Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation.

Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly. Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases. Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020-May 2021). Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany. Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5-11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.

General anesthesia increases temporal precision and decreases power of the brainstem auditory-evoked response-related segments of the electroencephalogram.

BACKGROUND: Brainstem auditory-evoked responses (BAEP) have been reported to be unchanged in the presence of drugs used for induction and maintenance of general anesthesia. The aim of this study was to investigate if the signal segments after the auditory stimulus that are used to average the evoked response change under the influence of general anesthesia. METHODS: BAEPs of 156 patients scheduled for elective surgery under general anesthesia were investigated. Anesthetic regimen was randomized as a combination of one of four hypnotic drugs supplemented by one of four opioids. Signal segments after the auditory stimulus were obtained at six different periods of anesthesia. Power and phase properties of wavelet-filtered single-sweep auditory-evoked activity accounting for the waveform of the averaged BAEP wave V and the stability of amplitude and latency of the averaged BAEP wave V over periods were analyzed. RESULTS: Amplitude and latency of wave V change slightly with no significant difference between the periods. During anesthesia, however, the power of single sweeps is significantly reduced, whereas phase-locking properties of the according signal segments are significantly enhanced. This effect is independent of the anesthetic or opioid used. CONCLUSIONS: General anesthesia affects phase and power of the segments of the electroencephalogram related to BAEP wave V. This study's results support the idea that temporally precise responses from a large number of neurons in the brainstem might play a crucial role in encoding and passing sensory information to higher subcortical and cortical areas of the brain.

EEG under anesthesia--feature extraction with TESPAR.

We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five DOA classes assessed by human experts whose judgements were based on EEG mid-latency auditory evoked potentials (MLAEPs) and clinical observations. We found that our system closely mimicked the behavior of the human expert, thus proving the utility of the method. Further analyses on the features extracted by our technique indicated that information related to DOA is mostly distributed across frequency bands and that the presence of high frequencies (> 80 Hz), which reflect mostly muscle activity, is beneficial for DOA detection.

The discriminant power of simultaneous monitoring of spontaneous electroencephalogram and evoked potentials as a predictor of different clinical states of general anesthesia.

Spontaneous or evoked electrical brain activity is increasingly used to monitor general anesthesia. Previous studies investigated the variables from spontaneous electroencephalogram (EEG), acoustic (AEP), or somatosensory evoked potentials (SSEP). But, by monitoring them separately, the available information from simultaneous gathering could be missed. We investigated whether the combination of simultaneous information from EEG, AEP, and SSEP shows a more discriminant power to differentiate between anesthesia states than from information derived from each measurement alone. Therefore, we assessed changes of 30 EEG, 21 SSEP, and 29 AEP variables recorded from 59 patients during four clinical states of general anesthesia: "awake," "light anesthesia," "surgical anesthesia," and "deep surgical anesthesia." The single and combined discriminant powers of EEG, AEP, and SSEP variables as predictors of these states were investigated by discriminant analysis. EEG variables showed a higher discriminant power than AEP or SSEP variables: 85%, 46%, and 32% correctly classified cases, respectively. The frequency of correctly classified cases increased to 90% and 91% with information from EEG + AEP and EEG + AEP + SSEP, respectively. Thus, future anesthesia monitoring should consider combined information simultaneously distributed on different electrophysiological measurements, rather than single variables or their combination from EEG or AEP or SSEP.

The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials.

This work shows methodological aspects of heuristic pattern recognition in auditory evoked potentials. A linear and a nonlinear transformation based on wavelet transform are presented. They result in a statistical error model and an entropy function related to the Gibbs function and describe changes in midlatency auditory evoked potentials induced by general anaesthesia. The same transformations were calculated using 12 common wavelets. We present a method to compare the two defined parametrizations with respect to their ability to discriminate two defined states which is responsive and unresponsive depending on the wavelet used for the analysis. Auditory evoked potentials of 60 patients undergoing general anaesthesia were analysed. We propose the defined statistical error model and the entropy function as a very robust measure of changes in auditory evoked potentials. The influence of the wavelets suggest that for each parametrization the goodness of the wavelet should be validated.

High-frequency components of auditory evoked potentials are detected in responsive but not in unconscious patients.

BACKGROUND: The dose-dependent suppression of midlatency auditory evoked potentials by general anesthetics has been proposed to measure depth of anesthesia. In this study, perioperatively recorded midlatency auditory evoked potentials were analyzed in a time-frequency space to identify significant changes induced by general anesthesia. METHODS: Perioperatively recorded auditory evoked potentials of 19 patients, recorded at varying levels of anesthesia, were submitted to a multiscale analysis using the wavelet analysis. Energy contents of the signal were calculated in frequency bands 0-57.1 Hz, 57.1-114.3 Hz, 114.3-228.6 Hz, and 228.6-457.1 Hz. A Friedman test and a Dunn multiple comparisons test were performed to identify significant differences. RESULTS: Statistical evaluation showed a highly significant decrease of the wavelet energies for the frequency bands 57.1-114.3 Hz (P < 0.0001), 114.3-228.6 Hz (P < 0.0001), and 228.6-457.1 Hz (P < 0.0001) for the measuring points representing deep general anesthesia. This decrease is accompanied by a decrease in the wavelet energy of the frequency band 0-57.1 Hz of no statistical significance (P = 0.021) (level of significance set to P = 0.01). The changes are most prominent in the poststimulus interval between 10 and 30 ms. CONCLUSIONS: This study describes the presence of high-frequency components of the auditory evoked potential. The amount of these components is higher during responsiveness when compared to unconsciousness. Temporal localization of the high-frequency components within the auditory evoked potential shows that they represent a response to the auditory stimulus. Further studies are required to identify the source of these high-frequency components.