Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery.

OBJECTIVES: Postoperative deviating physiologic values (vital signs) may represent postoperative stress or emerging complications. But they can also reflect chronic preoperative values. Distinguishing between the two circumstances may influence the utility of using vital signs in patient monitoring. Thus, we aimed to describe the occurrence of vital sign deviations before and after major vascular surgery, hypothesising that preoperative vital sign deviations were longer in duration postoperatively. METHODS: In this prospective observational study, arterial vascular patients were continuously monitored wirelessly - from the day before until 5 days after surgery. Recorded values were: heart rate, respiration rate, peripheral arterial oxygen saturation (SpO2 ) and blood pressure. The outcomes were 1. cumulative duration of SpO2 < 85% / 24 h, and 2. cumulative duration per 24 h of vital sign deviations. RESULTS: Forty patients were included with a median monitoring time of 21 h preoperatively and 42 h postoperatively. The median duration of SpO2 < 85% preoperatively was 14.4 min/24 h whereas it was 28.0 min/24 h during day 0 in the ward (p = .09), and 16.8 min/24 h on day 1 in the ward (p = 0.61). Cumulative duration of SpO2 < 80% was significantly longer on day 0 in the ward 2.4 min/24 h (IQR 0.0-4.6) versus 6.7 min/24 h (IQR 1.8-16.2) p = 0.01. CONCLUSION: Deviating physiology is common in patients before and after vascular surgery. A longer duration of severe desaturation was found on the first postoperative day in the ward compared to preoperatively, whereas moderate desaturations were reflected in postoperative desaturations. Cumulative duration outside thresholds is, in some cases, exacerbated after surgery.

Correction: A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

[This corrects the article DOI: 10.2196/35696.].

Transcutaneous blood gas monitoring and tissue perfusion during common femoral thromboendarterectomy.

BACKGROUND: Improving tissue perfusion can improve clinical outcomes in surgical patients, where monitoring may aid clinicians in detecting adverse conditions and guide interventions. Transcutaneous monitoring (TCM) of oxygen (tcpO2) and carbon dioxide (tcpCO2) is a well-proven technology and could potentially serve as a measure of local circulation, perfusion and metabolism, but the clinical use is not thoroughly explored. The purpose of this proof-of-concept study was to investigate whether TCM of blood gasses could detect changes in perfusion during major vascular surgery. METHODS: Ten patients with peripheral arterial disease scheduled for lower limb major arterial revascularization under general anaesthesia were consecutively included. TcpO2 and tcpCO2 were continuously recorded from anaesthesia induction until skin closure with a TCM monitor placed on both legs and the thorax. Peripheral oxygen saturation was kept ≥94% and mean arterial blood pressure ≥65 mmHg. The primary outcomes were changes in tcpO2 and tcpCO2 related to arterial clamping and declamping during the procedure and analyzed by paired statistics. RESULTS: Femoral artery clamping resulted in a significant decrease in tcpO2 (-2.1 kPa, IQR-4.2; -0.8), p=.017)), followed by a significant increase in response to arterial declamping (5.5 kPa, IQR 0-7.3), p=.017)). Arterial clamping resulted in a statistically significant increase in tcpCO2 (0.9 kPa, IQR 0.3-5.4), p=.008)) and a significant decrease following declamping (-0.7 kPa, IQR -2.6; -0.2), p=.011)). CONCLUSION: Transcutaneous monitoring of oxygen and carbon dioxide is a feasible method for detection of extreme changes in tissue perfusion during arterial clamping and declamping, and its use for improving patient outcomes should be explored. Clinical Trials identifier: NCT04040478. Registered on July 31, 2019.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209.

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting.

STUDY OBJECTIVES: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. METHODS: A deep neural network model was developed using 15,684 polysomnography studies from five different cohorts. We applied four different scenarios: (1) impact of varying timescales in the model; (2) performance of a single cohort on other cohorts of smaller, greater, or equal size relative to the performance of other cohorts on a single cohort; (3) varying the fraction of mixed-cohort training data compared with using single-origin data; and (4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. RESULTS: Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777-0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787-0.790]; 3: 0.808 ± 0.092, 95% CI [0.807-0.810]; 4: 0.821 ± 0.085, 95% CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts. CONCLUSIONS: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BACKGROUND: Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span. OBJECTIVE: This interdisciplinary study intends to develop a biological age model and explore its usefulness. METHODS: The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course. RESULTS: We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021. CONCLUSIONS: We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.

Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts.

STUDY OBJECTIVES: Up to 5% of adults in Western countries have undiagnosed sleep-disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies, however, have limited generalizability as they have been conducted using the apnea-ECG database, a small sample database that lacks complex SDB cases. METHODS: Here, we developed a fully automatic, data-driven algorithm that classifies apnea and hypopnea events based on the ECG using almost 10 000 polysomnographic sleep recordings from two large population-based samples, the Sleep Heart Health Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), which contain subjects with a broad range of sleep and cardiovascular diseases (CVDs) to ensure heterogeneity. RESULTS: Performances on average were sensitivity(Se)=68.7%, precision (Pr)=69.1%, score (F1)=66.6% per subject, and accuracy of correctly classifying apnea-hypopnea index (AHI) severity score was Acc=84.9%. Target AHI and predicted AHI were highly correlated (R2 = 0.828) across subjects, indicating validity in predicting SDB severity. Our algorithm proved to be statistically robust between databases, between different periodic leg movement index (PLMI) severity groups, and for subjects with previous CVD incidents. Further, our algorithm achieved the state-of-the-art performance of Se=87.8%, Sp=91.1%, Acc=89.9% using independent comparisons and Se=90.7%, Sp=95.7%, Acc=93.8% using a transfer learning comparison on the apnea-ECG database. CONCLUSIONS: Our robust and automatic algorithm constitutes a minimally intrusive and inexpensive screening system for the detection of SDB events using the ECG to alleviate the current problems and costs associated with diagnosing SDB cases and to provide a system capable of identifying undiagnosed SDB cases.

A comparative study of methods for automatic detection of rapid eye movement abnormal muscular activity in narcolepsy.

OBJECTIVE: To evaluate rapid eye movement (REM) muscular activity in narcolepsy by applying five algorithms to electromyogram (EMG) recordings, and to investigate its value for narcolepsy diagnosis. PATIENTS/METHODS: A modified version of phasic EMG metric (mPEM), muscle activity index (MAI), REM atonia index (RAI), supra-threshold REM EMG activity metric (STREAM), and Frandsen method (FR) were calculated from polysomnography recordings of 20 healthy controls, 18 clinic controls (subjects suspected with narcolepsy but finally diagnosed without any sleep abnormality), 16 narcolepsy type one without REM sleep behavior disorder (RBD), nine narcolepsy type one with RBD, and 18 narcolepsy type two. Diagnostic value of metrics in differentiating between groups was quantified by area under the receiver operating characteristic curve (AUC). Correlations among the metrics and cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) values were calculated using linear models. RESULTS: All metrics excluding STREAM found significantly higher muscular activity in narcolepsy one cases versus controls (p < 0.05). Moreover, RAI showed high sensitivity in the detection of RBD. The mPEM achieved the highest AUC in differentiating healthy controls from narcoleptic subjects. The RAI best differentiated between narcolepsy 1 and 2. Lower CSF-hcrt-1 values correlated with high muscular activity quantified by mPEM, sMAI, lMAI, PEM and FR (p < 0.05). CONCLUSIONS: This automatic analysis showed higher number of muscle activations in narcolepsy 1 compared to controls. This finding might play a supportive role in diagnosing narcolepsy and in discriminating narcolepsy subtypes. Moreover, the negative correlation between CSF-hcrt-1 level and REM muscular activity supported a role for hypocretin in the control of motor tone during REM sleep.

Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

Study Objectives: The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals. Methods: We developed an automatic AA detection algorithm based on intelligent feature selection and advanced machine learning using the electrocardiogram. The model was trained and tested with respect to CA systematically scored in 258 (181 training size/77 test size) polysomnographic recordings from the Wisconsin Sleep Cohort. Results: A precision value of 0.72 and a sensitivity of 0.63 were achieved when evaluated with respect to CA. Further analysis indicated that 81% of the non-CA-associated AAs were associated with leg movement (38%) or respiratory (43%) events. Conclusions: The presented algorithm shows good performance when considering that more than 80% of the false positives (FP) found by the detection algorithm appeared in relation to either leg movement or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyze the systemic and clinical impacts of arousals.

Breathing Disturbances Without Hypoxia Are Associated With Objective Sleepiness in Sleep Apnea.

Study Objectives: To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness. Methods: A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use. Results: H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (ß = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; ß = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (ß = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (ß = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity. Conclusions: In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation.

T-wave amplitude (TWA) has been proposed as a marker of the innervation of the myocardium. Until now, TWA has been calculated manually or with poor algorithms, thus making its use not efficient in a clinical environment. We introduce a new wavelet-based algorithm for the delineation QRS complexes and T-waves, and the automatic calculation of TWA. When validated in the MIT/BIH Arrhythmia database, the QRS detector achieved sensitivity and positive predictive value of 99.84% and 99.87%, respectively. The algorithm was validated also on the QT database and it achieved sensitivity of 99.50% for T-peak detection. In addition, the algorithm achieved delineation accuracy that is similar to the differences in delineation between expert cardiologists. We applied the algorithm for the evaluation of the influence in TWA of anticholinergic and antiadrenergic drugs (i.e., atropine and metoprolol) for healthy subjects. We found that the TWA decreased significantly with atropine and that metoprolol caused a significant increase in TWA, thus confirming the clinical hypothesis that the TWA is a marker of the innervation of the myocardium. The results of this paper show that the proposed algorithm can be used as a useful and efficient tool in clinical practice for the automatic calculation of TWA and its interpretation as a non-invasive marker of the autonomic ventricular innervation.

