Abstract: A system and method are provided that employ the variability of physiological waveforms to estimate the time to death after WLST, or time to inadequate organ perfusion. From the variability data one can derive an index subsequently used to determine the probability of death (or inadequate organ perfusion) within a given time frame in an automated fashion from bedside monitors in the intensive or post-anesthesia care unit. The resulting variability index can also be combined with the clinical variables used in other death prediction tools to enhance the performance and outcome when compared to existing models. In at least one implementation, variability monitoring at the bedside could be used to provide estimates of the probability that a patient will die within a certain time frame after WLST. These estimates could be used to reduce the distress of the patients' families, as well as optimize the use of resources surrounding donation.

Abstract: A decision support system is provided for the management of extubation in intensive care unit patients. Based on multi-organ variability analysis of physiological signals, the proposed system transforms acquired waveforms into clinical information such as the risk of failing extubation and the probability of passing extubation. Furthermore, a variety of mechanisms are provided for displaying the extracted information to support a clinician's decisions.

Abstract: A system and method are provided that employ the variability of physiological waveforms to estimate the time to death after WLST, or time to inadequate organ perfusion. From the variability data one can derive an index subsequently used to determine the probability of death (or inadequate organ perfusion) within a given time frame in an automated fashion from bedside monitors in the intensive or post-anesthesia care unit. The resulting variability index can also be combined with the clinical variables used in other death prediction tools to enhance the performance and outcome when compared to existing models. In at least one implementation, variability monitoring at the bedside could be used to provide estimates of the probability that a patient will die within a certain time frame after WLST. These estimates could be used to reduce the distress of the patients' families, as well as optimize the use of resources surrounding donation.

Abstract: A decision support system is provided for the management of extubation in intensive care unit patients. Based on multi-organ variability analysis of physiological signals, the proposed system transforms acquired waveforms into clinical information such as the risk of failing extubation and the probability of passing extubation. Furthermore, a variety of mechanisms are provided for displaying the extracted information to support a clinician's decisions.

Abstract: A system and method are provided for assessing quality for a variability analysis. The method comprises: obtaining at least one waveform corresponding to a corresponding physiological measurement; determining at least one measure of waveform quality of the at least one waveform; extracting from a waveform, at least one event time series; determining a measure of event time series quality of the at least one event time series; determining at least one measure of stationarity of the at least one event time series; computing a quality measure using the at least one measure of waveform quality and the at least one measure of stationarity; and displaying the quality measure.

Abstract: A system is provided for leveraging the power of the analysis of variability over time, and which uses an underlying framework that can handle variability analyses across a distributed system in a consistent manner, in part by constructing a standard variability data file that includes several manifestations of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient display tools, while a central entity can provide connectivity to the distributed environment and provide a way to update the equipment and software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone users.

Abstract: A system is provided for leveraging the power of the analysis of variability over time, and which uses an underlying framework that can handle variability analyses across a distributed system in a consistent manner, in part by constructing a standard variability data file that includes several manifestations of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient display tools, while a central entity can provide connectivity to the distributed environment and provide a way to update the equipment and software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone users.

Abstract: A system is provided for leveraging the power of the analysis of variability over time, and which uses an underlying framework that can handle variability analyses across a distributed system in a consistent manner, in part by constructing a standard variability data file that includes several manifestations of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient display tools, while a central entity can provide connectivity to the distributed environment and provide a way to update the equipment and software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone users.

Abstract: A system is provided for leveraging the power of the analysis of variability over time, and which uses an underlying framework that can handle variability analyses across a distributed system in a consistent manner, in part by constructing a standard variability data file that includes several manifestations of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient display tools, while a central entity can provide connectivity to the distributed environment and provide a way to update the equipment and software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone users.

Abstract: A method and apparatus (100) for providing continuous analysis and display of the variability of multiple patient parameters monitored by multiple bedside monitors (106a–106c) for each patient (102). Each monitor is connected to a patient interface and to a patient data storage unit (115) and a processor (113). Each monitored patient parameter is measured in real-time. Data artifacts are removed, and variability analysis is performed based upon a selected period of observation. Variability analysis yields variability of the patient parameters, which represents a degree to which the patient parameters fluctuate over time, to provide diagnostic information particularly useful in the detection, prevention and treatment of multiple organ dysfunction syndrome (MODS).

Abstract: A method and apparatus (100) for providing continuous analysis and display of the variability of multiple patient parameters monitored by multiple bedside monitors (106a-106c) for each patient (102). Each monitor is connected to a patient interface and to a patient data storage unit (115) and a processor (113). Each monitored patient parameter is measured in real-time. Data artifacts are removed, and variability analysis is performed based upon a selected period of observation. Variability analysis yields variability of the patient parameters, which represents a degree to which the patient parameters fluctuate over time, to provide diagnostic information particularly useful in the detection, prevention and treatment of multiple organ dysfunction syndrome (MODS).

Abstract: A method and apparatus (100) for providing continuous analysis and display of the variability of multiple patient parameters monitored by multiple beside monitors (106a-106c) for each patient (102). Each monitor is connected to a patient interface and to a patient data storage unit (115) and a processor (113). Each monitored patient parameter is measured in real-time. Data artifacts are removed, and variability analysis is performed based upon a selected period of observation. Variability analysis yields variability of the patient parameters, which represents a degree to which the patient parameters fluctuate over time, to provide diagnostic information particularly useful in the detection, prevention and treatment of multiple organ dysfunction syndrome (MODS).