Respiratory Traces Research Articles

Humans without a sense of smell breathe differently

Olfaction may play a restricted role in human behavior, yet paradoxically, its absence in anosmia is associated with diverse deleterious outcomes, culminating in reduced life expectancy. The mammalian nose serves two purposes: olfaction and breathing. Because respiratory patterns are impacted by odors, we hypothesized that nasal respiratory airflow may be altered in anosmia. We apply a wearable device that precisely logs nasal airflow for 24-hour-long sessions in participants with isolated congenital anosmia and controls. We observe significantly altered patterns of respiratory nasal airflow in anosmia in wake and in sleep. These differences allow classification of anosmia at 83% accuracy using the respiratory trace alone. Patterns of respiratory airflow have pronounced impact on health, emotion and cognition. We therefore suggest that a portion of the deleterious outcomes associated with anosmia may be attributed to altered patterns of respiratory nasal airflow rather than a direct result of lost odor perception per se.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Loss of Htr1B Leads to a Failure in the Neonate Autoresuscitation Reflex

Sudden Infant Death Syndrome (SIDS) is the leading cause of death in infants under the age of 1, with an unknown etiology, that takes the lives of 3,400 infants per year in the US alone. Interestingly, SIDS cases show males predominance (60%), however this sexual disparity in is not fully understood. SIDS is believed to follow a triple risk hypothesis model where the infant has an innate vulnerability, is at a critical developmental period, and is challenged with an exogenous stressor. A leading hypothesis indicates many SIDS cases are mechanistically driven by a failure in the autoresuscitation reflex. The autoresuscitation reflex is a series of gasps that occur to revitalize normal cardiorespiratory function when an infant falls into a hypoxic coma, characterized by an apnea and bradycardia, due to severe hypoxic conditions (i.e., rebreathing while sleeping in face down or with the face blocked). Although the cause of failure in the autoresuscitation reflex for SIDS remains unknown, post-mortem studies have found abnormalities related to the central serotonin (5-HT) neurotransmitter system, including several 5-HT receptors (5-HTr’s). Follow up studies in animal models show that 5-HT system perturbations result in a significant survival decrease in neonatal animals during anoxic (3% CO2/balance N2) gas challenges that test the autoresuscitation function, including pharmacological receptor manipulations. Furthermore, adult animal studies report that 5-HT receptor stimulation alters various respiratory parameters (i.e., tidal volume, respiratory rate, gasping). What remains to be understood is the cellular and molecular mechanisms through which 5-HT plays a role in the autoresuscitation reflex and how those mechanism may be disrupted in SIDS. We hypothesize that Htr1B is required for successful autoresuscitation. To test this hypothesis, we utilized an automated closed loop robotic platform for neonate cardio-respiratory measurements ( Looper) to assay P7 Htr1B loss of function mice for their abilities to survive repeated anoxic gas challenges testing the autoresuscitation reflex. Using Looper, we find that the loss of Htr1B decreases survival to repeated anoxic challenges compared to wild type (WT) controls in both male and female mice. Additionally, a subset of male heterozygous Htr1B mice survive fewer challenges compared to WT mice, whereas others survive a similar number of challenges as WT mice; a phenotype not observed in female mice. Our data supports the hypothesis that modulations to the serotonin system via Htr1B is important in eliciting the autoresuscitation response. Future studies aim to investigate additional respiratory parameters from respiratory trace data, in addition to recovery parameters, and functionally mapping out where in the brainstem and what neuronal populations require the expression of this receptor for the autoresuscitation reflex. R01 HL161142. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The Role of OPRM1 in the Neonatal Autoresuscitation Reflex and Sudden Infant Death Syndrome

