| 周志庆,刘欢,陈梦奇,杨丹,迟晨汝,匡霞,童小梨,陶秀彬.老年住院患者跌倒影响因素及风险列线图模型的构建[J].济宁医学院学报,2024,47(4):295-299 |
| 老年住院患者跌倒影响因素及风险列线图模型的构建 |
| Construction of a nomogram model for fall influencing factors and risk in elderly inpatients |
| 投稿时间：2023-11-23 |
| DOI：10.3969/j.issn.1000-9760.2024.04.006 |
| 中文关键词: 老年住院患者;跌倒;影响因素;预测模型 |
| 英文关键词: Elderly hospitalized patients;Falls;Influencing factor;Prediction model |
| 基金项目:安徽省人文社会科学重点项目(SJD202305);安徽省教育厅研究生教学研究项目(2022jyjxggyj338) |
| 作者 | 单位 | E-mail | | 周志庆 | 皖南医学院第一附属医院弋矶山医院护理部, 芜湖 241001 | | | 刘欢 | 皖南医学院第一附属医院弋矶山医院护理部, 芜湖 241001 | | | 陈梦奇 | 皖南医学院研究生学院, 芜湖 241002 | | | 杨丹 | 皖南医学院研究生学院, 芜湖 241002 | | | 迟晨汝 | 皖南医学院研究生学院, 芜湖 241002 | | | 匡霞 | 皖南医学院第一附属医院弋矶山医院护理部, 芜湖 241001 | | | 童小梨 | 皖南医学院第一附属医院弋矶山医院护理部, 芜湖 241001 | | | 陶秀彬 | 皖南医学院第一附属医院弋矶山医院护理部, 芜湖 241001 | 1325609568@qq.com |
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| 中文摘要: |
| 目的 评估老年住院患者跌倒风险并分析相关影响因素,构建风险预测模型,为住院老年患者的跌倒管理工作提供依据。方法 选取2022年2月-2022年8月在芜湖市某三级甲等医院614名老年住院患者展开横断面调查,采用一般资料调查表、衰弱评估量表、肌少症筛查量表、Morse跌倒风险评估量表,由经过培训的研究组成员对调查对象进行调查。跌倒风险评分大于45分则判定为跌倒高危人群,根据跌倒风险评分分为高危人群组(n=210)与非高危人群组(n=404)。使用二元logistic回归对单因素分析有意义的变量进行分析,并筛选出主要影响因素,使用校准曲线、Homster-Lemeshow拟合度检验、ROC曲线、DCA曲线分别对模型进行评价。结果 614例老年住院患者跌倒得分为(42.42±24.51)分,属于跌倒高危人群的共有210例,占34.2%。二元logistic回归分析结果显示,高龄、每周锻炼频次≤2次、合并肌少症(OR=2.682,95%CI:1.785～4.031)、合并衰弱(OR=2.103,95%CI:1.433～3.085)均是老年住院患者跌倒高危人群的独立危险因素(P＜0.05)。基于以上独立影响因素建立老年住院患者跌倒风险的列线图预测模型。ROC曲线下面积为0.717(95%CI:0.675~0.758),Homster-Lemeshow拟合度检验(χ2=13.332,P=0.101),均显示该模型具有较好的区分度和拟合度,临床决策曲线阈值概率在0.22~0.52,提示该模型具有较好的临床实用性。结论 本研究基于老年住院患者跌倒的危险因素,构建列线图模型,临床工作人员可以借此筛选跌倒高风险人群,实施个性化跌倒干预措施,减少跌倒事件的发生,提高其晚年生活质量。 |
| 英文摘要: |
| Objective To evaluate the risk of falls and construct a risk prediction model,and provide a basis for the fall management of hospitalized elderly patients. Methods A cross-sectional survey was conducted among 614 elderly inpatients in a tertiary hospital in Wuhu City from February 2022 to August 2022,using a general information questionnaire,frailty assessment scale,and sarcopenia screening scale.Trained research team members administered the Morse Fall Risk Assessment Scale to the participants,and those scoring above 45 points were classified as high-risk for falls.Based on this,the elderly inpatients were divided into a high-risk group (n=210) and a non-high-risk group (n=404).Binary Logistic regression was employed to analyze the statistically significant variables from the univariate analysis and identify the factors influencing patients' risk of falling.The model was evaluated using calibration curves, Hosmer-Lemeshow goodness-of-fit test,ROC curves,and DCA curves. Results Among 614 elderly hospitalized patients with falls scores (42.42±24.51),210 cases were in the group at high risk of falling,and the rate of high risk of falling was 34.2%.The results of the binary Logistic regression analysis showed that,Advanced age,the frequency of exercise was ≤2 times per week ,combined with sarcopenia (OR=2.682,95%CI:1.785~4.031),combined (OR=2.103,95%CI:1.433~3.085) were all independent factors affecting the high risk of falls in elderly inpatients (P＜0.05).A nomogram prediction model of fall risk in elderly inpatients was established based on the above independent influencing factors.The area under the ROC curve of this risk prediction model was 0.717 (95%CI:0.675~0.758),and the Homster-Lemeshow fit test (χ2=13.332,P=0.101) showed that the model has good discrimination and fit,and the clinical decision curve threshold probability was 0.22 to 0.52,suggesting that the model has good clinical utility. Conclusion Based on the risk factors of falls in elderly inpatients,this study constructed a nomogram model so which clinical staff can screen people at high risk of falling,implement personalized fall interventions to reduce the occurrence of fall events and improve their quality of life in their later years. |
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