Browsing by Author "Chen, Xiaoxiao"
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- ItemPrehospital heart rate and blood pressure increase the positive predictive value of the Glasgow Coma Scale for high-mortality traumatic brain injury.(2014-05-08) Reisner, Andrew; Chen, Xiaoxiao; Kumar, Kamal; Reifman, JaquesWe hypothesized that vital signs could be used to improve the association between a trauma patient s prehospital Glasgow Coma Scale GCS score and his or her clinical condition Previously abnormally low and high blood pressures have both been associated with higher mortality for patients with traumatic brain injury TBI We undertook a retrospective analysis of 1384 adult prehospital trauma patients Vital sign data were electronically archived and analyzed We examined the relative risk of severe head Abbreviated Injury Scale AIS 5 6 as a function of the GCS systolic blood pressure SBP heart rate HR and respiratory rate RR We created multi variate logistic regression models and using DeLong s test compared their area under receiver operating characteristic curves ROC AUCs for three outcomes head AIS 5 6 all cause mortality and either head AIS 5 6 or neurosurgical procedure We found significant bimodal relationships between head AIS 5 6 versus SBP and HR but not RR When the GCS was Under15 ROC AUCs were significantly higher for a multi variate regression model GCS SBP and HR versus GCS alone In particular patients with abnormalities in all parameters GCS SBP and HR were significantly more likely to have high mortality TBI versus those with abnormalities in GCS alone This could be useful for mobilizing resources e g neurosurgeons and operating rooms at the receiving hospital and might enable new prehospital management protocols where therapies are selected based on TBI mortality risk
- ItemA robust method to estimate instantaneous heart rate from noisy electrocardiogram waveforms.(2011-02-04) Gribok, Andrei V; Chen, Xiaoxiao; Reifman, JaquesWe propose a new algorithm for real time estimation of instantaneous heart rate HR from noise laden electrocardiogram ECG waveforms typical of unstructured ambulatory field environments The estimation of HR from ECG waveforms is an indirect measurement problem that requires differencing which invariably amplifies high frequency noise We circumvented noise amplification by considering the estimation of HR as the solution of a weighted regularized least squares problem which in addition directly provided analytically based confidence intervals CIs for the estimated HRs To evaluate the performance of the proposed algorithm we applied it to simulated data and to noise laden ECG records that were collected during helicopter transport of trauma injured patients to a trauma center We compared the proposed algorithm with HR estimates produced by a widely used vital sign travel monitor and a standard HR estimation technique followed by postprocessing with Kalman filtering or spline smoothing The simulation results indicated that our algorithm consistently produced more accurate HR estimates with estimation errors as much as 67 smaller than those attained by the postprocessing methods while the results with the field collected data showed that the proposed algorithm produced much smoother and reliable HR estimates than those obtained by the vital sign monitor Moreover the obtained CIs reflected the amount of noise in the ECG recording and could be used to statistically quantify uncertainties in the HR estimates We conclude that the proposed method is robust to different types of noise and is particularly suitable for use in ambulatory environments where data quality is notoriously poor