Cyclosporine A in the treatment of Interstitial Cystitis

Painful bladder syndrome/interstitial cystitis (PBS/IC) is a chronic urinary bladder disorder of unknown etiology characterized by symptoms of bladder pain and urinary frequency. PBS/IC is a chronic disease in which drug therapy has not led to significant success over the course of time. If the symptoms of PBS/IC are refractory to standard treatments, a possible cure might demand surgical intervention involving cystectomy. The eventual autoimmune etiology in mind, immunosuppressive drug therapy with cyclosporine A (CyA) was started to patients with refractory PBS/IC. CyA is a potent anti-inflammatory drug, a calcineurin inhibitor which inhibits T lymphocyte IL-2 produc-tion. T cells are present in abundance in inflammation of the bladder in PBS/IC. On the basis of a pilot, short-term study with CyA on PBS/IC, use of CyA was continued empirically over the long term. We conducted a prospective, randomized, six-month study in 64 patients comparing the effect of CyA with the FDA approved treatment, pentosan polysulfate sodium (PPS). We measured the drug effect on patient s symptoms, the potassium sensitivity test, and on urinary biomarkers. We further tested the impact of CyA, PPS, DMSO and BCG therapy on a health-related quality of life questionnaire and evaluated the response rate to treatment with these therapies. Long-term use of CyA was safe and effective in PBS/IC patients. The good clinical effect matured individually during the years in which CyA was continued. Cessation of medication led to the reappearance of symptoms, and restarting CyA to renewed alleviation, so that CyA was administered as continuous medication. The response rate to CyA increased during the study period, comprising 75% of CyA patients at six months. 19% of patients responded to PPS therapy. Adverse effects were more common in the CyA group, underlining the importance of monitoring the drug safety and appropriate titration of the dose. The potassium sensitivity test is positive in the majority of PBS/IC patients. Successful therapy of PBS/IC can alter nerve sensitivity to external potassium. This effect was seen more often after CyA therapy. Successful treatment of PBS/IC with CyA resulted to decreasing urinary levels of EGF. IL-6 levels in urine were higher among older patient with a longer history of PBS/IC. In these patients, reduced levels of urinary IL-6 were measured after CyA therapy. Patients who experience the best treatment response have improved quality of life according to the post-treatment health-related quality of life (HRQOL) questionnaire. CyA had more impact on the ma-jority of the aspects of QoL than PPS. Despite DMSO therapy being more successful than BCG in the count of responders, DMSO and BCG had equal effects on the HRQOL questionnaire.

Energy efficient area monitoring using information coverage in wireless sensor networks

In this paper, we are concerned with energy efficient area monitoring using information coverage in wireless sensor networks, where collaboration among multiple sensors can enable accurate sensing of a point in a given area-to-monitor even if that point falls outside the physical coverage of all the sensors. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The scheduling of sensor activity using the optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime.

Essential nursing care of the critically ill patient

This chapter is about essential nursing care. Because it is often referred to as basic nursing, nurses may not always perceive it as deserving of priority. Yet, how well patients are cared for has a direct effect on their sense of wellbeing and their recovery. ‘Interventional patient hygiene’ is a systematic, evidence-based approach to nursing actions designed to improve patient outcomes using a framework of hygiene, catheter care, skin care, mobility and oral care.1 This chapter focuses on the physical care, infection control, preventative therapies and transport of critically ill patients. The first two areas are closely linked: poor-quality physical care increases the risk of infection. The final areas are essential features of critical care nursing.

The effect of blood glucose on the vasculature in young patients with type 1 diabetes

