Xiao Qian, Hou Guangzhao, Liu Shihang, Zhou Shuai, Chen Wei, Zhang Yingze, Lv Hongzhi
Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, Shijiazhuang China.
Trauma Emergency Center, The Third Hospital of Hebei Medical University; No.139 Ziqiang Road, Shijiazhuang China.
PLoS One. 2025 Jun 3;20(6):e0323609. doi: 10.1371/journal.pone.0323609. eCollection 2025.
Metatarsal fractures rank among the ten most common fractures.Comprehensive studies on postoperative functional recovery remain limited. A reliable predictive model for recovery outcomes is essential for optimizing patient care.
To develop and validate a predictive model for postoperative functional recovery in metatarsal fracture patients and implement it as an interactive web-based calculator.
This retrospective study included 555 metatarsal fracture patients (2018-2022), with 425 in the training cohort and 130 in the validation cohort. The outcome variable was postoperative recovery as assessed by the AOFAS midfoot scoring system. LASSO regression identified significant predictors of recovery,the selected variables underwent binary logistic regression analysis to identify independent risk factors. A prediction model was constructed using the training cohort and visualized through a nomogram. Model validation was performed internally through bootstrapping and externally using the validation cohort. The model was implemented as an interactive web calculator using R Shiny.
At final follow-up, 71.71% of patients achieved good recovery (AOFAS score >80). The model identified ten independent risk factors, including residence location, smoking status, obesity, rehabilitation training, educational level, age, injury mechanism, infection, and anemia. The model demonstrated robust discrimination (c-index: 0.832 training, 0.838 validation) and calibration (H-L test: P = 0.994 training, P = 0.648 validation). DCA showed optimal clinical utility within 0.72-1.00 threshold probability. Protective factors included hilly areas (OR = 0.183), smoking (OR = 0.4), obesity (OR = 0.270), and undergoing rehabilitation training (OR = 0.237),while risk factors included low educational level (OR = 3.884), advanced age (OR = 2.751), high-energy injury (OR = 3.003), residence in mountainous regions (OR = 4.671), infection (OR = 16.946), and anemia (OR = 5.787). The interactive web calculator is accessible at https://metarecoverypredictor.shinyapps.io/DynNomapp/.
The validated prediction model effectively identifies risk factors for postoperative recovery in metatarsal fractures. This tool can aid clinicians in developing personalized treatment strategies and improving patient outcomes. The web-based calculator provides easy access for clinical application.
跖骨骨折位列十大常见骨折之中。关于术后功能恢复的全面研究仍然有限。一个可靠的恢复结果预测模型对于优化患者护理至关重要。
开发并验证一个跖骨骨折患者术后功能恢复的预测模型，并将其作为基于网络的交互式计算器来应用。
这项回顾性研究纳入了555例跖骨骨折患者（2018 - 2022年），其中425例在训练队列，130例在验证队列。结局变量是采用美国足踝外科协会（AOFAS）中足评分系统评估的术后恢复情况。套索回归确定了恢复的显著预测因素，对所选变量进行二元逻辑回归分析以确定独立危险因素。使用训练队列构建预测模型并通过列线图进行可视化。通过自抽样在内部进行模型验证，并使用验证队列在外部进行验证。该模型使用R Shiny作为交互式网络计算器来实现。
在最终随访时，71.71%的患者实现了良好恢复（AOFAS评分>80）。该模型确定了10个独立危险因素，包括居住地点、吸烟状况、肥胖、康复训练、教育水平、年龄、损伤机制、感染和贫血。该模型显示出强大的区分能力（c指数：训练集为0.832，验证集为0.838）和校准能力（Hosmer - Lemeshow检验：训练集P = 0.994，验证集P = 0.648）。决策曲线分析显示在阈值概率为0.72 - 1.00时具有最佳临床实用性。保护因素包括山区（OR = 0.183）、吸烟（OR = 0.4）、肥胖（OR = 0.270）和接受康复训练（OR = 0.237），而危险因素包括低教育水平（OR = 3.884）、高龄（OR = 2.751）、高能损伤（OR = 3.003）、居住在山区（OR = 4.671）、感染（OR = 16.946）和贫血（OR = 5.787）。交互式网络计算器可通过https://metarecoverypredictor.shinyapps.io/DynNomapp/访问。
经过验证的预测模型有效地识别了跖骨骨折术后恢复的危险因素。这个工具可以帮助临床医生制定个性化治疗策略并改善患者预后。基于网络的计算器为临床应用提供了便捷途径。
