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田勇, 董其娟, 李丽, 等. 老年2型糖尿病患者非创伤性骨折发生风险的列线图预测模型构建与评价[J]. 四川大学学报(医学版), 2023, 54(1): 176-180. DOI: 10.12182/20230160209
引用本文: 田勇, 董其娟, 李丽, 等. 老年2型糖尿病患者非创伤性骨折发生风险的列线图预测模型构建与评价[J]. 四川大学学报(医学版), 2023, 54(1): 176-180. DOI: 10.12182/20230160209
TIAN Yong, DONG Qi-juan, LI Li, et al. Establishment and Evaluation of a Nomogram Prediction Model for the Risks of Nontraumatic Fracture in Older Adults with Type 2 Diabetes Mellitus[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(1): 176-180. DOI: 10.12182/20230160209
Citation: TIAN Yong, DONG Qi-juan, LI Li, et al. Establishment and Evaluation of a Nomogram Prediction Model for the Risks of Nontraumatic Fracture in Older Adults with Type 2 Diabetes Mellitus[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(1): 176-180. DOI: 10.12182/20230160209

老年2型糖尿病患者非创伤性骨折发生风险的列线图预测模型构建与评价

Establishment and Evaluation of a Nomogram Prediction Model for the Risks of Nontraumatic Fracture in Older Adults with Type 2 Diabetes Mellitus

  • 摘要:
      目的  分析影响老年2型糖尿病患者非创伤性骨折发生的危险因素,构建列线图预测模型并给予评估。
      方法  收集278例老年2型糖尿病患者的临床资料作为建模组,另收集109例老年2型糖尿病患者的临床资料作为验证组,均根据患者患2型糖尿病后是否发生非创伤性骨折将其分为骨折组及无骨折组。以多因素logistic回归分析老年2型糖尿病发生非创伤性骨折风险的影响因素,使用R软件构建列线图预测模型,再评估该列线图的准确性及临床有效性(ROC曲线下面积、H-L拟合度曲线及校准曲线)。
      结果   建模组中老年2型糖尿病患者非创伤性骨折发生率为24.46%(68/278),两组患者年龄、糖尿病周围神经病变、吸烟史、饮酒史、血清甘油三酯(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、糖化血红蛋白(HbA1c)、高血压病史比较,差异有统计学意义(P<0.05)。年龄、糖尿病周围神经病变、HbA1c、高血压病史为老年2型糖尿病患者发生非创伤性骨折的独立危险因素(P<0.05)。据此构建的列线图预测模型内部验证结果显示:ROC曲线下面积为0.774,校准曲线斜率接近1,且H-L拟合度曲线χ2=12.643,P=0.125。以验证组患者进行外部验证,结果显示ROC曲线下面积为0.780,校准曲线的预测概率与实际概率相近,提示该模型区分度、准确度较好。
      结论  年龄、糖尿病周围神经病变、HbA1c、高血压病史为老年2型糖尿病患者发生非创伤性骨折的独立危险因素,以此建立的预测模型具有较高的准确度及区分度,医护人员可根据患者个体化因素采取针对性措施进行预防,进而减少老年2型糖尿病患者发生非创伤性骨折的可能性。

     

    Abstract:
      Objective  To analyze the risk factors for nontraumatic fractures in older adults with type 2 diabetes mellitus, to establish a nomogram prediction model, and to evaluate the model.
      Methods  The clinical data of 278 older adults with type 2 diabetes mellitus were collected as the modeling group, and the clinical data of 109 older adults with type 2 diabetes mellitus were collected as the validation group. In both groups, patients were divided into a fracture subgroup and a non-fracture subgroup according to whether there were nontraumatic fractures after patients developed type 2 diabetes mellitus. Multivariate logistic regression was done to identify factors influencing the risks of non-traumatic fracture in older patients with type 2 diabetes mellitus. R software was used to construct a nomogram prediction model, and then the accuracy and clinical validity of the nomogram (area under the ROC curve, H-L fit curve, and calibration curve) were evaluated.
      Results  In the modeling group, the incidence of nontraumatic fractures in older adults with type 2 diabetes mellitus was 24.46% (68/278). The two subgroups showed significant differences in age, diabetic peripheral neuropathy, smoking history, drinking history, serum triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycated hemoglobin (HbA1c), and hypertension history (P<0.05). Age, diabetic peripheral neuropathy, HbA1c and history of hypertension were independent risk factors for nontraumatic fractures in older patients with type 2 diabetes mellitus (P<0.05). A nomogram prediction model was constructed accordingly and the internal verification results of the prediction model were as follows: the area under the ROC curve was 0.774 (0.680-0.869), the slope of the calibration curve was close to 1, and the H-L fit curve was χ2=12.643, P=0.125. External validation was conducted with the patients in the validation group. The results showed that the area under the ROC curve was 0.780 (0.670-0.890). The prediction probability of the calibration curve was close to the actual probability, suggesting that the model had good discrimination and accuracy.
      Conclusion  Age, diabetic peripheral neuropathy, HbA1c, and hypertension history are independent risk factors for nontraumatic fractures in older adults with type 2 diabetes mellitus, and the prediction model established consequently has high accuracy and discrimination. Medical workers can take preventive measures based on individual patient factors to reduce the possibility of nontraumatic fractures in older adults with type 2 diabetes mellitus.

     

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