Predominant obstructive or central sleep apnea in patients with atrial fibrillation: influence of characterizing apneas versus apneas and hypopneas

Objective/backgroundSleep-disordered breathing (SDB) is common in patients with atrial fibrillation (Afib). Although a high proportion of respiratory events are hypopneas, previous studies have only used apneas to differentiate obstructive (OSA) from central (CSA) sleep apnea. This study investigated the impact of using apneas and hypopneas versus apneas only to define the predominant type of SDB in Afib patients with preserved ejection fraction.Patients/methodsThis retrospective analysis was based on high-quality cardiorespiratory polygraphy (PG) recordings (07/2007–03/2016) that were re-analyzed using 2012 American Academy of Sleep Medicine criteria, with differentiation of apneas and hypopneas as obstructive or central. Classification of predominant (>50% of events) OSA and CSA was defined based on apneas only (OSA_AI and CSA_AI) or apneas and hypopneas (OSA_AHI and CSA_AHI). SDB was defined as an apnea–hypopnea index ≥5/h.ResultsA total of 211 patients were included (146 male, age 68.7 ± 8.5 y). Hypopneas accounted for >50% of all respiratory events. Based on apneas only, 46% of patients had predominant OSA and 44% had predominant CSA. Based on apneas and hypopneas, the proportion of patients with OSA was higher (56%) and that with CSA was lower (36%). In the subgroup of patients with moderate to severe SDB (AHI ≥ 15/h), the proportion with predominant CSA was 55.2% based on apneas only versus 42.1% with apneas and hypopneas.ConclusionsIn hospitalized patients with Afib and SDB, use of apneas and hypopneas versus apneas alone had an important influence on the proportion of patients classified as having predominant OSA or CSA.     

Impact of nasal resistance on the distribution of apneas and hypopneas in obstructive sleep apnea

BackgroundThis study aims to investigate how increased nasal resistance affects respiratory variables in obstructive sleep apnea (OSA). In this setting, to assess how nasal resistance, as measured by 4-phase rhinomanometry, affects the distribution of hypopneas and apneas when measured with routine Sleep Polygraphy (PG).MethodsPG recordings were analysed and 4-phase rhinomanometry was conducted. Crude differences between groups were compared using Mann–Whitney Wilkoxon test. Odds for higher nasal resistance were modelled using logistic regression. All tests were two-sided. P < 0.05 was considered statistically significant.ResultsIn sum, 126 OSA patients referred to our center examined with PG were included. OSA Patients with a higher ratio of hypopneas relative to apneas are more than three times more likely (OR = 3.72, 95%CI [1.30–10.66], p = 0.015) to have increased nasal resistance as measured by 4-phase rhinomanometry, compared to those who have a lower ratio of hypopneas relative to apneas, regardless of OSA severity.The median Hypopnea to Apnea Ratio (HAR) in the low nasal resistance group was 0.8 compared to 2.6 in the high nasal resistance group (p = 0.000). The median apnea index in the low nasal resistance group was 13.6, in the high nasal group it was 5.2 (p = 0.001).ConclusionsOur investigation shows that OSA patients presenting with increased nasal resistance demonstrate significant differences in the distribution of hypopneas and apneas. OSA patients with increased nasal resistance exhibit a significantly lower apnea index and a higher hypopnea to apnea ratio compared to OSA patients presenting with low nasal resistance. Thus, analysis of the HAR in sleep studies is a useful tool to identify patients who may potentially have nasal obstruction as part of their respiratory pathophysiology. We therefore recommend that OSA patients with a predominance of hypopneas relative to apneas should undergo further nasal measurements.     

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based na?ve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both na?ve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.     

Validation of an automated computational method for skeletal muscle fibre morphometry analysis

Accurate and fast measurement of muscle fibre size and evaluation of fibre type proportions in large cross-sectional areas remains challenging as existing methods require extensive manual measurements. In this study, we assessed the fibre morphometry of ∼1000 fibres in mouse and human control and diseased muscle cross-sections. We compared fibre size, percentage fibre proportion and percentage fibre surface area results obtained by an automated method using MetaMorph® with those obtained manually using Image Pro. Data collection using MetaMorph® software was faster and produced similar results to those obtained using Image Pro. The ability to quickly and accurately measure large numbers of fibres with MetaMorph® allows the researcher to make a more precise assessment of fibre type and fibre size changes in human muscle biopsies and animal models of muscle disease.     

