Bispectral Index Can Reliably Detect Deep Sedation in Mechanically Ventilated Patients: A Prospective Multicenter Validation Study.

BACKGROUND: Excessively deep sedation is prevalent in mechanically ventilated patients and often considered suboptimal. We hypothesized that the bispectral index (BIS), a quantified electroencephalogram instrument, would accurately detect deep levels of sedation. METHODS: We prospectively enrolled 90 critically ill mechanically ventilated patients who were receiving sedation. The BIS was monitored for 24 hours and compared with the Richmond Agitation Sedation Scale (RASS) evaluated every 4 hours. Deep sedation was defined as a RASS of -3 to -5. Threshold values of baseline BIS (the lowest value before RASS assessment) and stimulated BIS (the highest value after standardized assessment) for detecting deep sedation were determined in a training set (45 patients, 262 RASS assessments). Diagnostic accuracy was then analyzed in a validation set (45 patients, 264 RASS assessments). RESULTS: Deep sedation was only prescribed in 6 (6.7%) patients, but 76 patients (84.4%) had at least 1 episode of deep sedation. Thresholds for detecting deep sedation of 50 for baseline and 80 for stimulated BIS were identified, with respective areas under the receiver-operating characteristic curve of 0.771 (95% confidence interval, 0.714-0.828) and 0.805 (0.752-0.857). The sensitivity and specificity of baseline BIS were 94.0% and 66.5% and of stimulated BIS were 91.0% and 66.5%. When baseline and stimulated BIS were combined, the sensitivity, specificity, and clinical utility index were 85.0% (76.1%-91.1%), 85.9% (79.5%-90.7%), and 66.9% (57.8%-76.0%), respectively. CONCLUSIONS: Combining baseline and stimulated BIS may help detect deep sedation in mechanically ventilated patients.

[Imaging spectrometry radiometric cross-calibration based on precise spectral response matching].

The present research describes the development of an improved cross-calibration method of on-orbit satellite sensor. The EO-1/Hyperion was taken as the referenced sensor and HJ-1A/HSI was taken as the uncalibrated sensor. The differences between the bands configurations were removed by the precise spectral response matching using the deconvolution method, which significantly reduced the radiometric calibration uncertainty of HSI sensor. The calibration coefficients of HSI for all 115 bands were acquired. The uncertainties of calibration coefficient from band 1 to band 60 stably lie in 5%-8%, and for all the other bands excerpt for the oxygen absorption which lies in at 760 nm and the water vapor absorption which lies in at 940 nm, the uncertainties of calibration coefficients are changed from 7% to 18%, which increased as the wavelength increased. Contrasted Compared with the traditional spectral matching method, the method proposed can improve the calibration accuracy by about 50%, which can meet the demand of the quantitive application for hyperspectral remote sensing data. It demonstrated the good precision and reliability of the method. It solved the spectral matching problem when the band configuration is big enough so that the cross calibration accuracy is too low and is difficult to apply in hyperspectral sensor cross-calibration, and provides a new method to frequently update the calibration coefficients for hyperspectral imager.

[Effect of remotely sensed data errors on the retrieving accuracy of territorial parameters--a case study on chlorophyll a concentration inversion of Taihu Lake].

The errors of the territorial parameters retrieved from remote sensing are decided by the data error and the model error. The data error is not simply added to the total errors of retrieval results. It would be reformed by the quantitative inversion model, and then, combined with the model errors and melts into the totals errors. Accordingly, during the quantitative process, taking advantage of the highest correlation coefficient or the least root mean square error as assessment standard for describing the chlorophyll a concentration vs remote sensing parameters is not reasonable. Focusing on the above problem, the study pointed out that the reason why the result of the optimized cost function is contrary with the practical is that different model has different influence on data errors. Combined with the in situ measurements of Taihu Lake, in October, 2003, it is known that due to the error magnification phenomena (TM2/TM3 algorithm is 2.28 times more than TM2/TM1 algorithm), although the regression coefficient of TM2/TM3 algorithm is higher than TM2/TM1 algorithm, the quantitative errors of TM2/TM3 algorithm are 7.938 5 microg x L(-1) more than TM2/TM1 algorithm. Moreover, the retrieval results show that distribution pattern of the results of TM2/TM3 algorithm is completely opposite to the TM2/TM1 algorithm. According to the former research achievements, the results of TM2/TM1 algorithm would be more reasonable. In summary, only when that the factor of data error is added to the optimized cost function is taken as a constrain condition in search for the optimal solution of the quantitative models, would the retrieval results be more reliable.

[The spectrum characteristics of an invasion plant: Eupatorium adenophorum Spreng].

