Evidence map›Paper›PMID 42118304›Full record

ArticleInternational orthopaedics2026

Rising adoption of imageless navigation in total hip arthroplasty for morbid obesity: do clinical outcomes improve? A matched cohort study.

Roshan Latifi, Jack Parker, Amir Human Hoveidaei, Amirhossein Salmannezhad, Zhongming Chen, Jakob Adolf, Rishi Thakral, Janet D Conway

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Article in International orthopaedics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Roshan LatifiOrthopedic Surgery Department, Aalborg University Hospital, Aalborg, Denmark.
Jack ParkerInternational Center for Limb Lengthening, Rubin Institute for Advanced Orthopedics, Sinai Hospital, Baltimore, United States.
Amir Human HoveidaeiInternational Center for Limb Lengthening, Rubin Institute for Advanced Orthopedics, Sinai Hospital, Baltimore, United States. hoveidaei.a.h@gmail.com.
Amirhossein SalmannezhadNeuromusculoskeletal Research Center, Iran University of Medical Sciences, Tehran, Iran, Islamic Republic of.
Zhongming ChenInternational Center for Limb Lengthening, Rubin Institute for Advanced Orthopedics, Sinai Hospital, Baltimore, United States.
Jakob AdolfDepartment of Paediatric Orthopaedics, Medical University of Sofia, Sofia, Bulgaria.
Rishi ThakralDepartment of Orthopedic Surgery, University of Oklahoma, Oklahoma City, United States.
Janet D ConwayInternational Center for Limb Lengthening, Rubin Institute for Advanced Orthopedics, Sinai Hospital, Baltimore, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTotal hip arthroplasty (THA) presents unique technical challenges in patients who have morbid obesity, and benefits of imageless navigation in this population remain unclear. This study compared complications, thromboembolic events, and emergency department utilization between imageless navigation-assisted and manual THA in patients who have morbid obesity.

methodsA retrospective cohort study was performed using the PearlDiver database. Patients who have morbid obesity (BMI ≥ 40) undergoing elective primary THA between 2010 and 2021 were identified. Cases performed with robotic assistance or image-based navigation were excluded. Patients were stratified by intraoperative technique into manual and imageless navigation cohorts and matched 1:3 on age, sex, year of procedure, and comorbidities. Surgical complications, thromboembolic events, revision procedures, and emergency department visits were evaluated at multiple postoperative time points and compared using univariable regression.

resultsAfter matching, 4,499 patients who have morbid obesity were included, comprising 3,367 manual (74.8%) and 1,132 imageless navigation (25.2%) cases. No significant differences were observed between cohorts in rates of surgical complications, including infection, dislocation, periprosthetic fracture, mechanical loosening, chronic pain, or leg length discrepancy at any evaluated time point (P > 0.05). Thromboembolic events were uncommon and did not differ between groups at 30 or 90 days. Emergency department visits and revision rates were also similar at all time points.

conclusionsAmong patients who have morbid obesity undergoing primary THA, imageless navigation was not associated with improved outcomes compared with manual techniques, suggesting postoperative risk is driven primarily by obesity-related factors. Surgeons should weigh resource utilization and patient characteristics when selecting operative technique.

Indexed as

Arthroplasty, Replacement, HipObesity, MorbidSurgery, Computer-AssistedAgedFemaleHumansMaleMiddle AgedPostoperative ComplicationsReoperationRetrospective StudiesTreatment OutcomeComputer-assisted surgeryImageless navigationMorbid obesityPostoperative complicationsTotal hip arthroplastyValue-based care

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.