Characterization of the acute pulse transit time response to obstructive apneas and hypopneas in preschool children with sleep-disordered breathing

Background

Surges in heart rate (HR) and blood pressure (BP) at apnea termination contribute to the hypertension seen in obstructive sleep apnea (OSA). Because childhood OSA prevalence peaks in the preschool years, we aimed to characterize the cardiovascular response to obstructive events in preschool-aged children.

Methods

Clinically referred children aged 3–5 years were grouped by obstructive apnea–hypopnea index (OAHI) into the following: primary snoring (PS) (OAHI ? 1 event/h [n = 21]), mild OSA (OAHI > 1– ? 5 [n = 32]), and moderate to severe (MS) OSA (OAHI > 5 [n = 28]). Beat-to-beat pulse transit time (PTT), an inverse continuous indicator of BP changes, and HR were averaged during the two halves (early and late) and during the peak after (post) each obstructive event and were expressed as percentage change from late- to post-event.

Results

We analyzed 422 events consisting of 55 apneas and 367 hypopneas. A significant post-event increase in HR and fall in PTT occurred in all severity groups (P < .05 for all). A greater response was associated with OSA, nonrapid eye movement sleep (NREM), cortical arousal, hypopneas, and oxygen desaturation (P < .05 for all).

Conclusions

Obstructive events elicit acute cardiovascular changes in preschool children. Such circulatory perturbations have been implicated in the development of hypertension, and our findings complement previous studies to suggest a cumulative impact of snoring on the cardiovascular system from childhood into adulthood.     

Validation of breath biomarkers for obstructive sleep apnea

Background and objectivesObstructive sleep apnea (OSA) is an underdiagnosed respiratory disease with negative metabolic and cardiovascular effects. The current gold standard for diagnosing OSA is in-hospital polysomnography, a time-consuming and costly procedure, often inconvenient for the patient. Recent studies revealed evidence for the potential of breath analysis for the diagnosis of OSA based on a disease-specific metabolic pattern. However, none of these findings were validated in a larger and broader cohort, an essential step for its application in clinics.MethodsIn the present study, we validated a panel of breath biomarkers in a cohort of patients with possible OSA (N = 149). These markers were previously identified in our group by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).ResultsHere, we could confirm significant differences between metabolic patterns in exhaled breath from OSA patients compared to control subjects without OSA as well as the association of breath biomarker levels with disease severity. Our prediction of the diagnosis for the patients from this completely independent validation study using a classification model trained on the data from the previous study resulted in an area under the receiver operating characteristic curve of 0.66, which is comparable to questionnaire-based OSA screenings.ConclusionsThus, our results suggest that breath analysis by SESI-HRMS might be useful to screen for OSA as an objective measure. However, its true predictive power should be tested in combination with OSA screening questionnaires.Clinical trial“Mass Spectral Fingerprinting in Obstructive Sleep Apnoea”, NCT02810158, www.ClinicalTrials.gov.     

An OSAHS evaluation method based on multi-features acoustic analysis of snoring sounds

Snoring is the most direct symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) and implies a lot of information about OSAHS symptoms. This paper aimed to identify OSAHS patients by analyzing acoustic features derived from overnight snoring sounds. Mel-frequency cepstral coefficients, 800 Hz power ratio, spectral entropy and other 10 acoustic features were extracted from snores, and Top-6 features were selected from the extracted 10 acoustic features by a feature selection algorithm based on random forest, then 5 kinds of machine learning models were applied to validate the effectiveness of Top-6 features on identifying OSAHS patients. The results showed that when the classification performance and computing efficiency were taken into account, the combination of logistic regression model and Top-6 features performed best and could successfully distinguish OSAHS patients from simple snorers. The proposed method provides a higher accuracy for evaluating OSAHS with lower computational complexity. The method has great potential prospect for the development of a portable sleep snore monitoring device.     

