144例非计划性转入神经外科ICU患者的临床分析与护理策略

目的 调查非计划性转入NICU患者的转入原因,分析其临床指标,探讨护理策略.方法 回顾性调查2019年1月1日-2020年10月31日我院NICU的175例非计划转入患者,利用自制量表收集患者的人口学信息、生理指标、是否死亡等资料,并对其进行分析.结果 共有144例患者纳入分析显示:16:00～23:59转入患者数量最...     

ICU患者非计划性拔管的原因分析与防范措施

非计划性拔管（Unplanned extubation,UEX）是指插管意外脱落或未经医护人员同意,患者将插管拔除,也包括医护人员操作不当所致拔管。ICU患者病情危重,变化快,管道多,无形中增加了非计划性拔管的风险。管道维系着患者生命,一旦发生UEX,可能造成患者损伤、住院天数延长、费用增加,甚至危及患者生命;还使重新插管率增加,     

Rass评分在预防ICU患者非计划性拔管中的应用效果观察

目的观察Rass评分在预防ICU患者非计划性拔管中的应用效果。方法将120例ICU收治的插管患者随机分为对照组与观察组,两组均采用ICU常规护理,对照组给予基础镇静,观察组给予Rass评分指导镇静。比较两组非计划性拔管发生率、病死率、住院时间以及护理过程中不良反应发生情况。结果观察组非计划性拔管发生率低于对照组(1.67%vs 11.67%,P0.05);观察组病死率低于对照组(1.67%VS 16.67%,P0.05);两组住院时间差异无统计学意义(P0.05);观察组并发症发生率低于对照组(3.33%VS 18.33%,P0.05)。结论 Rass评分可有效预防ICU患者非计划性拔管发生率,降低不良事件发生率及不良反应发生率,值得临床推广应用。     

ICU病人非计划性拔管的原因分析与护理防范

为提高ICU插管病人的护理质量，分析非计划性拔管的原因，并提出进行相关知识的培训，有效固定导管，合理使用镇静剂及肢体约束等防范措施。     

患者非计划性重返ICU原因分析及护理对策

总结了我院2013年01月至2014年12月期间,24例患者重返ICU治疗的原因。主要包括呼吸道系统问题、循环系统问题、神经系统问题以及手术并发症和感染性休克等。认为在加强基础护理的同时,严格做好交接班工作,及早发现病情变化,是减少患者重返ICU的重要措施。     

ICU病人非计划性拔管的原因分析与护理防范

为提高ICU插管病人的护理质量 ,分析非计划性拔管的原因 ,并提出进行相关知识的培训 ,有效固定导管 ,合理使用镇静剂及肢体约束等防范措施。     

ICU气管插管病人非计划性拔管的原因分析及对策

ICU气管插管病人中非计划性拔管的发生率为10.8%.通过对ICU住院病人的回顾性调查,发现非计划性拔管的发生与缺乏有效的固定、未使用镇静剂、未采取适当的肢体约束措施以及医疗护理操作不当等因素有关.针对这些因素探索相应的护理对策,使非计划性拔管的发生率下降至1.9%.     

非惩罚性报告制度在防控ICU患者非计划性拔管中的应用

目的：探讨非惩罚性报告制度在ICU患者非计划性拔管防控中的应用效果。方法成立不良事件分析小组,在ICU建立非惩罚性报告制度,对出现非计划性拔管事件采取不公开、非惩罚的处理原则,分析发生非计划性拔管的根本原因,提出改进意见和预防措施,不断修改完善护理工作流程及管理制度。结果实施后非计划性拔管上报率由13．3％提高到66．7％（P＜0．01）,非计划性拔管发生率由3．27％下降至1．14％（P＜0．01）。结论在ICU实施非惩罚性报告制度能明显改善护士对非计划性拔管的认知及上报态度,有助于找到不良事件发生的原因,从根本上杜绝非计划性拔管的发生,有利于预防和避免严重不良事件的发生。     

肿瘤ICU患者非计划性拔管临床特征分析及对策

目的分析湖南省肿瘤医院ICU非计划性拔管的临床特征及发生原因,探讨预防对策。方法调查2014年1~12月湖南省肿瘤医院ICU 38例非计划性拔管患者的一般情况、发生原因及采取的护理措施等。结果胃管、气管导管、中心静脉导管、外科引流管的意外拔(脱)管分别占发生总数的50%、15.8%、10.5%和10.5%;清醒患者占36.8%;未行有效约束占55.3%;管道固定不妥善占21.1%;护士预见拔管风险占31.6%;重置率占68.4%;拔管患者管床护士职称为护士占55.3%;工作年限1~5年达44.7%;患者自拔占94.7%;拔管患者出现ICU综合症者达73.7%。结论强化培训教育,实行预警教育,正确评估患者管道风险,做好宣教与心理护理,合理的约束与镇静、镇痛,护理人员合理配置与有效合作,预见性的提前做好预防措施,可防范ICU非计划性拔管的发生。     

ICU气管插管患者非计划性拔管的原因分析与护理对策

目的分析总结ICU气管插管患者非计划性拔管的原因及影响因素,探讨优化护理干预对减少非计划性拔管的效果。方法回顾性分析2010年6月~2012年6月我院ICU收治的240例气管插管患者的临床资料,其中128例为2011年7月前给予常规护理措施的患者,112例为2011年7月以后进行优化护理干预后的患者。总结引起非计划性拔管的原因,观察采取优化护理干预前后非计划性拔管比例的变化。结果给予常规护理措施的128例患者中有15例（11．72％）出现非计划性拔管,优化护理干预的112例患者中仅4例（3．57％）出现非计划性拔管,二者比较差异有显著意义（P〈0．05）。结论患者舒适度低、患者意识状况异常、导管固定不牢、护理操作不当、护患沟通不到位是导致非计划性拔管的主要原因。采取针对性的护理干预措施,可显著减少非计划性拔管的发生。     

危重症评分量表对急诊病人转入ICU及死亡的预测能力研究

目的:探讨危重症评分量表对急诊病人转入重症监护室(ICU)及死亡的预测能力。方法:采用方便抽样法选取2019年1月—2019年12月在我院急诊科就诊的250例病人作为研究对象,最终纳入228例病人,按转入科室分为ICU组(68例)及普通病房组(160例)。应用改良早期预警评分(MEWS)量表和急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评估病人病情,记录病人ICU转入率及死亡率,并应用受试者工作特征曲线(ROC)分析MEWS量表、APACHEⅡ量表在急诊病人ICU转入率及死亡率中的预测价值。结果:与普通病房组相比,ICU组MEWS评分、APACHEⅡ评分更高(P<0.001)。死亡组MEWS评分、APACHEⅡ评分高于非死亡组(P<0.001)。经ROC曲线分析可知,MEWS评分、APACHEⅡ评分诊断急诊病人转入ICU的最佳截断值分别为4分和20分,两者联合诊断急诊病人转入ICU的敏感度及特异度明显高于MEWS评分、APACHEⅡ评分单项诊断。MEWS评分、APACHEⅡ评分诊断急诊病人死亡最佳截断值分别为6分和25分,两者联合诊断急诊病人死亡的敏感度及特异度高于...     

儿科ICU患儿非计划拔管的危险因素分析与护理措施

目的探讨儿科ICU患儿非计划拔管的危险因素,并制定有效的护理措施,以减少非计划性拔管的发生。方法采用回顾性研究方法,选取某医院儿科重症监护室自2019年1—6月收治的215例患儿为研究对象,其中20例患儿发生非计划拔管,收集患儿的年龄、管路类型、是否约束、镇静水平、护士是否知晓非计划拔管的知识、胶布更换次数、工作年限和分管床位数的一般资料。结果多因素logistic回归分析显示,镇静剂的使用、胶布更换次数、护士对计划性拔管知识的知晓是影响因素(P<0.05)。结论护理人员应学会正确的评估患儿的镇静水平,提高胶布更换频次,加强护理人员非计划性拔管相关知识的培训等措施,能够减少的降低儿科ICU患儿非计划性拔管的发生。     

The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis

Purpose

Sepsis is believed to be responsible for substantial health care burden, but there is limited information about its magnitude and the factors affecting health outcomes in Asian population. The aim of the study was to assess the disease burden of sepsis and to test the usefulness of Charlson Comorbidity Index (CCI) and age as risk-adjusted hospital mortality predictors in patients with sepsis using hospital administrative database.

