四种评分系统对呼吸重症监护室病人预后评估的比较

目的　研究APACHEⅢ、SAPSⅡ、MPMⅡ 0和APACHEⅡ评分系统对呼吸重症监护室病人预后评估中的适用性和可行性 ,同时比较其优劣。方法　回顾性分析 2 16例呼吸重症监护室病人临床资料 ,进行APACHEⅢ、SAPSⅡ和APACHEⅡ评分 ,计算SAPSⅡ、APACHEⅡ和MPMⅡ 0的预计病死危险度。比较由SAPSⅡ、APACHEⅡ和MPMⅡ 0计算的病人院内病死概率和病人实际病死率之间的差异 ;同时用Hosmer-Lemeshow拟合优度检验和ROC曲线比较APACHEⅢ、SAPSⅡ和APACHEⅡ评分之间校准度和精确度的差异。结果　 2 16例病人中死亡 77例 ,存活 139例 ,实际病死率 35 6 5 %。死亡组与存活组病人的APACHEⅡ、APACHEⅢ及SAPSⅡ评分在存活组与死亡组之间差异均有统计学意义 (P <0 0 5 )。APACHEⅡ、APACHEⅢ和SAPSⅡ的AUROCC分别为 0 74 4、0 74 1和 0 75 4 ,APACHEⅡ -PHM与实际病死率无显著性差异 (P >0 0 5 ) ,而SAPSⅡ -PHM和MPMⅡ 0与实际病死率有显著性差异 (P <0 0 5 )。结论　各种病情评价系统均可用于评价呼吸重症监护室病人的预后 ,4种病情评价系统中以APACHEⅡ最适于RICU病人。     

Extracorporeal membrane oxygenation in a Scottish intensive care unit

I reflected on the training I had on an extraordinary treatment for profound respiratory failure. The result of training enabled us to successfully treat a young female with the influenza A virus with extracorporeal membrane oxygenation (ECMO). I report the positive outcome that occurred, while continuing to run a busy general intensive care unit (ICU). She was the first of six patients who were all successfully treated with ECMO. Ten trained and experienced critical care nurses and two doctors attended the ECMO training course provided by the national centre in the UK. Five patients had already received ECMO therapy in the Scottish specialist unit (over the period of 8 years). As our Scottish specialist unit purchased exactly the same equipment as the national centre, it was easier for the multidisciplinary team to utilize their new‐found knowledge and treat future patients with ECMO. With the predicted swine flu (H1N1) pandemic and the subsequent demand for critical care beds, funding was obtained to facilitate ECMO training. The potential need for increased provision of ECMO therapies was highlighted by recent events in Australia and New Zealand. Their most recent winter produced 68 patients requiring ECMO, whereas the previous year had manifested only three. Using our new equipment and adapted protocols from the national centre, we used these new skills to treat our first patient in October 2009. Johns' reflective practice tool was used to evaluate the care provided. Our patient was on ECMO for 9 days. She went on to make a remarkable recovery and was discharged from the ICU 1 week after ECMO was discontinued. She was discharged to the cardiothoracic high‐dependency unit, where she was successfully rehabilitated. We were able to successfully treat a young lady, while providing the care for all other patients. This was a complex treatment, one that uses many resources including time and finance. Now that we have all the equipment, the necessary training and the knowledge, we can continue to deliver this service to the public in our locality.     

Evaluation of pediatric intensive care scoring systems

There is no report analysing pediatric severity scoring systems in British Intensive Therapy Units (ICUs). Two previously reported pediatric severity scoring systems, the Admission Physiologic Stability Index (APSI) and the Organ System Failure (OSF) score were evaluated for 151 patients. The APSI was higher for children who died than for those who lived (p<0.001). This difference reflected the sharp distinction between the APSI for chilren who left intensive care within 24 h and those remaining in ICU longer than 24h (p<0.001). For children remaining in ICU longer than 24 h, there was a large overlap of APSI scores, and the APSI did not discriminate between children in the overlap region who lived and those who died (p=0.054). There was underscoring of neurological patients; the APSI did not differentiate neurological patients whole lived and those who died (p>0.10). The OSF also underscored neurological patients. Increasing number of organ systems failed was associated with increasing mortality. In contrast to previous reports, however, the mortality rate was unaffected by whether the total number of systems failed simultaneously or non-simultaneously. There is still a need for a comprehensive yet simple pediatric scoring system for comparing the efficacy and outcome of pediatric intensive care in different ICUs in different countries.     

Computer assisted data analysis in intensive care: the ICDEV project-development of a scientific database system for intensive care

Introduction: Patient Data Management Systems (PDMS) for ICUs collect, present and store clinical data. Various intentions make analysis of those digitally stored data desirable, such as quality control or scientific purposes. The aim of the Intensive Care Data Evaluation project (ICDEV), was to provide a database tool for the analysis of data recorded at various ICUs at the University Clinics of Vienna.Settings: General Hospital of Vienna, with two different PDMSs used: Care Vue 9000 (Hewlett Packard, Andover, USA) at two ICUs (one medical ICU and one neonatal ICU) and PICIS Chart+ (PICIS, Paris, France) at one Cardiothoracic ICU.Concept and methods: Clinically oriented analysis of the data collected in a PDMS at an ICU was the beginning of the development. After defining the database structure we established a client-server based database system under Microsoft Windows NI^TM and developed a user friendly data quering application using Microsoft Visual C++^TM and Visual Basic^TM;Results: ICDEV was successfully installed at three different ICUs, adjustment to the different PDMS configurations were done within a few days. The database structure developed by us enables a powerful query concept representing an ‘EXPERT QUESTION COMPILER’ which may help to answer almost any clinical questions. Several program modules facilitate queries at the patient, group and unit level. Results from ICDEV-queries are automatically transferred to Microsoft Excel^TM, for display (in form of configurable tables and graphs) and further processing.Conclusions: The ICDEV concept is configurable for adjustment to different intensive care information systems and can be use to support computerized quality control. However, as long as there exists no sufficient artifact recognition or data validation software for automatically recorded patient data, the reliability of these data and their usage for computer assisted quality control remain unclear and should be further studied. Supported by the Scientific Fund of the Mayor of Vienna     

Predicting mortality in intensive care unit survivors using a subjective scoring system

Most prognostic models rely on variables recorded within 24 hours of admission to predict the mortality rate of patients in the intensive care unit (ICU). Although a significant number of patients die after discharge from the ICU, there is a paucity of data related to predicting hospital mortality based on information obtained at ICU discharge. It is likely that experienced intensivists may be able to predict the likelihood of hospital death at ICU discharge accurately if they incorporate patients' age, preferences regarding life support, comorbidities, prehospital quality of life, and clinical course in the ICU into their prediction. However, if it is to be generalizable and reproducible and to perform well without bias, then a good prediction model should be based on objectively defined variables.     

Otorhinolaryngological problems occurring within the intensive care unit

OBJECTIVE: An overview of common otorhinolaryngological (ORL) problems and procedures in the intensive care unit (ICU) is presented. FOCUS: Diagnostic, management, and treatment aspects of some conditions are discussed with emphasis on the potential difficulties encountered in the ICU. Approach recommendations are outlined as well as a list of required basic equipment. CONCLUSIONS: Otorhinolaryngology should be included in intensive care continuing medical education programs.     

重症监护护理评分系统在CCU病区中的应用

冠心病监护病房(CCU)是心血管内科急危重症的诊治场所,由于CCU收治的病人病情严重程度不同,自然病程及救治成功率也不大相同,病人即使同一疾病,却因生理、心理、社会等因素存在个体差异,因而会表现出不同的临床情况。但是在临床护理工作中,医生开具的护理级别却相同,因此,这种被动的护理工作模式显然不能满足病人的实际护理需求和当代的临床需要,给予准确的护理评估,保证护理质量非常重要。为使     

Costs and cost-effectiveness of a telemedicine intensive care unit program in 6 intensive care units in a large health care system

Purpose

The purpose of this study is to estimate the costs and cost-effectiveness of a telemedicine intensive care unit (ICU) (tele-ICU) program.

Materials and Methods

We used an observational study with ICU patients cared for during the pre-tele-ICU period and ICU patients cared for during the post-tele-ICU period in 6 ICUs at 5 hospitals that are part of a large nonprofit health care system in the Gulf Coast region. We obtained data on a sample of 4142 ICU patients: 2034 in the pre-tele-ICU period and 2108 in the post-tele-ICU period. Economic outcomes were hospital costs, ICU costs and floor costs, measured for average daily costs, costs per case, and costs per patient.

Results

After the implementation of the tele-ICU, the hospital daily cost increased from $4302 to $5340 (24%); the hospital cost per case, from $21 967 to $31 318 (43%); and the cost per patient, from $20 231 to $25 846 (28%). Although the tele-ICU intervention was not cost-effective in patients with Simplified Acute Physiology Score II 50 or less, it was cost-effective in the sickest patients with Simplified Acute Physiology Score II more than 50 (17% of patients) because it decreased hospital mortality without increasing costs significantly.

Conclusions

Hospital administrators may conclude that a tele-ICU program aimed at the sickest patients is cost-effective.     

Assessment of severity of the state and intensive care of newborns in the intensive care units

A point-based evaluation of the severity condition was made on the bases of an analysis of clinical findings (cardiovascular, respiratory, central nervous, urinary and gastrointestinal systems) for 130 newborns who were admitted to the intensive care and resuscitation unit: 3-5 points--severe condition, 6-10 points--highly severe condition and 12-15 points--extremely severe condition. The severity assessment scale as applicable to newborns and based only on the clinical findings enables the practitioner to optimize the conducted therapy in accordance with a severity degree.     

Adoption of a sedation scoring system and sedation guideline in an intensive care unit

AIM: The paper presents a study assessing the rate of adoption of a sedation scoring system and sedation guideline. BACKGROUND: Clinical practice guidelines including sedation guidelines have been shown to improve patient outcomes by standardizing care. In particular sedation guidelines have been shown to be beneficial for intensive care patients by reducing the duration of ventilation. Despite the acceptance that clinical practice guidelines are beneficial, adoption rates are rarely measured. Adoption data may reveal other factors which contribute to improved outcomes. Therefore, the usefulness of the guideline may be more appropriately assessed by collecting adoption data. METHOD: A quasi-experimental pre-intervention and postintervention quality improvement design was used. Adoption was operationalized as documentation of sedation score every 4 hours and use of the sedation and analgesic medications suggested in the guideline. Adoption data were collected from patients' charts on a random day of the month; all patients in the intensive care unit on that day were assigned an adoption category. Sedation scoring system adoption data were collected before implementation of a sedation guideline, which was implemented using an intensive information-giving strategy, and guideline adoption data were fed back to bedside nurses. After implementation of the guideline, adoption data were collected for both the sedation scoring system and the guideline. The data were collected in the years 2002-2004. FINDINGS: The sedation scoring system was not used extensively in the pre-intervention phase of the study; however, this improved in the postintervention phase. The findings suggest that the sedation guideline was gradually adopted following implementation in the postintervention phase of the study. Field notes taken during the implementation of the sedation scoring system and the guideline reveal widespread acceptance of both. CONCLUSION: Measurement of adoption is a complex process. Appropriate operationalization contributes to greater accuracy. Further investigation is warranted to establish the intensity and extent of implementation required to positively affect patient outcomes.     

Identification of low-risk monitor patients within a medical-surgical intensive care unit

We studied the hospital course of 1148 consecutive intensive care unit (ICU) admissions to test the feasibility of identifying patients suitable for early transfer. Based on the type of treatment each admission received during the initial 16 hours in ICU, we divided the patients into active treatment or monitored categories. Which of the 513 monitored admissions received active treatment before discharge was analyzed with a multivariate logistic regression analysis, using variables such as age, sex, indication for admission, and a new severity-of-illness scale. The most important variable in identifying low-risk monitored patients was the severity of illness measure, which performed well in both estimation and validation data sets. Within the 513 monitored admissions, 154 had predicted risks of requiring active intensive therapy of less than 5 per cent. Only five persons actually received such treatment. This approach might assist in reducing the ever-increasing demand for intensive care.     

Quality of life in the five years after intensive care: a cohort study

Introduction  

Data on quality of life beyond 2 years after intensive care discharge are limited and we aimed to explore this area further. Our objective was to quantify quality of life and health utilities in the 5 years after intensive care discharge.     

重症监护护理评分系统在CCU患者分层护理中的应用效果

目的评价重症监护护理评分系统在CCU患者分层护理中的应用效果。方法选取2013年12月至2014年6月收治的175例患者为对照组,2014年7月至2015年1月收治的182例患者为观察组,对照组根据不同的病种给予常规护理,观察组责任护士根据重症监护护理评分系统对患者护理需求情况进行评分,主要包括血压、心律、通气功能、换气功能、重要脏器供血情况、营养及代谢情况等,依据其评分分值的高低分层级进行护理。结果观察组CCU患者护理质量、住院时间、医疗费用、对护理人员满意度及并发症发生率与对照组比较差异均具有统计学意义(P0.01或P0.05)。结论应用重症监护护理评分系统提高了患者护理质量,缩短CCU住院时间,降低了并发症发生率。     

Development of an e-mail database of US intensive care physicians

PURPOSE: Although surveying critical care physicians regarding their behaviors and attitudes may usefully inform clinical, ethical, and policy questions, few resources exist for surveying intensivists electronically. We sought to develop an e-mail database for all intensivists associated with US training programs in critical care medicine (academic intensivists) and to determine the feasibility of using this database to survey intensivists. MATERIALS AND METHODS: We obtained e-mail addresses for academic intensivists by consulting each training program's institutional Web site or contacting program directors directly. We sent presumed intensivists up to 3 e-mail invitations to participate in an initial survey. RESULTS: We identified 2858 potential intensivists and obtained operative e-mail addresses for 2494 (87%). Only 31 (9%) of the remaining intensivists were members of the Society of Critical Care Medicine, suggesting that most of those without identified addresses were not intensivists. During the conduct of an initial survey, 161 physicians self-identified themselves as nonintensivists; of the remaining 2333 presumed intensivists, 1026 (44%) responded and 44 (2%) opted out. The response rate of 44% is based on the conservative assumptions that the remaining 1263 physicians were intensivists and saw the e-mail invitation. CONCLUSIONS: This database provides a unique resource for investigators wishing to efficiently identify the views and practice patterns of US academic intensivists and provides a benchmark response rate of approximately 44% for electronic surveys of intensivists.     

Mortality prediction models for pediatric intensive care: comparison of overall and subgroup specific performance

Aim

To validate paediatric index of mortality (PIM) and pediatric risk of mortality (PRISM) models within the overall population as well as in specific subgroups in pediatric intensive care units (PICUs).

Methods

Variants of PIM and PRISM prediction models were compared with respect to calibration (agreement between predicted risks and observed mortality) and discrimination (area under the receiver operating characteristic curve, AUC). We considered performance in the overall study population and in subgroups, defined by diagnoses, age and urgency at admission, and length of stay (LoS) at the PICU. We analyzed data from consecutive patients younger than 16 years admitted to the eight PICUs in the Netherlands between February 2006 and October 2009. Patients referred to another ICU or deceased within 2 h after admission were excluded.

Results

A total of 12,040 admissions were included, with 412 deaths. Variants of PIM2 were best calibrated. All models discriminated well, also in patients <28 days of age (neonates), with overall higher AUC for PRISM variants (PIM = 0.83, PIM2 = 0.85, PIM2-ANZ06 = 0.86, PIM2-ANZ08 = 0.85, PRISM = 0.88, PRISM3-24 = 0.90). Best discrimination for PRISM3-24 was confirmed in 13 out of 14 subgroup categories. After recalibration PRISM3-24 predicted accurately in most (12 out of 14) categories. Discrimination was poorer for all models (AUC < 0.73) after LoS of >6 days at the PICU.

Conclusion

All models discriminated well, also in most subgroups including neonates, but had difficulties predicting mortality for patients >6 days at the PICU. In a western European setting both the PIM2(-ANZ06) or a recalibrated version of PRISM3-24 are suited for overall individualized risk prediction.     

Mortality predictions in the intensive care unit: comparing physicians with scoring systems

OBJECTIVE: Risk-prediction models offer potential advantages over physician predictions of outcomes in the intensive care unit (ICU). Our systematic review compared the accuracy of ICU physicians' and scoring system predictions of ICU or hospital mortality of critically ill adults. DATA SOURCE: MEDLINE (1966-2005), CINAHL (1982-2005), Ovid Healthstar (1975-2004), EMBASE (1980-2005), SciSearch (1980-2005), PsychLit (1985-2004), the Cochrane Library (Issue 1, 2005), PubMed "related articles," personal files, abstract proceedings, and reference lists. STUDY SELECTION: We considered all studies that compared physician predictions of ICU or hospital survival of critically ill adults to an objective scoring system, computer model, or prediction rule. We excluded studies if they focused exclusively on the development or economic evaluation of a scoring system, computer model, or prediction rule. DATA EXTRACTION AND ANALYSIS: We independently abstracted data and assessed study quality in duplicate. We determined summary receiver operating characteristic curves and areas under the summary receiver operating characteristic curves+/-se and summary diagnostic odds ratios. DATA SYNTHESIS: We included 12 observational studies of moderate methodological quality. The area under the summary receiver operating characteristic curves for seven studies was 0.85+/-0.03 for physician predictions compared with 0.63+/-0.06 for scoring system predictions (p=.002). Physicians' summary diagnostic odds ratios derived from the area under the summary receiver operating characteristic curves were significantly higher (12.43; 95% confidence interval 5.47, 27.11) than scoring systems' summary diagnostic odds ratios (2.25; 95% confidence interval 0.78, 6.52, p=.001). Combined results of all 12 studies indicated that physicians predict mortality more accurately than do scoring systems: ratio of diagnostic odds ratios (95% confidence interval) 1.92 (1.19, 3.08) (p=.007). CONCLUSIONS: Observational studies suggest that ICU physicians discriminate between survivors and nonsurvivors more accurately than do scoring systems in the first 24 hrs of ICU admission. The overall accuracy of both predictions of patient mortality was moderate, implying limited usefulness of outcome prediction in the first 24 hrs for clinical decision making.