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Fajardo Pérez M, Yamak-Altinpulluk E, Díez Tafur R, Salazar-Zamorano CH, Espinosa Morales K, Oliver-Fornies P, Rocha-Romero A, Aguilar Ureña R, Juarez-Lemus A, Galluccio F, Abd-Elsayed A. Novel ultrasound-guided supraclavicular stellate ganglion block. Pain Pract 2024; 24:808-814. [PMID: 38251786 DOI: 10.1111/papr.13350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
INTRODUCTION Stellate ganglion block (SGB) provides diagnostic and therapeutic benefits in pain syndromes in the head, neck, and upper extremity, including complex regional pain syndrome Types I and II, Raynaud's disease, hyperhidrosis, arterial embolism in the region of the arm. METHODS We present a novel ultrasound-guided supraclavicular stellate ganglion block. Considering the existing anatomical structures of the targeted area. RESULTS AND CONCLUSIONS We hope that we can provide fewer complications and additional benefits with this new approach.
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Affiliation(s)
- Mario Fajardo Pérez
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
| | - Ece Yamak-Altinpulluk
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Anesthesiology Clinical Research Office, Ataturk University, Erzurum, Turkey
- Outcomes Research Consortium, Cleveland, Ohio, USA
| | - Rodrigo Díez Tafur
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Centro MDRS - Sports, Spine & Pain Center: Lima Pain Institute, Lima, Peru
- Clínica Angloamericana British American Hospital, Lima, Peru
- Latin American Pain Society (LAPS), New York, New York, USA
| | - Carlos H Salazar-Zamorano
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Department of Anesthesia, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Karla Espinosa Morales
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Department of Anesthesia and Pain Medicine, Hospital de Trauma, Centro Integral de Salud de Puriscal, San José, Costa Rica
| | - Pablo Oliver-Fornies
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Department of Anesthesiology, Critical Care and Pain Medicine, Móstoles University Hospital, Móstoles, Spain
- Aragon Institute for Health Research, Zaragoza, Spain
| | - Andrés Rocha-Romero
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Department of Anesthesia and Pain Medicine, Hospital de Trauma, Centro Integral de Salud de Puriscal, San José, Costa Rica
- Department of Anesthesia and Pain Management, Centro Nacional de Rehabilitacion, Hospital de Trauma, San José, Costa Rica
| | - Ricardo Aguilar Ureña
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Department of Anesthesiology, Critical Care and Pain Medicine, Centro Nacional de Rehabilitacion, San José, Costa Rica
| | - Angel Juarez-Lemus
- Department of Pain Medicine, National Cancer Institute, Mexico City, Mexico
| | - Felice Galluccio
- Morphological Madrid Research Center (MoMaRC), Ultradissection Spain EchoTraining School, Madrid, Spain
- Fisiotech Lab Studio, Rheumatology and Pain Management, Firenze, Italy
- Center for Regional Anesthesia and Pain Medicine (CRAPM), Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Alaa Abd-Elsayed
- Anesthesiology Department, University of Wisconsin, Madison, Wisconsin, USA
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Zeman J, Pettinato AM, Ladha FA, Lico I, Crespo EM, Givertz MM. Recurrent premature ventricular complex-triggered idiopathic polymorphic ventricular arrhythmias in a patient with a structurally normal heart. HeartRhythm Case Rep 2023; 9:888-892. [PMID: 38204821 PMCID: PMC10774531 DOI: 10.1016/j.hrcr.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024] Open
Affiliation(s)
- Jan Zeman
- Department of Internal Medicine, University of Connecticut, Farmington, Connecticut
| | - Anthony M. Pettinato
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Feria A. Ladha
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ina Lico
- Department of Internal Medicine, University of Connecticut, Farmington, Connecticut
| | - Eric M. Crespo
- Department of Electrophysiology, Heart and Vascular Institute, Hartford Hospital, Hartford, Connecticut
| | - Michael M. Givertz
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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Hyun KY, Kim JJ, Im KS, Lee BS, Kim YJ. Machine learning analysis of primary hyperhidrosis for classification of hyperhidrosis type and prediction of compensatory hyperhidrosis. J Thorac Dis 2023; 15:4808-4817. [PMID: 37868857 PMCID: PMC10586983 DOI: 10.21037/jtd-23-471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/11/2023] [Indexed: 10/24/2023]
Abstract
Background Although sympathectomy is highly effective for improving symptom, compensatory hyperhidrosis (CH) is a major issue. In this study, characteristics of primary hyperhidrosis were investigated in terms of the heart rate variability (HRV) parameters. Classification of hyperhidrosis type and prediction of CH after sympathicotomy were also determined using machine learning analysis. Methods From March 2017 to December 2021, 128 subjects who underwent HRV tests before sympathicotomy were analyzed. T2 and T3 bilateral endoscopic sympathicotomy were routinely performed in patients with craniofacial and palmar hyperhidrosis, respectively. Data collected age, sex, body mass index (BMI), hyperhidrosis type, symptom improvement after sympathicotomy, the degrees of CH after sympathicotomy, and preoperative HRV findings. The independent risk factors associated with the degree of CH after sympathicotomy were investigated. Machine learning analysis was used to determine classification of hyperhidrosis type and prediction of the degree of CH. Results Preoperatively, patients with palmar hyperhidrosis presented with significantly larger standard deviation of normal-to-normal (SDNN), root mean square of successive differences (RMSSD), total power (TP), and low frequency (LF) than patients with craniofacial hyperhidrosis after controlling for age and sex (P=0.030, P=0.004, P=0.041, and P=0.022, respectively). More sympathetic nervous predominance was found in craniofacial type (P=0.019). Low degree of CH had significantly greater RMSSD (P=0.047), and high degree of CH showed more sympathetic nervous predominance (P=0.006). Multivariate analysis showed the type and expansion of sympathicotomy were significant factors for CH (P=0.001 and P=0.028, respectively). The neural network (NN) algorithm outperformed and showed a 0.961 accuracy, 0.961 F1 score, 0.961 precision, 0.961 recall, and 0.972 area under the curve (AUC) for classification of hyperhidrosis type. The random forest (RF) model outperformed showed a 0.852 accuracy, 0.853 F1 score, 0.856 precision, 0.852 recall, and 0.914 AUC for prediction of the degree of CH. Conclusions The present study showed the machine learning algorithm can classify types and predict CH after sympathicotomy for primary hyperhidrosis with considerable accuracy. Further large-scale studies are needed to validate the findings and provide management guidelines for primary hyperhidrosis.
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Affiliation(s)
- Kwan Yong Hyun
- Department of Thoracic and Cardiovascular Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Jae Jun Kim
- Department of Thoracic and Cardiovascular Surgery, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Kyong Shil Im
- Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Bong Sung Lee
- Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Yun Ji Kim
- Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
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