Jun Kishii

Juntendo University

Papers

2

Total Citations

23

H-Index

2

About

Jun Kishii is a clinical researcher specializing in anesthesiology and perioperative pain management, with a particular focus on optimizing postoperative analgesia for patients undergoing robot-assisted thoracic surgery (RATS). As minimally invasive robotic surgical techniques have expanded in thoracic oncology and mediastinal disease treatment, Kishii has positioned himself at the forefront of evaluating analgesic strategies tailored to this evolving surgical landscape. His most impactful work, cited 15 times, directly compared thoracic epidural analgesia against intercostal nerve block combined with intravenous patient-controlled analgesia in RATS patients, providing clinicians with much-needed evidence to guide pain management decisions in a previously understudied context. Building on this foundation, his 2023 study — already accumulating 8 citations — extended this inquiry to mediastinal disease cases, systematically evaluating general anesthesia alone versus combined epidural or ultrasound-guided thoracic paraspinal block approaches. Kishii's contributions are particularly valuable because they address a genuine gap in clinical knowledge: while robotic thoracic surgery has grown rapidly in adoption, its specific analgesic requirements had remained largely uninvestigated. His comparative, evidence-based methodology offers practical guidance for anesthesiologists navigating this emerging surgical frontier.

Research Focus

Key Achievements

2
H-Index
2
Papers
23
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
Postoperative analgesia in patients undergoing robot-assisted thoracic surgery: a comparison between thoracic epidural analgesia and intercostal nerve block combined with intravenous patient-controlled analgesia
15 citations · 2021
📈 Most Prolific Year: 2021 (1 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: Juntendo University

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago