目的 调查老年脆性骨折患者术前衰弱现况,并分析影响因素。方法 采用便利抽样法, 2024年11月—2025年3月, 选取于广州市某三甲医院骨科住院的207例老年脆性骨折患者, 使用一般资料调查表、简易衰弱评估量表、简版流调中心抑郁量表、广泛性焦虑量表和营养风险筛查量表2002开展问卷调查。采用有序分类Logistic回归, 分析老年脆性骨折患者术前衰弱的影响因素。结果 207例老年脆性骨折患者的术前衰弱前期占48.3%, 衰弱发生率为23.2%。有序分类Logistic 回归分析结果显示年龄（OR=1.131）、睡眠差（OR=2.557）、合并3种及以上慢性病（OR=3.990）、抑郁（OR=3.296）、营养不良风险（OR=4.005）为老年脆性骨折患者术前衰弱的危险因素,BMI正常（OR=0.206）是保护因素（均P<0.05）。结论 老年脆性骨折患者年龄, 睡眠情况, 多种共病,抑郁, 存在营养不良风险及BMI影响其术前衰弱水平, 重视衰弱的筛查及早期干预, 提升患者治疗效果和生活质量。

       Objective To explore the preoperative frailty status and influencing factors in elderly patients with osteoporotic fractures．Methods Using convenience sampling, 207 elderly patients with osteoporotic fractures admitted to the orthopedic department of a tertiary hospital in Guangzhou from November 2024 to March 2025 were selected．Data were collected using a general information questionnaire, the Frail Scale, the 10-item Center for Epidemiologic Studies Depression Scale（CES-D-10）, the Generalized Anxiety Disorder Scale（GAD-7）, and the Nutritional Risk Screening 2002（NRS-2002）．Logistic regression was used to analyze factors influencing preoperative frailty．Results Among 207 elderly patients with osteoporotic fractures, the incidence of early stages of frailty was 48.3%, and the incidence of frailty was 23.2%．Logistic regression analysis revealed the following risk factors for preoperative frailty：age（OR=1.131）, poor sleep quality（OR=2.557）, multiple chronic comorbidities（OR=3.990）, depression（OR=3.296）, nutritional risk（OR=4.005）．Normal body mass index（OR=0.206）was a protective factor．Conclusions Advanced age,poor sleep quality, multiple chronic comorbidities, depression, nutritional risk and body mass index are associated with frailty in elderly osteoporotic fracture patients．Health care providers should pay attention to frailty screening and early intervention, which can reverse or delay the progression of frailty and improve the treatment effect and quality of life of patients．

目的 探索基于患者居家护理服务需求且以三甲医院为实施主体的“互联网+护理服务”模式,以期为“互联网+护理服务”的开展提供借鉴。方法 成立“互联网+护理服务”小组,基于患者需求进行平台建设,组建“互联网+护理服务”团队,经过同质化培训持证上岗,提供专科护理、母婴护理以及基础护理共25项服务内容。服务结束后,调查患者对“互联网+护理服务”的满意度。结果 截至2022年10月,该平台注册护士438名,注册患者1 000人。共开展“互联网+护理服务”居家护理服务841人次,排名前三位的服务项目是母婴护理、留置/更换鼻饲管护理、留置/更换尿管护理。患者居家服务好评率为100％,对护理服务的整体满意度为（4.86±0.22）分。患者对护士指导居家护理知识的满意度最高,为（4.89±0.33）分,对平台收费的满意度最低,为（4.12±0.78）分。结论 “互联网+护理服务”为居家护理提供了更加方便快捷的渠道,使资源利用更有效,患者认可度高。

