[E-Health and reality - what are we facing in patient care?] / E-Health und die Realität ­ was sehen wir heute schon in der Klinik?

The terms e­Health and digitization are core elements of a change in our time. The main drivers of this change - in addition to a dynamic market - are the serious advantages for the healthcare sector in the processing of tasks and requirements. The large amounts of data, the intensively growing medical knowledge, the rapidly advancing technological developments and the goal of a personalized, customized therapy for the patient, make the application absolutely necessary. While e­Health describes the use of information and communication technologies in healthcare, the concept of digitization is associated with the underlying processes of change and innovation. Digital technologies include software and hardware based developments. The term clinical data intelligence describes the property of workability and also characterizes the collaboration of clinically relevant systems with which the medical user works. The hierarchy in digital processing maps the levels from pure data management through clinical decision support to automated process flows and autonomously operating units. The combination of patient data management and clinical decision support proves its value in terms of error reduction, prevention, quality and safety, especially in drug therapy. The aim of this overview is the presentation of the existing reality in medical centers with perspectives derived from the point of view of the medical user.

[Future trend medical apps. From the apps store directly into medical practice?]. / Zukunftstrend "Medical Apps". Vom App-Store direkt in die medizinische Anwendung?

In day to day medical care, patients, nursing staff and doctors currently face a bewildering and rapidly growing number of health-related apps running on various "smart" devices and there are also uncountable possibilities for the use of such technology. Concerning regulation, a risk-based approach is applied for development and use (including safety and security considerations) of medical and health-related apps. Considering safety-related issues as well as organizational matters, this is a sensible approach but requires honest self-assessment as well as a high degree of responsibility, networking and good quality management by all those involved. This cannot be taken for granted. Apart from regulatory aspects it is important to not only consider what is reasonable, helpful or profitable. Quality aspects, safety matters, data protection and privacy as well as liability issues must also be considered but are often not adequately respected. If software quality is compromised, this endangers patient safety as well as data protection, privacy and data integrity. This can for example result in unwanted advertising or unauthorized access to the stored data by third parties; therefore, local, regional and international regulatory measures need to be applied in order to ensure safe use of medical apps in all possible areas, including the operating room (OR) with its highly specialized demands. Lawmakers need to include impulses from all stakeholders in their considerations and this should include input from existing private initiatives that already deal with the use and evaluation of apps in a medical context. Of course, this process needs to respect pre-existing national, European as well as international (harmonized) standards.

Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms.

Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.

[Noise in intensive care units. Do the alarms for subspecialties differ]. / Lärm auf der Intensivstation: Unterscheiden sich die Alarme in den Fachdisziplinen?

INTRODUCTION: Cardiovascular monitoring alarms are frequent in intensive care units (ICUs) and lead to noise levels often exceeding 80 dB. The aim of this study was to evaluate if there are relevant differences between ICUs with different subspecialties in the frequency and distribution of alarm signals, their occurrence during the day, the types of alarms and the underlying vital parameters. METHODS: All alarm signals of the cardiovascular monitoring systems from randomly chosen patients at five different ICUs of the university hospital of Regensburg were evaluated. RESULTS: No significant differences between the ICUs regarding the frequency of alarm signals and only slight differences in the time distribution could be recognized (p=0.02). The most frequent alarm signals were from threshold alarms (61%) followed by technical alarms. The majority of alarms generated were related to invasive arterial blood pressure measurement. CONCLUSIONS: The frequency and distribution of ICU alarm signals seem to be comparable on different ICUs. Therefore, implementation of universal concepts for alarm reduction seems to be applicable regardless of the subspecialty of the ICU.

Users' opinions on intensive care unit alarms--a survey of German intensive care units.

Monitoring of physiologic parameters in critically ill patients is associated with an enormous number of alarms, leading to reduced clinical value with high sensitivity but low specificity. To evaluate opinions of intensive care unit (ICU) staff on current monitoring we conducted a survey of German ICUs. Furthermore, the survey aimed to assess requirements and requests for future alarm systems. The survey was conducted between May 2006 and June 2007 on a randomised sample of German ICUs. Questionnaires with 24 partly closed-ended partly open-ended questions were posted. Of 915 letters, 274 (30%) from 185 contacted ICUs were returned and evaluated. One hundred and sixty physicians, the majority (52%) working in a department of anaesthesiology, and 114 nurses returned the survey. Most responders (87%) estimated that less than 50% of current alarms result in clinical consequences (52% estimated less than 25%). We suggested trend alarms, smoothing of signals to reduce artefacts, generation of new combined alarms and integrative monitoring of different alarm systems as improvements of current ICU alarm systems, all of which were agreed to by the majority. Free text commentaries focused on the need for reducing alarms caused by artefacts and called for improvement of the monitor-user interfaces. Our survey demonstrates the dissatisfaction of clinical staff with the current alarm systems regarding alarm frequency and specificity in German ICUs, thereby confirming data raised in single institutions. ICU staff's acceptance for new alarm algorithms like signal extraction or detection of trends as a basis for smart monitoring appealed to the majority of users.

Health informatics.

Health informatics is the development and assessment of methods and systems for the acquisition, processing and interpretation of patient data with the help of knowledge from scientific research. This definition implies that health informatics is not tied to the application of computers but more generally to the entire management of information in healthcare. The focus is the patient and the process of care. The apparent information overload and the imperfection of medical decision making motivate the use of information systems for medical decision support. Health informatics provides tools to control processes in healthcare, acquire medical knowledge and communicate information between all people and organisations involved with healthcare. Although the development of medical information systems may often lag behind the available possibilities, the technological state of the current medical information systems is better than it is generally held to be. Health informatics should help healthcare professionals to provide better and more cost-effective care and enable healthcare systems to be more efficient and to adapt better to our patients' needs. Health informatics may reshape the way we deliver care to meet the demands of the future.

Online pattern recognition in intensive care medicine.

In intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a physiological time series are needed for suitable bedside decision support. There are various approaches to modeling time-dependent data and also several methodologies for pattern detection in time series. We compare several methodologies de-signed for online detection of measurement artifacts, level changes, and trends for a proper classification of the patient s state by means of a comparative case-study.

Noninvasive whole-body electrical bioimpedance cardiac output and invasive thermodilution cardiac output in high-risk surgical patients.

OBJECTIVE: To evaluate the reliability of whole-body impedance cardiography with two electrodes on either both wrists or one wrist and one ankle for the measurement of cardiac output compared with the thermodilution method. DESIGN: Prospective, clinical investigation SETTING: Surgical intensive care unit of a university-affiliated community hospital. PATIENTS: Simultaneous cardiac output measurements by noninvasive whole-body impedance cardiography (nCO) and invasive thermodilution (thCO) in 22 high-risk surgical patients scheduled for extended surgery requiring perioperative pulmonary artery catheter monitoring. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 109 sets of measurements consisting of 455 single comparison measurements between nCO and thCO were included in the analysis. The mean cardiac output difference between the two methods was 1.62 L/min with limits of agreement (2 SD) of +/- 4.64 L/min. The inter-measurement variance was slightly higher for nCO. The correlation coefficient between nCO and thCO was r2 = 0.061 (p < .001) for single measurements and r2 = 0.083 (p < .002) for sets of three to six measurements. The two most predictive factors for between-method differences were the absolute thCO value (r2 = 0.13; p < .001) and whether or not a continuous nitroglycerin infusion was used (p < .05, Student's t-test). CONCLUSIONS: Agreement between whole-body impedance cardiography and thermodilution in the measurement of cardiac output was unsatisfactory. Factors that can explain these differences are differences between the populations used for calibration of nCO and the study population, the influence of changing peripheral perfusion, and the effect of a supranormal hemodynamic state on the bioimpedance signal. Whole-body impedance cardiography cannot be recommended for assessing the hemodynamic state of high-risk surgical patients as studied in this investigation.

Knowledge discovery and knowledge validation in intensive care.

Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that support the development of operational protocols. The aim is to ensure high quality standards for the protocol through empirical validation during the development, as well as lower development cost through the use of machine learning and statistical techniques. We demonstrate our approach of integrating expert knowledge with data driven techniques based on our effort to develop an operational protocol for the hemodynamic system.

Dimension reduction for highdimensional online-monitoring data in intensive care.

Nowadays high dimensional data in intensive care medicine can be captured, stored, and retrieved with the help of clinical information systems. Intelligent alarm systems are needed for an adequate bedside decision support, in the course of which the detection of qualitative patterns in physiologic monitoring data such as outliers, level changes, or trends aims at a proper classification of the patients state. Statistical time series techniques have already been applied successfully to the analysis of single physiological variables. The simultaneous online analysis of the multivariate patient curve yields further challenges. We describe methods for reducing the dimension and for keeping the computational efforts necessary for monitoring low. We present preliminary results of an ongoing study on monitoring critically ill patients.

Statistical pattern detection in univariate time series of intensive care on-line monitoring data.

OBJECTIVES: To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis. DESIGN: Off-line time series analysis. SETTING: Surgical intensive care unit of a teaching hospital. PATIENTS: 19 patients requiring hemodynamic monitoring with a pulmonary artery catheter. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Hemodynamic data were acquired in 1-min intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure, and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low-order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95% confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context. CONCLUSION: Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.

[Perforation of the esophagus after esophageal manometry]. / Osophagusperforation nach Osophagusmanometrie.

HISTORY AND FINDINGS: A 75-year-old man was admitted for oesophageal manometry because of dysphagia for the past 2 years and retrosternal burning sensation unrelated to exercise. His general condition was appropriate for his age. INVESTIGATIONS: An oesophagogram showed corkscrew-like deformation of a diffuse oesophageal spasm. The first, but incomplete, manometry recorded clearly propulsive contractions with markedly raised and prolonged pressure, as in "nutcracker oesophagus". The lower oesophageal sphincter could not be demonstrated initially. Subsequent pH measurements provided no evidence for increased gastrooesophageal reflux. TREATMENT AND FURTHER COURSE: After the first manometry conservative treatment was initiated with molsidomine, nifedipine and nitrospray sublingual, but the dysphagia was not significantly improved. A second manometry was performed before a planned surgical exploration. Placing of the catheter was again difficult and mild resistance experienced. Endoscopy revealed only minimal, presumably superficial, mucosal lesions. 2 days later bilateral pleural effusions together with mediastinitis occurred. Conservative treatment was continued until finally a distal oesophageal perforation was demonstrated. At surgery the perforation was seen and a oesophagectomy with gastric pull-through and intrathoracic anastomosis performed. However, the patient died of septic multi-organ failure. CONCLUSIONS: Oesophageal manometry is a safe but invasive method with few complications for measuring oesophageal motility. Although this has not previously been reported, oesophageal perforation with mediastinitis may end fatally, if the particular circumstances are unfavourable. In addition to special anatomical features, type and state of the manometric catheter may present a risk factor.

Time series analysis in critical care monitoring.

Time series analysis techniques facilitate statistical analysis of variables in the course of time. Continuous monitoring of the critically ill offers an especially wide range of applications. Several studies from different work groups show that autoregression, integration, moving average (ARIMA) models help to identify pathologic outliers and trends in physiologic variables in surgical critical care. The effect of therapeutic interventions on physiologic target variables has been estimated with interrupted ARIMA models. The time series before the therapeutic intervention were compared to changes under intervention using the same model including an intervention regressor. In most patients clinically relevant therapeutic effects could be statistically identified. Similarly, noneffective therapeutic maneuvers could be detected early, and eventually changes in therapeutic strategy initiated. These techniques appear to be most appropriate with electronic online measurements at short time intervals, e.g., heart rate, invasive pressures, regional oxygenation. But even on the basis of short time series of critical care monitoring variables, ARIMA models can successfully be employed for the analysis of laboratory variables and of therapeutic interventions. Nevertheless, due to high demands for manpower and to statistical methodological limitations, the general use of this methodology in clinical practice apart from controlled clinical studies cannot be recommended today. Nevertheless, time series analysis techniques bear a great potential for clinical applications. Ongoing studies will in the future allow us to apply time series analyses to a wide group of clinical problems. In clinical practice, time series analyses support a more analytical and reproducible approach toward the evaluation of pathologic changes and therapeutic effects in the individual patient. Present research focuses on the development of automatic methods for time series analysis that allow instantaneous statistical analysis at the bedside and algorithms for multivariate time series analysis. This would offer an option to the healthcare professional for a more reliable evaluation of the individual treatment. Therefore, it appears rewarding to invest further efforts into the development of medical time series analysis techniques.