Background. Patients with type 1 diabetes are at markedly increased risk of vascular complications. In this respect it is noteworthy that hyperglycaemia that is shown to cause endothelial dysfunction, has clearly been shown to be a risk factor for diabetic microvascular disease. However, the role of hyperglycaemia as a predictor of macrovascular disease is not as clear as for microvascular disease, although type 1 diabetes itself increases the risk of cardiovascular disease substantially. Furthermore, it is not known whether it is the short-term or the long-term hyperglycaemia that confers possible risk. In addition, the role of glucose variability as a predictor of complications is to a large extent unexplored. Interestingly, although hyperglycaemia increases the risk of pre-eclampsia in women with type 1 diabetes, it is unclear whether pre-eclampsia, a condition characterized by endothelial dysfunction, is also a risk factor for microvascular complication, diabetic nephropathy. Aims. This doctoral thesis investigated the role of acute hyperglycaemia and glucose variability on arterial stiffness and cardiac ventricular repolarisation in male patients with type 1 diabetes as well as in healthy male volunteers. The thesis also explored whether acute hyperglycaemia leads to an inflammatory response, endothelial dysfunction and oxidative stress. Finally, the role of pre-eclampsia, as a predictor of diabetic nephropathy in type 1 diabetes was examined. Subjects and methods. In order to study glucose variability and the daily glycaemic control, 22 male patients with type 1 diabetes, without any diabetic complications, were monitored for 72-h with a continuous glucose monitoring system. At the end of the 72-h glucose monitoring period a 2-h hyperglycaemic clamp was performed both in the patients with type 1 diabetes and in the 13 healthy age-matched male volunteers. Blood pressure, arterial stiffness and QT time were measured to detect vascular changes during acute hyperglycaemia. Blood samples were drawn at baseline (normoglycaemia) and during acute hyperglycaemia. In another patient sample, women with type 1 diabetes were followed during their pregnancy and restudied eleven years later to elucidate the role of pre-eclampsia and pregnancy-induced hypertension as potential risk factors for diabetic nephropathy. Results and conclusions. Acute hyperglycaemia increased arterial stiffness as well as caused a disturbance in the myocardial ventricular repolarisation, emphasizing the importance of a strict daily glycaemic control in male patients with type 1 diabetes. An inflammatory response was also observed during acute hyperglycaemia. Furthermore, a high mean daily blood glucose but not glucose variability per se is associated with arterial stiffness. While glucose variability in turn correlated with central blood pressure, the results suggest that the glucose metabolism is closely linked to the haemodynamic changes in male patients with uncomplicated type 1 diabetes. Notably, the results are not directly applicable to females. Finally, a history of a pre-eclamptic pregnancy, but not pregnancy-induced hypertension was associated with increased risk of diabetic nephropathy.

Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a valid screening tool in chemotherapy outpatients

Purpose In the oncology population where malnutrition prevalence is high, more descriptive screening tools can provide further information to assist triaging and capture acute change. The Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a component of a nutritional assessment tool which could be used for descriptive nutrition screening. The purpose of this study was to conduct a secondary analysis of nutrition screening and assessment data to identify the most relevant information contributing to the PG-SGA SF to identify malnutrition risk with high sensitivity and specificity. Methods This was an observational, cross-sectional study of 300 consecutive adult patients receiving ambulatory anti-cancer treatment at an Australian tertiary hospital. Anthropometric and patient descriptive data were collected. The scored PG-SGA generated a score for nutritional risk (PG-SGA SF) and a global rating for nutrition status. Receiver operating characteristic curves (ROC) were generated to determine optimal cut-off scores for combinations of the PG-SGA SF boxes with the greatest sensitivity and specificity for predicting malnutrition according to scored PG-SGA global rating. Results The additive scores of boxes 1–3 had the highest sensitivity (90.2 %) while maintaining satisfactory specificity (67.5 %) and demonstrating high diagnostic value (AUC = 0.85, 95 % CI = 0.81–0.89). The inclusion of box 4 (PG-SGA SF) did not add further value as a screening tool (AUC = 0.85, 95 % CI = 0.80–0.89; sensitivity 80.4 %; specificity 72.3 %). Conclusions The validity of the PG-SGA SF in chemotherapy outpatients was confirmed. The present study however demonstrated that the functional capacity question (box 4) does not improve the overall discriminatory value of the PG-SGA SF.

Patient doses in ct, dental cone beam ct and projection radiography in Finland, with emphasis on paediatric patients

Diagnostic radiology represents the largest man-made contribution to population radiation doses in Europe. To be able to keep the diagnostic benefit versus radiation risk ratio as high as possible, it is important to understand the quantitative relationship between the patient radiation dose and the various factors which affect the dose, such as the scan parameters, scan mode, and patient size. Paediatric patients have a higher probability for late radiation effects, since longer life expectancy is combined with the higher radiation sensitivity of the developing organs. The experience with particular paediatric examinations may be very limited and paediatric acquisition protocols may not be optimised. The purpose of this thesis was to enhance and compare different dosimetric protocols, to promote the establishment of the paediatric diagnostic reference levels (DRLs), and to provide new data on patient doses for optimisation purposes in computed tomography (with new applications for dental imaging) and in paediatric radiography. Large variations in radiation exposure in paediatric skull, sinus, chest, pelvic and abdominal radiography examinations were discovered in patient dose surveys. There were variations between different hospitals and examination rooms, between different sized patients, and between imaging techniques; emphasising the need for harmonisation of the examination protocols. For computed tomography, a correction coefficient, which takes individual patient size into account in patient dosimetry, was created. The presented patient size correction method can be used for both adult and paediatric purposes. Dental cone beam CT scanners provided adequate image quality for dentomaxillofacial examinations while delivering considerably smaller effective doses to patient compared to the multi slice CT. However, large dose differences between cone beam CT scanners were not explained by differences in image quality, which indicated the lack of optimisation. For paediatric radiography, a graphical method was created for setting the diagnostic reference levels in chest examinations, and the DRLs were given as a function of patient projection thickness. Paediatric DRLs were also given for sinus radiography. The detailed information about the patient data, exposure parameters and procedures provided tools for reducing the patient doses in paediatric radiography. The mean tissue doses presented for paediatric radiography enabled future risk assessments to be done. The calculated effective doses can be used for comparing different diagnostic procedures, as well as for comparing the use of similar technologies and procedures in different hospitals and countries.

