Conceptio › Archive › ClinicalTrials.gov
ClinicalTrials.gov public full text Public Domain

Deep Neuromuscular Block for Laparoscopic Surgery

University of Melbourne · 2017
ClinicalTrials.gov · Datasets · License: Public Domain · 2017
Open Source ↗
laparoscopy
Trial summary: deep neuromuscular block is proposed as a technique to improve operative conditions for laparoscopy. Early clinical data would suggest that there may also be patient benefits beyond the operative period related to lower intra-abdominal pressure, and improved surgical exposure. In order to safely conduct deep neuromuscular blockade, it is essential to use Sugammadex to reverse the neuromuscular block. Conventional practice is to provide moderate neuromuscular block and reverse with neostigmine. It is not possible to safely reverse deep neuromuscular block using neostogmine, as the majority of block must have worn off for neostigmine to be effective. in order to identify whether deep neuromuscular block improves quality of recovery after surgery, the investigators will conduct a randomised trial of deep versus moderate neuromuscular block, whilst minimising variance in other anaesthetic techniques and drugs used. the outcome measured will be the post-operative quality of recovery over multiple time periods using the Postoperative Quality of Recovery Scale (PostopQRS). 350 patients will be enrolled over 4 centres.
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 1294236
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive