A systematic review of machine learning models for management, prediction and classification of ARDS.

AIM: Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD: In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS: Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION: For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.

Beyond-band discrete soliton interaction in binary waveguide arrays.

We investigate different scenarios of interaction between two beyond-band discrete solitons (BBDSs), which are a new class of solitons in binary waveguide arrays and have been investigated just recently. In the quasi-continuous regime when solitons intensity is low and, thus, solitons are broad, two BBDSs with the same envelope in binary waveguide arrays interact with each other practically like two well-known fundamental solitons governed by the nonlinear Schrödinger equation in a single optical fiber. However, this similarity disappears if the discrete nature of the system is enhanced by increasing the intensity of BBDSs. In that case, two initially in-phase BBDSs with the same detuning cannot periodically collide during propagation. We also show that single-peaked BBDSs are more robust and less mobile than double-peaked BBDSs with the same detuning. This robustness stops two identical single-peaked BBDSs from interaction even at initial separations when double-peaked BBDSs can still strongly interact with each other or with single-peaked BBDSs.

Complexity Phase Diagram for Interacting and Long-Range Bosonic Hamiltonians.

We classify phases of a bosonic lattice model based on the computational complexity of classically simulating the system. We show that the system transitions from being classically simulable to classically hard to simulate as it evolves in time, extending previous results to include on-site number-conserving interactions and long-range hopping. Specifically, we construct a complexity phase diagram with easy and hard "phases" and derive analytic bounds on the location of the phase boundary with respect to the evolution time and the degree of locality. We find that the location of the phase transition is intimately related to upper bounds on the spread of quantum correlations and protocols to transfer quantum information. Remarkably, although the location of the transition point is unchanged by on-site interactions, the nature of the transition point does change. Specifically, we find that there are two kinds of transitions, sharp and coarse, broadly corresponding to interacting and noninteracting bosons, respectively. Our Letter motivates future studies of complexity in many-body systems and its interplay with the associated physical phenomena.

A new class of out-gap discrete solitons in binary waveguide arrays.

We analytically and numerically investigate beyond-band discrete solitons, which present a completely new class of stable localized out-gap solitons with detunings being located beyond the two bands of the linear plane waves in a periodic binary waveguide array. Each of the even and odd components of these discrete solitons does not change its sign across the transverse direction of the binary waveguide array. The even and odd components of these newly found discrete solitons can be approximately presented by two hyperbolic secant functions with the only difference in their peaks. This approximation is especially good in the low-intensity regime in which the detuning of these solitons can asymptotically reach the two limits of a linear spectrum. These distinguishing features altogether make the newly found discrete solitons different from all other classes of discrete solitons investigated earlier in binary waveguide arrays. Two transformation rules for constructing even and odd components of these discrete solitons are also found for various combinations of signs of the propagation mismatch σ and nonlinear coefficient γ.

Lieb-Robinson Light Cone for Power-Law Interactions.

The Lieb-Robinson theorem states that information propagates with a finite velocity in quantum systems on a lattice with nearest-neighbor interactions. What are the speed limits on information propagation in quantum systems with power-law interactions, which decay as 1/r^{α} at distance r? Here, we present a definitive answer to this question for all exponents α>2d and all spatial dimensions d. Schematically, information takes time at least r^{min{1,α-2d}} to propagate a distance r. As recent state transfer protocols saturate this bound, our work closes a decades-long hunt for optimal Lieb-Robinson bounds on quantum information dynamics with power-law interactions.

Destructive Error Interference in Product-Formula Lattice Simulation.

Quantum computers can efficiently simulate the dynamics of quantum systems. In this Letter, we study the cost of digitally simulating the dynamics of several physically relevant systems using the first-order product-formula algorithm. We show that the errors from different Trotterization steps in the algorithm can interfere destructively, yielding a much smaller error than previously estimated. In particular, we prove that the total error in simulating a nearest-neighbor interacting system of n sites for time t using the first-order product formula with r time slices is O(nt/r+nt^{3}/r^{2}) when nt^{2}/r is less than a small constant. Given an error tolerance ϵ, the error bound yields an estimate of max{O(n^{2}t/ϵ),O(n^{2}t^{3/2}/ϵ^{1/2})} for the total gate count of the simulation. The estimate is tighter than previous bounds and matches the empirical performance observed in Childs et al. [Proc. Natl. Acad. Sci. U.S.A. 115, 9456 (2018)PNASA60027-842410.1073/pnas.1801723115]. We also provide numerical evidence for potential improvements and conjecture an even tighter estimate for the gate count.

Validating the inspired sinewave technique to measure the volume of the 'baby lung' in a porcine lung-injury model.

BACKGROUND: Bedside lung volume measurement could personalise ventilation and reduce driving pressure in patients with acute respiratory distress syndrome (ARDS). We investigated a modified gas-dilution method, the inspired sinewave technique (IST), to measure the effective lung volume (ELV) in pigs with uninjured lungs and in an ARDS model. METHODS: Anaesthetised mechanically ventilated pigs were studied before and after surfactant depletion by saline lavage. Changes in PEEP were used to change ELV. Paired measurements of absolute ELV were taken with IST (ELVIST) and compared with gold-standard measures (sulphur hexafluoride wash in/washout [ELVSF6] and computed tomography (CT) [ELVCT]). Measured volumes were used to calculate changes in ELV (ΔELV) between PEEP levels for each method (ΔELVIST, ΔELVSF6, and ΔELVCT). RESULTS: The coefficient of variation was <5% for repeated ELVIST measurements (n=13 pigs). There was a strong linear relationship between ELVIST and ELVSF6 in uninjured lungs (r2=0.97), and with both ELVSF6 and ELVCT in the ARDS model (r2=0.87 and 0.92, respectively). ELVIST had a mean bias of -12 to 13% (95% limits=±17 - 25%) compared with ELVSF6 and ELVCT. ΔELVIST was concordant with ΔELVSF6 and ΔELVCT in 98-100% of measurements, and had a mean bias of -73 to -77 ml (95% limits=±128 - 186 ml) compared with ΔELVSF6 and -1 ml (95% limits ±333 ml) compared with ΔELVCT. CONCLUSIONS: IST provides a repeatable measure of absolute ELV and shows minimal bias when tracking PEEP-induced changes in lung volume compared with CT in a saline-lavage model of ARDS.

Dynamical Phase Transitions in Sampling Complexity.

We make the case for studying the complexity of approximately simulating (sampling) quantum systems for reasons beyond that of quantum computational supremacy, such as diagnosing phase transitions. We consider the sampling complexity as a function of time t due to evolution generated by spatially local quadratic bosonic Hamiltonians. We obtain an upper bound on the scaling of t with the number of bosons n for which approximate sampling is classically efficient. We also obtain a lower bound on the scaling of t with n for which any instance of the boson sampling problem reduces to this problem and hence implies that the problem is hard, assuming the conjectures of Aaronson and Arkhipov [Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing (ACM Press, New York, New York, USA, 2011), p. 333]. This establishes a dynamical phase transition in sampling complexity. Further, we show that systems in the Anderson-localized phase are always easy to sample from at arbitrarily long times. We view these results in light of classifying phases of physical systems based on parameters in the Hamiltonian. In doing so, we combine ideas from mathematical physics and computational complexity to gain insight into the behavior of condensed matter, atomic, molecular, and optical systems.

Enhancing detection of topological order by local error correction.

The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states-locally error-corrected decoration (LED)-by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order.

Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography.

The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. Paired measurements of lung heterogeneity were then taken with both the IST and CT. Lung heterogeneity measured by the IST was calculated by (a) the ratio of tracer gas outputs measured at oscillation periods of 180 s and 60 s, and (b) by the standard deviation of the modelled log-normal distribution of ventilations and perfusions in the simulation lung. In the CT images, lungs were manually segmented and divided into different regions according to voxel density. A quantitative CT method to calculate the heterogeneity (the Cressoni method) was applied. The IST and CT show good Pearson correlation coefficients in lung heterogeneity measurements (ventilation: 0.71, and perfusion, 0.60, p < 0.001). Within individual animals, the coefficients of determination average ventilation (R2 = 0.53) and perfusion (R2 = 0.68) heterogeneity. Strong concordance rates of 98% in ventilation and 89% when the heterogeneity changes were reported in pairs measured by CT scanning and IST methods. This quantitative method to identify heterogeneity has the potential to replicate CT lung heterogeneity, and to aid individualised care in ARDS.

L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging.

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging.

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet,1 that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.

Continuous measurement of arterial oxygenation in mechanically ventilated horses.

BACKGROUND: The possibility of accurately and continuously measuring arterial oxygen partial pressure (PaO2 ) in horses may facilitate the management of hypoxaemia during general anaesthesia. OBJECTIVES: The aim of this study was to evaluate the ability of a novel fibreoptic sensor to measure PaO2 (PaO2Sensor ) continuously and in real time in horses undergoing ventilatory manoeuvres during general anaesthesia. STUDY DESIGN: In vivo experimental study. METHODS: Six adult healthy horses were anaesthetised and mechanically ventilated in dorsal recumbency. A fibreoptic sensor was placed in one of the facial arteries through a catheter to continuously measure and record PaO2Sensor . After an alveolar recruitment manoeuvre, a decremental positive end-expiratory pressure (PEEP) titration using 20-minute steps of 5 cm H2 O from 20 to 0 cm H2 O was performed. An arterial blood sample was collected at 15 minutes of ventilation at each PEEP level for PaO2 measurement using an automated blood gas machine (PaO2Ref ). The agreement between PaO2Sensor and PaO2Ref was assessed by Pearson's correlation, Bland-Altman plot and four-quadrant plot analysis. In the last minute of ventilation at each PEEP level, a slow tidal inflation/deflation manoeuvre was performed. RESULTS: The mean relative bias between PaO2Sensor and PaO2Ref was 4% with limits of agreement between -17% and 29%. The correlation coefficient between PaO2Sensor and PaO2Ref was 0.98 (P < .001). The PaO2Sensor and PaO2Ref concordance rate for changes was 95%. Measurements of PaO2Sensor during the slow inflation/deflation manoeuvre at PEEP 15 and 10 cm H2 O were not possible because of significant noise on the PaO2 signal generated by a small blood clot. MAIN LIMITATIONS: Small sample size. CONCLUSION: The tested fibreoptic probe was able to accurately and continuously measure PaO2Sensor in anaesthetised horses undergoing ventilatory manoeuvres. A heparinised system in the catheter used by the fibreoptic sensor should be used to avoid blood clots and artefacts in the PaO2 measurements.

OxVent: Design and evaluation of a rapidly-manufactured Covid-19 ventilator.

BACKGROUND: The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS: The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS: Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION: The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING: Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.

Optimal State Transfer and Entanglement Generation in Power-Law Interacting Systems.

We present an optimal protocol for encoding an unknown qubit state into a multiqubit Greenberger-Horne-Zeilinger-like state and, consequently, transferring quantum information in large systems exhibiting power-law (1/rα) interactions. For all power-law exponents α between d and 2d+1, where d is the dimension of the system, the protocol yields a polynomial speed-up for α>2d and a superpolynomial speed-up for α≤2d, compared to the state of the art. For all α>d, the protocol saturates the Lieb-Robinson bounds (up to subpolynomial corrections), thereby establishing the optimality of the protocol and the tightness of the bounds in this regime. The protocol has a wide range of applications, including in quantum sensing, quantum computing, and preparation of topologically ordered states. In addition, the protocol provides a lower bound on the gate count in digital simulations of power-law interacting systems.

Bedside monitoring of lung volume available for gas exchange.

