Surgery and Sports Medicine Department, Croix-Rousse Hospital, 103 Grande rue de la Croix Rousse, 69004, Lyon, France. 2. FIFA Medical Center of Excellence, Orthopaedics Surgery and Sports Medicine Department, Croix-Rousse Hospital, 103 Grande rue de la Croix Rousse, 69004, Lyon, France. esappey@gmail.com. 3. Melbourne Orthopaedic Group, 33 The Avenue, Windsor, VIC, 3181, Australia. 4. LIBM - EA 7424, Interuniversity Laboratory of Biology of Mobility, Claude Bernard Lyon 1 University, Lyon, France. 5. Univ Lyon, Claude Bernard Lyon 1 University, IFSTTAR, LBMC UMR_T9406, F69622, Lyon, France.
Abstract
PURPOSE: The aim of this systematic review was to compare relevant health economic consequences of the CT-based robotic-arm-assisted system versus conventional Uni-compartmental Knee Arthroplasty (UKA). METHODS: In November 2020, A PRISMA systematic review was conducted using four databases (Pubmed, Scopus, Cochrane and Google Scholar) to identify all comparative studies reporting health economic assessments, such as robotic system costs, consumable costs, surgical revision rate, operating time, length of stay, and inpatient care costs. RESULTS: A total of nine comparative studies published between 2014 and 2020 were included in this systematic review. There was a moderate risk of bias as assessed using the ROBINS-I Tool. The CT-based robotic-arm-assisted system seemed to be associated with a lower risk of revision, decreased analgesia requirements during hospitalization, a shorter length of stay, and lower inpatient care costs compared to a conventional technique. CONCLUSION: CT-based robotic-arm-assisted system for UKA appears to be an economically viable solution with a positive health economic impact as it tends to decrease revision rate compared to conventional UKA, improve post-operative rehabilitation and analgesia management. Post-operative inpatient care costs seem lower with the robotic-assisted system but depend on institutional case volume and differ among health systems. More studies are needed to confirm cost-effectiveness of CT-based robotic-arm-assisted system based on different health systems. LEVEL OF EVIDENCE: Systematic review, Level IV.
PURPOSE: The aim of this systematic review was to compare relevant health economic consequences of the CT-based robotic-arm-assisted system versus conventional Uni-compartmental Knee Arthroplasty (UKA). METHODS: In November 2020, A PRISMA systematic review was conducted using four databases (Pubmed, Scopus, Cochrane and Google Scholar) to identify all comparative studies reporting health economic assessments, such as robotic system costs, consumable costs, surgical revision rate, operating time, length of stay, and inpatient care costs. RESULTS: A total of nine comparative studies published between 2014 and 2020 were included in this systematic review. There was a moderate risk of bias as assessed using the ROBINS-I Tool. The CT-based robotic-arm-assisted system seemed to be associated with a lower risk of revision, decreased analgesia requirements during hospitalization, a shorter length of stay, and lower inpatient care costs compared to a conventional technique. CONCLUSION: CT-based robotic-arm-assisted system for UKA appears to be an economically viable solution with a positive health economic impact as it tends to decrease revision rate compared to conventional UKA, improve post-operative rehabilitation and analgesia management. Post-operative inpatient care costs seem lower with the robotic-assisted system but depend on institutional case volume and differ among health systems. More studies are needed to confirm cost-effectiveness of CT-based robotic-arm-assisted system based on different health systems. LEVEL OF EVIDENCE: Systematic review, Level IV.
Authors: Rushabh M Vakharia; Nipun Sodhi; Wayne B Cohen-Levy; Ajit M Vakharia; Michael A Mont; Martin W Roche Journal: J Knee Surg Date: 2019-10-22 Impact factor: 2.757
Authors: Nizar N Mahomed; Jane Barrett; Jeffrey N Katz; John A Baron; John Wright; Elena Losina Journal: J Bone Joint Surg Am Date: 2005-06 Impact factor: 5.284