1.解放军总医院第一医学中心:,皮肤科,北京
3.解放军总医院第一医学中心:,康复科,北京
4.解放军总医院第一医学中心:,血管外科,北京
2.陆军军医大学(第三军医大学)士官学校基础部,石家庄
5.南方医科大学南方医院整形美容外科,广州
LI Xia, E-mail: 1648248514@qq.com.
Received:26 November 2024,
Revised:05 March 2025,
Published:30 May 2025
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申 琳,白小嘉,王 刚,等.PFNA内固定术后患者营养不良预测模型构建及其准确性分析[J].陆军军医大学学报,2025,47(10):1092-1101.DOI:10.16016/j.2097-0927.202411118.
SHEN L,BAI X J,WANG G,et al.Construction and accuracy analysis of a malnutrition prediction model for patients after proximal femoral nail anti rotation internal fixation[J].J Army Med Univ,2025,47(10):1092-1101.DOI:10.16016/j.2097-0927.202411118.
申 琳,白小嘉,王 刚,等.PFNA内固定术后患者营养不良预测模型构建及其准确性分析[J].陆军军医大学学报,2025,47(10):1092-1101.DOI:10.16016/j.2097-0927.202411118. DOI:
SHEN L,BAI X J,WANG G,et al.Construction and accuracy analysis of a malnutrition prediction model for patients after proximal femoral nail anti rotation internal fixation[J].J Army Med Univ,2025,47(10):1092-1101.DOI:10.16016/j.2097-0927.202411118. DOI:
目的
2
探索股骨近端防旋髓内钉(proximal femoral nail anti-rotatio, PFNA)内固定术老年患者术后营养风险相关因素并建立营养不良预测模型。
方法
2
纳入2021年1月至2024年6月于我院接受PFNA内固定术老年患者574例,分为营养不良组(389例)和营养良好组(185例),比较2组患者生理、心理、社会、经济、环境、医学等因素共39个指标的差异,采用Logistic分析筛选营养风险危险因素并构建列线图模型。
结果
2
年龄偏高、BMI指数偏低、术后SAS评分偏高、骨折前较少锻炼、农民、经济压力较高,及术前白蛋白、前蛋白、血红蛋白偏低,骨折前Barthel指数偏低是PFNA内固定术患者营养风险的独立危险因素(
P
<
0.05)。纳入上述因素构建列线图预测模型,预测该类患者发生营养不良风险的AUC(95%
CI
)=0.995(0.987~1.000),在阈概率
>
0.02时采用本模型预测PFNA内固定术患者术后营养不良风险均可临床获益。
结论
2
基于年龄、BMI指数、经济压力、骨折前锻炼情况及术前白蛋白等指标构建老年患者PFNA内固定术术后营养风险预测模型,有较高的准确性和临床应用价值。
Objective To explore the related factors of postoperative nutritional risk in elderly patients with proximal femoral nail anti rotation (PFNA) internal fixation and establish a prediction model of malnutrition. Methods A total of 574 elderly patients who underwent PFNA internal fixation in the First Medical Center of Chinese PLA General Hospital from January 2021 to June 2024 were included and divided into malnutrition group (
n
=389) and good nutrition group (
n
=185). The differences in 39 indicators in
aspects of physiological
psychological
social
economic
environmental and medical fields were compared between the 2 groups. Logistic analysis was used to screen the nutritional risk factors
and then a nomogram model was constructed based on these factors. Results Advanced age
lower BMI
higher postoperative Self-Rating Anxiety Scale (SAS) score
less exercise before fracture
being farmers
higher economic pressure
lower preoperative albumin
preprotein and hemoglobin
and lower Barthel index before fracture were independent risk factors for nutritional risk in patients undergoing PFNA internal fixation (
P
<
0.05). The nomogram prediction model based on the above factors had an AUC value of 0.995 (95%
CI
: 0.987~1.000) in predicting the risk of malnutrition in these patients. When the threshold probability
>
0.02
this model could be clinically beneficial in predicting the risk of postoperative malnutrition in patients after PFNA internal fixation. Conclusion Our nutritional risk prediction model based on age
BMI
economic pressure
pre-fracture exercise and preoperative albumin and other indicators is constructed for the elderly patients after PFNA internal fixation
and the model has high accuracy and clinical application value.
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