The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Gershom Zajicek - One of the best experts on this subject based on the ideXlab platform.
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Computer Assisted Physiologic Monitoring and Stability Assessment in Vascular Surgical Patients Undergoing General Anesthesia – Preliminary Data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram G. Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, C. William Hanson, Clifford S. Deutschman, Gershom ZajicekAbstract:Background. Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO_2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods. After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results. The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion. The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
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computer assisted Physiologic Monitoring and stability assessment in vascular surgical patients undergoing general anesthesia preliminary data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, Clifford S. Deutschman, William C Hanson, Gershom ZajicekAbstract:Background.Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods.After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results.The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion.The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
Lisa E Hensley - One of the best experts on this subject based on the ideXlab platform.
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natural history of aerosol exposure with marburg virus in rhesus macaques
Viruses, 2016Co-Authors: Evan C Ewers, Joshua C Johnson, William D Pratt, Nancy A Twenhafel, Joshua D Shamblin, Ginger Donnelly, Heather L Esham, Carly Wlazlowski, Miriam A Botto, Lisa E HensleyAbstract:Marburg virus causes severe and often lethal viral disease in humans, and there are currently no Food and Drug Administration (FDA) approved medical countermeasures. The sporadic occurrence of Marburg outbreaks does not allow for evaluation of countermeasures in humans, so therapeutic and vaccine candidates can only be approved through the FDA animal rule—a mechanism requiring well-characterized animal models in which efficacy would be evaluated. Here, we describe a natural history study where rhesus macaques were surgically implanted with telemetry devices and central venous catheters prior to aerosol exposure with Marburg-Angola virus, enabling continuous Physiologic Monitoring and blood sampling without anesthesia. After a three to four day incubation period, all animals developed fever, viremia, and lymphopenia before developing tachycardia, tachypnea, elevated liver enzymes, decreased liver function, azotemia, elevated D-dimer levels and elevated pro-inflammatory cytokines suggesting a systemic inflammatory response with organ failure. The final, terminal period began with the onset of sustained hypotension, dehydration progressed with signs of major organ hypoperfusion (hyperlactatemia, acute kidney injury, hypothermia), and ended with euthanasia or death. The most significant pathologic findings were marked infection of the respiratory lymphoid tissue with destruction of the tracheobronchial and mediastinal lymph nodes, and severe diffuse infection in the liver, and splenitis.
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real time Monitoring of cardiovascular function in rhesus macaques infected with zaire ebolavirus
The Journal of Infectious Diseases, 2011Co-Authors: Mark G Kortepeter, James V Lawler, Anna N Honko, Mike Bray, Joshua C Johnson, Bret K Purcell, Gene G Olinger, Robert G Rivard, Matthew J Hepburn, Lisa E HensleyAbstract:Nine rhesus macaques were implanted with multisensor telemetry devices and internal jugular vein catheters before being infected with Zaire ebolavirus. All animals developed viremia, fever, a hemorrhagic rash, and typical changes of Ebola hemorrhagic fever in clinical laboratory tests. Three macaques unexpectedly survived this usually lethal disease, making it possible to compare Physiological parameters in lethally challenged animals and survivors. After the onset of fever, lethal illness was characterized by a decline in mean arterial blood pressure, an increase in pulse and respiratory rate, lactic acidosis, and renal failure. Survivors showed less pronounced change in these parameters. Four macaques were randomized to receive supplemental volumes of intravenous normal saline when they became hypotensive. Although those animals had less severe renal compromise, no apparent survival benefit was observed. This is the first report of continuous Physiologic Monitoring in filovirus-infected nonhuman primates and the first to attempt cardiovascular support with intravenous fluids.
Amit Maliar - One of the best experts on this subject based on the ideXlab platform.
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Computer Assisted Physiologic Monitoring and Stability Assessment in Vascular Surgical Patients Undergoing General Anesthesia – Preliminary Data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram G. Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, C. William Hanson, Clifford S. Deutschman, Gershom ZajicekAbstract:Background. Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO_2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods. After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results. The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion. The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
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computer assisted Physiologic Monitoring and stability assessment in vascular surgical patients undergoing general anesthesia preliminary data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, Clifford S. Deutschman, William C Hanson, Gershom ZajicekAbstract:Background.Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods.After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results.The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion.The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
Yacov Berlatzky - One of the best experts on this subject based on the ideXlab platform.
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Computer Assisted Physiologic Monitoring and Stability Assessment in Vascular Surgical Patients Undergoing General Anesthesia – Preliminary Data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram G. Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, C. William Hanson, Clifford S. Deutschman, Gershom ZajicekAbstract:Background. Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO_2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods. After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results. The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion. The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
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computer assisted Physiologic Monitoring and stability assessment in vascular surgical patients undergoing general anesthesia preliminary data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, Clifford S. Deutschman, William C Hanson, Gershom ZajicekAbstract:Background.Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods.After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results.The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion.The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
Leonid A. Eidelman - One of the best experts on this subject based on the ideXlab platform.
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Computer Assisted Physiologic Monitoring and Stability Assessment in Vascular Surgical Patients Undergoing General Anesthesia – Preliminary Data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram G. Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, C. William Hanson, Clifford S. Deutschman, Gershom ZajicekAbstract:Background. Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO_2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods. After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results. The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion. The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
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computer assisted Physiologic Monitoring and stability assessment in vascular surgical patients undergoing general anesthesia preliminary data
Journal of Clinical Monitoring and Computing, 2000Co-Authors: Yoram Weiss, Amit Maliar, Leonid A. Eidelman, Yacov Berlatzky, Clifford S. Deutschman, William C Hanson, Gershom ZajicekAbstract:Background.Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure – the health stability magnitude (HSM). The HSM is computed for a pre-determined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. Materials and methods.After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with there cord from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. Results.The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient’s time course, projected on this diagram, revealed deviations form “normal” physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. Conclusion.The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can beviewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.