Nocturnal Oximetry-based Evaluation of Habitually Snoring Children

Rationale: The vast majority of children around the world undergoing adenotonsillectomy for obstructive sleep apnea–hypopnea syndrome (OSA) are not objectively diagnosed by nocturnal polysomnography because of access availability and cost issues. Automated analysis of nocturnal oximetry (nSpO2), which is readily and globally available, could potentially provide a reliable and convenient diagnostic approach for pediatric OSA. Methods: DeidentifiednSpO2 recordings froma total of 4,191 children originating from13 pediatric sleep laboratories around the worldwere prospectively evaluated after developing and validating an automated neural network algorithm using an initial set of single-channel nSpO2 recordings from 589 patients referred for suspected OSA. Measurements and Main Results: The automatically estimated apnea–hypopnea index (AHI) showed high agreement with AHI from conventional polysomnography (intraclass correlation coefficient, 0.785) when tested in 3,602 additional subjects. Further assessment on the widely used AHI cutoff points of 1, 5, and 10 events/h revealed an incremental diagnostic ability (75.2, 81.7, and 90.2% accuracy; 0.788, 0.854, and 0.913 area under the receiver operating characteristic curve, respectively). Conclusions: Neural network–based automated analyses of nSpO2 recordings provide accurate identification of OSA severity among habitually snoring children with a high pretest probability of OSA. Thus, nocturnal oximetry may enable a simple and effective diagnostic alternative to nocturnal polysomnography, leading to more timely interventions and potentially improved outcomes.Supported in part by project VA037 U16 from the Consejer´ıa de Educacio´ n de la Junta de Castilla y Leo´ n and the European Regional Development Fund (FEDER), project RTC-2015-3446-1 from the Ministerio de Econom´ıa y Competitividad and FEDER, and project 153/2015 of the Sociedad Espan˜ ola de Neumolog´ıa y Cirug´ıa Tora´ cica (SEPAR). L.K.-G. is supported by NIH grant 1R01HL130984. M.F.P. was supported by a Fellowship Educational grant award from the Kingdom of Saudi Arabia. D.´A. was in receipt of a Juan de la Cierva grant from the Ministerio de Econom´ıa y Competitividad. The funders played no role in the study design, data collection, data analysis, interpretation, and writing of the manuscript

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Synthesis of large-pore zeolites from chiral structure-directing agents with two l-prolinol units

In this work, we perform an in-depth experimental and computational study about the structure-directing effect of two new chiral organic quaternary ammonium dications bearing two N-methyl-prolinol units linked by a xylene spacer in para or meta relative orientation, displaying four enantiopure stereogenic centers in (S) configuration. Synthesis results show that the para-xylene derivative is an efficient structure-directing agent, promoting the crystallization of ZSM-12 (in pure-silica composition), beta zeolite (as pure-silica, or in the presence of Al or Ge), and a mixture of polymorphs C, A and B of zeolite beta (in the presence of Ge). In contrast, the meta-xylene derivative showed a much poorer structure-directing activity, yielding only amorphous materials unless Ge is present in the gel, where beta and polymorph C (together with A and B) zeolites crystallized. Molecular simulations showed that the para-xylene dication displays a cylindrical shape suitable for confining in zeolite pores, while the meta-xylene derivative has an angular shape that shifts from the typical dimensions required for 12MR zeolite channels. Despite enantio-purity of the para-xylene dication with (S, S, S, S) configuration, no enrichment in polymorph A of the zeolite beta samples obtained was observed by Transmission Electron Microscopy. With the aid of molecular simulations, the failure in transferring chirality to the zeolite is explained by the loose fit of this SDA in the large-pores of zeolite beta, and a lack of close geometrical fit with the chiral element of polymorph A, as evidenced by the very similar interaction of the cation with the two enantiomorphic space groups of polymorph A. Nevertheless, the molecular-level knowledge gained in this work can provide insights for the future design of more efficient SDAs towards the synthesis of chiral zeolites

Impact of coronavirus syndromes on physical and mental health of health care workers: Systematic review and meta-analysis

