Abstract: A method is presented for predicting onset of pulmonary congestion symptoms. The overall idea is to track rising left ventricular filling pressure (LVFP) in heart failure patients by exploiting significant changes in a pulsatile arterial waveform obtained with a mobile device and thereby avert hospitalizations. The stimulus for the changes may be metronomic deep breathing performed by the patient or a natural occurring arrhythmia such as atrial fibrillation or premature beats. For either stimulus, the extent of the amplitude variations depends on where the patient is on the Starling curve. If the patient is on the steep part of the curve, the variations will be large and LVFP will be low. If the patient is on the flatter part of the curve, the variations will be smaller and LFVP will be higher. These variations can be normalized in various ways to arrive at a congestion prediction index (CPI).

Abstract: In a medical monitoring system, one or more biometric monitors collect data of a plurality of monitored biometric parameters. An expression evaluator evaluates a user-defined expression incorporating one or more of the monitored biometric parameters to generate data for a user-defined biometric parameter. A further processing component performs processing and display of data of at least one parameter selected from the monitored biometric parameters and the user-defined biometric parameter. The further processing component operates in the same way regardless of whether the at least one parameter is selected from the monitored biometric parameters or the user-defined biometric parameter.

Abstract: A method for predicting a patient's outcome variable, such as a probability of mortality/recovery, includes accessing at least one of a plurality past patients' data fields including physiological and/or laboratory data, and a time of stay indicating how long each patient had been under care at the time. An outcome variable estimation algorithm is generated by data mining from a plurality of past patients' physiological and/or laboratory data, corresponding time of stay, and associated outcome variables. A current patient's outcome variable is determined from the current patient's physiological and/or laboratory data, the current time of stay, and the outcome variable estimation algorithm.

Abstract: An apparatus (10) for predicting patient respiratory stability includes a patient data memory (24) which stores patient data for a patient (12) and an analyzer (34) in communication with the memory computes a measure of patient respiratory stability. The analyzer applies one or more rules to the patient data that are based on a plurality of parameters which in combination, have been identified as being predictive of patient respiratory instability, such as mean airway pressure (MAWP), plateau pressure (PP), arterial oxygen saturation (SaO2 or SpO2), and heart rate (HR). Based on the application of the rules, the analyzer determines the measure of patient respiratory stability.

Abstract: A medical system (10) and method monitor a patient. Patient data for the patient received. The patient data includes vital sign measurements and laboratory results. A vital signs instability index (VIX) regarding a physiological condition of the patient is calculated from the received vital sign measurements. A laboratory instability index (LIX) regarding the physiological condition is calculated from the received laboratory results. The VIX and the LIX are integrated into an indicator of patient deterioration.

Abstract: A system (202) generates patient alarms using a stepped alarm scheme. The system (202) includes one or more processors (220) programmed to receive physiological scores and/or physiological parameter values; compare the physiological scores and/or the physiological parameter values to a plurality of alarm levels; in response to a physiological score and/or physiological parameter value falling within an uninhibited zone of the alarm levels, issue an alarm; and set a first inhibition period for the uninhibited alarm level after issuing the alarm.

Abstract: In a patient monitoring system (10), shorter interval physiological parameters and longer interval clinical data are collected from a monitored patient (12). A composite acuity score generator (70) generates or updates a composite acuity score indicative of wellbeing of the patient (12) based at least on the sensed physiological parameters and the longer interval data. A monitor (22, 56) displays current values of at least one of selected sensed physiological parameters, longer interval data, and the composite acuity score.

Abstract: The application relates, in part, to a therapeutic composition comprising a pharmaceutically active agent and a diarrheagenic agent.

Abstract: In a medical monitoring system, one or more biometric monitors collect data of a plurality of monitored biometric parameters. An expression evaluator evaluates a user-defined expression incorporating one or more of the monitored biometric parameters to generate data for a user-defined biometric parameter. A further processing component performs processing and display of data of at least one parameter selected from the monitored biometric parameters and the user-defined biometric parameter. The further processing component operates in the same way regardless of whether the at least one parameter is selected from the monitored biometric parameters or the user-defined biometric parameter.

Abstract: A medical system (10) and method monitor a patient. Patient data for the patient received. The patient data includes vital sign measurements and laboratory results. A vital signs instability index (VEX) regarding a physiological condition of the patient is calculated from the received vital sign measurements. A laboratory instability index (LIX) regarding the physiological condition is calculated from the received laboratory results. The VEX and the LIX are integrated into an indicator of patient deterioration.

