Nurse-led chest drain removal in a cardiac high dependency unit

Within the cardiac high dependency unit it is currently a member of the surgical team who makes the decision for a patient's chest drain to be removed after cardiac surgery. This has often resulted in delays in discharging one patient and therefore in admitting the next. A pilot study was carried out using a working standard that had been developed, incorporating an algorithmic model. The results have enabled nursing staff in a cardiac high dependency unit to undertake this responsibility independently.

Construção de barreiras para diminuir as falhas nas mensurações glicêmicas realizadas pela enfermagem em pacientes críticos com insulina intravenosa

Esta pesquisa tem como objeto os fatores que influenciam a mensuração glicêmica realizada pela enfermagem em pacientes que recebem insulina contínua intravenosa utilizando glicosímetros portáteis à beira leito. Vários fatores podem influenciar a mensuração glicêmica, tais como a amostra sanguínea, a calibração do aparelho, a estocagem das fitas-teste, o hematócrito dos pacientes, o uso de vasoaminas e falhas do operador. A partir da Tese de que: A identificação dos fatores que influenciam a mensuração glicêmica realizada pela enfermagem através de glicosímetros é determinante para a eficiência das barreiras de salvaguarda voltadas para a minimização de falhas na sua execução, a fim de garantir resultados glicêmicos confiáveis e, consequentemente, realizar a titulação da insulina com segurança, teve-se como objetivo geral propor ações de enfermagem que funcionem como barreiras para diminuir as falhas nas mensurações glicêmicas realizadas pela enfermagem em pacientes que recebem infusão contínua de insulina. Espera-se contribuir com ações para garantir a adequação e o controle rigoroso da insulina administrada. Estudo observacional, transversal, prospectivo com abordagem quantitativa na análise dos dados, em uma unidade intensiva cirúrgica cardiológica de um hospital público do Rio de Janeiro. As variáveis do estudo foram submetidas a tratamentos estatísticos não paramétricos e às medidas de associação. Foram investigados 42 pacientes com observação de 417 glicemias. Predominaram pacientes do sexo feminino (57,14%), média de idade de 48 (15,85) anos, sem insuficiência renal e sem tratamento dialítico (90,48%). Observou-se PAM com média de 77(10,29) mmHg, uso de vasoaminas (80,95%), PaO2 ≥ 90mmHg em 85,71% e hematócrito <35% em 71,42%. Encontrou-se uma incidência de hipoglicemia de 35,7%, sendo a população dividida em dois grupos, o primeiro (G1) com pacientes que apresentaram hipoglicemia ≤ 60mg/dl (n=15), e o segundo (G2), com pacientes sem hipoglicemia (n=27). O hematócrito baixo foi a característica clínica que apresentou maior associação com a hipoglicemia. Pacientes com esta condição apresentaram 5,60 vezes mais risco de apresentarem hipoglicemia. O uso de vasoaminas elevou 3,3 vezes o risco de hipoglicemia em pacientes com estas medicações. A realização de cirurgias de emergência, a presença de insuficiência renal com tratamento dialítico, e a elevação da PaO2 acima de 90mmHg também apresentaram associação positiva com a hipoglicemia. Das 417 mensurações observadas, predominou o uso de amostra sanguínea de origem arterial. Observou-se que em todas as etapas da técnica de mensuração houve desvio de execução, com exceção de compressão da polpa digital. Os desvios observados que mostraram associação positiva (RR>1) para pacientes com hipoglicemia foram: a falta de calibração do glicosímetro, a falta de verificação da validade/integridade da fita teste, a falta da higienização das mãos e a falta da coleta de até 1 ml de sangue. Construiu-se uma revisão da técnica de mensuração glicêmica com enfoque nos fatores que podem comprometer o resultado glicêmico levando em conta o risco de hipoglicemia. Tornou-se evidente que a compreensão apropriada dos fatores que influenciam a glicemia e a mensuração glicêmica é indispensável para o enfermeiro na obtenção de resultados glicêmicos confiáveis, e assim, evitar erros na titulação das doses de insulina administrada.

Unmet health needs in patients with coronary heart disease: implications and potential for improvement in caring services

BACKGROUND: Improving the quality of health care services requires tailoring facilities to fulfil patients' needs. Satisfying patients' healthcare needs, listening to patients' opinions and building a closer provider-user partnership are central to the NHS. Few published studies have discussed cardiovascular patients' health needs, but they are not comprehensive and fail to explore the contribution of outcome to needs assessment. METHOD: A comprehensive self-administered health needs assessment (HNA) questionnaire was developed for concomitant use with generic (Short Form-12 and EuroQOL) and specific (Seattle Angina Questionnaire) health-related quality of life (HRQL) instruments on 242 patients admitted to the Acute Cardiac Unit, Nottingham. RESULTS: 38% reported difficulty accessing health facilities, 56% due to transport and 32% required a travelling companion. Mean HRQOL scores were lower in those living alone (P < 0.05) or who reported unsatisfactory accommodation. Dissatisfaction with transport affected patients' ease of access to healthcare facilities (P < 0.001). Younger patients (<65 y) were more likely to be socially isolated (P = 0.01). Women and patients with chronic disease were more likely to be concerned about housework (P < 0.05). Over 65 s (p < 0.05) of higher social classes (p < 0.01) and greater physical needs (p < 0.001) had more social needs, correlating moderately (0.32 < r < 0.63) with all HRQL domains except SAQ-AS. Several HRQL components were highly correlated with the HNA physical score (p < 0.001). CONCLUSIONS: Patients wanted more social (suitable accommodation, companionship, social visits) and physical (help aids, access to healthcare services, house work) support. The construct validity and intra-class reliability of the HNA tool were confirmed. Our results indicate a gap between patients' health needs and available services, highlighting potential areas for improvement in the quality of services

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Nurse-determined assessment of cardiac output. Comparing a non-invasive cardiac output device and pulmonary artery catheter: a prospective observational study.

Background The accurate measurement of Cardiac output (CO) is vital in guiding the treatment of critically ill patients. Invasive or minimally invasive measurement of CO is not without inherent risks to the patient. Skilled Intensive Care Unit (ICU) nursing staff are in an ideal position to assess changes in CO following therapeutic measures. The USCOM (Ultrasonic Cardiac Output Monitor) device is a non-invasive CO monitor whose clinical utility and ease of use requires testing. Objectives To compare cardiac output measurement using a non-invasive ultrasonic device (USCOM) operated by a non-echocardiograhically trained ICU Registered Nurse (RN), with the conventional pulmonary artery catheter (PAC) using both thermodilution and Fick methods. Design Prospective observational study. Setting and participants Between April 2006 and March 2007, we evaluated 30 spontaneously breathing patients requiring PAC for assessment of heart failure and/or pulmonary hypertension at a tertiary level cardiothoracic hospital. Methods SCOM CO was compared with thermodilution measurements via PAC and CO estimated using a modified Fick equation. This catheter was inserted by a medical officer, and all USCOM measurements by a senior ICU nurse. Mean values, bias and precision, and mean percentage difference between measures were determined to compare methods. The Intra-Class Correlation statistic was also used to assess agreement. The USCOM time to measure was recorded to assess the learning curve for USCOM use performed by an ICU RN and a line of best fit demonstrated to describe the operator learning curve. Results In 24 of 30 (80%) patients studied, CO measures were obtained. In 6 of 30 (20%) patients, an adequate USCOM signal was not achieved. The mean difference (±standard deviation) between USCOM and PAC, USCOM and Fick, and Fick and PAC CO were small, −0.34 ± 0.52 L/min, −0.33 ± 0.90 L/min and −0.25 ± 0.63 L/min respectively across a range of outputs from 2.6 L/min to 7.2 L/min. The percent limits of agreement (LOA) for all measures were −34.6% to 17.8% for USCOM and PAC, −49.8% to 34.1% for USCOM and Fick and −36.4% to 23.7% for PAC and Fick. Signal acquisition time reduced on average by 0.6 min per measure to less than 10 min at the end of the study. Conclusions In 80% of our cohort, USCOM, PAC and Fick measures of CO all showed clinically acceptable agreement and the learning curve for operation of the non-invasive USCOM device by an ICU RN was found to be satisfactorily short. Further work is required in patients receiving positive pressure ventilation.