Eupatorium adenophorum Spreng. (EAS) is a toxic invasion plant and has caused significant economic and environmental impacts in China. The EAS has the characteristics of widely distributing and quickly spreading. The traditional detecting and supervising methods become invalid when applied for managing the spatial distribution of EAS. Based on the analyzing results of the spectrum features of EAS, the present paper tried to structure the identifying models by remote sensing. The main objective of this paper is to develop an available method for detecting and mapping the spatial distribution of EAS. The study shows that the spectrum of EAS has two reflecting peaks and one absorbing trough. The corresponding wavelengths of those peaks are 560, 730 and 674 nm, respectively. The absorption characteristics of EAS at 647 nm are that the absorbing depth is 0.504 3-1.910 3, the absorbing width is 13.778 9-17.251 8 nm and the area at the left absorption band is greater than the right, and the corresponding area ratio of left to right is 1.771 9-2.444 1. The white flowers of EAS make the reflectance higher at visible bands, and the first-order derivatives of EAS spectral show a wave peak at 420 nm. Compared with the spectral feature of other representative materials, the absorbing characteristics at 647 nm, such as absorption width and absorption depth, and the peak at 420 nm of derivatives spectral are special features of EAS spectral, which can be used as remotely sensed parameters for detecting and mapping the EAS at florescence.

[Research of coupling effects among various water quality components].

Based on aquatic optics Monte Carlo hyperspectral simulation, the interactions between spectral characteristics of chlorophyll a, total suspended matter (TSM) and colored dissolved organic matter (CDOM) were discussed. The result shows that the nonlinear and spectrally varying interactions between different water components are extremely highly asymmetric. The existing of chlorophyll a and CDOM has little effects on the characteristic wavebands selection and information retrieval of TSM, while the effects of TSM on chlorophyll a are obvious. With the stepwise additions of TSM, the response of chlorophyll a to its concentration becomes weak. When the concentration of TSM increases to a certain degree, the spectral response of chlorophyll a concentration will disappear. Even at the sensitivity waveband of chlorophyll a such as 670 nm, when the TSM is in high concentration, the spectral reflectance will not change with chlorophyll a concentration, which lead to difficulty to extract the chlorophyll a concentration in turbid water dominated by suspended matter. The existing of CDOM causes the blue and green band ratio algorithm to fail when the chlorophyll a is in middle to high concentration. The spectral effects on CDOM of the water body dominated by TSM are more obvious than that dominated by chlorophyll a. There are strong inhibition effects of TSM on the CDOM spectral properties in the short bands. The research results can provide theoretical basis for characteristic waveband selection, the application scope of water component concentration inversion algorithm and the waveband setting for case 2 water remote sensing.

[Study on quantitative model for suspended sediment concentration in Taihu Lake].

The complicated compositions of Case II waters result in the complex properties of spectral curves. The present paper analyzed the in situ measurements data of spectral curves, and further realized the relationships between the properties of spectral curves and suspended sediment concentration. The study found that the max peak of spectral curves was moving to the direction of shortwavelength as increasing suspended sediment concentration, namely the blue shift of wavelength; the area enclosed by spectral curve and coordinate axis in the range of sensitive bands had preferably linear relationship with the suspended sediment concentration (curve area model); the trapezoidal area model which was an approximation of curve area model could also excellently reflect those relationships, and be greatly suitable for multi-spectral satellite imagery retrieval such as LandSat/TM, MODIS and so on. The inversion results of trapezoidal area model for LandSat/TM imagery on October 27, 2003 in Taihu Lake showed that the suspended sediment concentration ranged from 30 to 80 mg x L(-1), the distribution pattern was higher in the west, south and central lake and lower in the east lake; compared with the in situ measurements in the regions, and the relative error of retrieval model was 6.035%.

Emergence agitation during recovery from intracranial surgery under general anaesthesia: a protocol and statistical analysis plan for a prospective multicentre cohort study.

INTRODUCTION: Emergence agitation after intracranial surgery is an important clinical issue during anaesthesia recovery. The aim of this multicentre cohort study is to investigate the incidence of emergence agitation, identify the risk factors and determine clinical outcomes in adult patients after intracranial surgery under general anaesthesia. Additionally, we will deliberately clarify the relationship between postoperative pneumocephalus and agitation. METHODS AND ANALYSIS: The present study is a prospective multicentre cohort study. Five intensive care units (ICUs) in China will participate in the study. Consecutive adult patients admitted to the ICUs after intracranial surgery will be enrolled. Sedation-Agitation Scale (SAS) or Richmond Agitation-Sedation Scale (RASS) will be used to evaluate the patients 12 h after the enrolment. Agitation is defined as an SAS score of 5-7, or an RASS score of +2 to +4. According to the maximal SAS and RASS score, patients will be divided into two cohorts: the agitation group and the non-agitation group. Factors potentially related to emergence agitation will be collected at study entry, during anaesthesia and operation, during postoperative care. Univariate analyses between the agitation and the non-agitation groups will be performed. The stepwise backward logistic regression will be carried out to identify the independent predictors of agitation. Patients will be followed up for 72 h after the operation. Accidental self-extubation of the endotracheal tube and removal of other catheters will be documented. The use of sedatives and analgesics will be collected. ETHICS AND DISSEMINATION: Ethics approval has been obtained from each of five participating hospitals. Study findings will be disseminated through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT02318199.