Older patients request more information : a survey of use of written patient education materials in general practice

Objectives: To determine GPs' reported use of written education materials with older patients and older patients' reported receipt of these materials. To determine GPs' and older patients' perceptions of written materials.---------- Method: Using self-report questionnaires, two populations were surveyed; a randomised sample of 50 GPs (29 males and 21 females) practising in Brisbane's southern suburbs and a convenience sample of 188 older community-dwelling people (aged over 64 years).----------- Results: All GPs reported using written materials with patients, although 28% had not given any to the Last 10 patients. This increased to 46% when patients were older. Twenty percent of patients wanted more written information from their GP, while some GPs believed that older patients preferred verbal information and gave out written information only when they perceived patient interest. All GPs reported giving written materials at the time of consultation and over two thirds discussed the content with patients. Just over 50% of patients reported receiving written information from GPs in the Last six months and only hall of these again discussed it directly with their GP. Overall, patients were more positive than GPs about the value of written education materials.---------- Conclusions: Older patients' desire for written information may be better met if they are more assertive in requesting this of GPs and GPs may better serve their patients' needs if they make written information more readily available to them. Better access to materials and more financial incentives to give them out might also increase GPs' use of written materials.

Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

Cellular senescence and longevity of osteophyte-derived mesenchymal stem cells compared to patient-matched bone marrow stromal cells

This study aimed to determine the cellular aging of osteophyte-derived mesenchymal cells (oMSCs) in comparison to patient-matched bone marrow stromal cells (bMSCs). Extensive expansion of the cell cultures was performed and early and late passage cells (passages 4 and 9, respectively) were used to study signs of cellular aging, telomere length, telomerase activity, and cell-cycle-related gene expression. Our results showed that cellular aging was more prominent in bMSCs than in oMSCs, and that oMSCs had longer telomere length in late passages compared with bMSCs, although there was no significant difference in telomere lengths in the early passages in either cell type. Telomerase activity was detectable only in early passage oMSCs and not in bMSCs. In osteophyte tissues telomerase-positive cells were found to be located perivascularly and were Stro-1 positive. Fifteen cell-cycle regulator genes were investigated and only three genes (APC, CCND2, and BMP2) were differentially expressed between bMSC and oMSC. Our results indicate that oMSCs retain a level of telomerase activity in vitro, which may account for the relatively greater longevity of these cells, compared with bMSCs, by preventing replicative senescence. J. Cell. Biochem. 108: 839-850, 2009. (c) 2009 Wiley-Liss, Inc.

Measuring the effect of patient comfort rounds on practice environment and patient satisfaction : A pilot study

Hourly rounding in the acute hospital setting has been proposed as an intervention to increase patient satisfaction and safety, and improve the nursing practice environment, but the innovation has not been adequately tested. A quasiexperimental pretest post-test non-randomized parallel group trial design was used to test the effect of hourly patient comfort rounds on patient satisfaction and nursing perceptions of the practice environment, and to evaluate research processes and instruments for a proposed larger study. A Patient Satisfaction Survey instrument was developed and used in conjunction with the Practice Environment Scale of the Nursing Work Index. Results on patient satisfaction showed no significant changes. Significant changes were found for three of the five practice environment subscales. Consistent with the aim of a pilot study, this research has provided important information related to design, instruments and process that will inform a larger sufficiently powered study.

Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Setting hospital infection control policy : a decision-making framework incorporating health economics and healthcare epidemiology