Staff perspectives of a cardiology short stay unit

Objective To evaluate staff perceptions about working environment, efficiency and the clinical safety of a cardiovascular intervention short stay unit (SSU) during the first year of operation. Design Postal questionnaire. Setting Cardiac catheterisation laboratory (CCL), coronary care unit (CCU), general cardiology ward (GCW) and the short stay unit (SSU) of a tertiary referral hospital situated in the mid coastal region of NSW. Subjects Cardiologists (including visiting medical officers [VMO]), cardiology fellows, cardiology advanced trainees and nurses. Results Responses on the working environment of the SSU and the discharge process were statistically significant. A substantial proportion of both nurses and doctors had concerns about patient safety, even though no adverse events were formally recorded in the database. Conclusions Though the participants of the survey agree on the efficiency of the SSU in providing beds to the hospital, they disagree on aspects that are important in the functioning of the SSU, including the working environment, patient selection and clinical safety. The results highlight potential issues that could be improved or addressed and are relevant to the rollout of SSUs across NSW.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

A prospective pilot study evaluating the ‘cardiac decompensation score’ in the setting of intraaortic balloon counterpulsation

The study objective was to determine whether the ‘cardiac decompensation score’ could identify cardiac decompensation in a patient with existing cardiac compromise managed with intraaortic balloon counterpulsation (IABP). A one-group, posttest-only design was utilised to collect observations in 2003 from IABP recipients treated in the intensive care unit of a 450 bed Australian, government funded, public, cardiothoracic, tertiary referral hospital. Twenty-three consecutive IABP recipients were enrolled, four of whom died in ICU (17.4%). All non-survivors exhibited primarily rising scores over the observation period (p < 0.001) and had final scores of 25 or higher. In contrast, the maximum score obtained by a survivor at any time was 15. Regardless of survival, scores for the 23 participants were generally decreasing immediately following therapy escalation (p = 0.016). Further reflecting these changes in patient support, there was also a trend for scores to move from rising to falling at such treatment escalations (p = 0.024). This pilot study indicates the ‘cardiac decompensation score’ to accurately represent changes in heart function specific to an individual patient. Use of the score in conjunction with IABP may lead to earlier identification of changes occurring in a patient's cardiac function and thus facilitate improved IABP outcomes.

Development and pilot test of a peer-support based Cardiac-Diabetes Self-Management program : a study protocol

Background: People with cardiac disease and type 2 diabetes have higher hospital readmission rates (22%)compared to those without diabetes (6%). Self-management is an effective approach to achieve better health outcomes; however there is a lack of specifically designed programs for patients with these dual conditions. This project aims to extend the development and pilot test of a Cardiac-Diabetes Self-Management Program incorporating user-friendly technologies and the preparation of lay personnel to provide follow-up support. Methods/Design: A randomised controlled trial will be used to explore the feasibility and acceptability of the Cardiac-Diabetes Self-Management Program incorporating DVD case studies and trained peers to provide follow-up support by telephone and text-messaging. A total of 30 cardiac patients with type 2 diabetes will be randomised, either to the usual care group, or to the intervention group. Participants in the intervention group will received the Cardiac-Diabetes Self-Management Program in addition to their usual care. The intervention consists of three faceto- face sessions as well as telephone and text-messaging follow up. The face-to-face sessions will be provided by a trained Research Nurse, commencing in the Coronary Care Unit, and continuing after discharge by trained peers. Peers will follow up patients for up to one month after discharge using text messages and telephone support. Data collection will be conducted at baseline (Time 1) and at one month (Time 2). The primary outcomes include self-efficacy, self-care behaviour and knowledge, measured by well established reliable tools. Discussion: This paper presents the study protocol of a randomised controlled trial to pilot evaluates a Cardiac- Diabetes Self-Management program, and the feasibility of incorporating peers in the follow-ups. Results of this study will provide directions for using such mode in delivering a self-management program for patients with both cardiac condition and diabetes. Furthermore, it will provide valuable information of refinement of the intervention program.

Using user-friendly telecommunications to improve cardiac and diabetes self-management programme : a pilot study

Previous studies exploring the incidence and readmission rates of cardiac patients admitted to a coronary care unit (CCU) with type 2 diabetes [1] have been undertaken by the first author. Interviews of these patients regarding their experiences in managing their everyday conditions [2] provided the basis for developing the initial cardiac–diabetes self-management programme (CDSMP) [3]. Findings from each of these previous studies highlighted the complexity of self-management for patients with both conditions and contributed to the creation of a new self-management programme, the CDSMP, based on Bandura’s (2004) self-efficacy theory [4]. From patient and staff feedback received for the CDSMP [3], it became evident that further revision of the programme was needed to improve self-management levels of patients and possibility of incorporating methods of information technology (IT). Little is known about the applicability of different methods of technology for delivering self-management programmes for patients with chronic diseases such as those with type 2 diabetes and cardiac conditions. Although there is some evidence supporting the benefits and the great potential of using IT in supporting self-management programmes, it is not strong, and further research on the use of IT in such programmes is recommended [5–7]. Therefore, this study was designed to pilot test feasibility of the CDSMP incorporating telephone and text-messaging as follow-up approaches.

