Risk factors analysis and risk prediction model construction of non-specific low back pain: an ambidirectional cohort study

Lu et al. Journal of Orthopaedic Surgery and Research https://doi.org/10.1186/s13018-023-03945-9 (2023) 18:545 RESEARCH ARTICLE Journal of Orthopaedic Surgery and Research Open Access Risk factors analysis and risk prediction model construction of non‑specific low back pain: an ambidirectional cohort study Wenjie Lu1, Zecheng Shen2, Yunlin Chen1, Xudong Hu1, Chaoyue Ruan1, Weihu Ma1 and Weiyu Jiang1* Abstract Purpose Non-specific low back pain (NLBP) is a common clinical condition that affects approximately 60–80% of adults worldwide. However, there is currently a lack of scientific prediction and evaluation systems in clinical practice. The purpose of this study was to analyze the risk factors of NLBP and construct a risk prediction model. Methods We collected baseline data from 707 patients who met the inclusion criteria and were treated at the Sixth Hospital of Ningbo from December 2020 to December 2022. Logistic regression and LASSO regression were used to screen independent risk factors that influence the onset of NLBP and to construct a risk prediction model. The sensitivity and specificity of the model were evaluated by tenfold cross-validation, and internal validation was performed in the validation set. Results Age, gender, BMI, education level, marital status, exercise frequency, history of low back pain, labor intensity, working posture, exposure to vibration sources, and psychological status were found to be significantly associated with the onset of NLBP. Using these 11 predictive factors, a nomogram was constructed, and the area under the ROC curve of the training set was 0.835 (95% CI 0.756–0.914), with a sensitivity of 0.771 and a specificity of 0.800. The area under the ROC curve of the validation set was 0.762 (95% CI 0.665–0.858), with a sensitivity of 0.800 and a specificity of 0.600, indicating that the predictive value of the model for the diagnosis of NLBP was high. In addition, the calibration curve showed a high degree of consistency between the predicted and actual survival probabilities. Conclusion We have developed a preliminary predictive model for NLBP and constructed a nomogram to predict the onset of NLBP. The model demonstrated good performance and may be useful for the prevention and treatment of NLBP in clinical practice. Keywords Non-specific low back pain, Risk factors, Prediction model, Nomogram *Correspondence: Weiyu Jiang 1 Department of Spinal Surgery, Ningbo Sixth Hospital, Ningbo 315040, Zhejiang, China 2 Zhejiang University of Traditional Chinese Medicine Third Clinical Medical College, Hangzhou 310000, Zhejiang, China Introduction NLBP refers to lumbosacral pain and discomfort originating from the waist, without specific causes or structural factors, with or without radiating pain in the lower limbs. It is a prevalent clinical condition, with approximately 60–80% of adults reporting a history of NLBP, particularly among those under the age of 45 [1, 2]. While modern medical research has identified numerous complex factors contributing to NLBP, there is no clear understanding of the pathological anatomy underlying © The Author(s) 2023. 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The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Lu et al. Journal of Orthopaedic Surgery and Research (2023) 18:545 these abnormal changes. Currently, there exist diverse clinical treatment methods for NLBP. For instance, Filippo Migliorini and Alice Baroncini discovered that a combination of non-steroidal anti-inflammatory drugs, acupuncture, and transcutaneous electrical nerve stimulation can effectively alleviate pain and improve disability levels in NLBP patients [3–5]. Lorenzo Giordano et al. also found that value-added therapy is a viable option for NLBP patients who have not responded to conservative treatment [6]. Moreover, Luca Miranda et al. systematically examined 303 NLBP patients and observed that mesenchymal stem cells may inhibit nociceptors, reduce catabolism, and facilitate the repair of damaged or degenerated tissue, thereby alleviating pain [7]. However, most of these treatments offer temporary relief rather than a fundamental solution, often leaving residual symptoms. As a result, patients’ expectations regarding clinical outcomes are frequently unmet, significantly impacting their physical and mental well-being [8–10]. Therefore, early screening and effective prevention of NLBP have become critical concerns for healthcare professionals. In the era of personalized medicine, accurate prediction of disease occurrence and prognosis has gained increasing importance. Constructing disease risk prediction models has proven effective in reducing disease incidence as demonstrated by numerous scholars [11, 12]. Despite notable progress in the diagnosis and treatment of NLBP in China, the lack of basic epidemiological data and a scientific prediction and evaluation system hinders successful prevention and prognosis assessment of NLBP. Therefore, the objective of this study is to identify the most significant risk factors and develop a robust risk prediction model for NLBP. The primary purpose is to provide valuable assistance to clinicians and patients in enhancing the prevention and treatment strategies for NLBP. Evidence before this study We searched PubMed, Medline and CSTJ for peerreviewed, original studies published from database inception to December 2022, with the terms “non-specific low back pain”, “NLBP”, “risk factors”, and “predictive model”. It is hoped that this study can include as many risk factors as possible to improve the clinical significance of the prediction model. Participants and methods Participants Referring to all the risk factors obtained by searching before the survey, we conducted an ambidirectional cohort study and performed a questionnaire survey on outpatients who visited the Ningbo Sixth Hospital from December 2020 to December 2022. The inclusion criteria were as follows: age between 16 and 60 years; Page 2 of 13 clinical diagnosis of lumb (...truncated)