Background: Reducing rates of healthcare acquired infection has been identified by the Australian Commission on Safety and Quality in Health Care as a national priority. One of the goals is the prevention of central venous catheter-related bloodstream infection (CR-BSI). At least 3,500 cases of CR-BSI occur annually in Australian hospitals, resulting in unnecessary deaths and costs to the healthcare system between $25.7 and $95.3 million. Two approaches to preventing these infections have been proposed: use of antimicrobial catheters (A-CVCs); or a catheter care and management ‘bundle’. Given finite healthcare budgets, decisions about the optimal infection control policy require consideration of the effectiveness and value for money of each approach. Objectives: The aim of this research is to use a rational economic framework to inform efficient infection control policy relating to the prevention of CR-BSI in the intensive care unit. It addresses three questions relating to decision-making in this area: 1. Is additional investment in activities aimed at preventing CR-BSI an efficient use of healthcare resources? 2. What is the optimal infection control strategy from amongst the two major approaches that have been proposed to prevent CR-BSI? 3. What uncertainty is there in this decision and can a research agenda to improve decision-making in this area be identified? Methods: A decision analytic model-based economic evaluation was undertaken to identify an efficient approach to preventing CR-BSI in Queensland Health intensive care units. A Markov model was developed in conjunction with a panel of clinical experts which described the epidemiology and prognosis of CR-BSI. The model was parameterised using data systematically identified from the published literature and extracted from routine databases. The quality of data used in the model and its validity to clinical experts and sensitivity to modelling assumptions was assessed. Two separate economic evaluations were conducted. The first evaluation compared all commercially available A-CVCs alongside uncoated catheters to identify which was cost-effective for routine use. The uncertainty in this decision was estimated along with the value of collecting further information to inform the decision. The second evaluation compared the use of A-CVCs to a catheter care bundle. We were unable to estimate the cost of the bundle because it is unclear what the full resource requirements are for its implementation, and what the value of these would be in an Australian context. As such we undertook a threshold analysis to identify the cost and effectiveness thresholds at which a hypothetical bundle would dominate the use of A-CVCs under various clinical scenarios. Results: In the first evaluation of A-CVCs, the findings from the baseline analysis, in which uncertainty is not considered, show that the use of any of the four A-CVCs will result in health gains accompanied by cost-savings. The MR catheters dominate the baseline analysis generating 1.64 QALYs and cost-savings of $130,289 per 1.000 catheters. With uncertainty, and based on current information, the MR catheters remain the optimal decision and return the highest average net monetary benefits ($948 per catheter) relative to all other catheter types. This conclusion was robust to all scenarios tested, however, the probability of error in this conclusion is high, 62% in the baseline scenario. Using a value of $40,000 per QALY, the expected value of perfect information associated with this decision is $7.3 million. An analysis of the expected value of perfect information for individual parameters suggests that it may be worthwhile for future research to focus on providing better estimates of the mortality attributable to CR-BSI and the effectiveness of both SPC and CH/SSD (int/ext) catheters. In the second evaluation of the catheter care bundle relative to A-CVCs, the results which do not consider uncertainty indicate that a bundle must achieve a relative risk of CR-BSI of at least 0.45 to be cost-effective relative to MR catheters. If the bundle can reduce rates of infection from 2.5% to effectively zero, it is cost-effective relative to MR catheters if national implementation costs are less than $2.6 million ($56,610 per ICU). If the bundle can achieve a relative risk of 0.34 (comparable to that reported in the literature) it is cost-effective, relative to MR catheters, if costs over an 18 month period are below $613,795 nationally ($13,343 per ICU). Once uncertainty in the decision is considered, the cost threshold for the bundle increases to $2.2 million. Therefore, if each of the 46 Level III ICUs could implement an 18 month catheter care bundle for less than $47,826 each, this approach would be cost effective relative to A-CVCs. However, the uncertainty is substantial and the probability of error in concluding that the bundle is the cost-effective approach at a cost of $2.2 million is 89%. Conclusions: This work highlights that infection control to prevent CR-BSI is an efficient use of healthcare resources in the Australian context. If there is no further investment in infection control, an opportunity cost is incurred, which is the potential for a more efficient healthcare system. Minocycline/rifampicin catheters are the optimal choice of antimicrobial catheter for routine use in Australian Level III ICUs, however, if a catheter care bundle implemented in Australia was as effective as those used in the large studies in the United States it would be preferred over the catheters if it was able to be implemented for less than $47,826 per Level III ICU. Uncertainty is very high in this decision and arises from multiple sources. There are likely greater costs to this uncertainty for A-CVCs, which may carry hidden costs, than there are for a catheter care bundle, which is more likely to provide indirect benefits to clinical practice and patient safety. Research into the mortality attributable to CR-BSI, the effectiveness of SPC and CH/SSD (int/ext) catheters and the cost and effectiveness of a catheter care bundle in Australia should be prioritised to reduce uncertainty in this decision. This thesis provides the economic evidence to inform one area of infection control, but there are many other infection control decisions for which information about the cost-effectiveness of competing interventions does not exist. This work highlights some of the challenges and benefits to generating and using economic evidence for infection control decision-making and provides support for commissioning more research into the cost-effectiveness of infection control.