Understanding data flow and security requirements in wireless Body Area Networks for healthcare

The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.

Methodological perspectives for register-based health system performance assessment : Developing a hip fracture monitoring system in Finland

The resources of health systems are limited. There is a need for information concerning the performance of the health system for the purposes of decision-making. This study is about utilization of administrative registers in the context of health system performance evaluation. In order to address this issue, a multidisciplinary methodological framework for register-based data analysis is defined. Because the fixed structure of register-based data indirectly determines constraints on the theoretical constructs, it is essential to elaborate the whole analytic process with respect to the data. The fundamental methodological concepts and theories are synthesized into a data sensitive approach which helps to understand and overcome the problems that are likely to be encountered during a register-based data analyzing process. A pragmatically useful health system performance monitoring should produce valid information about the volume of the problems, about the use of services and about the effectiveness of provided services. A conceptual model for hip fracture performance assessment is constructed and the validity of Finnish registers as a data source for the purposes of performance assessment of hip fracture treatment is confirmed. Solutions to several pragmatic problems related to the development of a register-based hip fracture incidence surveillance system are proposed. The monitoring of effectiveness of treatment is shown to be possible in terms of care episodes. Finally, an example on the justification of a more detailed performance indicator to be used in the profiling of providers is given. In conclusion, it is possible to produce useful and valid information on health system performance by using Finnish register-based data. However, that seems to be far more complicated than is typically assumed. The perspectives given in this study introduce a necessary basis for further work and help in the routine implementation of a hip fracture monitoring system in Finland.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

PERFECT Stroke : PERFormance, Effectiveness, and Costs of treatment episodes in Stroke

Stroke is a major cause of death and disability, incurs significant costs to healthcare systems, and inflicts severe burden to the whole society. Stroke care in Finland has been described in several population-based studies between 1967 and 1998, but not since. In the PERFECT Stroke study presented here, a system for monitoring the Performance, Effectiveness, and Costs of Treatment episodes in Stroke was developed in Finland. Existing nationwide administrative registries were linked at individual patient level with personal identification numbers to depict whole episodes of care, from acute stroke, through rehabilitation, until the patients went home, were admitted to permanent institutional care, or died. For comparisons in time and between providers, patient case-mix was adjusted for. The PERFECT Stroke database includes 104 899 first-ever stroke patients over the years 1999 to 2008, of whom 79% had ischemic stroke (IS), 14% intracerebral hemorrhage (ICH), and 7% subarachnoid hemorrhage (SAH). A 18% decrease in the age and sex adjusted incidence of stroke was observed over the study period, 1.8% improvement annually. All-cause 1-year case-fatality rate improved from 28.6% to 24.6%, or 0.5% annually. The expected median lifetime after stroke increased by 2 years for IS patients, to 7 years and 7 months, and by 1 year for ICH patients, to 4 years 5 months. No change could be seen in median SAH patient survival, >10 years. Stroke prevalence was 82 000, 1.5% of total population of Finland, in 2008. Modern stroke center care was shown to be associated with a decrease in both death and risk of institutional care of stroke patients. Number needed to treat to prevent these poor outcomes at one year from stroke was 32 (95% confidence intervals 26 to 42). Despite improvements over the study period, more than a third of Finnish stroke patients did not have access to stroke center care. The mean first-year healthcare cost of a stroke patient was ~20 000 , and among survivors ~10 000 annually thereafter. Only part of this cost was incurred by stroke, as the same patients cost ~5000 over the year prior to stroke. Total lifetime costs after first-ever stroke were ~85 000 . A total of 1.1 Billion , 7% of all healthcare expenditure, is used in the treatment of stroke patients annually. Despite a rapidly aging population, the number of new stroke patients is decreasing, and the patients are more likely to survive. This is explained in part by stroke center care, which is effective, and should be made available for all stroke patients. It is possible, in a suitable setting with high-quality administrative registries and a common identifier, to avoid the huge workload and associated costs of setting up a conventional stroke registry, and still acquire a fairly comprehensive dataset on stroke care and outcome.