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.     

Functional cerebral asymmetries during the menstrual cycle: a cross-sectional and longitudinal analysis

This study aims at answering two basic questions regarding the mechanisms with which hormones modulate functional cerebral asymmetries. Which steroids or gonadotropins fluctuating during the menstrual cycle affect perceptual asymmetries? Can these effects be demonstrated in a cross-sectional (follicular and midluteal cycle phases analyzed) and a longitudinal design, in which the continuous hormone and asymmetry fluctuations were measured over a time course of 6 weeks? To answer these questions, 12 spontaneously cycling right-handed women participated in an experiment in which their levels of progesterone, estradiol, testosterone, LH, and FSH were assessed every 3 days by blood-sample based radioimmunoassays (RIAs). At the same points in time their asymmetries were analyzed with visual half-field (VHF) techniques using a lexical decision, a figure recognition, and a face discrimination task. Both cross-sectional and longitudinal analyzes showed that an increase of progesterone is related to a reduction in asymmetries in a figure recognition task by increasing the performance of the left-hemisphere which is less specialized for this task. Cross-sectionally, estradiol was shown to have significant relationships to the accuracy and the response speed of both hemispheres. However, since these effects were in the same direction, asymmetry was not affected. This was not the case in the longitudinal design, where estradiol affected the asymmetry in the lexical decision and the figural comparison task. Overall, these data show that hormonal fluctuations within the menstrual cycle have important impacts on functional cerebral asymmetries. The effect of progesterone was highly reliable and could be shown in both analysis schemes. By contrast, estradiol mainly, but not exclusively, affected both hemispheres in the same direction.     

Präklinisches Vorgehen bei Massenanfall von Verletzten und Erkrankten (MANV) und Terror-MANV

Mass casualty incidents (MCI) are an immense challenge for preclinical emergency services. The usual principles of personalized treatment cannot be maintained in these circumstances. Because of the imbalance of personnel and material to the number of victims, it is necessary to identify vitally compromised (so-called red flag) patients by a reproducible triage algorithm and to schedule them for immediate (surgical) treatment. Life-preserving measures follow the ABCDE scheme and the premise of rapid transport to an appropriate hospital for a surgical intervention. The key goal of managing every MCI is to successfully treat the largest number of patients with the limited resources available and to avert as many fatal courses as possible. There is an urgent need to address threatening scenarios, such as terrorist attacks in the German healthcare system. All personnel must be trained in recognizing and handling these situations. The tactical action is largely determined by the individual operational situation. The leading motto, however, is “stop the bleeding and clear the scene”.     

Ruhigstellung von Frakturen in der präklinischen Notfallmedizin

Notfall + Rettungsmedizin - In Deutschland ereignen sich etwa 2,6 Mio. Verkehrsunfälle/Jahr mit einer relevanten Zahl an Extremitäten‑, Becken- und...     

Lärm auf der Intensivstation

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.     

Graphical models for multivariate time series from intensive care monitoring

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical methodology. Recently graphical models have been developed for analysing the partial correlations between the components of multivariate time series. We apply this technique to the haemodynamic system of critically ill patients monitored in intensive care. In this way we can appraise the practical value of the new procedure by re-identifying known associations within the haemodynamic system. From separate analyses for different pathophysiological states we can even conclude that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique seems useful for automatic statistical analysis of high-dimensional physiological time series and it can provide new insights into physiological mechanisms. Moreover, we can use it to achieve an adequate dimension reduction of the variables needed for online monitoring at the bedside.     

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. Received: 29 April 1998 Accepted: 5 October 1998     

Collection of annotated data in a clinical validation study for alarm algorithms in intensive care—a methodologic framework

Introduction

Monitoring of physiologic parameters in critically ill patients is currently performed by threshold alarm systems with high sensitivity but low specificity. As a consequence, a multitude of alarms are generated, leading to an impaired clinical value of these alarms due to reduced alertness of the intensive care unit (ICU) staff. To evaluate a new alarm procedure, we currently generate a database of physiologic data and clinical alarm annotations.

Methods

Data collection is taking place at a 12-bed medical ICU. Patients with monitoring of at least heart rate, invasive arterial blood pressure, and oxygen saturation are included in the study. Numerical physiologic data at 1-second intervals, monitor alarms, and alarm settings are extracted from the surveillance network. Bedside video recordings are performed with network surveillance cameras.

Results

Based on the extracted data and the video recordings, alarms are clinically annotated by an experienced physician. The alarms are categorized according to their technical validity and clinical relevance by a taxonomy system that can be broadly applicable. Preliminary results showed that only 17% of the alarms were classified as relevant, and 44% were technically false.

Discussion

The presented system for collecting real-time bedside monitoring data in conjunction with video-assisted annotations of clinically relevant events is the first allowing the assessment of 24-hour periods and reduces the bias usually created by bedside observers in comparable studies. It constitutes the basis for the development and evaluation of “smart” alarm algorithms, which may help to reduce the number of alarms at the ICU, thereby improving patient safety.