OBJECTIVE: The study aims to create a diagnostic support tool to indicate the likelihood of the presence of lumbar spinal stenosis (LSS) using a cluster of elements from the patient history and observational findings. DESIGN: The study is case based and case controlled. SETTING: The study was performed in the tertiary care of a medical center. SUBJECTS: There were a total of 1,448 patients who presented with a primary complaint of back pain with or without leg pain. METHODS: All patients underwent a standardized clinical examination. The diagnosis of LSS was made by one of two experienced orthopaedic surgeons based on clinical findings and imaging. Data from the patient history and observational findings were then statistically analysed using bivariate analysis and contingency tables. RESULTS: The most diagnostic combination included a cluster of: 1) bilateral symptoms; 2) leg pain more than back pain; 3) pain during walking/standing; 4) pain relief upon sitting; and 5) age>48 years. Failure to meet the condition of any one of five positive examination findings demonstrated a high sensitivity of 0.96 (95% CI=0.94-0.97) and a low negative likelihood ratio (LR-) of 0.19 (95% CI=0.12-0.29). Meeting the condition of four of five examination findings yielded a LR+ of 4.6 (95% CI=2.4-8.9) and a post-test probability of 76%. CONCLUSION: The high sensitivity of the diagnostic support tool provides the potential to reduce the incidence of unnecessary imaging when the diagnosis of LSS is statistically unlikely. In patients where the condition of four of the five findings was present, the post-test probability of 76% suggests that imaging and further workup are indicated. This is an inexpensive but powerful tool, with a potential to increase diagnostic efficiency and reduce cost by narrowing the indications for imaging.
OBJECTIVE: The study aims to create a diagnostic support tool to indicate the likelihood of the presence of lumbar spinal stenosis (LSS) using a cluster of elements from the patient history and observational findings. DESIGN: The study is case based and case controlled. SETTING: The study was performed in the tertiary care of a medical center. SUBJECTS: There were a total of 1,448 patients who presented with a primary complaint of back pain with or without leg pain. METHODS: All patients underwent a standardized clinical examination. The diagnosis of LSS was made by one of two experienced orthopaedic surgeons based on clinical findings and imaging. Data from the patient history and observational findings were then statistically analysed using bivariate analysis and contingency tables. RESULTS: The most diagnostic combination included a cluster of: 1) bilateral symptoms; 2) leg pain more than back pain; 3) pain during walking/standing; 4) pain relief upon sitting; and 5) age>48 years. Failure to meet the condition of any one of five positive examination findings demonstrated a high sensitivity of 0.96 (95% CI=0.94-0.97) and a low negative likelihood ratio (LR-) of 0.19 (95% CI=0.12-0.29). Meeting the condition of four of five examination findings yielded a LR+ of 4.6 (95% CI=2.4-8.9) and a post-test probability of 76%. CONCLUSION: The high sensitivity of the diagnostic support tool provides the potential to reduce the incidence of unnecessary imaging when the diagnosis of LSS is statistically unlikely. In patients where the condition of four of the five findings was present, the post-test probability of 76% suggests that imaging and further workup are indicated. This is an inexpensive but powerful tool, with a potential to increase diagnostic efficiency and reduce cost by narrowing the indications for imaging.
Authors: Christian Jaeger Cook; Chad E Cook; Michael P Reiman; Anand B Joshi; William Richardson; Alessandra N Garcia Journal: Eur Spine J Date: 2019-07-16 Impact factor: 3.134
Authors: Sean D Rundell; Ayumi Saito; Eric N Meier; Stephanie T Danyluk; Jeffrey G Jarvik; Kelley Seebeck; Janna L Friedly; Patrick J Heagerty; Sandra K Johnston; Monica Smersh; Maggie E Horn; Pradeep Suri; Amy M Cizik; Adam P Goode Journal: BMC Musculoskelet Disord Date: 2022-07-21 Impact factor: 2.562
Authors: Christy Tomkins-Lane; Markus Melloh; Jon Lurie; Matt Smuck; Michele C Battié; Brian Freeman; Dino Samartzis; Richard Hu; Thomas Barz; Kent Stuber; Michael Schneider; Andrew Haig; Constantin Schizas; Jason Pui Yin Cheung; Anne F Mannion; Lukas Staub; Christine Comer; Luciana Macedo; Sang-Ho Ahn; Kazuhisa Takahashi; Danielle Sandella Journal: Spine (Phila Pa 1976) Date: 2016-08-01 Impact factor: 3.241