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Stewart J, Sahgal A, Chan AKM, Soliman H, Tseng CL, Detsky J, Myrehaug S, Atenafu EG, Helmi A, Perry J, Keith J, Jane Lim-Fat M, Munoz DG, Zadeh G, Shultz DB, Das S, Coolens C, Alcaide-Leon P, Maralani PJ. Pattern of Recurrence of Glioblastoma Versus Grade 4 IDH-Mutant Astrocytoma Following Chemoradiation: A Retrospective Matched-Cohort Analysis. Technol Cancer Res Treat 2022; 21:15330338221109650. [PMID: 35762826 PMCID: PMC9247382 DOI: 10.1177/15330338221109650] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background and Purpose: To quantitatively compare the recurrence
patterns of glioblastoma (isocitrate dehydrogenase-wild type) versus grade 4
isocitrate dehydrogenase-mutant astrocytoma (wild type isocitrate dehydrogenase
and mutant isocitrate dehydrogenase, respectively) following primary
chemoradiation. Materials and Methods: A retrospective matched
cohort of 22 wild type isocitrate dehydrogenase and 22 mutant isocitrate
dehydrogenase patients were matched by sex, extent of resection, and corpus
callosum involvement. The recurrent gross tumor volume was compared to the
original gross tumor volume and clinical target volume contours from
radiotherapy planning. Failure patterns were quantified by the incidence and
volume of the recurrent gross tumor volume outside the gross tumor volume and
clinical target volume, and positional differences of the recurrent gross tumor
volume centroid from the gross tumor volume and clinical target volume.
Results: The gross tumor volume was smaller for wild type
isocitrate dehydrogenase patients compared to the mutant isocitrate
dehydrogenase cohort (mean ± SD: 46.5 ± 26.0 cm3 vs
72.2 ± 45.4 cm3, P = .026). The recurrent gross
tumor volume was 10.7 ± 26.9 cm3 and 46.9 ± 55.0 cm3
smaller than the gross tumor volume for the same groups
(P = .018). The recurrent gross tumor volume extended outside
the gross tumor volume in 22 (100%) and 15 (68%) (P= .009) of
wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase patients,
respectively; however, the volume of recurrent gross tumor volume outside the
gross tumor volume was not significantly different (12.4 ± 16.1 cm3
vs 8.4 ± 14.2 cm3, P = .443). The recurrent gross
tumor volume centroid was within 5.7 mm of the closest gross tumor volume edge
for 21 (95%) and 22 (100%) of wild type isocitrate dehydrogenase and mutant
isocitrate dehydrogenase patients, respectively. Conclusion: The
recurrent gross tumor volume extended beyond the gross tumor volume less often
in mutant isocitrate dehydrogenase patients possibly implying a differential
response to chemoradiotherapy and suggesting isocitrate dehydrogenase status
might be used to personalize radiotherapy. The results require validation in
prospective randomized trials.
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Affiliation(s)
- James Stewart
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook 151192Odette Cancer Centre, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, 7938University of Toronto, 7989University Health Network, Toronto, Ontario, Canada
| | - Ali Helmi
- Department of Medical Imaging, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - James Perry
- Division of Neurology, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Julia Keith
- Department of Laboratory Medicine & Pathobiology, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - David G Munoz
- Department of Pathology, 7938University of Toronto, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Gelareh Zadeh
- Division of Neurosurgery, Department of Surgery, 7938University of Toronto, 7989University Health Network, Toronto, Ontario, Canada
| | - David B Shultz
- Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7989University Health Network, Toronto, Ontario, Canada
| | - Sunit Das
- Division of Neurosurgery, Department of Surgery, 7938University of Toronto, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Catherine Coolens
- Department of Radiation Oncology, 7938University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, 7989University Health Network, Toronto, Ontario, Canada
| | - Paula Alcaide-Leon
- Department of Medical Imaging, 7938University of Toronto, 7989University Health Network, Toronto, Ontario, Canada
| | - Pejman Jabehdar Maralani