Validation of an automated punctate mechanical stimuli delivery system designed for fMRI studies in rodents

Functional magnetic resonance imaging (fMRI) is increasingly being used for animal studies studying the transmission of nociceptive information. Application of noxious mechanical stimuli is widely used for animal and human assessment of pain processing. Any accessory hardware used in animal imaging studies must, however, be sufficiently small to fit in the magnet bore diameter and be non-magnetic. We have developed a system that can apply mechanical stimuli simultaneously with fMRI. This system consists of a standardized instrument to deliver mechanical stimuli (VonFrey monofilament) and a gas-pressured mechanical transducer. These components were integrated with a computer console that controlled the period of stimuli to match acquisition scans. Preliminary experiments demonstrated that the force-stimulus transducer did not influence MRI signal to noise ratio. Mechanical stimulation of the hindpaw significantly increased blood oxygen level dependent (BOLD) signal intensity in several midbrain regions involved in the processing of nociceptive information in the rat (p<0.001, uncorrected for multiple comparisons). This system can be applied to both animal and human imaging studies and has a wide range of applications for studies of nociceptive processing.     

Validation of the NetSCID: an automated web-based adaptive version of the SCID

The present study developed and validated a configurable, adaptive, web-based version of the Structured Clinical Interview for DSM, the NetSCID. The validation included 24 clinicians who administered the SCID and 230 participants who completed the paper SCID and/or the NetSCID. Data-entry errors, branching errors, and clinician satisfaction were quantified. Relative to the paper SCID, the NetSCID resulted in far fewer data-entry and branching errors. Clinicians ‘preferred’ using the NetSCID and found that the NetSCID was easier to administer.     

High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm

ObjectiveThe goal of this study was to determine the consistency of human reviewer spike detection and then develop a computer algorithm to make the intracranial spike detection process more objective and reliable.MethodsThree human reviewers marked interictal spikes on samples of intracranial EEGs from 10 patients. The sensitivity, precision and agreement in channel ranking by activity were calculated between reviewers. A computer algorithm was developed to parallel the way human reviewers detect spikes by first identifying all potential spikes on each channel using frequency filtering and then block scaling all channels at the same time in order to exclude potential spikes that fall below an amplitude and slope threshold. Its performance was compared to the human reviewers on the same set of patients.ResultsHuman reviewers showed surprisingly poor inter-reviewer agreement, but did broadly agree on the ranking of channels for spike activity. The computer algorithm performed as well as the human reviewers and did especially well at ranking channels from highest to lowest spike frequency.ConclusionsOur algorithm showed good agreement with the different human reviewers, even though they demonstrated different criteria for what constitutes a ‘spike’ and performed especially well at the clinically important task of ranking channels by spike activity.SignificanceAn automated, objective method to detect interictal spikes on intracranial recordings will improve both research and the surgical management of epilepsy patients.     

Validation of the Spanish version of the EURO-D scale: an instrument for detecting depression in older people

OBJECTIVE: To standardize the EURO-D scale as a method for detecting depression in Spanish older people. METHODS: In a first phase, a sample of 1080 community older people was assessed with the GMS-AGECAT by lay interviewers. In a second phase, all the probable cases and a similar number of randomly selected probable non-cases were assessed using DSM-III-R diagnosis by psychiatrists. To test reliability, internal consistency with the Cronbach alpha coefficient and test-retest reliability (Kappa value) were obtained. Validity, sensitivity and specificity, predictive values, Receiver Operating Characteristic (ROC) curve, and Specific Likelihood Ratios (SSLR) were calculated. RESULTS: A high internal consistency was obtained in both phases of the study (alpha = 0.75; alpha = 0.79, respectively). Test-retest reliability was acceptable (weighted kappa = 0.60). For the prediction of DSM-III-R diagnosis validity coefficients (cut-off point 3/4) were: sensitivity 91.8%, specificity 76.6% and area under the ROC curve 0.92 [95% Confidence Intervals (0.89-0.95)]. Illiteracy and cognitive difficulties had a small negative effect on the performance of the scale. CONCLUSIONS: The EURO-D scale is a reliable and valid instrument for detecting probable cases of depression in older people in Spain. Illiteracy and cognitive difficulties should be considered when interpreting the results.     