Opioid receptors are widely expressed throughout the brain and integral in nociception as well as homeostatic regulation of autonomic processes including respiration through modulation of respiratory rhythmogenesis and chemosensory responses. Perturbations to homeostatic mu opioid receptor (MOR) signaling via opiate overdose results in respiratory depression, aberrant breathing, and often death. With the rise of opioid use over the last two decades, many infants are being diagnosed with Neonatal Abstinence Syndrome (NAS), a congenital cardio-respiratory disorder resulting from prenatal exposure to opioids. Infants born with NAS have a fourfold increased risk for Sudden Infant Death Syndrome (SIDS). SIDS and related Sudden Unexpected Infant Death takes the lives of approximately 3,400 infants annually under the age of one and its etiology remains mostly unknown. Currently SIDS is explained via the triple risk model that proposes that SIDS occurs in a vulnerable infant during a critical developmental period when triggered by an external stressor, converging on the failure of the neonate autoresuscitation reflex in most cases. Recently a study identified an association between a mutation in the OPRM1 gene encoding MORs and SIDS cases. However, how this gene and perturbations in its native function modulates respiratory regulation are poorly understood. We hypothesize that native OPRM1 function is integral to protective neonate respiratory reflexes and that perturbation of native OPRM1 signaling negatively affects the neonatal autoresuscitation reflex. Male and female OPRM1 loss of function mice were assayed for their autoresuscitation reflex on our novel testbed, Looper. Mice were exposed to repeated bouts of anoxia and allowed to autoresuscitate until death occurred. Preliminary data suggest that loss of OPRM1 function results in a higher mortality rate on the autoresuscitation assay with OPRM1 KO mice surviving significantly less anoxic challenges compared to wild type controls. No sex differences were observed between male and female survival for all genotype groups. Results indicate that perturbation of the native function of OPRM1 produces a vulnerability to autoresuscitation dysfunction and mitigated survival. Our data demonstrate the first functional assessment of OPRM1 in neonatal cardiovascular function and autoresuscitation and reveal an important risk factor that may be implicated in perpetuating SIDS and NAS phenotypes. Additional analyses of distinct parameters from respiratory trace data for OPRM1 loss of function mice will aid in a deeper, mechanistic understanding of features implicated in autoresuscitation dysfunction. Future studies aim to characterize specific splice variants of OPRM1, known to produce neomorphic function and model the SIDS associated VUS, to further characterize the implication of OPRM1 and its variants in congenital respiratory diseases. Funding sources NIH/NHLBI R01HLN161142-01 and R01HL130249-07. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Butyrylcholinesterase Is Not Required for the Neonatal Autoresuscitation Reflex

Sudden Infant Death Syndrome (SIDS) is the leading cause of infant death under the age of one year of age, taking the lives of more than 1,400 infants annually, and comprising approximately 41% of all Sudden Unexpected Infant Death (SUID) cases. SIDS etiology remains vastly unknown but is best explained via a triple risk model that proposes SIDS occurs in a vulnerable infant during a critical developmental period when triggered by an external stressor. In SIDS it is hypothesized that there is first a failure in arousal followed by failure in the protective autoresuscitation reflex; a defense mechanism for an infant that is in a hypoxic coma, that promotes deep gasping to attempt to restart cardiovascular function. Many SIDS case studies reveal associations with genetic variants and abnormal biomarkers; however, it is unclear whether these variants are causal. Recently a case study identified a broad association between reduced blood BChE activity and SIDS cases, yet the functional relationship between BChE and respiratory regulation remains to be investigated. We aimed to functionally test the relationship between BChE and the neonatal autoresuscitation reflex utilizing a rapid pipeline for precision genetic modeling and high-throughput functional testing developed by the Ray Lab. BChE loss of function mice, generated through targeted genetic editing, were tested for neonatal autoresuscitation on our automated closed loop phenotyping platform, Looper. Postnatal day 7-8 mice were exposed to 3% CO2 and 97% N2 stimuli to induce a state of hypoxia resulting in detection of bradycardia, initial hyperventilation, hypoventilation, and apnea followed by restoration of room air conditions to facilitate the autoresuscitation reflex and cardio-respiratory recovery. Male and female mice were exposed to repeated anoxic gas challenges and allowed to autoresuscitate until death occurred. Data suggest that loss of BChE function does not affect neonatal autoresuscitation. BChE knockout mice and heterozygous mice survived similar amounts of anoxic challenges as wild type controls. No sex differences were observed between the number of anoxic challenges survived in males and females for all genotypes. Our data demonstrate the first functional assessment of BChE in neonatal cardio-respiratory regulation and the autoresuscitation response and highlight that BChE is not required for successful neonatal autoresuscitation. Additional analyses of respiratory parameters from respiratory trace data for BChE loss of function mice will aid in a further understanding if BChE loss of function is implicated in other aspects respiratory regulation. Funding: Precision Core funding U54 OD030165. NIH/NHLBI R01HLN161142-01 and R01HL130249-07. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

Detecting central apneas using multichannel signals in premature infants.

Objective. Monitoring of apnea of prematurity, performed in neonatal intensive care units by detecting central apneas (CAs) in the respiratory traces, is characterized by a high number of false alarms. A two-step approach consisting of a threshold-based apneic event detection algorithm followed by a machine learning model was recently presented in literature aiming to improve CA detection. However, since this is characterized by high complexity and low precision, we developed a new direct approach that only consists of a detection model based on machine learning directly working with multichannel signals.Approach. The dataset used in this study consisted of 48 h of ECG, chest impedance and peripheral oxygen saturation extracted from 10 premature infants. CAs were labeled by two clinical experts. 47 features were extracted from time series using 30 s moving windows with an overlap of 5 s and evaluated in sets of 4 consecutive moving windows, in a similar way to what was indicated for the two-step approach. An undersampling method was used to reduce imbalance in the training set while aiming at increasing precision. A detection model using logistic regression with elastic net penalty and leave-one-patient-out cross-validation was then tested on the full dataset.Main results. This detection model returned a mean area under the receiver operating characteristic curve value equal to 0.86 and, after the selection of a FPR equal to 0.1 and the use of smoothing, an increased precision (0.50 versus 0.42) at the expense of a decrease in recall (0.70 versus 0.78) compared to the two-step approach around suspected apneic events.Significance. The new direct approach guaranteed correct detections for more than 81% of CAs with lengthL≥ 20 s, which are considered among the most threatening apneic events for premature infants. These results require additional verifications using more extensive datasets but could lead to promising applications in clinical practice.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. Experiments were performed on a prototype 1.0T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. The end-to-end latency of the system was measured to be (389 ± 15)ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3)mm, compared to the (3.2 ± 1.9)mm of the offline LSTM, the (3.3 ± 1.4)mm of the online LR and the (4.4 ± 2.4)mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy withMLC-tracking.

Evaluation of Patient-Specific Quality Control (QC) for Markerless Dynamic Tumor Tracking (MDTT) Deliveries

To report on the patient-specific QC for MDTT for liver and lung tumors treated using the dome-of-liver/diaphragm as a surrogate for the target position and compare to the QC of conventional fiducial-based tracking. Vero4DRT is a linear accelerator that has a gimbal-mounted waveguide and collimation system allowing the radiation beam to perform dynamic tumor tracking based on a patient's respiratory motion. A correlation model is built between an external IR marker placed on the chest and an internal structure, which may be implanted fiducial surrogates or in the case of MDTT, an anatomic landmark. During treatment an orthogonal kV image pair is taken every second. The auto detected internal structure position versus the predicted position is recorded in treatment log files. Five IMRT tracking plans were delivered to two commercial motion phantoms with in-house additions for patient-specific tracking QC. For point dose measurements, a 0.6cc farmer chamber was placed inside the dynamic tracking phantom which contains fiducials for fiducial-based tracking delivery and an anatomic landmark for MDTT. For 2D dose distribution measurements, a radiographic film insert for the Quasar Respiratory Motion Phantom was constructed. The phantom contains both fiducials and has a liver-dome shape at one end to facilitate MDTT. Both motion platforms were interfaced with software that enabled patient-specific respiratory motion traces acquired during a 4DCT scan. Each plan was delivered three times on each phantom: 1) in static mode, 2) during fiducial-based tracking, and 3) during MDTT. Chamber measurements taken during static and tracked delivery were compared to the average chamber dose from the treatment planning software (TPS). Film distributions measured during tracked deliveries were compared to the static film distribution using gamma analysis with FILMQA PRO software. Finally, dynamic tracking treatment statistics, extracted from log files, were compared between fiducial and markerless tracking deliveries. All chamber measurements resulted in dose differences <2.5% compared with the TPS. Dose differences between fiducial-based and MDTT were 0.26% on average (range 0.03-0.62%). For film dose map analysis, gamma pass rates were >95% for all plans for all tracking methods. The average gamma pass rate for 3%/3mm was 99.4% (fiducial-based) vs 98.4% (markerless). For 2%/2mm, the average gamma pass rate was 96.4% (fiducial-based tracking) vs 95.6% (MDTT). Dynamic treatment statistics from logs files reported an average 3D absolute deviation from predicted position of 0.52mm (±0.24) for fiducial tracking and 1.19mm (±0.50) for MDTT. Both fiducial-based and MDTT plans meet passing criteria for patient-specific QC. 3D absolute deviation of detected versus predicted position are larger for MDTT compared to fiducial tracking, however no significant impact on dose delivery was measured.