- Department of Medical Imaging, 7938University of Toronto, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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2
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Jabehdar Maralani P, Tseng CL, Baharjoo H, Wong E, Kapadia A, Dasgupta A, Howard P, Chan AKM, Atenafu EG, Lu H, Tyrrell P, Das S, Payabvash S, Detsky J, Husain Z, Myrehaug S, Soliman H, Chen H, Heyn C, Symons S, Sahgal A. The Initial Step Towards Establishing a Quantitative, Magnetic Resonance Imaging-Based Framework for Response Assessment of Spinal Metastases After Stereotactic Body Radiation Therapy. Neurosurgery 2021; 89:884-891. [PMID: 34392364 PMCID: PMC8645191 DOI: 10.1093/neuros/nyab310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/09/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND There are no established threshold values regarding the degree of growth on imaging when assessing response of spinal metastases treated with stereotactic body radiation therapy (SBRT). OBJECTIVE To determine a magnetic resonance imaging-based minimum detectable difference (MDD) in gross tumor volume (GTV) and its association with 1-yr radiation site-specific (RSS) progression-free survival (PFS). METHODS GTVs at baseline and first 2 post-SBRT scans (Post1 and Post2, respectively) for 142 spinal segments were contoured, and percentage volume change between scans calculated. One-year RSS PFS was acquired from medical records. The MDD was determined. The MDD was compared against optimal thresholds of GTV changes associated with 1-yr RSS PFS using Youden's J index, and receiver operating characteristic curves between timepoints compared to determine which timeframe had the best association. RESULTS A total of 17 of the 142 segments demonstrated progression. The MDD was 10.9%. Baseline-Post2 demonstrated the best performance (area under the curve [AUC] 0.90). Only Baseline-Post2 had an optimal threshold > MDD at 14.7%. Due to large distribution of GTVs, volumes were split into tertiles. Small tumors (GTV < 2 cc) had optimal thresholds of 42.0%, 71.3%, and 37.2% at Baseline-Post1 (AUC 0.81), Baseline-Post2 (AUC 0.89), and Post1-Post2 (AUC 0.77), respectively. Medium tumors (2 ≤ GTV ≤ 8.3 cc) all demonstrated optimal thresholds < MDD, with AUCs ranging from 0.65 to 0.84. Large tumors (GTV > 8.3 cc) had 2 timepoints where optimal thresholds > MDD: Baseline-Post2 (13.3%; AUC 0.97) and Post1-Post2 (11.8%; AUC 0.66). Baseline-Post2 had the best association with RSS PFS for all tertiles. CONCLUSION Given a MDD of 10.9%, for small GTVs, larger (>37%) changes were required before local failure could be determined, compared to 11% to 13% for medium/large tumors.
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Affiliation(s)
| | - Chia-Lin Tseng
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | | | - Erin Wong
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Anish Kapadia
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Peter Howard
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, University Health Network, Toronto, Canada
| | - Hua Lu
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Pascal Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Sunit Das
- Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Canada
| | | | - Jay Detsky
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Chris Heyn
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Sean Symons
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
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3
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Jabehdar Maralani P, Myrehaug S, Mehrabian H, Chan AKM, Wintermark M, Heyn C, Conklin J, Ellingson BM, Rahimi S, Lau AZ, Tseng CL, Soliman H, Detsky J, Daghighi S, Keith J, Munoz DG, Das S, Atenafu EG, Lipsman N, Perry J, Stanisz G, Sahgal A. Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma. Radiother Oncol 2021; 156:258-265. [PMID: 33418005 PMCID: PMC8186561 DOI: 10.1016/j.radonc.2020.12.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022]
Abstract
Background: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. Methods: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. Results: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS. Conclusion: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada.
