Browsing by Author "Reisner, Andrew T"
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- ItemDecision tool for the early diagnosis of trauma patient hypovolemia.(2008-06-05) Chen, Liangyou; McKenna, Thomas M; Reisner, Andrew T; Gribok, Andrei; Reifman, JaquesWe present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital when reliable acquisition of vital sign data may be difficult The decision tool uses basic vital sign variables as input into linear classifiers which are then combined into an ensemble classifier The classifier identifies hypovolemic patients with an area under a receiver operating characteristic curve AUC of 0 76 standard deviation 0 05 for 100 randomly reselected patient subsets The ensemble classifier is robust classification performance degrades only slowly as variables are dropped and the ensemble structure does not require identification of a set of variables for use as best feature inputs into the classifier The ensemble classifier consistently outperforms best features based linear classifiers the classification AUC is greater and the standard deviation is smaller pUnder0 05 The simple computational requirements of ensemble classifiers will permit them to function in small fieldable devices for continuous monitoring of trauma patients
- ItemDiagnosis of hemorrhage in a prehospital trauma population using linear and nonlinear multiparameter analysis of vital signs.(2007-11-16) Chen, Liangyou; Reisner, Andrew T; McKenna, Thomas M; Gribok, Andrei; Reifman, JaquesIn this study we analyzed a dataset of time series vital signs data collected by standard Propaq travel monitor during helicopter transport of 898 civilian trauma casualties from the scene of injury to a receiving trauma center The goals of the analysis are two fold First to determine which combination of the automatically collected and qualified vital signs provides the best discrimination between casualties with and without major hemorrhage Second to determine whether nonlinear classifiers provide improved discrimination over simpler linear classifiers Major hemorrhage is defined by the presence of injuries consistent with hemorrhage in casualties who received one or more units of blood We randomly selected a subset of the casualties to train and test the classifiers with multiple combinations of the vital signs variables and used the area under the receiver operating characteristic curve ROC AUC as a decision metric Based on the results of 100 simulations we observe that i the best two features obtained are systolic blood pressure and heart rate mean AUC 0 75 from a linear classifier and ii the use of nonlinear classifiers does not improve discrimination These results support earlier findings that the interaction of systolic blood pressure and heart rate is useful for the identification of trauma hemorrhage and that linear classifiers are adequate for many real world applications
- ItemIs heart rate variability better than routine vital signs for prehospital identification of major hemorrhage?(2014-12-23) Edla, Shwetha; Reisner, Andrew T; Liu, Jianbo; Convertino, Victor A; Carter, Robert; Reifman, JaquesDuring initial assessment of trauma patients metrics of heart rate variability HRV have been associated with high risk clinical conditions Yet despite numerous studies the potential of HRV to improve clinical outcomes remains unclear Our objective was to evaluate whether HRV metrics provide additional diagnostic information beyond routine vital signs for making a specific clinical assessment identification of hemorrhaging patients who receive packed red blood cell PRBC transfusion
- ItemA method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms.(2006-05-03) Yu, Chenggang; Liu, Zhenqiu; McKenna, Thomas; Reisner, Andrew T; Reifman, JaquesThe development and application of data driven decision support systems for medical triage diagnostics and prognostics pose special requirements on physiologic data In particular that data are reliable in order to produce meaningful results The authors describe a method that automatically estimates the reliability of reference heart rates HRr derived from electrocardiogram ECG waveforms and photoplethysmogram PPG waveforms recorded by vital signs monitors The reliability is quantitatively expressed through a quality index QI for each HRr
- ItemTachycardic and non-tachycardic responses in trauma patients with haemorrhagic injuries.(0000-00-00) Reisner, Andrew T; Edla, Shwetha; Liu, Jianbo; Liu, Jiankun; Khitrov, Maxim Y; Reifman, JaquesBACKGROUND Analyses of large databases have demonstrated that the association between heart rate HR and blood loss is weaker than what is taught by Advanced Trauma Life Support training However those studies had limited ability to generate a more descriptive paradigm because they only examined a single HR value per patient METHODS In a comparative retrospective analysis we studied the temporal characteristics of HR through time in adult trauma patients with haemorrhage based on documented injuries and transfusion of 3 units of red blood cells RBCs We analysed archived vital sign data of up to 60 min during either pre hospital or emergency department care RESULTS We identified 133 trauma patients who met the inclusion criteria for major haemorrhage and 1640 control patients without haemorrhage There were 55 haemorrhage patients with a normal median HR and 78 with tachycardia Median HR was 0 8 and 0 7 bpm per 10 min respectively Median time to documented hypotension was 8 and 5 min respectively RBCs were not significantly different median volumes were 6 IQR 4 13 and 10 units IQR 5 16 respectively Time to hypotension and mortality were not significantly different Tachycardic patients were significantly younger P Under 0 05 Only 10 patients with normal HR developed transient temporary tachycardia and only 11 tachycardic patients developed a transient temporary normal HR CONCLUSIONS The current analysis suggests that some trauma patients with haemorrhage are continuously tachycardic while others have a normal HR For both cohorts hypotension typically develops within 30 min without any consistent temporal increases or trends in HR