Abudukadier Maimaitiyibubaji, Zhang Yuxin, Li Maozhao, Muhetaer Munire, Mijiti Yibulayinjiang, Simayi Zumulaiti, Aireti Maimaitijiang, Tian Jingshun, Maimaiti Maimaitishawutiaji
Department of Hand and Foot Microsurgery, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children's Hospital, Urumqi, Xinjiang, 830000, People's Republic of China.
Department of Spine Surgery, The First People's Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, People's Republic of China.
Infect Drug Resist. 2024 Dec 27;17:5895-5907. doi: 10.2147/IDR.S497404. eCollection 2024.
Tuberculous spondylitis (TS) and brucellar spondylitis (BS) exhibit certain similarities in clinical presentation and imaging characteristics, making differential diagnosis challenging. Developing a reliable differential diagnosis model can assist clinicians in distinguishing between these two conditions at an early stage, allowing for targeted prevention and treatment strategies.
Patients diagnosed with TS and BS were retrospectively collected and randomized into training and validation cohorts (ratio 7:3). The least absolute shrinkage and selection operator (LASSO) regression was used to reduce data dimensionality and select variables. Multivariate logistic regression was used to build predictive models. A nomogram was constructed to provide a visual representation of the model. Receiver operating characteristic (ROC) curve, calibration plots and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.
A total of 183 patients included (101 cases of TB, 82 cases of BS) our study. Our results showed that these variables including time from symptom onset to admission, anorexia, adenosine deaminase (ADA) and psoas abscess were important to differentiate TS and BS. The area under the curve (AUC) of ROC curve was 0.820 [95% (0.749, 0.892)] and 0.899 [95% (0.823, 0.976)] for the training and validation cohort, respectively. The results of calibration curve and DCA confirmed that the nomogram performed well in differentiating TS patient from BS.
The combination of time from symptom onset to admission, anorexia, ADA and psoas abscess demonstrated good differential properties for TS and BS. We developed a new nomogram model that can effectively differentiate TS and BS based on these four characteristics, which could be a valid and useful clinical tool for clinicians to aid in early differential diagnosis and targeted treatment.
结核性脊柱炎（TS）和布鲁菌性脊柱炎（BS）在临床表现和影像学特征上存在一定相似性，这使得鉴别诊断具有挑战性。开发一个可靠的鉴别诊断模型可以帮助临床医生在早期区分这两种疾病，从而制定有针对性的预防和治疗策略。
回顾性收集诊断为TS和BS的患者，并随机分为训练组和验证组（比例为7:3）。使用最小绝对收缩和选择算子（LASSO）回归来降低数据维度并选择变量。采用多变量逻辑回归建立预测模型。构建列线图以直观展示该模型。使用受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）来评估列线图的预测性能。
本研究共纳入183例患者（101例结核性脊柱炎，82例布鲁菌性脊柱炎）。结果显示，从症状出现到入院的时间、厌食、腺苷脱氨酶（ADA）和腰大肌脓肿等变量对于区分TS和BS很重要。训练组和验证组的ROC曲线下面积（AUC）分别为0.820 [95% （0.749, 0.892）]和0.899 [95% （0.823, 0.976）]。校准曲线和DCA的结果证实，列线图在区分TS患者和BS患者方面表现良好。
从症状出现到入院的时间、厌食、ADA和腰大肌脓肿的组合对TS和BS具有良好的鉴别特性。我们开发了一种新的列线图模型，该模型可以基于这四个特征有效区分TS和BS，这可能是临床医生进行早期鉴别诊断和靶向治疗的有效且有用的临床工具。