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hatef Mehrabian
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Max Wintermark
- Department of Radiology, Stanford University, United States
| | - Chris Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, United States
| | - Benjamin M Ellingson
- Department of Radiological Sciences and Psychiatry, University of California Los Angeles, United States
| | - Saba Rahimi
- Department of Biomedical Engineering, University of Toronto, Canada
| | - Angus Z Lau
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Shadi Daghighi
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Julia Keith
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - David G Munoz
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - Sunit Das
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | | | - Nir Lipsman
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | - James Perry
- Department of Medicine, Division of Neurology, University of Toronto, Canada
| | - Greg Stanisz
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
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4
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Chan RW, Chen H, Myrehaug S, Atenafu EG, Stanisz GJ, Stewart J, Maralani PJ, Chan AKM, Daghighi S, Ruschin M, Das S, Perry J, Czarnota GJ, Sahgal A, Lau AZ. Quantitative CEST and MT at 1.5T for monitoring treatment response in glioblastoma: early and late tumor progression during chemoradiation. J Neurooncol 2020; 151:267-278. [PMID: 33196965 DOI: 10.1007/s11060-020-03661-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/07/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Quantitative MRI (qMRI) was performed using a 1.5T protocol that includes a novel chemical exchange saturation transfer/magnetization transfer (CEST/MT) approach. The purpose of this prospective study was to determine if qMRI metrics at baseline, at the 10th and 20th fraction during a 30 fraction/6 week standard chemoradiation (CRT) schedule, and at 1 month following treatment could be an early indicator of response for glioblastoma (GBM). METHODS The study included 51 newly diagnosed GBM patients. Four regions-of-interest (ROI) were analyzed: (i) the radiation defined clinical target volume (CTV), (ii) radiation defined gross tumor volume (GTV), (iii) enhancing-tumor regions, and (iv) FLAIR-hyperintense regions. Quantitative CEST, MT, T1 and T2 parameters were compared between those patients progressing within 6.9 months (early), and those progressing after CRT (late), using mixed modelling. Exploratory predictive modelling was performed to identify significant predictors of early progression using a multivariable LASSO model. RESULTS Results were dependent on the specific tumor ROI analyzed and the imaging time point. The baseline CEST asymmetry within the CTV was significantly higher in the early progression cohort. Other significant predictors included the T2 of the MT pools (for semi-solid at fraction 20 and water at 1 month after CRT), the exchange rate (at fraction 20) and the MGMT methylation status. CONCLUSIONS We observe the potential for multiparametric qMRI, including a novel pulsed CEST/MT approach, to show potential in distinguishing early from late progression GBM cohorts. Ultimately, the goal is to personalize therapeutic decisions and treatment adaptation based on non-invasive imaging-based biomarkers.
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Affiliation(s)
- Rachel W Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Greg J Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aimee K M Chan
- Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Shadi Daghighi
- Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sunit Das
- Division of Neurosurgery, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - James Perry
- Division of Neurology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Angus Z Lau
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
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5
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Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: early experience from the COVID-19 pandemic. Anaesthesia 2020; 75:1579-1582. [PMID: 32227594 PMCID: PMC7228334 DOI: 10.1111/anae.15057] [Citation(s) in RCA: 187] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2020] [Indexed: 01/06/2023]
Affiliation(s)
- A K M Chan
- Department of Anaesthesia and Intensive Care, Prince of Wales Hospital, Shatin, Hong Kong
| | - C P Nickson
- School of Public Health and Preventative Medicine, Monash University, Melbourne, Vic., Australia
| | - J W Rudolph
- Center for Medical Simulation, Harvard Medical School, Boston, MA, USA
| | - A Lee
- Department of Anaesthesia and Intensive Care, Chinese University of Hong Kong, Shatin, Hong Kong
| | - G M Joynt
- Department of Anaesthesia and Intensive Care, Chinese University of Hong Kong, Shatin, Hong Kong
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Ng LY, Chan AKM, Lam TWY. The use of high-flow nasal oxygen during airway management in a child with epidermolysis bullosa dystrophica and a difficult airway. Anaesth Rep 2019; 7:96-99. [PMID: 32051961 DOI: 10.1002/anr3.12031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2019] [Indexed: 11/11/2022] Open
Abstract
The role of high-flow nasal oxygen in paediatric anaesthesia has been emerging in recent years. However, literature regarding its benefits in paediatric difficult airway management is limited. In this case report, we describe the use of high-flow nasal oxygen during airway management of a child with a difficult airway due to epidermolysis bullosa dystrophica in whom the use of a facemask would have been potentially harmful. Deep sedation was achieved with propofol and remifentanil while maintaining spontaneous breathing before flexible bronchoscopic tracheal intubation was attempted. However, on attempted tracheal intubation difficulty was encountered due to poor visualisation and contact bleeding. Tracheal intubation was eventually successful after converting to videolaryngoscopy. Oxygenation was maintained throughout the process despite deep sedation and a long procedure time. Moreover, no skin abrasions or mucosal injury resulted from the use of high-flow nasal oxygen. We conclude that high-flow nasal oxygen has a valuable role during airway management for a child with a predicted difficult airway when the use of a facemask would have been potentially harmful.
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Affiliation(s)
- L Y Ng
- Department of Anaesthesia and Intensive Care Prince of Wales Hospital Hong Kong HKSAR
| | - A K M Chan
- Department of Anaesthesia and Intensive Care Prince of Wales Hospital Hong Kong HKSAR
| | - T W Y Lam
- Department of Anaesthesia and Intensive Care Prince of Wales Hospital Hong Kong HKSAR
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