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C0068 Prediction model for minimising the risk of median nerve puncture with dry needling approach at pronator teres muscle
  1. Daniel Muñoz García1,
  2. Raúl Ferrer Peña2,
  3. Juan Antonio Valera Calero3,
  4. Rubén Conde Lima3,
  5. Israel del Río Santamaría4
  1. 1Daniel Muñoz García Madrid, Madrid, España
  2. 2Centro Superior de Estudios Universitarios La Salle, Madrid, España
  3. 3Fisioterapeuta en el ejercicio libre de la profesión, Madrid, España
  4. 4Servicio de Rehabilitación Hospital Recoletas, Burgos, España


Background The pronator teres is located in the anterior forearm; anatomically, it is described as having two heads, i.e., the ulnar head and the humeral head. The median nerve runs between the two heads and leaves the muscle 5 to 8 cm distal the lateral epicondyle; however, some variations are possible in this distribution. Pronator teres syndrome is a median nerve compression condition that causes 5% of median neuropathies, and it is a common cause of medial epicondylitis and carpal tunnel syndrome. One treatment for these conditions is dry needling approach for the pronator teres trigger points. The use of the ultrasound prevents the formation of lesions in the medial nerve, which it is an undesirable effect of this technique.

The main aim of this study was to correlate anthropometric measures of the forearm in healthy subjects with the depth of the pronator teres to guide the decision of the choice of the length of needle in order to avoid injury to the nerve during the dry needle approach.

Methods In a cross-sectional study with a total of 65 participants, a predictive model for median nerve depth in the pronator teres was constructed using a Decision Tree Analysis algorithm (SPSS 22.0, IBM, Armonk, NY, USA). This provides a clinically comprehensive classification algorithm for clinical practice that will allow clinicians to profile the individual risk for a given patient using two needle lengths (13 mm or 25 mm). The decision tree was developed using an exhaustive Chi-squared Automatic Interaction Detector (CHAID), i.e., a recursive partitioning method that builds classification trees for predicting categorical predictor variables by automatically selecting a cut-off for all parameters, including body mass index (kg/m2), forearm length, forearm circumference, and pronator teres thickness.

Results The algorithm showed a sensitivity of 87.7% for predicting depth using the forearm circumference. When the patient’s forearm circumference is less than or equal to 27.5 cm, the predictive value for using the 13 mm needle is 92%, and when the patient’s forearm circumference is more than 27.5 cm, the 25 mm needle can be uses with 100% confidence when the forearm length also does not exceed 27 cm.

Conclusions Research using ultrasound can help improve clinical decision making when proceeding with dry needling to avoid damage to the median nerve. Nevertheless, ultrasound guided dry needling approach in clinical setting is recommended.

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