BACKGROUND: Bedside measurement of lung volume may provide guidance in the personalised setting of respiratory support, especially in patients with the acute respiratory distress syndrome at risk of ventilator-induced lung injury. We propose here a novel operator-independent technique, enabled by a fibre optic oxygen sensor, to quantify the lung volume available for gas exchange. We hypothesised that the continuous measurement of arterial partial pressure of oxygen (PaO2) decline during a breath-holding manoeuvre could be used to estimate lung volume in a single-compartment physiological model of the respiratory system. METHODS: Thirteen pigs with a saline lavage lung injury model and six control pigs were studied under general anaesthesia during mechanical ventilation. Lung volumes were measured by simultaneous PaO2 rate of decline (VPaO2) and whole-lung computed tomography scan (VCT) during apnoea at different positive end-expiratory and end-inspiratory pressures. RESULTS: A total of 146 volume measurements was completed (range 134 to 1869 mL). A linear correlation between VCT and VPaO2 was found both in control (slope = 0.9, R2 = 0.88) and in saline-lavaged pigs (slope = 0.64, R2 = 0.70). The bias from Bland-Altman analysis for the agreement between the VCT and VPaO2 was - 84 mL (limits of agreement ± 301 mL) in control and + 2 mL (LoA ± 406 mL) in saline-lavaged pigs. The concordance for changes in lung volume, quantified with polar plot analysis, was - 4º (LoA ± 19°) in control and - 9° (LoA ± 33°) in saline-lavaged pigs. CONCLUSION: Bedside measurement of PaO2 rate of decline during apnoea is a potential approach for estimation of lung volume changes associated with different levels of airway pressure.

Lung simulation to support non-invasive pulmonary blood flow measurement in Acute Respiratory Distress Syndrome in animals.

Patients undergoing mechanical lung ventilation are at risk of lung injury. A noninvasive bedside lung monitor may benefit these patients. The Inspired Sinewave Test (IST) can measure cardio-pulmonary parameters noninvasively. We propose a lung simulation to improve the measurement of pulmonary blood flow using IST. The new method was applied to 12 pigs' data before lung injury (control) and after lung injury (ARDS model). Results using the lung simulation shown improvements in correlation in both simulated data (R2 increased from 0.98 to 1) and pigs' data (R2 increased from <0.001 to 0.26). Paired blood flow measurements were performed by both the IST (noninvasive) and thermodilution (invasive). In the control group, the bias of the two methods was negligible (0.02L/min), and the limit of agreement was from -1.20 to 1.18 L/min. The bias was -0.68 L/min in the ARDS group and with a broader limit of agreement (-2.49 to 1.13 L/min).Clinical Relevance- the inspired sinewave test can be used to measure cardiac output noninvasively in mechanically ventilated subjects with and without acute respiratory distress syndrome.

Assessment of Ventilatory Heterogeneity in Chronic Obstructive Pulmonary Disease Using the Inspired Sinewave Test.

