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A Practical Dosing Algorithm for Deep Neuromuscular Blockade during Total Intravenous Anesthesia: ROCURITHM.

Kim I Albers-Warlé1, Gabby T J A Reijnders-Boerboom1, Veerle Bijkerk1

  • 1Department of Anesthesiology, Radboudumc, Nijmegen, The Netherlands.

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This study developed a dosing algorithm for deep neuromuscular blockade (NMB) using rocuronium during total intravenous anesthesia. The algorithm optimizes rocuronium dosage to maintain a posttetanic count (PTC) of 1-2, improving surgical conditions.

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Area of Science:

  • Anesthesiology
  • Pharmacology
  • Surgical Safety

Background:

  • Deep neuromuscular blockade (NMB) use is increasing, but consensus on optimal application is lacking.
  • Guidelines recommend deep NMB for specific indications to improve surgical conditions.
  • Optimal dosing strategies for deep NMB during total intravenous anesthesia require investigation.

Purpose of the Study:

  • To investigate an optimal dosing strategy for deep neuromuscular blockade (NMB).
  • To develop and validate a rocuronium dosing algorithm for deep NMB during total intravenous anesthesia.
  • To achieve and maintain a posttetanic count (PTC) of 1 to 2.

Main Methods:

  • Pooled data from three trials (n=424) on deep NMB during laparoscopic surgery with total intravenous anesthesia.
  • Analyzed rocuronium dose, infusion start time, and adjustment strategies.
  • Validated the developed algorithm in a separate cohort (n=32) and compared it to non-algorithm use (n=180).

Main Results:

  • The algorithm achieved a mean of 76% of measurements within the target PTC of 1-2 in the validation cohort.
  • Rocuronium dose averaged 1.0 ± 0.27 mg·kg-1·h-1 for PTC 1-2 during 116 min surgery.
  • The algorithm demonstrated significantly better performance than anesthesiology residents without the algorithm.

Conclusions:

  • A dosing algorithm for deep neuromuscular blockade with rocuronium during total intravenous anesthesia is proposed.
  • This algorithm aids in achieving and maintaining target neuromuscular blockade levels.
  • The validated algorithm shows improved efficacy compared to standard practice.