Abstract: A clinical decision support system (16) monitors one or more patients. The system (16) includes one or more processors (84) programmed to receive patient data for the patients. For each patient, one or more monitoring rules are selected from a plurality of monitoring rules based on patient data availability and/or patient context. A determination is made as to whether a patient is deteriorating using the selected monitoring rules for the patient. In response to determining the patient is deteriorating, an alert is generated.

Abstract: Medical vital signs (110) are captured, recorded, processed, and a signal quality assessment (160) is computed based on signal waveform components such as slope, amplitude, time to rise, time at peak, and degree to which signal peaks (420) and valleys (430). The signal assessment (160) may be used as a basis for rating the quality (130) of the underlying vital signal, to increase the quality of the signal by removing noisy segments and physiologically impossible peaks (42) and valleys (434), to detect a parameter value (120), to label a waveform (140), or to prompt an alarm (550) to indicate the signal has reached a critical level and issue a warning to the user of the vital data. The signal and the assessment are stored in an indexed, searchable data storage memory (590) from which the signals may be retrieved and displayed (300).

Abstract: A system (202) generates patient alarms using a stepped alarm scheme. The system (202) includes one or more processors (220) programmed to receive physiological scores and/or physiological parameter values; compare the physiological scores and/or the physiological parameter values to a plurality of alarm levels; in response to a physiological score and/or physiological parameter value falling within an uninhibited zone of the alarm levels, issue an alarm; and set a first inhibition period for the uninhibited alarm level after issuing the alarm.

Abstract: A method for predicting a patient's outcome variable, such as a probability of mortality/recovery, includes accessing at least one of a plurality past patients' data fields including physiological and/or laboratory data, and a time of stay indicating how long each patient had been under care at the time. An outcome variable estimation algorithm is generated by data mining from a plurality of past patients' physiological and/or laboratory data, corresponding time of stay, and associated outcome variables. A current patient's outcome variable is determined from the current patient's physiological and/or laboratory data, the current time of stay, and the outcome variable estimation algorithm.

Abstract: The application relates, in part, to a therapeutic composition comprising a pharmaceutically active agent and a diarrheagenic agent.

Abstract: The present invention relates to a therapeutic composition comprising a pharmaceutically active agent and a diarrheagenic agent, wherein the diarrheagenic agent induces diarrhea to reduce the harmful or undesired side effects of the pharmaceutically active agent when the pharmaceutically active agent is ingested at a dose higher than the prescribed dose.

Abstract: Medical vital signs (110) are captured, recorded, processed, and a signal quality assessment (160) is computed based on signal waveform components such as slope, amplitude, time to rise, time at peak, and degree to which signal peaks (420) and valleys (430). The signal assessment (160) may be used as a basis for rating the quality (130) of the underlying vital signal, to increase the quality of the signal by removing noisy segments and physiologically impossible peaks (424) and valleys (434), to detect a parameter value (120), to label a waveform (140), or to prompt an alarm (550) to indicate the signal has reached a critical level and issue a warning to the user of the vital data. The signal and the assessment are stored in an indexed, searchable data storage memory (590) from which the signals may be retrieved and displayed (300).

Abstract: An apparatus (10) for predicting patient respiratory stability includes a patient data memory (24) which stores patient data for a patient (12) and an analyzer (34) in communication with the memory computes a measure of patient respiratory stability. The analyzer applies one or more rules to the patient data that are based on a plurality of parameters which in combination, have been identified as being predictive of patient respiratory instability, such as mean airway pressure (MAWP), plateau pressure (PP), arterial oxygen saturation (SaO2 or SpO2), and heart rate (HR). Based on the application of the rules, the analyzer determines the measure of patient respiratory stability.

Abstract: The present application relates to improving healthcare practices in clinical facilities. In a facility that utilizes electronic patient charts (16), a correlation processor (24) discovers correlations between events as defined by a user of the system. The user first selects an anchor event. Then the user selects a related event. Both the anchor events and the related events can be run through appropriate filters to eliminate unwanted results. The user then defines a relationship between the anchor event and the related event. The correlation processor (24) then searches through the patient charts (16) for the correlation as defined by the user. Results are displayed for the user in a format designated by the user.

Abstract: The application relates, in part, to a therapeutic composition comprising a pharmaceutically active agent and a diarrheagenic agent.