BACKGROUND: There is marked variability in the symptoms and outcomes of patients with chronic obstructive pulmonary disease (COPD) which are poorly predicted by spirometry/FEV1%pred. Furthermore, as spirometry requires the performance of potentially distressing respiratory manoeuvres which are to some extent user-effort dependent, there is need for non-invasive and simple-to-perform techniques to identify subtypes of COPD which are more closely related to clinically relevant outcomes. MATERIALS AND METHODS: The inspired sinewave test (IST) sinusoidally modulates the inspired concentration of a tracer gas (N2O) over successive tidal breaths. A single-compartment tidal-ventilation lung model processes the amplitude/phase of the expired N2O sinewave and estimates cardiopulmonary variables including: effective lung volume and indices of ventilatory heterogeneity (VH; ELV180/FRCpleth and ELV180/ELVpred). 83 COPD patients and 53 healthy controls performed the IST test, standard pulmonary function tests (Spirometry, body plethysmography and the single breath test of carbon monoxide uptake), and symptom severity questionnaires (COPD assessment test, CAT; mMRC dyspnoea-scale, mMRC-DS; Cough+Mucus score; C+M score). RESULTS: ELV180/FRCpleth and ELV180/ELVpred were significantly lower in patients with COPD vs healthy participants (0.34±0.11 vs 0.68±0.14 and 0.7±0.27 vs 0.98±0.15, respectively; P<0.05). Multivariable regression analysis demonstrated that ELV180/FRCpleth was a stronger and independent predictor of CAT, mMRC-DS and C+M score vs FEV1%pred. ELV180/ELVpred was a stronger and independent and better predictor of C+M score vs FEV1%pred. Phenotyping patients, based upon ELV180/ELVpred and FRC%pred, uncovered significant symptomatic differences between groups. CONCLUSION: The IST indices of VH were superior and independent predictors of symptom severity vs FEV1%pred and has potential as a non-invasive and simple-to-perform method to stratify patients into subgroups related to clinically relevant features of COPD.

Dexmedetomidine infusion for analgesia and prevention of emergence agitation in children with obstructive sleep apnea syndrome undergoing tonsillectomy and adenoidectomy.

BACKGROUND: Dexmedetomidine, a specific α(2) agonist, has an analgesic-sparing effect and reduces emergence agitation. We compared an intraoperative dexmedetomidine infusion with bolus fentanyl to reduce perioperative opioid use and decrease emergence agitation in children with obstructive sleep apnea syndrome undergoing adenotonsillectomy (T&A). METHODS: One hundred twenty-two patients with obstructive sleep apnea syndrome undergoing T&A, ages 2 to 10 years, completed this prospective, randomized, U.S. Food and Drug Administration-approved study. After mask induction with sevoflurane, group D received IV dexmedetomidine 2 µg · kg(-1) over 10 minutes, followed by 0.7 µg · kg(-1) · h(-1), and group F received IV fentanyl bolus 1 µg · kg(-1). Anesthesia was maintained with sevoflurane, oxygen, and nitrous oxide. Fentanyl 0.5 to 1 µg · kg(-1) was given to subjects in both groups for an increase in heart rate or systolic blood pressure 30% above preincision values that continued for 5 minutes. Observers in the postanesthesia care unit (PACU) were blinded to treatment groups. Pain was evaluated using the objective pain score in the PACU on arrival, at 5 minutes, at 15 minutes, then every 15 minutes for 120 minutes. Emergence agitation was evaluated at the same intervals by 2 scales: the Pediatric Anesthesia Emergence Delirium scale and a 5-point scale described by Cole. Morphine (0.05 to 0.1 mg · kg(-1)) was given for pain (score >4) or severe agitation (score 4 or 5) lasting more than 5 minutes. RESULTS: In group D, 9.8% patients needed intraoperative rescue fentanyl in comparison with 36% in group F (P = 0.001). Mean systolic blood pressure and heart rate were significantly lower in group D (P < 0.05). Minimum alveolar concentration values were significantly different between the 2 groups (P = 0.015). The median objective pain score was 3 for group D and 5 for group F (P = 0.001). In group D, 10 (16.3%) patients required rescue morphine, in comparison with 29 (47.5%) in group F (P = 0.002). The frequency of severe emergence agitation on arrival in the PACU was 18% in group D and 45.9% in group F (P = 0.004); at 5 minutes and at 15 minutes, it was lower in group D (P = 0.028). The duration of agitation on the Cole scale was statistically lower in group D (P = 0.004). In group D, 18% of patients and 40.9% in group F had an episode of Spo(2) below 95% (P = 0.01). CONCLUSIONS: An intraoperative infusion of dexmedetomidine combined with inhalation anesthetics provided satisfactory intraoperative conditions for T&A without adverse hemodynamic effects. Postoperative opioid requirements were significantly reduced, and the incidence and duration of severe emergence agitation was lower with fewer patients having desaturation episodes.