Validation of the Retardation Rating Scale for detecting depression in geriatric inpatients

OBJECTIVES: Validation in the elderly of the Retardation Rating Scale (RRS), which includes items related to motor and mental retardation but not vegetative items, and may be particularly well-suited for the diagnosis of depression in the elderly. METHODS: One hundred and sixty-five geriatric inpatients (105 depressed), aged 65 and over, without dementia, neuroleptic medication and increased risk of slowed mobility, were assessed with the RRS and three validated 'gold-standard' scales for geriatric depression (Hamilton Depression Rating Scale, Montgomery and Asberg Depression Rating Scale, Geriatric Depression Scale). Factor analysis used varimax rotation, Cronbach's, Spearman's and Ferguson's coefficients and the Mann-Whitney U-test to evaluate construct and internal consistency. Convergent validity and Receiver Operating Characteristics curves were also analyzed. RESULTS: Factor analysis retained three interpretable domains: (1) motor items (45% of the variance); (2) mental items and (3) the cognitive items. Internal consistency was high (alpha = 0.91). Each item was strongly correlated with the total RRS score and associated with depression. The RRS showed good convergent validity and its total score increased with depression severity. A cut-off score of 10 yielded 79% sensitivity and 80% specificity, with 80% of the patients properly classified, that is 15% more than standard observer scales. CONCLUSION: RRS is a valid screening tool for depression and improves recognition of depression in geriatric inpatients.     

Validation of a semi-automated scintigraphic technique for detecting episodic, real-time colonic flow

The relationships between the movement of colonic content and regional pressures have only been partially defined. During the analysis of a combined colonic scintigraphic and manometric study, a quantitative technique for determining discrete, episodic, real-time colonic flow was developed. Our aim was to validate this technique through the construction of a computer-generated phantom model of known antegrade and retrograde motility. The anthropoid phantom was rasterized into a 6-mm voxel model to create a 3D voxel phantom of the colon with four distinct colonic segments. Associating a time/activity curve with each segment simulated dynamic behaviour. Activity in the model was based on data obtained from human colonic scintigraphic recordings using 30 MBq of (99m)Tc sulphur colloid. The flow was simulated by modifying the input time/activity functions to represent episodes of net flow of 2%, 5% or 10% of segmental content. Our quantitative technique was applied to the phantom model to measure the accuracy with which simulated flows were detected. Our quantitative technique proved to be a sensitive and specific means of detecting the presence and the magnitude of discrete episodes of colonic flow and therefore, should improve our ability to correlate colonic flow and motor patterns.     

An automated system for the mapping and quantitative analysis of immunocytochemistry of an inducible nuclear protein

We describe here an automated system that accurately maps tissue sections stained by immunocytochemistry for an inducible nuclear protein. The sections are scanned with a computer-controlled microscope setup hooked to a CCD camera. Raw images captured at high resolution are filtered using highly selective criteria for the recognition of labeled cell nuclei. The total population of recognized labeled nuclei is then divided into separate bins, according to their labeling intensities. Finally, information about both the position and labeling intensity of labeled nuclei is represented in average density maps. The system was optimized for the quantitative mapping of neuronal cells expressing the inducible gene ZENK in the brain of songbirds, in response to stimulation with song, but should be of general applicability for the mapping of inducible nuclear proteins.     

Validation of Addenbrooke's cognitive examination for detecting early dementia in a Japanese population

There is a clear need for brief, but sensitive and specific, cognitive screening instruments for dementia. We assessed the diagnostic accuracy of the Japanese version of Addenbrooke's Cognitive Examination (ACE) in identifying early dementia in comparison with the conventional Mini-Mental State Examination (MMSE). Standard tests for evaluating dementia screening tests were applied. A total of 201 subjects (Alzheimer's disease (AD) = 65, frontotemporal dementia (FTD) = 24, vascular dementia = 26, dementia with Lewy bodies = 11, mild cognitive impairment (MCI) = 13, and controls = 62) participated in this study. The reliability of the ACE was very good (alpha coefficient = 0.82). In our patient series, the sensitivity for diagnosing dementia with an ACE score of ≤ 74 was 0.889 with a specificity of 0.987, and the sensitivity of an ACE score of ≤ 80 was 0.984 with a specificity of 0.867. The Japanese version of the ACE is a very accurate instrument for the detection of early dementia, and should be widely used in clinical practice.     

SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis

ObjectivesFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.MethodsSix ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability.ResultsBy applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/? 0.2 SEM), specificity of 91.6% (+/? 0.1 SEM) and classification accuracy of 87.9% (+/? 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394).ConclusionWe have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE).SignificanceLongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.