Utilidad de las señales de oximetría y flujo aéreo en el diagnóstico simplificado de la apnea obstructiva del sueño. Diseño de un test automático domiciliario

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by recurrent episodes of total (apnea) or partial (hypopnea) absence of airflow during sleep. Untreated OSA produces a significant decrease in quality of life and is associated with the main causes of mortality in industrialized countries.However, OSA is considered an underdiagnosed chronic disease. Continuous positive airway pressure (CPAP) is the most common therapeutic option. Nocturnal polysomnography (PSG) in a specialized sleep unit is the reference diagnostic method, although it has low availability and accessibility. Consequently, in recent years there has been a significant demand for abbreviated methods, most of them at home, to reduce waiting lists. The fundamental hypothesis that the use of automatic processing techniques based on machine learning tools could allow maximizing the diagnostic accuracy of a reduced set of combined biomedical signals: overnight oximetry and airflow recorded at patient&#8217;s home. The main objective was to evaluate whether the joint analysis by means of machine learning algorithms of unsupervised SpO2 and AF signals acquired at patient's home leads to a significant increase in diagnostic performance compared to single-channel approaches. A prospective observational study was carried out in which a population referred consecutively to the Sleep Unit showing moderate-to-high clinical suspicion of having OSA was analyzed.All patients underwent an unsupervised PSG at home(gold standard) from which the SpO2 and AF signals were extracted, which were subsequently processed offline.The apnea-hypopnea index(AHI) derived from the PSG was used to confirm or rule out the presence of the disease.Three different approaches for screening patients with suspected OSA were assessed in terms of the source of information used: single-channel based on SpO2, single-channel based on AF, and two-channel combining information from both SpO2 and AF.The automatic processing of the SpO2 and AF signals was developed in 4 stages: preprocessing, feature extraction, feature selection, and pattern recognition. Unsupervised SpO2 and AF recordings were parameterized using the fast correlation-based filter(FCBF)algorithm.The following machine learning methods were used: linear regression(MLR), multilayer perceptron neural networks(MLP) and support vector machines(SVM). The population was divided into independent training and test groups. Agreement between the estimated and the actual AHIderived from at-home PSG was assessed, and typical OSA cutoff points(5, 15, and 30 events/h) were applied. A total of 299 unattended PSGs were performed at home, with a validity percentage of 85.6%. The highest agreement between the estimated AHI and the PSG AHI was reached by the SVMSpO2+AF model, with an CCI 0.93 and a 4-class kappa index 0.71, as well as with an overall accuracy for the 4 OSA severity categories equal to 81.25%, significantly higher than the individual analysis of the SpO2 signal and the airflow signal.The SVMSpO2+AF model achieved the highest diagnostic performance of all algorithms for the detection of severe OSA, with an accuracy of 95.83% and AUC ROC 0.98. In addition, the AUC ROC of the dual-channel models was significantly higher (p<0.01) than that achieved by all the single-channel approaches for the cutoff of 15events/h. The proposed methodology based on the joint automatic analysis of the SpO2 and AF signals acquired at home showed a high complementarity that led to a remarkable increase in diagnostic performance compared to single-channel approaches. The automatic models outperformed the conventional indices(desaturation and airflow-derived indexes) both in terms of correlation and concordance with the AHI from PSG, as well as in terms of overall diagnostic accuracy, providing a moderate increase in diagnostic performance, particularly in the detection of moderate-to-severe OSA.Our findings suggest that the joint analysis of oximetry and airflow signals by means of machine learning methods allows a simplified as well as accurate screening of OSA at patient's home.La Apnea Obstructiva del Sueño (AOS) es un trastorno respiratorio crónico infradiagnosticado caracterizado por la repetición recurrente de episodios de ausencia total (apnea) o parcial (hipopnea) del flujo aéreo (FA) durante el sueño, que disminuye la calidad de vida y aumenta la mortalidad. La CPAP es el tratamiento más habitual, no invasivo, eficaz y coste-efectivo, por lo que favorecer el proceso de diagnóstico es fundamental. La PSG nocturna es el método diagnóstico de referencia, presentando baja disponibilidad y accesibilidad, lo que ha contribuido a desbordar los recursos disponibles, retrasando el diagnóstico y el tratamiento. En contexto de la simplificación diagnóstica portátil, en auge, el uso de únicamente una (monocanal) o dos (bi-canal) señales, como las de SpO2 y FA ha sido ampliamente explorado, aunque la mayoría en entornos hospitalarios controlados. La hipótesis se fundamenta en que las técnicas de procesado automático basadas en machine learning podrían maximizar la precisión diagnóstica de un conjunto reducido de señales combinadas. El objetivo consistió en evaluar si el análisis conjunto mediante algoritmos de aprendizaje automático de las señales de SpO2 y FA no supervisadas adquiridas en el domicilio aumenta el rendimiento diagnóstico en comparación con los enfoques de un solo canal. Se llevó a cabo un estudio observacional prospectivo en pacientes con sospecha moderada-alta de AOS. Se realizó una PSG no supervisada en su domicilio (gold standard de referencia), de la que se extrajeron las señales de SpO2 y FA, procesadas offline posteriormente. El índice de apnea-hipopnea (IAH) derivado de la PSG se empleó para confirmar o descartar la presencia de la enfermedad. Se implementaron y compararon 3 metodologías de screening en función de la fuente de información empleada: (1) monocanal basado en SpO2, (2) monocanal basado en FA, (3) bi-canal combinando SpO2 y FA. El procesado automático de las señales de SpO2 y FA se desarrolló en 4 etapas: preprocesado, extracción de características, selección de características (mediante fast correlation-based filter, FCBF) y reconocimiento de patrones. Cada enfoque de screening se empleó para estimar automáticamente el IAH utilizando los siguientes métodos de machine learning: (1) regresión lineal múltiple (MLR), (2) redes neuronales perceptrón multicapa (MLP) y (3) máquinas vector soporte (SVM). La población se dividió en grupos independientes de entrenamiento (60%) y test (40%). Se realizaron un total de 299 PSGs domiciliarias. Los modelos de enfoque combinado bi-canal alcanzaron valores de concordancia entre el IAH estimado y el IAH de la PSG domiciliaria y de rendimiento diagnóstico para todos los puntos de corte típicos de AOS (5, 15 y 30 e/h) superiores al enfoque monocanal. La mayor concordancia fue alcanzada por el modelo SVMSpO2+FA (CCI 0.93, kappa4 clases 0.71, precisión global 81.25%), significativamente superior a los análisis individuales. El modelo SVMSpO2+FA alcanzó el mayor rendimiento diagnóstico de todos los algoritmos para la detección de AOS grave (precisión 95.83% y AUC ROC 0.98). Además, el AUC ROC de los modelos bi-canal fue superior (p <0.01) al de los enfoques monocanal para el punto de corte de 15 e/h. La metodología propuesta basada en el análisis automático conjunto de las señales de SpO2 y FA adquiridas en el domicilio mostró una alta complementariedad y un notable aumento del rendimiento diagnóstico en comparación con los enfoques monocanal. Los modelos automáticos superaron globalmente a los índices clásicos (de desaturación y de eventos de flujo aéreo), aportando un incremento moderado del rendimiento diagnóstico particularmente en la detección de AOS moderado-grave. Los resultados obtenidos indican que el análisis conjunto de las señales de oximetría y flujo mediante métodos de aprendizaje automático permite un screening simplificado a la vez que preciso de la AOS en el domicilio del paciente.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

