多学科团队管理对PICC患者导管维护效果及并发症的影响

目的 探讨学科团队管理对PICC患者导管维护效果及并发症的影响。方法 选取2021年1—12月PICC置管患者116例为研究对象,根据患者入院时间将患者分为对照组（2021年1—6月,n=58）和观察组（2021年7—12月,n=58）,对照组行PICC常规性护理,观察组实施多学科团队管理。比较两组患者导管置管效果、导管留置并发症发生率、PICC导管维护满意度评分。结果 观察组导管维护依从率、一次性穿刺成功率较对照组高（P<0.05）,观察组导管留置时间较对照组长（P<0.05）。与对照组比较,观察组导管留置期间并发症发生率较低（P<0.05）。与对照组比较,观察组患者对导管维护的满意度评分较高（P<0.05）。结论 多学科团队管理可提升PICC患者导管维护效果,有效降低导管留置期间并发症发生率,提升患者导管维护满意度。     

临床护理路径在血液透析患者静脉留置双腔管护理中的应用研究

目的探讨临床护理路径在血液透析患者留置深静脉双腔透析导管护理的应用效果。方法选择80例我院血液透析中心接受颈内静脉留置双腔透析导管行透析治疗患者,随机分为对照组（n=39）和观察组（n=41）,对照组接受常规护理,观察组按照临床护理路径进行护理。比较两组患者的置管时间、并发症发生率及对治疗满意度等。结果观察组患者并发症发生率明显低于对照组（P〈0．01）;观察组置管时间长,对治疗满意度高,与对照组比较差异均有统计学意义（P〈0．05）。结论应用临床护理路径对血液透析患者留置深静脉双腔透析导管治疗进行护理可提高护理的质效,具有重要的临床推广价值。     

个体化封管液在血液透析留置导管中的应用及效果观察

目的探讨个体化封管液在血液透析留置导管中的应用效果。方法选取2019年3月-2020年4月在我院行中心静脉置管血液透析的56例患者,随机分为观察组和对照组,各28例。观察组使用个体化封管液,对照组使用0.9%生理盐水9 mL+肝素钠1 mL配制封管液封管。比较两组患者导管留置时间及满意度、导管并发症发生率及凝血相关指标情况。结果观察组透析导管有效留置时间及导管留置满意度均明显高于对照组(P0.05);观察组并发症发生率少于对照组(P0.05);封管前后两组凝血酶时间(TT)、凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)比较差异无统计学意义(P0.05)。结论个体化封管液可使留置导管患者延长导管使用时间,减少并发症的发生率,提高满意度,值得推广应用。     

健康教育对预防留置双J管离院患者并发症的影响

目的探讨留置双J管病员住院期间实施健康教育的有效方法及预防出院后带管期间发生并发症的效果。方法选择手术后需要留置双J管的患者400例,随机分成观察组200例（采用个性化的健康教育措施）和对照组200例（采用传统护理方法 ）,比较两组的并发症情况。结果观察组并发症发生率为1.50%,而对照组则高达6.50%。结论个性化健康教育能有效降低留置双J管离院患者并发症的发生率。     

耐高压双腔PICC导管在造血干细胞移植中的应用效果

目的探讨耐高压双腔PICC导管在造血干细胞移植中的临床应用效果。方法选取B超引导下经外周静脉置入双腔耐高压PICC导管至中心静脉行造血干细胞移植的患者60例作为观察组,选取经锁骨下静脉置人CVC导管至中心静脉行造血干细胞移植的患者60例作为对照组。对比两组患者穿刺置管一般情况,院内导管留置时间、置管并发症发生情况。结果PICC导管一般可以保留1年,但大部分患者移植术后要求不再留用。观察组患者院内导管留置时间显著长于对照组（P〈0．05）,置管并发症发生率显著低于对照组（P〈0．05）。结论在造血干细胞移植过程中留置耐高压型双腔PICC导管安全性高,不易发生导管相关性感染等并发症,并在移植中显示出较好的优越性。     

