ArticleHealthcare (Basel, Switzerland)2023
Development and Validation of a Risk-Prediction Nomogram for Chronic Low Back Pain Using a National Health Examination Survey: A Cross-Sectional Study.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- Considerations on the Obstacles That Lead to Slow Recruitment in a Pain Management Clinical Trial: Experiences from the Belgian PELICAN (PrEgabalin Lidocaine Capsaicin Neuropathic Pain) Pragmatic Study.Pain research & management · 2023Trial
- Development and validation of a nomogram model for predicting the risk of hypertension in Bangladesh.Heliyon · 2024Article
- Body Mass Index and Its Influence on Chronic Low Back Pain in the Spanish Population: A Secondary Analysis from the European Health Survey (2020).Biomedicines · 2023Article
- Validity of On-Line Supervised Fitness Tests in People with Low Back Pain.Healthcare (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral prognostic factors have been reported for chronic low back pain (CLBP). However, there are no studies on the prediction of CLBP development in the general population using a risk prediction model. This cross-sectional study aimed to develop and validate a risk prediction model for CLBP development in the general population, and to create a nomogram that can help a person at risk of developing CLBP to receive appropriate counseling on risk modification.
methodsData on CLBP development, demographics, socioeconomic history, and comorbid health conditions of the participants were obtained through a nationally representative health examination and survey from 2007 to 2009. Prediction models for CLBP development were derived from a health survey on a random sample of 80% of the data and validated in the remaining 20%. After developing the risk prediction model for CLBP, the model was incorporated into a nomogram.
resultsData for 17,038 participants were analyzed, including 2693 with CLBP and 14,345 without CLBP. The selected risk factors included age, sex, occupation, education level, mid-intensity physical activity, depressive symptoms, and comorbidities. This model had good predictive performance in the validation dataset (concordance statistic = 0.7569, Hosmer-Lemeshow chi-square statistic = 12.10,
conclusionsThe risk prediction model presented by a nomogram, which is a score-based prediction system, can be incorporated into the clinical setting. Thus, our prediction model can help individuals at risk of developing CLBP to receive appropriate counseling on risk modification from primary physicians.
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