Methods

A retrospective cohort study of hospital discharge database from 2004 to 2007 to identify cases with sepsis, comorbidity, and organ failure using the International Statistical Classification of Diseases and Related Health Problems, 9th Revision, Australian Modification codes was conducted.

Results

Of 305?637 hospitalized patients over 4 years, 6929 (2.27%) patients had sepsis, with 1216 (17.5%) patients associated with intensive care unit (ICU) admission. The mortality rates increased consistently in patients with CCI ranging from none to low, moderate and high grade for both patients with ICU admission (39.4%, 51.6%, 55.9%, and 54.3% respectively; P < .001) and patients without ICU admission (6.4%, 8.7%, 17.1%, and 25.3% respectively; P < .001). Logistic regression analysis showed that CCI (odds ratio, 11.8; high versus none) and age (odds ratio, 8.46; aged 85 years and older versus aged 18-54 years old) were significant and independent predictors of hospital mortality. Similar results were seen with hospital length of stay by zero-truncated negative binomial regression model analysis.

Conclusion

The sepsis-related mortality and resource utilization are high in this population as well. Comorbidities and advanced age were some of the most important contributors to hospital mortality and resource utilization.     

BIS监测联合Ramsay镇静评分在预防ICU患者非计划性气管拔管中的应用

目的 探讨脑电双频指数(BIS)监测联合Ramsay镇静评分在预防ICU患者非计划性气管拔管中的应用价值.方法 选择93例神志清醒的气管插管患者作为研究对象,采用随机数字法将患者分为实验组47例与对照组46例,实验组采用BIS监测联合Ramsay镇静评分进行镇静管理,对照组采用Ramsay镇静评分进行镇静管理,对2组患者气管插管期间非计划性拔管发生率进行比较.结果 实验组非计划性拔管发生率显著低于对照组.结论 BIS监测联合Ramsay镇静评分比单纯Ramsay镇静评分法更适合于气管插管患者的镇静管理.     

急诊室危重患者滞留时间影响因素Logistic回归分析

目的 探索影响急诊抢救室危重患者滞留时间的相关因素,为急诊管理者制定相关措施提供依据.方法 回顾性分析一家综合性医院2010年1月至2011年6月急诊抢救室危重患者的信息,通过二分类Logistic回归分析滞留时间的影响因素,并进一步比较主要影响因素特征.结果(1)2010年1月至2011年6月急诊抢救室共有危重患者11 468例,滞留的中位数时间为11h,滞留时间＞6h的有6 525例,占56.9％.(2)影响急诊危重患者滞留时间超过6h的主要因素有主诊科室、绿色通道、就诊时间段.其次为收入ICU、交通事故、120送入、初步诊断个数、离开抢救室去向、性别、节假日就诊、就诊月份.而患者的年龄、职业、户籍不是影响患者滞留时间超过6h的因素.结论 该家医院危重患者在抢救室滞留的时间偏长,主要受主诊科室、绿色通道、就诊时间段等因素影响,值得重视并进一步研究.     