Objective To explore the“Internet + Nursing Service”model based on the needs of patients' home care, which the grade A class 3 hospital hospital was the main institution of implementation, and to provide the reference for implementing “Internet + Nursing Service”．Methods We set up a working group, built a platform according to needs, and set up the “Internet + Nursing service”team, who worked with the certificate after homogenization training．The program provided 25 kinds of services, including specialist nursing, maternal and child care, basic nursing, provides high-quality and convenient home care services．After the service, the patients' satisfaction was investigated．Results By October 2021, the platform had 438 registered nurses and 1000 registered patients．A total of 841 people were provided with “Internet + Nursing service”home care services, and the top three services were maternal and infant care, nasal feeding tube care with indentation/replacement, and urinary tube care with indentation/replacement．The favorable rate of home service was 100%, and the overall satisfaction with nursing services was（4.86±0.22）points．Patients had the highest level of satisfaction with nurses' guidance on home nursing knowledge, which was（4.89±0.33）points, and the lowest level of satisfaction with platform fees, which was（4.12±0.78）points．Conclusions “Internet + Nursing Service”provides a more convenient and fast access for home nursing, making resource utilization more effective and with high patient recognition．

介绍我院应对新型冠状病毒感染防控工作的护理管理措施,包括发热病区的组建,护理应急队伍的建设,工作流程指引的修订和完善,院感防护措施的严格落实,后勤保障的严密管理等,并进行护理质量控制与持续改进,为其他医院的防控工作提供借鉴。

It introduces nursing management measures of novel coronavirus infection prevention and control in our hospital, including the establishment of fever ward, the construction of nursing emergency team, the revision and improvement of working process guidelines, the strict implementation of hospital sensory protection measures, tight management of logistical support, and quality control and continuous improvement of nursing. It offers experiences for other hospitals.

       目的   基于Nomogram初步构建膝骨关节炎（KOA）患者术前衰弱的风险预测模型。方法   便利选取172例于2021年12月—2022年8月在广州市某三甲医院关节外科接受择期膝关节置换术的KOA患者为研究对象，依据衰弱的发生与否分为衰弱组（n=111）和非衰弱组（n=61），通过单因素分析筛选变量，纳入Logistic回归分析，并构建列线图模型。结果   单因素分析结果显示年龄、BMI、膝关节疼痛年限、合并症、抑郁、焦虑、疼痛、睡眠障碍、营养状况等在不同组间比较差异存在统计学的意义（P＜0.05）。多因素Logistic回归分析表明，BMI异常（OR=3.360）、膝关节疼痛年限＞5年（OR=14.188）、抑郁（OR=5.608）、睡眠障碍（OR=25.480）是KOA患者术前衰弱的独立危险因素（P＜0.05）。基于此，建立了预测膝骨关节炎患者术前衰弱风险的列线图预测模型。结果显示C-index为0.915，校正曲线接近理想曲线，ROC曲线下面积（AUC）为0.919（95%CI：0.878~0.961），可见该预测模型具有较好的区分度和准确度。结论   根据BMI、膝关节疼痛年限、抑郁以及睡眠障碍这四个独立危险因素，可以准确地预测膝骨关节炎患者术前衰弱的风险。

    Objective  To develop a nomogram for predicting the risk of preoperative frailty in knee osteoarthritis patients．Methods  A convenience sample of 172 patients who underwent elective knee arthroplasty at a Grade-A hospital in Guangzhou from December 2021 to August 2022 was selected．The patients were divided into two groups based on the presence of preoperative frailty：frailty group（n=111）and non-frailty group（n=61）．The variables with statistical differences were screened by univariate analysis for multivariate logistic regression analysis，and the nomogram prediction model was established．Results  Univariate analysis identified significant differences between the groups in age，BMI，years of knee pain，complications，depression，anxiety，pain，sleep disturbance，and nutrition（P＜0.05）．Multivariate logistic regression showed that abnormal BMI（OR=3.360），years of knee pain ＞ 5（OR=14.188），depression（OR=5.608），and sleep disorders（OR=25.480）were independent  risk factors for preoperative frailty in knee osteoarthritis patients（P＜0.05）．Based on these findings，a nomogram prediction model was established．Model verification results demonstrated that the nomogram had good differentiation and accuracy in predicting the risk of preoperative frailty，with a C-index of 0.915，an area under the ROC curve of 0.919（95% CI：0.878~0.961），and a calibration curve slope close to 1．Conclusions  The nomogram，based on four independent risk factors（BMI，years of knee pain，depression，and sleep disturbance），effectively predicts the risk of preoperative frailty in knee osteoarthritis patients．