Erratum: High-Resolution Respirometry to Assess Mitochondrial Function in Human Spermatozoa.

An erratum was issued for:High-Resolution Respirometry to Assess Mitochondrial Function in Human Spermatozoa. The Protocol and Representative Resultsections wereupdated. Step 2.4.12 of the Protocol was updated from: Finally, inject 1 µL of 5 mM antimycin A (2.5 µM final concentration). This is a complex II inhibitor to discriminate between the mitochondrial and residual oxygen consumption (non-mitochondrial respiration). For the analysis of complex I, add 1 µL of 1 mM rotenone (0.5 µM final concentration), an inhibitor of this complex, instead of AA. Measure the oxygen consumption until the signal decreases and stabilizes. to: Finally, inject 1 µL of 5 mM antimycin A (2.5 µM final concentration). This is a complex III inhibitor to discriminate between the mitochondrial and residual oxygen consumption (non-mitochondrial respiration). For the analysis of complex I, add 1 µL of 1 mM rotenone (0.5 µM final concentration), an inhibitor of this complex, instead of AA. Measure the oxygen consumption until the signal decreases and stabilizes. Figure 3 in the Representative Results section was updated from: Figure 3: Determination of the optimal concentration of digitonin for the permeabilization of human sperm cells. The respiration rates were measured at 37 °C in MRM medium with glutamate, malate, and adenosine diphosphate. (A) Representative respiratory trace. The blue line is the O2 concentration, and the red line represents the O2 flow per volume correlated. (B) Mitochondria respiration rate means ± standard error, n = 4. The red arrow represents the optimal concentration. Abbreviation: dig = digitonin. Please click here to view a larger version of this figure. to: Figure 3: Determination of the optimal concentration of digitonin for the permeabilization of human sperm cells. The respiration rates were measured at 37 °C in MRM medium with glutamate, malate, and adenosine diphosphate. (A) Representative respiratory trace. The blue line is the O2 concentration, and the red line represents the O2 flow per volume correlated. (B) Mitochondria respiration rate means ± standard error, n = 4. The red arrow represents the optimal concentration. Abbreviation: dig = digitonin. Please click here to view a larger version of this figure.

4D Monte Carlo dose reconstructions using surface motion measurements

PurposeTo investigate the feasibility of a 4D Monte Carlo based dose reconstruction method to study the dosimetric impact of respiratory motion using surface motion measurements for patients undergoing VMAT treatments for Non-Small Cell Lung Cancer. MethodsThe 4Ddefdosxyznrc/EGSnrc algorithm was used to reconstruct VMAT doses delivered to the patients using machine log files and respiratory traces measured with the RADPOS 4D dosimetry system. The RADPOS sensor was adhered to the patient’s abdomen prior to each treatment fraction and its position was used as a surrogate for tumour motion. Treatment log files were synchronized with the patient respiratory traces. Patient specific respiratory models were generated from deformable registration of the inhale and exhale 4DCT images and the respiratory traces.The reconstructed doses were compared to planned doses calculated with DOSXYZnrc/EGSnrc on the average-intensity and the exhale phase CT images. ResultsRespiratory motion measurements and log files were acquired for 2 patients over 5 treatment fractions each. The motion was predominantly along the anterior/posterior direction (A/P). The average respiratory amplitudes were 8.7 ± 2.7 mm and 10.0 ± 1.2 mm for Patient 1 and 2, respectively. Both patients displayed inter- and intra-fractional variations in the baseline position. Small inter-fractional differences were observed in the reconstructed doses for each patient. Differences between the reconstructed and planned doses were attributed to differences in organ volumes. ConclusionThe 4D reconstruction method was successfully implemented for the two patients studied. Small differences between the planned and reconstructed doses were observed due to the small tumour motion of these patients.

Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Signals

Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Methods: The respiratory traces from 36 patients who underwent DIBH RT were collected. The patients’ RT treatment plans were generated for both DIBH and free-breathing (FB) modalities. The patients were divided into two classes (patient eligible or not), based on the decrease of maximum dose to the left anterior descending (LAD) artery achieved with DIBH, compared to that achieved with FB and ΔDL. Patients with ΔDL &gt; median value of ΔDL within the patient cohort were assumed to be those selected for DIBH. A BLSTM-RNN was trained for classification of patients eligible for DIBH by analysis of their respiratory signals, as acquired during acquisition of the pre-treatment computed tomography (CT), for selecting the window for DIBH. The dataset was split into training (60%) and test groups (40%), and the hyper-parameters, including the number of hidden layers, the optimizer, the learning rate, and the number of epochs, were selected for optimising model performance. The BLSTM included 2 layers of 100 neural units, each followed by a dropout layer with 20% dropout, and was trained in 35 epochs using the Adam optimizer, with an initial learning rate of 0.0003. Results: The system achieved accuracy, specificity, and sensitivity of, F1 score and area under the receiving operating characteristic curve (AUC) of 71.4%, 66.7%, 80.1%, 72.4%, and 69.4% in the test dataset, respectively. Conclusions: The proposed BLSTM-RNN classified patients in the test set eligible for DIBH with good accuracy. These results look promising for building an accurate and robust decision system to provide automated assistance to the radiotherapy team in assigning patients to DIBH.

Abstract 181: In-hospital Cardiac Arrest Debrief In A Pediatric Intensive Care Unit

Background: Debrief of in-hospital cardiac arrests (IHCA) has been shown to improve outcomes. There can be limited data available to quantitatively review a resuscitation, but careful analysis can provide insight for improvement. Methods: A standard process for debriefing all cardiac arrests was developed in our institution’s pediatric intensive care unit (PICU). Characteristics of the event were documented, and each arrest was independently reviewed by a lead arrest review investigator including telemetry data, chart review, and provider interviews when available. Data was stored in a secure redcap database. Results: From April 1, 2021 to March 31, 2022 there were n=25 IHCAs in 22 patients. Eighteen (72%) were classified as severe bradycardia with hypotension and/or poor perfusion; 6 (24%) were pulseless electrical activity; and 1 was unstable VT/VF. Two patients had a shockable rhythm during their arrest. The median arrest duration was 6 minutes with the longest duration being 104 minutes. Out of the 25 arrests, 18 (72%) had return of spontaneous circulation. Out of the 22 patients who had at least 1 arrest, 9 (41%) survived to hospital discharge and 12 (55%) died; 1 patient is still admitted. In addition to outcomes data, the debrief process led to many process improvements including the utility of a backboard (Figure 1A) and use of the respiratory rate tracing (thoracic impedance) in assessing compressions (Figure 1B). Discussion: IHCA in pediatric patients confers a significant risk of mortality and morbidity. Development of a comprehensive debrief process will help to identify areas for improvement in all facets of arrest care.

System requirements to improve adaptive 4-dimensional computed tomography (4D CT) imaging

Four-Dimensional Computed Tomography (4D CT) is of increasing importance in stereotactic body radiotherapy (SBRT) treatments affected by respiratory motion. However, 4D CT images are commonly impacted by irregular breathing, causing image artifacts that can propagate through to treatment, negatively effecting local control. REspiratory Adaptive CT (REACT) is a real-time gating method demonstrated to reduce motion artifacts by avoiding imaging during irregular respiration. Despite artifact reduction seen through in silico and clinical phantom-based studies, REACT has not been able to remove all artifacts. Here, we explore several hardware and software latencies (gantry rotation time, couch shifts, acquisition delays and phase calculation method) inherently linked to REACT and 4D CT in general and investigate their contribution to artifacts beyond those caused by irregular breathing. Imaging was simulated using the digital extended cardiac-torso (XCAT) phantom for fifty patient-measured respiratory traces. Imaging protocols included conventional cine 4D CT and five REACT scans with systematically varied parameters to test the effect of different latencies on artifacts. Artifacts were quantified by comparing the image normalized cross correlation across couch transition points and determining the volume error compared to a static phantom ground truth both as a total error and individually across pixel rows in the main plane of motion. Artifacts were determined for each lung, the whole heart and lung tumour and were compared back to conventional 4D CT and REACT with standard clinical scanning parameters. The gantry rotation time and acquisition delay were found to have the largest impact on reducing image artifacts and should be the focus of future development. The phase calculation method was also found to influence motion artifacts and should potentially be assessed on a patient-to-patient basis. Finally, the correlation between an increase in artifacts and baseline drift suggests that longer scan times allowing drift to occur may impact image quality.