Background: Health care workers (HCW) are at high risk of developing physical/mental health outcomes related to coronavirus syndromes. Nature and frequency of these outcomes are undetermined. Methods: PRISMA/MOOSE-compliant (PROSPERO-CRD42020180205) systematic review of Web of Science/grey literature until 15th April 2020, to identify studies reporting physical/mental health outcomes in HCW infected/exposed to Severe Acute Respiratory Syndrome -SARS-, Middle East Respiratory Syndrome -MERS-, Novel coronavirus -COVID-19-. Proportion random effect meta-analyses, I2 statistic, quality assessment and sensitivity analysis. Results: 115 articles were included (n=60,458 HCW, age 36.1±7.1, 77.1% female). Physical health outcomes: 75.9% HCW infected by SARS/MERS/COVID-19 reported fever (95%CI=65.9–83.7%, k=12, n=949), 47.9% cough (95%CI=39.2–56.8%, k=14, n=970), 43.6% myalgias (95%CI=31.9–56.0%, k=13, n=898), 42.3% chills (95%CI=20.2–67.9%, k=7, n=716), 41.2% fatigue (95%CI=18.2–68.8%, k=6, n=386), 34.6% headaches (95%CI=23.1–48.2%, k=11, n=893), 31.2% dyspnoea (95%CI=23.2–40.5%, k=12, n=1003), 25.3% sore throat (95%CI=18.8–33.2%, k=8, n=747), 22.2% nausea/vomiting (95%CI=14.9–31.8%, k=6, n=662), 18.8% diarrhoea (95%CI=11.9–28.4%, k=9, n=824). Mental health outcomes: 62.5% HCW exposed to SARS/MERS/COVID-19 reported general health concerns (95%CI=57.0–67,8%, k=2, n=2254), 43.7% fear (95%CI=33.9–54.0%, k=4, n=584), 37.9% insomnia (95%CI=30.9–45.5%, k=6, n=5067), 37.8% psychological distress (95%CI=28.4–48.2%, k=15, n=24,346), 34.4% burnout (95%CI=19.3–53.5%, k=3, n=1337), 29.0% anxiety features (95%CI=14.2–50.3%, k=6, n=9191), 26.3% depressive symptoms (95%CI=12.5–47.1%, k=8, n=9893), 20.7% post-traumatic stress disorder features (95%CI=13.2–31%, k=11, n=3826), 16.1% somatisation (95%CI=0.2–96.0%, k=2, n=2184), 14.0% stigmatisation feelings (95%CI=6.4–28.1%, k=2, n=411). Limitations: Limited amount of evidence for some outcomes and suboptimal design in several studies included. Conclusions: SARS/MERS/COVID-19 have a substantial impact on the physical and mental health of HCW, which should become a priority for public health strategies

A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow

Producción CientíficaThe most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90–0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.This work has been partially supported by “Sociedad Española de Neumología y Cirugía Torácica” (SEPAR) under project 66/2016; “Gerencia Regional de Salud de Castilla y León” under project GRS 1472/A/17; “Ministerio de Ciencia Innovación y Universidades” and European Regional Development Fund (FEDER) under project DPI2017-84280-R; and by CIBER-BBN (ISCIII), co-funded with FEDER funds. F. Vaquerizo-Villar was in receipt of a “Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)” grant from the “Ministerio de Educación, Cultura y Deporte” (FPU16/02938). V. Barroso-García was funded by the grant “Ayuda para financiar la contratación predoctoral de personal investigador” from the “Consejería de Educación de la Junta de Castilla y León” and the European Social Fund

A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea

Producción CientíficaThis study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen’s kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.This work was supported by 'Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación’ and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC 2017 6516-1, by “European Commission” and “FEDER” under project 'Análisis y correlación entre la epigenética y la actividad cerebral para evaluar el riesgo de migraña crónica y episódica en mujeres' (‘Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014–2020’), by Sociedad Española de Neumología y Cirugía Torácica (SEPAR) under project 649/2018, by Sociedad Española de Sueño (SES) under project “Beca de Investigación SES 2019”, and by ‘Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain’ through ‘Instituto de Salud Carlos III’ co-funded with FEDER funds. The Childhood Adenotonsillectomy Trial (CHAT) was supported by National Institutes of Health (NIH) grants HL083075, HL083129, UL1-RR-024134, and UL1 RR024989. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). F. Vaquerizo-Villar was in receipt of a ‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’ grant from the Ministerio de Educación, Cultura y Deporte (FPU16/02938). D. Álvarez is supported by a "Ramón y Cajal" grant (RYC2019-028566-I) from the 'Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación’ co-funded by the European Social Fund (ESF). V. Barroso-García and E. Santamaría-Vazquez were in a receipt of a ‘Ayuda para financiar la contratación predoctoral de personal investigador’ grant from the Consejería de Educación de la Junta de Castilla y León and the ESF. L. Kheirandish-Gozal and D. Gozal were supported by NIH grants HL130984, HL140548, and AG061824

Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home

Producción CientíficaUntreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.Sociedad Española de Neumología y Cirugía Torácica (SEPAR) project 153/2015Junta de Castilla y León (Consejería de Educación) y el Fondo Europeo de Desarrollo Regional (FEDER), projects (RTC-2015-3446-1) y (TEC2014-53196-R)Ministerio de Economía y Competitividad (MINECO) y FEDER, y el proyecto POCTEP 0378_AD_EEGWA_2_P de la Comisión Europea. L.National Institutes of Health (NIH) grant 1R01HL130984-01Ministerio de Asuntos Económicos y Transformación Digital, grant IJCI-2014-2266

Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterize complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry

Medical nutrition therapy and clinical outcomes in critically ill adults: a European multinational, prospective observational cohort study (EuroPN)

BACKGROUND: Medical nutrition therapy may be associated with clinical outcomes in critically ill patients with prolonged intensive care unit (ICU) stay. We wanted to assess nutrition practices in European intensive care units (ICU) and their importance for clinical outcomes. METHODS: Prospective multinational cohort study in patients staying in ICU ≥ 5 days with outcome recorded until day 90. Macronutrient intake from enteral and parenteral nutrition and non-nutritional sources during the first 15 days after ICU admission was compared with targets recommended by ESPEN guidelines. We modeled associations between three categories of daily calorie and protein intake (low:  20 kcal/kg; > 1.2 g/kg) and the time-varying hazard rates of 90-day mortality or successful weaning from invasive mechanical ventilation (IMV). RESULTS: A total of 1172 patients with median [Q1;Q3] APACHE II score of 18.5 [13.0;26.0] were included, and 24% died within 90 days. Median length of ICU stay was 10.0 [7.0;16.0] days, and 74% of patients could be weaned from invasive mechanical ventilation. Patients reached on average 83% [59;107] and 65% [41;91] of ESPEN calorie and protein recommended targets, respectively. Whereas specific reasons for ICU admission (especially respiratory diseases requiring IMV) were associated with higher intakes (estimate 2.43 [95% CI: 1.60;3.25] for calorie intake, 0.14 [0.09;0.20] for protein intake), a lack of nutrition on the preceding day was associated with lower calorie and protein intakes (− 2.74 [− 3.28; − 2.21] and − 0.12 [− 0.15; − 0.09], respectively). Compared to a lower intake, a daily moderate intake was associated with higher probability of successful weaning (for calories: maximum HR 4.59 [95% CI: 1.5;14.09] on day 12; for protein: maximum HR 2.60 [1.09;6.23] on day 12), and with a lower hazard of death (for calories only: minimum HR 0.15, [0.05;0.39] on day 19). There was no evidence that a high calorie or protein intake was associated with further outcome improvements. CONCLUSIONS: Calorie intake was mainly provided according to the targets recommended by the active ESPEN guideline, but protein intake was lower. In patients staying in ICU ≥ 5 days, early moderate daily calorie and protein intakes were associated with improved clinical outcomes. Trial registration NCT04143503, registered on October 25, 2019. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-03997-z

Detección automática de la apnea del sueño infantil utilizando técnicas de deep learning y explainable artificial intelligence en señales de flujo aéreo

La alta prevalencia de la apnea obstructiva del sueño (AOS) pediátrica y las limitaciones de la prueba diagnóstica estándar han fomentado el estudio de estrategias alternativas que ayuden a su diagnóstico automático. Los métodos propuestos suelen basarse en técnicas de feature engineering, lo que implica una complejidad y subjetividad inherente. Otros utilizan técnicas de deep learning, que mejoran el rendimiento diagnóstico pero carecen de transparencia e interpretabilidad. En este trabajo proponemos evaluar un modelo explicable basado en redes neuronales convolucionales (CNN) para estimar la severidad de la AOS infantil utilizando la señal de flujo (FA). Para ello, se analizaron 1638 registros de FA, que fueron divididos en segmentos de 10 minutos. El modelo CNN estimó el número de eventos apneicos por segmento. Después, se aplicó el algoritmo Grad-CAM para identificar las regiones de FA en las que se fija la CNN al hacer su predicción. El modelo propuesto mostró una alta concordancia entre el índice de apnea-hipopnea estimado y el real (coeficiente de correlación intraclase = 0.87 en el grupo de test), así como un alto rendimiento diagnóstico (kappa de 4 clases = 0.38 y precisiones del 81.05%, 85.62% y 92.81% para 1, 5 y 10 eventos/h en el grupo de test). Grad-CAM reveló que la CNN se centra en el comienzo y el final del evento apneico, es decir, donde la señal FA cambia bruscamente de amplitud. Así, nuestra propuesta sería muy útil para identificar automáticamente los patrones respiratorios asociados con la AOS infantil y ayudar en su diagnóstico.Este estudio ha sido financiado por el Ministerio de Ciencia e Innovación - AEI y ERDF (PID2020-115468RB-I00 y PDC2021-120775-I00) y por el CIBER-BBN (CB19/01/00012). G.C. Gutiérrez Tobal ha recibido una ayuda postdoctoral de la Universidad de Valladolid. D. Álvarez es beneficiario de una ayuda Ramón y Cajal (RYC2019-028566-I) del Ministerio de Ciencia e Innovación - AEI, cofinanciada por el FSE. D. Gozal ha recibido financiación del NIH (AG061824 y HL166617)