Sarah L Hillary1, Brian H Brown2, Nicola J Brown2, Saba P Balasubramanian3. 1. University of Sheffield, Sheffield, UK. sarah.hillary@doctors.org.uk. 2. University of Sheffield, Sheffield, UK. 3. Sheffield Teaching Hospitals NHS Foundation Trust, University of Sheffield, Sheffield, UK.
Abstract
BACKGROUND: Electrical impedance (EI) measures tissue resistance to alternating current across several frequencies and may help identify tissue type. A recent rabbit model demonstrated that electrical impedance spectroscopy (EIS) may facilitate identification of parathyroid glands and potentially improve outcomes following surgery. This study looks at the EI patterns of soft tissues in the human neck to determine whether parathyroid tissue can be accurately identified. METHODS: This was a phase 1, single-arm interventional study involving 56 patients undergoing thyroid and/or parathyroid surgery. Up to 12 EI readings were taken from in vivo and ex vivo thyroid and parathyroid glands, adipose tissue and muscle of each patient. Each reading consists of a series of measurements over 14 frequencies from each tissue. EI patterns were analysed. Two patients were excluded due to data loss due to device malfunction. RESULTS: The median age of participants was 53.5 (range 20-85) years. Thirty-five participants had surgery for thyroid pathology, 17 for parathyroid pathology and four for both. Six hundred and six EIS spectra were reviewed for suitability. One hundred and eighty-four spectra were rejected leaving 422 spectra for analysis. The impedance patterns of the soft tissues differed by histological type. The EI ratio of low (152 Hz) to high (312 kHz) frequencies demonstrated a significant difference between the soft tissues (p = 0.006). Using appropriate thresholds, parathyroid tissue can be distinguished from thyroid tissue with a sensitivity of 76% and specificity of 60%. CONCLUSIONS: This study demonstrates the feasibility of using EIS to aid parathyroid identification and preservation. Further changes to the device and modelling of the EI patterns across the range of frequencies may improve accuracy and facilitate intraoperative use. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02901873).
BACKGROUND: Electrical impedance (EI) measures tissue resistance to alternating current across several frequencies and may help identify tissue type. A recent rabbit model demonstrated that electrical impedance spectroscopy (EIS) may facilitate identification of parathyroid glands and potentially improve outcomes following surgery. This study looks at the EI patterns of soft tissues in the human neck to determine whether parathyroid tissue can be accurately identified. METHODS: This was a phase 1, single-arm interventional study involving 56 patients undergoing thyroid and/or parathyroid surgery. Up to 12 EI readings were taken from in vivo and ex vivo thyroid and parathyroid glands, adipose tissue and muscle of each patient. Each reading consists of a series of measurements over 14 frequencies from each tissue. EI patterns were analysed. Two patients were excluded due to data loss due to device malfunction. RESULTS: The median age of participants was 53.5 (range 20-85) years. Thirty-five participants had surgery for thyroid pathology, 17 for parathyroid pathology and four for both. Six hundred and six EIS spectra were reviewed for suitability. One hundred and eighty-four spectra were rejected leaving 422 spectra for analysis. The impedance patterns of the soft tissues differed by histological type. The EI ratio of low (152 Hz) to high (312 kHz) frequencies demonstrated a significant difference between the soft tissues (p = 0.006). Using appropriate thresholds, parathyroid tissue can be distinguished from thyroid tissue with a sensitivity of 76% and specificity of 60%. CONCLUSIONS: This study demonstrates the feasibility of using EIS to aid parathyroid identification and preservation. Further changes to the device and modelling of the EI patterns across the range of frequencies may improve accuracy and facilitate intraoperative use. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02901873).
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