IntroductionAdequate ventilatory support of critically ill patients depends on prompt recognition of ventilator asynchrony, as asynchrony is associated with worse outcomes.We compared an automatic method of patient-ventilator asynchrony monitoring, based on airway flow frequency analysis, to the asynchrony index (AI) determined visually from airway tracings.MethodsThis was a prospective, sequential observational study of 110 mechanically ventilated adults. All eligible ventilated patients were enrolled. No clinical interventions were performed. Airway flow and pressure signals were sampled digitally for two hours. The frequency spectrum of the airway flow signal, processed to include only its expiratory phase, was calculated with the Cooley-Tukey Fast Fourier Transform method at 2.5 minute intervals. The amplitude ratio of the first harmonic peak (H1) to that of zero frequency (DC), or H1/DC, was taken as a measure of spectral organization. AI values were obtained at 30-minute intervals and compared to corresponding measures of H1/DC.ResultsThe frequency spectrum of synchronized patients was characterized by sharply defined peaks spaced at multiples of mean respiratory rate. The spectrum of asynchronous patients was less organized, showing lower and wider H1 peaks and disappearance of higher frequency harmonics. H1/DC was inversely related to AI (n = 110; r2 = 0.57; P < 0.0001). Asynchrony, defined by AI > 10%, was associated H1/DC < 43% with 83% sensitivity and specificity.ConclusionsSpectral analysis of airway flow provides an automatic, non-invasive assessment of ventilator asynchrony at fixed short intervals. This method can be adapted to ventilator systems as a clinical monitor of asynchrony.

A strong association exists between positive DeltaSO(2) and Delta[Lac] and survival in critically ill patients. Whether therapy aimed at increasing DeltaSO(2) and Delta[Lac] results in improved ICU survival remains to be determined.

One of the challenges facing current Noisy-Intermediate-Scale-Quantum devices (NISQ) is achieving efficient quantum circuit measurement or readout. The process of extracting classical data from the quantum domain, termed in this work as quantum-to-classical (Q2C) data decoding, generally incurs significant overhead, since the quantum circuit needs to be sampled repeatedly to obtain useful data readout. In this paper, we propose and evaluate time-efficient and depth-optimized Q2C methods based on the multidimensional, multileveldecomposable, quantum wavelet transform (QWT) whose packet and pyramidal forms are leveraged and optimized. We also propose a zero-depth technique that uses selective placement of measurement gates to perform the QWT operation. To demonstrate their efficiency, the proposed techniques are quantitatively evaluated in terms of execution time, circuit depth, and accuracy in comparison to existing Q2C techniques. Experimental evaluations of the proposed Q2C methods are performed using real high-resolution multispectral images on a 27-qubit state-of-the-art quantum computing device from IBM Quantum.

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