Novel method for detection of Sleep Apnoea using respiration signals.

Polysomnography (PSG) studies are considered the "gold standard" for the diagnosis of Sleep Apnoea (SA). Identifying cessations of breathing from long-lasting PSG recordings manually is a labour-intensive and time-consuming task for sleep specialist, associated with inter-scorer variability. In this study a simplified, semi-automatic, three-channel method for detection of SA patients is proposed in order to increase analysis reliability and diagnostic accuracy in the clinic. The method is based on characteristic features, such as respiration stoppages pr. hour and the total number of oxygen desaturations > 3%, extracted from the thorax and abdomen respiration effort belts, and the oxyhemoglobin saturation (SaO2), fed to an Elastic Net classifier and validated according to American Academy of Sleep Medicine (AASM) using the patients' AHI value. The method was applied to 109 patient recordings and resulted in a very high SA classification with accuracy of 97.9%. The proposed method reduce the time spent on manual analysis of respiration stoppages and the inter- and intra-scorer variability, and may serve as an alternative screening method for SA.

Subdural to subgaleal EEG signal transmission: the role of distance, leakage and insulating affectors.

OBJECTIVE: To estimate the area of cortex affecting the extracranial EEG signal. METHODS: The coherence between intra- and extracranial EEG channels were evaluated on at least 10 min of spontaneous, awake data from seven patients admitted for epilepsy surgery work up. RESULTS: Cortical electrodes showed significant extracranial coherent signals in an area of approximately 150 cm(2) although the field of vision was probably only 31 cm(2) based on spatial averaging of intracranial channels taking into account the influence of the craniotomy and the silastic membrane of intracranial grids. Selecting the best cortical channels, it was possible to increase the coherence values compared to the single intracranial channel with highest coherence. The coherence seemed to increase linearly with an accumulation area up to 31 cm(2), where 50% of the maximal coherence was obtained accumulating from only 2 cm(2) (corresponding to one channel), and 75% when accumulating from 16 cm(2). CONCLUSION: The skull is an all frequency spatial averager but dominantly high frequency signal attenuator. SIGNIFICANCE: An empirical assessment of the actual area of cerebral sources generating the extracranial EEG provides better opportunities for clinical electroencephalographers to determine the location of origin of particular patterns in the EEG.

Attenuated heart rate response is associated with hypocretin deficiency in patients with narcolepsy.

STUDY OBJECTIVE: Several studies have suggested that hypocretin-1 may influence the cerebral control of the cardiovascular system. We analyzed whether hypocretin-1 deficiency in narcolepsy patients may result in a reduced heart rate response. DESIGN: We analyzed the heart rate response during various sleep stages from a 1-night polysomnography in patients with narcolepsy and healthy controls. The narcolepsy group was subdivided by the presence of +/- cataplexy and +/- hypocretin-1 deficiency. SETTING: Sleep laboratory studies conducted from 2001-2011. PARTICIPANTS: In total 67 narcolepsy patients and 22 control subjects were included in the study. Cataplexy was present in 46 patients and hypocretin-1 deficiency in 38 patients. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: All patients with narcolepsy had a significantly reduced heart rate response associated with arousals and leg movements (P < 0.05). Heart rate response associated with arousals was significantly lower in the hypocretin-1 deficiency and cataplexy groups compared with patients with normal hypocretin-1 levels (P < 0.04) and patients without cataplexy (P < 0.04). Only hypocretin-1 deficiency significantly predicted the heart rate response associated with arousals in both REM and non-REM in a multivariate linear regression. CONCLUSIONS: Our results show that autonomic dysfunction is part of the narcoleptic phenotype, and that hypocretin-1 deficiency is the primary predictor of this dysfunction. This finding suggests that the hypocretin system participates in the modulation of cardiovascular function at rest.

Channel selection for automatic seizure detection.

OBJECTIVE: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. METHODS: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. RESULTS: Using only three channels, a seizure detection sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded directly on the epileptic focus. CONCLUSIONS: Based on our dataset, automatic seizure detection can be done using only three EEG channels without loss of performance. These channels should be selected based on maximum variance and not, as often done, using the focal channels. SIGNIFICANCE: With this simple automatic channel selection method, we have shown a computational efficient way of making automatic seizure detection.