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Surgical Microtia Reconstruction in Hemifacial Microsomia Patients: Current State and Future Directions. Plast Reconstr Surg Glob Open 2022; 10:e4486. [PMID: 36032368 PMCID: PMC9400929 DOI: 10.1097/gox.0000000000004486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/27/2022] [Indexed: 11/27/2022]
Abstract
Background: Hemifacial microsomia (HFM) is one of the most common congenital craniofacial disorders. Among many other features, microtia is present in the large majority of these patients. However, mainly due to the unilateral hypoplastic anatomy, microtia reconstruction among this patient population remains a reconstructive challenge for plastic surgeons. Given that no clear standards exist, an evidence-based synthesis of the literature was devised. Methods: A systematic search of Pubmed, Medline, and Embase was carried out, in accordance with the PRISMA guidelines. Studies discussing surgical microtia reconstruction for HFM patients were retained. Qualitative data regarding study design, challenges addressed, specific recommendations, and their respective strengths/limitations were extracted from each. Retrieved recommendations were consolidated and assigned a level of evidence grade. Results: Although only 11 studies were included in this review, these provided 22 main recommendations regarding the eight HFM-specific challenges identified, which were of either grade C (n = 5) or D (n = 17). Included studies addressed construct location (n = 7), the low hairline (n = 6), soft tissue construct coverage (n = 6), earlobe reconstruction (n = 6), construct projection (n = 5), anomalies of the relevant neurovascular systems (n = 2), retroauricular construct coverage (n = 2), and sizing of the construct (n = 2). Conclusions: Given the many persisting reconstructive challenges regarding surgical microtia reconstruction for HFM patients, the authors present a comprehensive and evidence-based consolidation of recommendations specific to these challenges. The authors hope this systematic review can appropriately guide plastic surgeons and will ultimately improve care for this patient population.
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Howell AE, Robinson JW, Wootton RE, McAleenan A, Tsavachidis S, Ostrom QT, Bondy M, Armstrong G, Relton C, Haycock P, Martin RM, Zheng J, Kurian KM. Testing for causality between systematically identified risk factors and glioma: a Mendelian randomization study. BMC Cancer 2020; 20:508. [PMID: 32493226 PMCID: PMC7268455 DOI: 10.1186/s12885-020-06967-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 05/17/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Whilst epidemiological studies have provided evidence of associations between certain risk factors and glioma onset, inferring causality has proven challenging. Using Mendelian randomization (MR), we assessed whether associations of 36 reported glioma risk factors showed evidence of a causal relationship. METHODS We performed a systematic search of MEDLINE from inception to October 2018 to identify candidate risk factors and conducted a meta-analysis of two glioma genome-wide association studies (5739 cases and 5501 controls) to form our exposure and outcome datasets. MR analyses were performed using genetic variants to proxy for candidate risk factors. We investigated whether risk factors differed by subtype diagnosis (either glioblastoma (n = 3112) or non-glioblastoma (n = 2411)). MR estimates for each risk factor were determined using multiplicative random effects inverse-variance weighting (IVW). Sensitivity analyses investigated potential pleiotropy using MR-Egger regression, the weighted median estimator, and the mode-based estimator. To increase power, trait-specific polygenic risk scores were used to test the association of a genetically predicated increase in each risk factor with glioma onset. RESULTS Our systematic search identified 36 risk factors that could be proxied using genetic variants. Using MR, we found evidence that four genetically predicted traits increased risk of glioma, glioblastoma or non-glioblastoma: longer leukocyte telomere length, liability to allergic disease, increased alcohol consumption and liability to childhood extreme obesity (> 3 standard deviations from the mean). Two traits decreased risk of non-glioblastoma cancers: increased low-density lipoprotein cholesterol (LDLc) and triglyceride levels. Our findings were similar across sensitivity analyses that made allowance for pleiotropy (genetic confounding). CONCLUSIONS Our comprehensive investigation provides evidence of a causal link between both genetically predicted leukocyte telomere length, allergic disease, alcohol consumption, childhood extreme obesity, and LDLc and triglyceride levels, and glioma. The findings from our study warrant further research to uncover mechanisms that implicate these traits in glioma onset.
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Affiliation(s)
- A E Howell
- Brain Tumour Research Centre, Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - J W Robinson
- Brain Tumour Research Centre, Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - R E Wootton
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, BS8 2BN, UK
| | - A McAleenan
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - S Tsavachidis
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, UK
| | - Q T Ostrom
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, UK
| | - M Bondy
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, UK
| | - G Armstrong
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, UK
| | - C Relton
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - P Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - R M Martin
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- The National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - J Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - K M Kurian
- Brain Tumour Research Centre, Institute of Clinical Neurosciences, University of Bristol, Bristol, UK.
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