Medical multiparametric time course prognoses applied to kidney function assessments.

In this paper, we describe an approach to utilize case-based reasoning methods for trend prognoses for the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. Since using conventional methods for reasoning over time does not fit for course predictions with poor medical knowledge of typical course patterns, we have developed abstraction methods suitable for integration into our case-based reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. Subsequently, we generate course-characteristic trend descriptions of the renal function over the course of time. Using case-based reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as probable prognoses to the user. We applied case-based reasoning methods in a domain which seemed reserved for statistical methods and conventional temporal reasoning.

Development of a knowledge-base for automatic monitoring of renal function of intensive care patients over time.

Renal dysfunction is a major problem in the management of critically ill patients. Monitoring of renal parameters over time is a prerequisite for detection of any significant deterioration of kidney function. Thus, we developed a knowledge-base for the dynamic monitoring of renal function of critically ill patients. A database with renal parameters of 750 intensive care patients was analyzed for distribution of parameters within predefined intervals of the creatinine clearance. Additionally, a subgroup of 11 patients with (quite) normal renal function over 11 days was selected and the daily variability of renal parameters was analyzed. An interdisciplinary expert team selected a set of nine clinically relevant renal parameters and formulated, on the basis of the data analysis and the parameter set, eight definitions of renal function, which represent four levels of renal performance. These definitions were arranged into an hierarchical structure, considering only clinically relevant changes of renal function. A change from one functional state to another inside of 2 days indicates a relevant alteration of renal function. Monitoring of time courses can additionally be performed by statistical analysis of the daily variability of parameters and comparison with their 'normal' variability. Moreover, rules were established for the plausibility check of results and interpretations of single parameters and parameter sets formulated.

A case-based consiliarius for therapy recommendation (ICONS): computer-based advice for calculated antibiotic therapy in intensive care medicine.

We report here on the system ICONS which utilizes case-based reasoning for medical decision support. As an application domain we have chosen the medical field of 'calculated antibiotic therapy' in an intensive care medicine setting. The system ICONS which runs on a personal computer suggests adequate antibiotic therapy regimen satisfying medical and economic conditions. To speed up the process of finding an adequate antibiotic therapy for a current patient, case-based reasoning is used for finding previously documented similar cases and for modifying them according to the requirements of the current patient. To reduce the memory capacity for the documentation of cases, collections of similar cases are clustered to prototypes. Medical knowledge is represented within a hierarchy of such prototypes and cases and an additional context-sensitive background knowledge-base. A knowledge acquisition tool was programmed that allows revisions of the background medical knowledge-base by simple and comprehensive methods. In addition to the advantage of producing site-specific and time-dependent knowledge, case-based reasoning is a practical method for speeding-up the process of generating and evaluating hypotheses in medical classification tasks.

Prognoses of multiparametric medical time courses applied to kidney function assessments.

In this paper, we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. Since using conventional methods for reasoning over time does not fit for course predictions with poor medical knowledge of typical course patterns, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We subsequently generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user. We applied Case-Based Reasoning methods in a domain which seemed reserved for statistical methods and conventional temporal reasoning.

Focusing on resistance development in a case based teleconsultation system for antibiotics therapy advice.

In this paper, we describe an approach to support physicians when they select a calculated antibiotic therapy for intensive care patients who have developed an infection as an additional complication. As advice is needed quickly and the pathogen is not yet known, we use an expected pathogen spectrum based on medical background knowledge and known resistances, which both will be adapted to the results of the laboratory. Case-Based Reasoning retrieval methods provide the advice for similar previous patients. Their solutions are adapted to be applicable to the new medical situation of the current patient. Furthermore, we present the recent resistance developments of the antibiotics to the physician.

Visualization of large datasets in intensive care.

At the "Institut für Anaesthesiologie der Ludwig-Maximilians-Universität" in Munich a computer-based system for the analysis and interpretation of renal function and fluid and electrolyte metabolism of critical care patients has been developed. This paper describes requirements and implementation aspects of the presentation of data to the physician. Key issue is, how to transform the enormous--and, as we all know, constantly increasing--amount of plain data available in modern intensive care units (ICUs) into relevant information which can be easily turned into therapeutic actions. These issues have been discussed in literature extensively over many years, but with the upcoming of moderately priced, though powerful graphical UNIX workstations an extended functionality is feasible.

Knowledge-based scheduling of duty rosters for physicians.

Applications of artificial intelligence methods to problems of common sense in medicine are rare. Our approach deals with a special class of resource allocation problems concerning fairness in the everyday life in a hospital. We treated the construction of a duty roster in a medical environment. We developed a fairness reasoning machine embedded in the expert system PEP for constructing a duty roster. Furthermore, we elicited and generalized knowledge about fairness between physicians. PEP has been used routinely. We observed a clear short cut of work time of the user in running it, and a 'fair' long-term allocation of physicians in the duty roster.

