坐骨神经痛临床诊断模型

数据集获取（Figshare）

• 源站标题：Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain
• DOI：10.1371/journal.pone.0191852
• 发布日期：2018-04-05
• 文件数：2 个
• 文件总体积：73.33 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Clinical_diagnostic_model_for_sciatica_developed_in_primary_care_patients_with_low_back-related_leg_pain/6100586

命令行下载

curl -L -o "S1 Table.docx" "https://ndownloader.figshare.com/files/10999718"
curl -L -o "S1 Dataset.xlsx" "https://ndownloader.figshare.com/files/10999727"

文件清单

• S1 Table.docx（16.67 KB）
• S1 Dataset.xlsx（56.66 KB）

文件总体积 73.33 KB（源站 API 实测）。

数据说明

Background
Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare.
Methods
Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard.
Results
Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii).
Conclusion
Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0191852（source: https://plos.figshare.com/articles/dataset/Clinical_diagnostic_model_for_sciatica_developed_in_primary_care_patients_with_low_back-related_leg_pain/6100586）。