Nursing care of the mechanically ventilated patient: what does the evidence say? Part one.

The care of the mechanically ventilated patient is at the core of a nurse's clinical practice in the Intensive Care Unit (ICU). Published work relating to the numerous nursing issues of the care of the mechanically ventilated patient in the ICU is growing significantly. Literature focuses on patient assessment and management strategies for patient stressors, pain and sedation. Yet this literature is fragmentary by nature. The purpose of this paper is to provide a single comprehensive examination of the evidence related to the care of the mechanically ventilated patient. In part one of this two-part paper, the evidence on nursing care of the mechanically ventilated patient is explored with specific focus on patient safety: particularly patient and equipment assessment. Part two of the paper examines the evidence related to the mechanically ventilated patient's comfort, the patient/family unit, patient position, hygiene, management of stressors, pain management and sedation.

Nursing care of the mechanically ventilated patient: What does the evidence say?: Part two

The care of the mechanically ventilated patient is a fundamental component of a nurse's clinical practice in the intensive care unit (ICU). Published work relating to the numerous nursing issues of the care of the mechanically ventilated patient in the ICU is growing significantly, yet is fragmentary by nature. The purpose of this paper is to provide a single comprehensive examination of the evidence related to the care of the mechanically ventilated patient. In part one of this two-part paper, the evidence on nursing care of the mechanically ventilated patient was explored with specific focus on patient safety: particularly patient and equipment assessment. This article, part two, examines the evidence related to the mechanically ventilated patient's comfort: patient position, hygiene, management of stressors (such as communication, sleep disturbance and isolation), pain management and sedation.

Part 2. Chemical and physical restraints in the management of mechanically ventilated patients in the ICU: A patient perspective

An important goal of the care for the mechanically ventilated patient is to minimize patient discomfort and anxiety. This is partly achieved by frequent use of chemical and physical restraints. The majority of patients in intensive care will receive some form of sedation. The goal and use of sedation has changed considerably over the past few decades with literature evidencing trends toward overall lighter sedation levels and daily interruption of sedation. Conversely, the use of physical restraint for the ventilated patient in ICU differs considerably between nations and continents. A large portion of the literature on the use of physical restraint is from general hospital wards and residential homes, and not from the ICU environment. Recent literature suggests minimal use of physical restraint in the ICU, and that reduction programmes have been initiated. However, very few papers illuminate the patient's experience of physical and chemical restraints as a treatment strategy. In Part 1 of this two-part review, the evidence on chemical and physical restraints was explored with specific focus on definitions of terms, unplanned extubation, agitation, delirium as well as the impact of nurse–patient ratios in the ICU on these issues. This paper, Part 2, examines the evidence related to chemical and physical restraints from the mechanically ventilated patient's perspective.

Development of patient-specific computer simulation tools for endoscopic scoliosis surgery

Endoscopic approaches for anterior correction of idiopathic scoliosis are a relatively new surgical technique. This paper describes the development of patient-specific finite element modelling techniques to investigate the biomechanics of single rod anterior scoliosis correction. Spinal geometry is obtained from pre-operative CT scans and material properties for osteo-ligamentous spinal tissues are based on existing literature. The techniques being developed will allow pre-surgical prediction of stresses, forces and deformations in spinal tissues, rods and screws under post-operative physiological loads.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.