Adaptation and abstraction as steps towards case-based reasoning in the real medical world: case-based selection strategies for antibiotics therapy.

In this paper we describe an approach to making suitable case-based reasoning methods for real medical world problems. As an example: for the class of therapeutic problems, we choose therapy advice as antibiotics for patients in an intensive care unit, who have an infectious disease as an additional complication. As rapid advice is needed and the agent is unknown, we use an expected agent spectrum based on medical background knowledge and known resistances, which will both be adapted with the results of the laboratory. Case-based reasoning retrieval methods provide the advice for similar former patients. The old solutions are adapted to be applicable to the new medical situation of the current patient. Because of the large and incrementally increasing number of cases, we use prototypes as a structural aid. We present some experimental results of studies about the performance of our prototype design.

Multiparametric time course prognoses by means of case-based reasoning and abstractions of data and time.

In this paper we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. We have chosen the monitoring of the kidney function in an Intensive Care Unit (ICU) setting as an example for diagnostic problems. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We abstract these parameters to a daily kidney function state. Subsequently, we use these states to generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user.

[Autologous transfusion in scoliosis operations: preoperative autologous blood donations and intraoperative mechanical autotransfusion]. / Autologe Transfusion bei Skolioseoperationen: Präoperative Eigenblutspende und intraoperative maschinelle Autotransfusion.

Autologous transfusion was used to reduce the high demand for blood accompanied with scoliosis surgery. Half of the blood loss could be saved by intraoperative autotransfusion (35 patients). This resulted in a corresponding reduction in homologous transfusion. An elimination of the need for homologous blood could be achieved only by the combination of preoperative blood donation and intraoperative autotransfusion (37 patients). With freezing the predeposit blood was independent of storage time and autologous plasma was available. Thus, the risks of transfusion can be avoided.

Information transfer in high dependency environments: an ergonomic analysis.

We have studied the information flow in HDE (with special focus on the information transfer process) using data provided by a group of experienced health care professionals. A model of the information flow in HDE was built up. It postulates the existence of quanta of information (due to the artificial fragmentation of the information flow produced by the clinical working processes: organization in shifts, demand of simultaneous activities from different staff members, etc.). This fragmentation is described by using the so-called Clinical Information Process Units (CIPUs), which correspond to patient care activities going on in parallely and serially linked blocks, performed by the staff in the specific environments. Due to a transfer in responsibility over the patient the CIPUs are linked by information transfer events which are described using transfer modules (TraMs). We exemplified 32 CIPUs related to the clinical environments (PreOp, Surgery, Recovery Intensive Care, Ward, Diagnostics, Outpatient) and the health care professional groups (Anesthesiologist/Intensivist, Surgeon, Nurse, Physician, Diagnostic Physician, Physical Therapist). A matrix was established providing the transfer situations among the CIPUs enabling a systematic classification of the TraMs. The contents of the TraMs are built up of information link elements, which are assembled according to the specific settings of the transfer situation given by the emitter, receiver and purpose. In summary we modelled the process of information transfer in HDE through CIPUs, TraMs and information links in a way, which may be useful to design information technology applications or to reorganize the information management in HDE.

Effects of Pringle manoeuvre and ischaemic preconditioning on haemodynamic stability in patients undergoing elective hepatectomy: a randomized trial.

BACKGROUND: The Pringle manoeuvre and ischaemic preconditioning are applied to prevent blood loss and ischaemia-reperfusion injury, respectively, during liver surgery. In this prospective clinical trial we report on the intraoperative haemodynamic effects of the Pringle manoeuvre alone or in combination with ischaemic preconditioning. METHODS: Patients (n=68) were assigned randomly to three groups: (i) resection with the Pringle manoeuvre; (ii) with ischaemic preconditioning before the Pringle manoeuvre for resection; (iii) without pedicle clamping. RESULTS: Following the Pringle manoeuvre the mean arterial pressure increased transiently, but significantly decreased after unclamping as a result of peripheral vasodilation. Ischaemic preconditioning improved cardiovascular stability by lowering the need for catecholamines after liver reperfusion without affecting the blood sparing benefits of the Pringle manoeuvre. In addition, ischaemic preconditioning protected against reperfusion-induced tissue injury. CONCLUSIONS: Ischaemic preconditioning provides both better intraoperative haemodynamic stability and anti-ischaemic effects thereby allowing us to take full advantage of blood loss reduction by the Pringle manoeuvre.