Publications by authors named "F Cumhur Öner"

348 Publications

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.

Acta Orthop 2021 Jun 10:1-6. Epub 2021 Jun 10.

Department of Orthopedic Surgery, Orthopedic Oncology Service, Massachusetts General Hospital - Harvard Medical School, Boston, USA.

Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed.Material and methods - We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics.Results - Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635-26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73-0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis.Interpretation - ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.
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http://dx.doi.org/10.1080/17453674.2021.1932928DOI Listing
June 2021

Vacuum plasma sprayed porous titanium coating on polyetheretherketone for ACDF improves the osteogenic ability: An in vitro and in vivo study.

Biomed Microdevices 2021 Apr 5;23(2):21. Epub 2021 Apr 5.

Spine Research Center of Wannan Medical College, No.22 Wenchang West Road, Wuhu, 241001, Anhui, China.

Cervical degenerative disease is a common and frequently occurring disease, which seriously affects the health and quality of the life of patients worldwide. Anterior cervical decompression and interbody fusion is currently recognized as the gold standard for the treatment of degenerative cervical spondylosis. Polyetheretherketone (PEEK) has become the prevailing material for cervical fusion surgery. Although PEEK has excellent biocompatibility, it is difficult to form bone connection at its bone-implant interface due to its low surface hydrophilicity and conductivity. It is widely accepted that Ti has excellent osteogenic activity and biocompatibility. In this study, a Ti-PEEK composite cage was prepared by coating Ti on the surface of a PEEK cage using a vacuum plasma spraying technique to enhance the osteogenic property of PEEK. The Ti-PEEK samples were evaluated in terms of their in vitro cellular behaviors and in vivo osteointegration, and the results were compared to a pure PEEK substrate. The skeleton staining and MTS assay indicated that the MC3T3-E1 cells spread and grew well on the surface of Ti-PEEK cages. The osteogenic gene expression and western blot analysis of osteogenic protein showed upregulated bone-forming activity of MC3T3-E1 cells in Ti-PEEK cages. Furthermore, a significant increase in new bone formation was demonstrated on Ti-PEEK implants in comparison with PEEK implants at 12 weeks in a sheep cervical spine fusion test. These results proved that the Ti-PEEK cage exhibited enhanced osseointegrative properties compared to the PEEK cage both in vitro and in vivo.
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http://dx.doi.org/10.1007/s10544-021-00559-yDOI Listing
April 2021

Resolvin E1 Regulates Th17 Function and T Cell Activation.

Front Immunol 2021 17;12:637983. Epub 2021 Mar 17.

The Forsyth Institute, Cambridge, MA, United States.

Resolvin E1 (RvE1) is a specialized pro-resolving lipid mediator derived from eicosapentaenoic acid and plays a critical role in resolving inflammation and tissue homeostasis. T17 cells are a distinct group of T helper (T) cells with tissue-destructive functions in autoimmune and chronic inflammatory diseases the secretion of IL-17. Dendritic cell (DC)-mediated antigen presentation regulates the T17-induced progression of inflammation and tissue destruction. In this study, we hypothesized that the RvE1 would restore homeostatic balance and inflammation by targeting the T17 function. We designed three experiments to investigate the impact of RvE1 on different phases of T17 response and the potential role of DCs: First CD4 T cells were induced by IL-6/TGF to measure the effect of RvE1 on T17 differentiation in an inflammatory milieu. Second, we measured the impact of RvE1 on DC-stimulated T17 differentiation in a co-culture model. Third, we measured the effect of RvE1 on DC maturation. RvE1 blocked the CD25, CCR6 and IL-17 expression; IL-17, IL-21, IL-10, and IL-2 production, suggesting inhibition of T cell activation, T17 stimulation and chemoattraction. RvE1 also suppressed the activation of DCs by limiting their pro-inflammatory cytokine production. Our findings collectively demonstrated that the RvE1 targeted the T17 activation and the DC function as a potential mechanism for inflammatory resolution and acquired immune response.
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http://dx.doi.org/10.3389/fimmu.2021.637983DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009993PMC
March 2021

Variation in global treatment for subaxial cervical spine isolated unilateral facet fractures.

Eur Spine J 2021 Apr 2. Epub 2021 Apr 2.

Department of Orthopaedic Surgery, Rothman Institute at Thomas Jefferson University Hospital, 925 Chestnut St, 5th Floor, Philadelphia, PA, 19107, USA.

Purpose: To determine the variation in the global treatment practices for subaxial unilateral cervical spine facet fractures based on surgeon experience, practice setting, and surgical subspecialty.

Methods: A survey was sent to 272 members of the AO Spine Subaxial Injury Classification System Validation Group worldwide. Questions surveyed surgeon preferences with regard to diagnostic work-up and treatment of fracture types F1-F3, according to the AO Spine Subaxial Cervical Spine Injury Classification System, with various associated neurologic injuries.

Results: A total of 161 responses were received. Academic surgeons use the facet portion of the AO Spine classification system less frequently (61.6%) compared to hospital-employed and private practice surgeons (81.1% and 81.8%, respectively) (p = 0.029). The overall consensus was in favor of operative treatment for any facet fracture with radicular symptoms (N2) and for any fractures categorized as F2N2 and above. For F3N0 fractures, significantly less surgeons from Africa/Asia/Middle East (49%) and Europe (59.2%) chose operative treatment than from North/Latin/South America (74.1%) (p = 0.025). For F3N1 fractures, significantly less surgeons from Africa/Asia/Middle East (52%) and Europe (63.3%) recommended operative treatment than from North/Latin/South America (84.5%) (p = 0.001). More than 95% of surgeons included CT in their work-up of facet fractures, regardless of the type. No statistically significant differences were seen in the need for MRI to decide treatment.

Conclusion: Considerable agreement exists between surgeon preferences with regard to unilateral facet fracture management with few exceptions. F2N2 fracture subtypes and subtypes with radiculopathy (N2) appear to be the threshold for operative treatment.
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http://dx.doi.org/10.1007/s00586-021-06818-zDOI Listing
April 2021

Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.

J Orthop Res 2021 Mar 18. Epub 2021 Mar 18.

Orthopedic Oncology Service, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.
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http://dx.doi.org/10.1002/jor.25036DOI Listing
March 2021