CyberKnife Xsight versus fiducial-based target-tracking: a novel 3D dosimetric comparison in a dynamic phantom

BackgroundThe CyberKnife Xsight lung-tracking system (XLTS) provides an alternative to fiducial-based target-tracking systems (FTTS) for non-small-cell lung cancer (NSCLC) patients without invasive fiducial insertion procedures. This study provides a method for 3D independent dosimetric verification of the accuracy of the FTTS compared to the XLTS without relying on log-files generated by the CyberKnife system.MethodsA respiratory motion trace was taken from a 4D-CT of a real lung cancer patient and applied to a modified QUASAR™ respiratory motion phantom. A novel approach to 3D dosimetry was developed using Gafchromic EBT3 film, allowing the 3D dose distribution delivered to the moving phantom to be reconstructed. Treatments were planned using the recommended margins for one and three fiducial markers and XLTS 2-view, 1-view and 0-view target-tracking modalities. The dose delivery accuracy was analysed by comparing the reconstructed dose distributions to the planned dose distributions using gamma index analysis.ResultsFor the 3%/2 mm gamma criterion, gamma passing rates up to 99.37% were observed for the static deliveries. The 3-fiducial and 1-fiducial-based deliveries exhibited passing rates of 93.74% and 97.82%, respectively, in the absence of target rotation. When target rotation was considered, the passing rate for 1-fiducial tracking degraded to 91.24%. The passing rates observed for XLTS 2-view, 1-view and 0-view target-tracking were 92.78%, 96.22% and 76.08%, respectively.ConclusionsExcept for the XLTS 0-view, the dosimetric accuracy of the XLTS was comparable to the FTTS under equivalent treatment conditions. This study gives us further confidence in the CyberKnife XLTS and FTTS systems.

Predictors of in-hospital mortality of COVID-19 patients and the role of telemetry in an internal medicine ward during the third phase of the pandemic.

The first pandemic phase of COVID-19 in Italy was characterized by high in-hospital mortality ranging from 23% to 38%. During the third pandemic phase there has been an improvement in the management and treatment of COVID-19, so mortality and predictors may have changed. A prospective study was planned to identify predictors of mortality during the third pandemic phase. From 15 December 2020 to 15 May 2021, 208 patients were hospitalized (median age: 64 years; males: 58.6%); 83% had a median of 2 (IQR,1-4) comorbidities; pneumonia was present in 89.8%. Patients were monitored remotely for respiratory function and ECG trace for 24 hours/day. Management and treatment were done following the timing and dosage recommended by international guidelines. 79.2% of patients necessitated O2-therapy. ARDS was present in 46.1% of patients and 45.4% received non-invasive ventilation and 11.1% required ICU treatment. 38% developed arrhythmias which were identified early by telemetry and promptly treated. The in-hospital mortality rate was 10%. At multivariate analysis independent predictors of mortality were: older age (R-R for≥70 years: 5.44), number of comorbidities ≥3 (R-R 2.72), eGFR ≤60 ml/min (RR 2.91), high d-Dimer (R-R for≥1,000 ng/ml:7.53), and low PaO2/FiO2 (R-R for <200: 3.21). Management and treatment adherence to recommendations, use of telemetry, and no overcrowding appear to reduce mortality. Advanced age, number of comorbidities, severe renal failure, high d-Dimer and low P/F remain predictors of poor outcome. The data help to identify current high-risk COVID-19 patients in whom management has yet to be optimized, who require the greatest therapeutic effort, and subjects in whom vaccination is mandatory.

MASS SPECTROMETRIC DIAGNOSIS OF RECOVERY AFTER RESPIRATORY ILLNESS USING MACHINE LEARNING METHODS

The express methodology of evaluation of mass-spectrometric parameters of gas composition of exhaled air for differential diagnostics of acute respiratory viral infections and tracing of dynamics of recovery after the disease was developed.