[Treatment of postoperative lung failure with prone positioning]. / Behandlung des postoperativen Lungenversagens mit Bauchlagerung.

Of forty-three consecutive patients with severe adult respiratory distress syndrome (ARDS) treated in the prone position pulmonary function improved significantly in 39 patients during the first 12 h in prone position. Changes were most pronounced in patients with high QS/QT, as well as in patients in the early stages of ARDS. Twenty-eight patients could be weaned from the ventilator, and 22 patients were able to leave the hospital. These results suggest that prone position has an important role in the overall therapeutic approach to ARDS and should be used as early as possible.

[Increased wound healing disorders in patients with inguinal hernia caused by administration of antithrombotic agents in the abdominal wall]. / Vermehrte Wundheilungsstörungen bei Leistenhernienpatienten durch Applikation der Thromboseprophylaxe in die Bauchdecken.

In a prospective, randomized study, including 108 patients, we investigated the effect of different locations for the subcutaneous injection of low dosed or low molecular heparin following standard herniotomia. In the group with injection into the contralateral abdominal wall the rate of local surgical complications was four times higher compared to those patients with injections into the thigh. Thus we conclude that after herniotomia low dosed or low molecular heparin should be applied into the subcutis of the thigh.

Postoperative course of functional and cellular variables in liver resection.

The determination of total serum bile acids (BA) is a sensitive variable for detection of altered liver function. This study investigated the course of serum bile acids in 44 liver-resected patients with different factors possibly compromising liver function. These factors were 1) amount of resected parenchyma; 2) duration of intraoperative ischemia; and 3) patient's age. The course of BA was compared with that of transaminases, bilirubin, lactate, and NH3. Serum BA showed a course correlated to the amount of resected liver parenchyma and differentiated between groups with < or = 35% and > 35% resected parenchyma. Whereas BA were more accurate in paralleling the resected tissue in the first postoperative days, a rise of bilirubin indicated complications in the postoperative course. As BA did not increase in a case of pulmonary-induced multiorgan failure, the specificity of this variable for liver function is implied. Different amounts of resection could not be distinguished by determination of transaminases. Different ischemic periods did not result in significant differences in the postoperative course of BA or bilirubin. However, marked elevations of transaminases depending on the duration of hepatic inflow occlusion were seen. None of the traced variables were related to the patient's age.

Acquisition of ICU data: concepts and demands.

As the issue of data overload is a problem in critical care today, it is of utmost importance to improve acquisition, storage, integration, and presentation of medical data, which appears only feasible with the help of bedside computers. The data originates from four major sources: (1) the bedside medical devices, (2) the local area network (LAN) of the ICU, (3) the hospital information system (HIS) and (4) manual input. All sources differ markedly in quality and quantity of data and in the demands of the interfaces between source of data and patient database. The demands for data acquisition from bedside medical devices, ICU-LAN and HIS concentrate on technical problems, such as computational power, storage capacity, real-time processing, interfacing with different devices and networks and the unmistakable assignment of data to the individual patient. The main problem of manual data acquisition is the definition and configuration of the user interface that must allow the inexperienced user to interact with the computer intuitively. Emphasis must be put on the construction of a pleasant, logical and easy-to-handle graphical user interface (GUI). Short response times will require high graphical processing capacity. Moreover, high computational resources are necessary in the future for additional interfacing devices such as speech recognition and 3D-GUI. Therefore, in an ICU environment the demands for computational power are enormous. These problems are complicated by the urgent need for friendly and easy-to-handle user interfaces. Both facts place ICU bedside computing at the vanguard of present and future workstation development leaving no room for solutions based on traditional concepts of personal computers.(ABSTRACT TRUNCATED AT 250 WORDS)