Validating respiratory index of auto-titrating positive airway pressure device with polysomnography

Purpose

Positive airway pressure (PAP) devices have been widely used as the first line of treatment in obstructive sleep apnea (OSA). Most advanced PAP devices support the estimation of respiratory index (RI) using the patient’s mask airflow. In addition to the compliance factor for PAP device use, which is important for monitoring patient sleep health, RI is also becoming important for monitoring. However, there are few reports that validate RI of a PAP device with polysomnography.

Methods

Between January 2015 and December 2017, 50 participants were enrolled who were diagnosed with OSA and prescribed auto-titration PAP (APAP) devices. The RIs of participants were measured at night using APAP devices, concurrently with electroencephalography, respiratory inductance plethysmography sensors, and other polysomnographic sensors in a sleep laboratory. The respiratory-related data of APAP were prospectively analyzed with the manually scored polysomnographic data.

Results

The apnea-hypopnea index and apnea index showed a statistically close relationship between the auto-scored respiratory data from the APAP device and the manually scored respiratory data from polysomnographic sensors. Obstructive apnea and central apnea indices showed relatively low correlations. The differences between the auto-scored RI and manually scored RI were influenced by BMI, waist circumference, weight, oxygen saturation, and respiratory distress indices of diagnostic polysomnographic factors.

Conclusions

The RIs of APAP devices have a tendency to be underestimated or mismatched when compared with polysomnography. Sleep specialists are advised to consider additional anthropometric and diagnostic factors to account for these differences during PAP treatment.

     

Biological variation and reference change values of serum Mac‐2–binding protein glycosylation isomer (M2BPGi)

BackgroundLimited data are available with regard to biological variations of the Mac‐2–binding protein glycosylation isomer (M2BPGi), a liver fibrosis biomarker.MethodsLong‐term biological variation of M2BPGi was investigated using longitudinally measured M2BPGi test results from healthy Korean adult subjects. One‐way analysis of variance (ANOVA) tests were used to calculate the reference change value (RCV) of M2BPGi based on biological variation estimates. Furthermore, asymmetric RCV was calculated according to a recent publication of the European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Biological Variation and Task Group for the Biological Variation Database (EFLM TG‐BVD).ResultsA total of 363 test results from 174 Korean subjects undergoing general health checkups were requested from 13 local clinics and hospitals during a 38‐month period. The within‐subjects biological variation (CV_I), between‐subject biological variation (CV_G), analytical variation (CV_A), RCV, and individuality index (II) values for serum M2BPGi were 23.3%, 30.0%, 4.3%, 65.6%, and 0.78, respectively. Asymmetric RCV calculated using formulae by a recent EFLM TG‐BVD publication ranged from −41.9 to 72.0%. Desirable analytical performance specifications for M2BPGi derived from biological variation were as follows: imprecision 11.6%, bias 9.6%, and total allowable error 28.7%.ConclusionsRCV based on biological estimates may be helpful for evaluating and interpreting serial M2BPGi measurements by physicians and in clinical laboratories.     

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Innate immunity against <Emphasis Type="Italic">Legionella pneumophila</Emphasis> during pulmonary infections in mice

Legionella pneumophila is an etiological agent of the severe pneumonia known as Legionnaires’ disease (LD). This gram-negative bacterium is thought to replicate naturally in various freshwater amoebae, but also replicates in human alveolar macrophages. Inside host cells, legionella induce the production of non-endosomal replicative phagosomes by injecting effector proteins into the cytosol. Innate immune responses are first line defenses against legionella during early phases of infection, and distinguish between legionella and host cells using germline-encoded pattern recognition receptors such as Toll-like receptors , NOD-like receptors, and RIG-I-like receptors, which sense pathogen-associated molecular patterns that are absent in host cells. During pulmonary legionella infections, various inflammatory cells such as macrophages, neutrophils, natural killer (NK) cells, large mononuclear cells, B cells, and CD4+ and CD8+ T cells are recruited into infected lungs, and predominantly occupy interstitial areas to control legionella. During pulmonary legionella infections, the interplay between distinct cytokines and chemokines also modulates innate host responses to clear legionella from the lungs. Recognition by NK cell receptors triggers effector functions including secretion of cytokines and chemokines, and leads to lysis of target cells. Crosstalk between NK cells and dendritic cells, monocytes, and macrophages provides a major first-line defense against legionella infection, whereas activation of T and B cells resolves the infection and mounts legionella-specific memory in the host.     

Diagnostic Performance of a New Convolutional Neural Network Algorithm for Detecting Developmental Dysplasia of the Hip on Anteroposterior Radiographs

ObjectiveTo evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs.Materials and MethodsOf 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience.ResultsThe area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618–0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001).ConclusionThe proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.