Feasibility of using telecommunications technology in a Cardiac-Diabetes Self-Management Program

Background: Evidence demonstrates self-management programs are an effective approach to assist patients with chronic diseases such as type 2 diabetes or cardiac conditions to modify their lifestyle for better managing their conditions. Using information technology (IT) has great potential to support self-management programs and assist patients to fulfill their goals in managing their conditions more efficiently and effectively. Examples of different types of technology used in self-management programs that have limited research support include: text messages, telephone followup, web-based programs, and other internet-assisted education. But little is known about the applicability and feasiability of different forms of technology for patients with chronic diseases such as those with type 2 diabetes and critical cardiac conditions. Furthermore, although there is some evidence of the benefits of using IT in supporting self-management programs, further research on the use of IT in such programs is recommended. Objective: To develop and pilot test an integrated Cardiac- Diabetes Self-Management Program (CDSMP) incorporating telephone and text-message follow-up. Methods: A pilot study using randomised controlled trial is conducted in the coronary care unit (CCU) in a Brisbane metropolitan hospital in Australia to collect data on patients with type 2 diabetes admitted to CCU. The main outcomes included self-efficacy levels, knowledge, and quality of life. Results: Initial results reveal that patients with diabetes admitted to the CCU in the experimental group did improve their self-efficacy, and knowledge levels. Acknowledgements: This Project is funded by QUT Early Career Researcher Research Grant

How to Improve Cardiac Surgery Decision Making? In Data Warehousing Perspective

The health system is one sector dealing with very large amount of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Therefore, there is a need for very effective system to capture, collate and distribute this health data. There are number of technologies have been identified to integrate data from different sources. Data warehousing is one technology can be used to manage clinical data in the healthcare. This paper addresses how data warehousing assist to improve cardiac surgery decision making. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. In order to deal with other units efficiently, it is important to integrate disparate data to a single point interrogation. We propose implementing a data warehouse for the cardiac surgery unit at TPCH. The data warehouse prototype developed using SAS enterprise data integration studio 4.2 and data was analysed using SAS enterprise edition 4.3. This improves access to integrated clinical and financial data with, improved framing of data to the clinical context, giving potentially better informed decision making for both improved management and patient care.

Risk factors for impaired respiratory function during nurse-administered procedural sedation and analgesia in the cardiac catheterisation laboratory : a matched case-control study

Background: Side effects of the medications used for procedural sedation and analgesia in the cardiac catheterisation laboratory are known to cause impaired respiratory function. Impaired respiratory function poses considerable risk to patient safety as it can lead to inadequate oxygenation. Having knowledge about the conditions that predict impaired respiratory function prior to the procedure would enable nurses to identify at-risk patients and selectively implement intensive respiratory monitoring. This would reduce the possibility of inadequate oxygenation occurring. Aim: To identify pre-procedure risk factors for impaired respiratory function during nurse-administered procedural sedation and analgesia in the cardiac catheterisation laboratory. Design: Retrospective matched case–control. Methods: 21 cases of impaired respiratory function were identified and matched to 113 controls from a consecutive cohort of patients over 18 years of age. Conditional logistic regression was used to identify risk factors for impaired respiratory function. Results: With each additional indicator of acute illness, case patients were nearly two times more likely than their controls to experience impaired respiratory function (OR 1.78; 95% CI 1.19–2.67; p = 0.005). Indicators of acute illness included emergency admission, being transferred from a critical care unit for the procedure or requiring respiratory or haemodynamic support in the lead up to the procedure. Conclusion: Several factors that predict the likelihood of impaired respiratory function were identified. The results from this study could be used to inform prospective studies investigating the effectiveness of interventions for impaired respiratory function during nurse-administered procedural sedation and analgesia in the cardiac catheterisation laboratory.