Impact de l’interface AMARA VIEW™ sur les voies aériennes supérieures au cours de la ventilation non invasive nocturne

Nasal masks can result in mouth leaks with implications on sleep quality. To reduce these leaks, oronasal masks are proposed. It has been shown that an oronasal mask can induce obstructive events in the upper airways (UA). Because of its fit around the nose, the Amara View™ facial mask may have less consequence on UA obstruction. This retrospective study assessed the impact of the Amara View™ mask on UA. The study was conducted on patients with chronic respiratory failure treated by home NIV with standard facial mask. Replacement of the standard facial mask by an Amara View™ mask was performed when the residual apnea hypopnea index recorded from the ventilator software exceeded 5/h. All patients underwent nocturnal respiratory polygraphy when on NIV with a standard facial mask and with an Amara View™ mask. Respiratory polygraphic traces were interpreted following the analysis methods published by the SomnoNIV Group. Seven patients were studied. Although nocturnal oxygen saturation was similar between both masks, the index of UA obstruction without ventilatory blunting was significantly lower during NIV with the Amara View™ mask compared to the standard facial mask (P=0.01). Nocturnal NIV using an Amara View™ facial interface may avoid UA obstruction without ventilatory command blunting.

Optimized Detection of Central Apneas Preceding Late-Onset Sepsis in Premature Infants.

In neonatal intensive care units, respiratory traces of premature infants developing late onset sepsis (LOS) may also show episodes of apneas. However, since clinical patient monitors often underdetect apneas, clinical experts are required to investigate patients' traces looking for these events. In this work we present a method to optimize an existing algorithm for central apnea (CA) detection and how we used it together with human annotations to investigate the occurrence of CAs preceding LOS.The algorithm was optimized by using a previously-annotated dataset consisting of 90 hours, extracted from 10 premature infants. This allowed to double precision (19.7% vs 9.3%, median values per patient) without affecting recall (90.5% vs 94.5%) compared to the original algorithm. This choice caused the missed identification of just 1 additional CA (4 vs 3) in the whole dataset. The optimized algorithm was then used to annotate a second dataset consisting of 480 hours, extracted from 10 premature infants diagnosed with LOS. Annotations were corrected by two clinical experts.A significantly higher number of CA annotations was found in the 6 hours prior to sepsis onset (p-value < 0.05). The use of the optimized algorithm followed by human annotations proved to be a suitable, time-efficient method to annotate CAs before sepsis in premature infants, enabling future use in large datasets.

Visually guided respiratory motion management for Ethos adaptive radiotherapy.

PurposeEthos adaptive radiotherapy (ART) is emerging with AI‐enhanced adaptive planning and high‐quality cone‐beam computed tomography (CBCT). Although a respiratory motion management solution is critical for reducing motion artifacts on abdominothoracic CBCT and improving tumor motion control during beam delivery, our institutional Ethos system has not incorporated a commercial solution. Here we developed an institutional visually guided respiratory motion management system to coach patients in regular breathing or breath hold during intrafractional CBCT scans and beam delivery with Ethos ART.MethodsThe institutional visual‐guidance respiratory motion management system has three components: (1) a respiratory motion detection system, (2) an in‐room display system, and (3) a respiratory motion trace management software. Each component has been developed and implemented in the clinical Ethos ART workflow. The applicability of the solution was demonstrated in installation, routine QA, and clinical workflow.ResultsAn air pressure sensor has been utilized to detect patient respiratory motion in real time. Either a commercial or in‐house software handled respiratory motion trace display, collection and visualization for operators, and visual guidance for patients. An extended screen and a projector on an adjustable stand were installed as the in‐room visual guidance solution for the closed‐bore ring gantry medical linear accelerator utilized by Ethos. Consistent respiratory motion traces and organ positions on intrafractional CBCTs demonstrated the clinical suitability of the proposed solution in Ethos ART.ConclusionThe study demonstrated the utilization of an institutional visually guided respiratory motion management system for Ethos ART. The proposed solution can be easily applied for Ethos ART and adapted for use with any closed bore‐type system, such as computed tomography and magnetic resonance imaging, through incorporation with appropriate respiratory motion sensors.