长期留置双腔静脉导管血液透析患者的全程护理干预

目的探讨长期留置双腔静脉导管血液透析患者的全程护理干预的效果。方法选择血液透析长期留置双腔静脉导管患者38例,随机分为观察组和对照组,各19例。对照组采用常规置管护理、健康教育;观察组建立档案,采用全程护理干预。结果观察组长期留置双腔静脉导管并发症明显低于对照组（P〈0．01）。结论护理干预可有效降低血液透析患者长期留置双腔静脉导管的并发症,提高患者自我护理能力及家属配合程度。长期留置双腔静脉导管血液透析患者得到系统的全程护理,增加医疗护理安全。     

中心静脉导管与静脉留置针引流恶性胸腔积液的效果比较

目的比较采用中心静脉导管与静脉留置针引流胸腔积液的临床效果。方法142例恶性胸腔积液患者随机分为对照组和研究组各7l例,对照组采用静脉留置针置管引流,研究组采用中心静脉导管置管引流,并给予术后导管及留置针护理,观察两组患者胸腔积液治疗效果及护理相关并发症发生情况。结果研究组留管时间长于对照组,留管次数及胸腔积液控制时间少于对照组,差异均有统计学意义（P〈0．05）。研究组穿刺成功率、胸膜反应发生率、穿刺点及胸腔感染率与对照组比较差异无统计学意义（P〉0．05）,研究组堵管发生率及脱管率低于对照组,差异有统计学意义（P〈0．05）。结论中心静脉导管置管引流恶性胸腔积液效果优于静脉留置针,护理相关并发症发生率较低,便于临床护理。     

双腔耐高压注射型经外周置入中心静脉导管在ICU中的应用效果

目的探讨重症监护病房(ICU)应用双腔耐高压注射型经外周置入中心静脉导管(PICC)的临床效果。方法回顾分析本院ICU行静脉置管术的557例患者的临床资料,观察组283例采用双腔耐高压注射型PICC置管术,对照组274例采用双腔中心静脉导管(CVC)置管术,对比分析2组患者的疗效及安全性指标。结果观察组单根导管治疗完成率显著高于对照组,静脉导管留置时间显著长于对照组(P0.05),2组患者一次置管成功率差异则无统计学意义(P0.05)。在置管操作过程中,2组患者气胸、血胸、心律失常、置管误入动脉、穿刺点渗血的并发症发生率差异无统计学意义(P0.05),观察组原发异位率显著高于对照组(P0.05)。静脉导管留置期间,观察组导管相关血流感染、非计划拔管的发生率显著低于对照组,静脉炎发生率显著高于对照组(P0.05),2组患者血栓形成、静脉导管移位、脱出、堵管的并发症发生率差异无统计学意义(P0.05)。结论与双腔CVC置管术相比,ICU应用双腔耐高压注射型PICC置管术具有留置期间安全性、有效性更佳等优点。     

泌尿外科术后留置双J管患者经延续性护理干预的价值探析

目的 探究执行延续性护理干预对泌尿外科术后留置双 J 管患者的影响。方法 2021 年 8 月至 2022 年 8 月我院泌尿外科 手术后 110 例留置双 J 管患者,随机均分对照组和观察组,每组各 55 例。对照组采用常规护理,观察组实施延续性护理干预。 比较两组的自我护理能力、生活质量、并发症发生率及治疗依从性。结果 对照组和观察组干预前的自我护理能力、生活质量 无统计学意义（P>0.05）。观察组的自我护理能力、生活质量优于对照组（P<0.05）。观察组治疗依从性高于对照组（P<0.05）。 观察组并发症发生率低于对照组（P<0.05）。结论 泌尿外科术后留置双 J 管患者实施延续性护理,能够提高自我护理能力,改 善生活质量,提高治疗依从性,降低并发症发生率。     

血液透析患者深静脉双腔留置导管并发症的预防及护理

目的 探讨血液透析患者深静脉双腔留置导管的相关并发症预防及护理.方法 选取在我院行血液透析治疗的患者84例为研究对象,随机分为对照组和观察组各42例,所有患者均为深静脉双腔留置导管建立血液透析通道,对照组实施常规护理,观察组实施并发症预防护理,对比分析两组患者并发症的发生率.结果 观察组并发症发生率为11.22%,对照组并发症发生率为25.75%,两组患者并发症的发生率比较差异具有统计学意义(P<0.05).结论 血液透析患者留置深静脉双腔导管后,易发生感染、渗血等并发症,需采取针对性预防护理策略及时防范病情恶化,才能有效促进患者快速恢复.     

2002-2006年浙江省遂昌县肺结核病监测结果分析

目的 分析和评价浙江省遂昌县5年来肺结核病管理、控制、监测、防治新模式的运作和成效.方法 对全县结核病发病情况进行监测、流行病学调查和评估.结果 2002-2006年遂昌县结核病可疑患者就诊人数占总人口的比例分别为80.75/10万、70.94/10万、107.87/10万、92.78/10万、146.96/10万;新痰涂片阳性(涂阳)登记率分别为15.09/10万、15.96/10万、26.97/10万、24.18/10万、24.42/10万.5年新登记涂阳病人男性与女性发病率比例分别为:4.19:1、2.04:1、2.32:1、4.04:1、2.81:1,总比例为3.21:1(x2=53.28,P＜0.01).农村居民发病人数占总发病率的91.92%.结论 按照现代防治策略的要求,通过免费查治、报病奖励、培训宣教、加大转诊等一系列措施的施行,不断提高了传染性肺结核病例的发现率和治愈率.     

Evaluation of Medical Image Registration Techniques Based on Nature and Domain of the Transformation

A lot of research has been done during the past 20 years in the area of medical image registration for obtaining detailed, important, and complementary information from two or more images and aligning them into a single, more informative image. Nature of the transformation and domain of the transformation are two important medical image registration techniques that deal with characters of objects (motions) in images. This article presents a detailed survey of the registration techniques that belong to both categories with detailed elaboration on their features, issues, and challenges. An investigation estimating similarity and dissimilarity measures and performance evaluation is the main objective of this work. This article also provides reference knowledge in a compact form for researchers and clinicians looking for the proper registration technique for a particular application.     

A Real-Time Intrasubject Elastic Registration Algorithm for Dynamic 2-D Ultrasound Images

A new dynamic registration technique is designed to elastically align a sequence of 2-D ultrasound (US) images. The proposed algorithm tracks features over an image sequence in real-time, as opposed to our previous technique which registers images without utilizing prior information. The registration results were evaluated using a customized validation framework for elastic US registration algorithms. Experiments were performed on 600 simulated images as well as 20 image sequences obtained from 10 volunteer subjects, each sequence containing 50 images. Strategies for qualitative and quantitative evaluation consisted of visual assessment, feature overlap, similarity measures, and simulation experiments. The registration method has comparable performance to our previous registration technique; however, has the advantage of lower computational requirements and hence, is potentially more suitable for clinical applications. Rigorous performance evaluations attest to the fast speed of registration at an average of 5.5 frames per second on a conventional computing platform. (E-mail: purang@cs.queensu.ca)     

Automatic registration using implicit shape representations: applications in intraoperative 3D rotational angiography to preoperative CTA registration

Purpose  A solution for automatic registration of 3D Rotational Angiography (XA) to CT/MR of the liver. Targeted for use in treatment planning of liver interventions. Methods  A shape-based approach to registration is proposed that does not require specification of landmarks nor is it prone to local minima like purely intensity-based registration methods. Through the use of vessel characteristics, accurate registration is possible even in the presence of deformations induced by catheters and respiratory motion. Results  Registration was performed on eight pairs of multiphase CT angiography and 3D rotational digital angiography datasets. Quantitative validation of the registration accuracy using vessel landmarks was performed on these datasets. The validation study showed that the method has a registration error of 9.41  ±  4.13 mm. In addition, the computation time is well below 60 s making it attractive for clinical application. Conclusion  A new method for fully automatic 3DXA to CT/MR image registration was developed and found to be efficient and accurate using clinically realistic datasets.     

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.     

Deformable MR-CT image registration using an unsupervised,dual-channel network for neurosurgical guidance

PurposeThe accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue – e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance.MethodThe framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks.ResultsThe proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s.ConclusionThe deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.     

SpineRegNet: Spine Registration Network for volumetric MR and CT image by the joint estimation of an affine-elastic deformation field

Spine registration for volumetric magnetic resonance (MR) and computed tomography (CT) images plays a significant role in surgical planning and surgical navigation system for the radiofrequency ablation of spine intervertebral discs. The affine transformation of each vertebra and elastic deformation of the intervertebral disc exist at the same time. This situation is a major challenge in spine registration. Existing spinal image registration methods failed to solve the optimal affine-elastic deformation field (AEDF) simultaneously, only consider the overall rigid or elastic alignment with the help of a manual spine mask, and encounter difficulty in meeting the accuracy requirements of clinical registration application. In this study, we propose a novel affine-elastic registration framework named SpineRegNet. The SpineRegNet consists of a Multiple Affine Matrices Estimation (MAME) Module for multiple vertebrae alignment, an Affine-Elastic Fusion (AEF) Module for joint estimation of the overall AEDF, and a Local Rigidity Constraint (LRC) Module for preserving the rigidity of each vertebra. Experiments on T2-weighted volumetric MR and CT images show that the proposed approach achieves impressive performance with mean Dice similarity coefficients of 91.36%, 81.60%, and 83.08% for the mask of the vertebrae on Datasets A-C, respectively. The proposed technique does not require a mask or manual participation during the tests and provides a useful tool for clinical spinal disease surgical planning and surgical navigation systems.     

Adaptive registration using local information measures

Rapidly advancing registration methods increasingly employ warping transforms. High degrees of freedom (DOF) warpings can be specified by manually placing control points or instantiating a regular, dense grid of control points everywhere. The former approach is laborious and prone to operator bias, whereas the latter is computationally expensive. We propose to improve upon the latter approach by adaptively placing control points where they are needed. Local estimates of mutual information (MI) and entropy are used to identify local regions requiring additional DOF.     

Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences

Objective  We present a novel method for intraoperative image-based bone surface reconstruction and its validation. Materials and methods  In the preoperative stage, we construct a CT-like intensity atlas of the anatomy of interest. In the intraoperative stage, we deformably register this atlas to fluoroscopic X-ray images of the patient anatomy. We iteratively refine the atlas-to-patient registration by establishing explicit correspondences between bone surfaces in the atlas and their projections in the X-ray images. The advantage of our method is its use of CT-quality intensity data for correspondence establishment, which eliminates the edge-detection problem and diminishes the miscorrespondence problem. We validate our method on two datasets: (1) an in vitro dry femur; (2) Digitally Reconstructed Radiographs, which were generated from 17 clinical CTs, and simulate realistic in vivo femurs. Results  The mean surface approximation error of our femur atlas was 0.85 ± 0.16 mm. On Digitally Reconstructed Radiographs, the mean surface reconstruction error was 1.40 ± 0.55 mm. On a dry femur, the mean surface reconstruction error was 1.44 mm. Conclusion  The results show that our reconstruction method is on par with the state of the art in reconstruction of ex vivo femurs. In addition, the results demonstrate that our method is effective in realistic simulations of the in vivo scenario.     

我国现行护士注册管理制度中相关问题的初步研究

目的探讨建立更加合理的护士注册管理制度。方法采用Delphi法,对125名护理教育、护理管理和临床护理专家进行调查。结果74%的专家建议分层注册,75%的专家认为不可以免试注册,对护士何时注册存在分歧。结论建议按照注册助理护士、注册护士、注册专科护士分别注册,取消现行的护士执业考试免试资格,进一步对注册护士的注册时间及相关问题进行研究。