OBJECTIVE: The purposes of this study were to identify the most important determinants from the patient history and clinical examination in diagnosing musculoskeletal chest pain (MSCP) in patients with acute noncardiac chest pain when supported by a structured protocol and to construct a decision tree for identification of MSCP in acute noncardiac chest pain. METHODS: Consecutive patients with noncardiac chest pain (n = 302) recruited from an emergency cardiology department were assessed. Using data from self-report questionnaires, interviews, and clinical assessment, patient characteristics were associated with the MSCP diagnosis, and the decision-making process of the clinician was reconstructed using recursive procedures in the tradition of constructing Classification and Regression Trees. RESULTS: Thirty-eight percent of patients had MSCP. There was no single determinant that predicted the condition completely. However, many items with high associations could be identified, mainly with high negative predictive value. The decision-making process was reconstructed giving rise to a 5-step, linear decision tree without branches. CONCLUSIONS: Clinicians use a combination of indicators including systematic palpation of the spine and chest wall and items from the case history to diagnose MSCP. However, the high negative predictive values of the main determinants suggest that the MSCP diagnosis may be a diagnosis by exclusion.
OBJECTIVE: The purposes of this study were to identify the most important determinants from the patient history and clinical examination in diagnosing musculoskeletal chest pain (MSCP) in patients with acute noncardiac chest pain when supported by a structured protocol and to construct a decision tree for identification of MSCP in acute noncardiac chest pain. METHODS: Consecutive patients with noncardiac chest pain (n = 302) recruited from an emergency cardiology department were assessed. Using data from self-report questionnaires, interviews, and clinical assessment, patient characteristics were associated with the MSCP diagnosis, and the decision-making process of the clinician was reconstructed using recursive procedures in the tradition of constructing Classification and Regression Trees. RESULTS: Thirty-eight percent of patients had MSCP. There was no single determinant that predicted the condition completely. However, many items with high associations could be identified, mainly with high negative predictive value. The decision-making process was reconstructed giving rise to a 5-step, linear decision tree without branches. CONCLUSIONS: Clinicians use a combination of indicators including systematic palpation of the spine and chest wall and items from the case history to diagnose MSCP. However, the high negative predictive values of the main determinants suggest that the MSCP diagnosis may be a diagnosis by exclusion.
Authors: Maria M Wertli; Tenzin D Dangma; Sarah E Müller; Laura M Gort; Benjamin S Klauser; Lina Melzer; Ulrike Held; Johann Steurer; Susann Hasler; Jakob M Burgstaller Journal: PLoS One Date: 2019-02-01 Impact factor: 3.240