The influence of length of stay in the ICU on power of discrimination of a multipurpose severity score (SAPS)

Objective To assess how the power of discrimination of a multipurpose severity score (Simplified Acute Physiology Score; SAPS) changes in relation to the length of stay (LOS) in the intensive care unit (ICU).Design In order to compute the SAPS probability, a model derived from logistic regression was developed in a cohort of 8059 patients. Measures of calibration (goodness-of-fit statistics) and discrimination [receiver operating characteristic (ROC) curve and relative area under the curve (AUC)] were adopted in a developmental set (5389 patients) and a validation set (2670 patients), both randomly selected. Once the logit was developed and the model validated, the whole database (8059 patients) was again assembled. To evaluate the accuracy of first-day SAPS probability over time, area under the ROC curve was computed for each of the initial 10 days of ICU care and for day 15.Setting 24 Italian ICUs.Patients A total of 8059 patients out of 10065 consecutive admissions over a period of 3 years (1990–1992) were included in this study. Patients whose SAPS was not correctly compiled (n=687), patients younger than 18 years (n=442), and patients whose LOS was less than 24 h (n=877) were excluded from this analysis.Interventions None.Measurements and results The logistic model gave good results in terms of calibration and discrimination, both in the developmental set (goodness-of-fit:X ²=9.24,p=0.32; AUC=0.79±0.01) and in the validation set (goodness-of-fit:X ²=8.95,p=0.537; AUC=0.78±0.01). The AUC for the whole database showed a loss in discrimination closely related to LOS: 0.79±0.01 at a day 1 and 0.59±0.02 at day 15.Conclusion The logistic model that we developed meets high standards for discrimination and calibration. However, SAPS loses its discriminative power over time; accuracy of prediction is maintained at an acceptable level only in patients who stay in the ICU no longer than 5 days. The stay in the ICU represents a complex variable, which is not predictable, that influences the performance of SAPS on the first day.ARCHIDIA (Archivio Diagnostico): A complete list of study participants appears in theAppendix     

妇科MEWS护理模式对妇科重度感染患者应用效果

Objective: To explore the application effect of the revised gynecological modified early warning score (Modified early warning score,MEWS) care model in patients with gynecological sepsis.Methods: 30 patients treated for severe infection from January 2019 to April 2021 were selected as the study subjects.By randomization, they were divided into controls at 1:1 (n=15) and into experimental groups (n=15).The control group was given the patient the clinical routine care intervention, and the experimental group gave the patient the MEWS care intervention.The incidence of risk events was compared between the two groups, and the quality of life assessment scale (short from 36 questionnaire,SF-36) was used to score the emotional function, social function and cognitive momentum after 1 month of care in the two groups.Results:The experimental group had emotional function, social function, cognitive momentum and other scores were higher than the control group, which showed statistical significance (P <0.05); after care, the experimental group had better vital signs than the control group, and the results showed statistical significance (P <0.05).Conclusion: The modified early warning score to improve the quality of life and reduce the probability of clinical nursing risk events is of great significance to promote the clinical rehabilitation effect of patients.     

Case-mix-adjusted length of stay and mortality in 23 Finnish ICUs

Objectives  To create a tool for benchmarking intensive care units (ICUs) with respect to case-mix adjusted length of stay (LOS) and to study the association between clinical and economic measures of ICU performance. Design  Observational cohort study. Setting  Twenty-three ICUs in Finland. Patients  A total of 80,854 consecutive ICU admissions during 2000–2005, of which 63,304 met the inclusion criteria. Interventions  None. Measurements and results  Linear regression was used to create a model that predicted ICU LOS. Simplified Acute Physiology Score (SAPS) II, age, disease categories according to Acute Physiology and Chronic Health Evaluation III, single highest Therapeutic Intervention Scoring System score collected during the ICU stay and presence of other ICUs in the hospital were included in the model. Probabilities of hospital death were calculated using SAPS II, age, and disease categories as covariates. In the validation sample, the created model accounted for 28% of variation in ICU LOS across individual admissions and 64% across ICUs. The expected ICU LOS was 2.53 ± 2.24 days and the observed ICU LOS was 3.29 ± 5.37 days, P < 0.001. There was no association between the mean observed − mean expected ICU LOS and standardized mortality ratios of the ICUs (Spearman correlation 0.091, P = 0.680). Conclusions  We developed a tool for the assessment of resource use in a large nationwide ICU database. It seems that there is no association between clinical and economic quality indicators. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.