Oximetry use in obstructive sleep apnea

Producción CientíficaIntroduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered. Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.This research has been partially supported by the projects DPI2017-84280-R and RTC-2015-3446-1 from Ministerio de Economía, Industria y Competitividad and European Regional Development Fund (FEDER), the project 66/2016 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad

El AMD3100 (plerixafor) puede mejorar la movilización de células hematopoyéticaspara hacer trasplantes autólogos : Informe de un caso.

La quimioterapia a dosis alta rescatada con trasplante de células hematopoyéticas autólogas se ha convertido en el tratamiento de elección en los pacientes con mieloma múltiple menores de 65 años de edad. El éxito del autotrasplante depende de la infusión de un número suficiente de células CD34+. Algunos pacientes no movilizan adecuadamente células CD34 de la médula ósea a la sangre periférica para hacer los trasplantes, principalmente aquellos quienes han recibido quimioterapia previa. En estos casos, además de los agentes movilizantes habituales como el G-CSF, la adición de plerixafor incrementa la colección de células CD34+. Se presenta el caso de un paciente con mieloma múltiple quien había sido sometido a un trasplante de células hematopoyéticas autólogas y que, al intentar un segundo trasplante, no movilizó adecuadamente células CD34 al usar filgrastim. La adición de plerixafor permitió hacer adecuadamente la movilización de las células hematopoyética y trasplantar al paciente. No existen en la literatura nacional publicaciones sobre este tema

Assessment of nocturnal autonomic cardiac imbalance in positional obstructive sleep apnea. A multiscale nonlinear approach

Producción CientíficaPositional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8–63.0 years, median body mass index (BMI) 27.7, IQR 26.0–31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0–67.0 years, median BMI 29.8, IQR 26.6–34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20–0.31 vs. 0.22, IQR 0.18–0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34–0.48 vs. 0.37, IQR 0.29–0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (Proyects DPI2017-84280-R and RTC-2017-6516-1)Sociedad Española de Neumología y Cirugía Torácica (Proyects 649/2018 and 66/2016)Gerencia Regional de Salud de Castilla y León (Proyecto GRS 1950/A/19)Ministerio de Educación, Cultura y Deporte (Proyecto FPU16/02938)Gerencia Regional de Salud de Castilla y León - (Projecto INT/M/15/20

Design and Construction for Hydroxides Based Air Conditioning System with Solar Collectors for Confined Roofs

In this chapter, the methodology to determinate heat load is revised and presented. The main parameters must be fixed as function of climatization, internal thermic conditions (comfort, temperature, and humid) and the activities. According with literature, the roof structural requirements were checked. These are an important parameter because it represents the limits to the system such as load by devices (weight of equipment), orientation in solar systems (operating conditions), and building materials. The method of calculation of solar available is shown; the aim is to achieve the major collection of solar energy. Finally, the plate heat exchangers can be fabricated in gasketed, welded or module welded design characterized by the model in which the flow channels for the two heat exchanging media are sealed. The kind of exchanger is suitable depending on your requirements. The thermodynamic method of calculation of sizing the exchangers is reviewed. The aim of this section is to find the suitable devices for the operation of air-conditioning absorption system based on hydroxide

Correction : Chaparro et al. Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain: Large-Scale Epidemiological Study. J. Clin. