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A severity-based classification framework for dry needling unintended responses and adverse events: An expert consensus Delphi

Musculoskelet Sci Pract. 2026 Jul 11;85:103618. doi: 10.1016/j.msksp.2026.103618. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop an expert-derived, consensus based framework for defining and classifying unintended responses (UR) and adverse events (AE) following dry needling (DN) through an exploratory, descriptive modified Delphi process.

METHODS: One hundred DN experts were invited to participate in a 3-round electronic Delphi. Eligibility required ≥3 years of DN practice, ≥5 years of clinical experience, and DN-related teaching and/or scholarship. A-priori consensus criteria were ≥80% agreement, median ≥3, and interquartile range ≤1. Round-1 used open-ended questions exploring UR and AE definitions and classifications. Rounds 2 and 3 required participants to rate agreement with statements derived from thematic analysis on a 4-point Likert scale. Descriptive statistics and Kendall’s coefficient of concordance and Wilcoxon rank-sum tests were calculated.

RESULTS: Sixty-five experts met inclusion criteria and completed Round 1. Fifty-two participants (80%) from nine countries completed all three rounds. Key consensus findings included: (1) UR categories including bleeding/bruising, neurologic responses, and autonomic responses; (2) classification of AE along a severity spectrum; (3) prioritizing severity and functional impact over response type when distinguishing UR from AE; and (4) four operational AE categories ranging from Minor Expected to Severe. Notably, pain and discomfort did not achieve consensus as an UR category.

CONCLUSION: This exploratory Delphi produced a preliminary, expert-derived framework for defining and classifying DN AE along a severity spectrum, pending prospective validation. Emphasizing severity and functional impact over prescriptive event typologies, this framework may support more consistent clinical documentation, patient communication, and research reporting.

PMID:42447540 | DOI:10.1016/j.msksp.2026.103618

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