Department of Physical Therapy, Faculty of Medicine, University of British Columbia, 2177 Westbrook Mall, Vancouver, V6T 1Z3, Canada.
Arthritis Research Canada, Richmond, Canada.
BMC Musculoskelet Disord. 2020 Apr 17;21(1):252. doi: 10.1186/s12891-020-03237-x.
Only a small proportion of anterior cruciate ligament (ACL) tears are diagnosed on initial healthcare consultation. Current clinical guidelines do not acknowledge that primary point-of-care practitioners rely more heavily on a clinical history than special clinical tests for diagnosis of an ACL tear. This research will assess the accuracy of combinations of patient-reported variables alone, and in combination with clinician-generated variables to identify an ACL tear as a preliminary step to designing a primary point-of-care clinical decision support tool.
Electronic medical records (EMRs) of individuals aged 15-45 years, with ICD-9 codes corresponding to a knee condition, and confirmed (ACL) or denied (ACL) first-time ACL tear seen at a University-based Clinic between 2014 and 2016 were eligible for inclusion. Demographics, relevant diagnostic indicators and ACL status based on orthopaedic surgeon assessment and/or MRI reports were manually extracted. Descriptive statistics calculated for all variables by ACL status. Univariate between group comparisons, clinician surveys (n = 17), availability of data and univariable logistic regression (95%CI) were used to select variables for inclusion into multivariable logistic regression models that assessed the odds (95%CI) of an ACL-tear based on patient-reported variables alone (consistent with primary point-of-care practice), or in combination with clinician-generated variables. Model performance was assessed by accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (95%CI).
Of 1512 potentially relevant EMRs, 725 were included. Participant median age was 26 years (range 15-45), 48% were female and 60% had an ACL tear. A combination of patient-reported (age, sport-related injury, immediate swelling, family history of ACL tear) and clinician-generated (Lachman test result) variables were superior for ACL tear diagnosis [accuracy; 0.95 (90,98), sensitivity; 0.97 (0.88,0.98), specificity; 0.95 (0.82,0.99)] compared to the patient-reported variables alone [accuracy; 84% (77,89), sensitivity; 0.60 (0.44,0.74), specificity; 0.95 (0.89,0.98)].
A high proportion of individuals without an ACL tear can be accurately identified by considering patient-reported age, injury setting, immediate swelling and family history of ACL tear. These findings directly inform the development of a clinical decision support tool to facilitate timely and accurate ACL tear diagnosis in primary care settings.
只有一小部分前交叉韧带 (ACL) 撕裂在初次就诊时得到诊断。目前的临床指南并未承认，一线医护人员在诊断 ACL 撕裂时，更多地依赖临床病史而非特殊的临床检查。本研究旨在评估仅基于患者报告变量，以及结合临床医生生成变量组合，对 ACL 撕裂进行识别的准确性，这是设计一线医护人员临床决策支持工具的初步步骤。
从 2014 年至 2016 年在一所大学诊所就诊的 15-45 岁人群中，选取符合国际疾病分类第 9 版 (ICD-9) 编码对应膝关节疾病，且经骨科医生评估和/或 MRI 报告确诊（ACL）或排除（ACL）初次 ACL 撕裂的患者的电子病历（EMR）纳入研究。手动提取人口统计学、相关诊断指标和 ACL 状态等数据，基于骨科医生评估和/或 MRI 报告进行 ACL 撕裂诊断。通过 ACL 状态计算所有变量的描述性统计。采用单变量组间比较、临床医生问卷调查（n=17）、数据可用性和单变量逻辑回归（95%置信区间），筛选变量纳入多变量逻辑回归模型，以评估基于患者报告变量（与一线医护实践一致）或结合临床医生生成变量的 ACL 撕裂可能性的比值比（95%置信区间）。通过准确性、敏感性、特异性、阳性和阴性预测值以及阳性和阴性似然比（95%置信区间）评估模型性能。
在 1512 份潜在相关的 EMR 中，纳入了 725 份。参与者的中位年龄为 26 岁（范围 15-45 岁），48%为女性，60%存在 ACL 撕裂。患者报告的（年龄、与运动相关的损伤、即刻肿胀、ACL 撕裂家族史）和临床医生生成的（Lachman 试验结果）变量的组合，在 ACL 撕裂诊断方面优于仅基于患者报告的变量[准确性：0.95（0.90，0.98），敏感性：0.97（0.88，0.98），特异性：0.95（0.82，0.99）]。
通过考虑患者报告的年龄、损伤情况、即刻肿胀和 ACL 撕裂家族史，可以准确识别出大部分无 ACL 撕裂的患者。这些发现直接为开发临床决策支持工具提供了信息，以促进在初级保健环境中及时、准确地诊断 ACL 撕裂。
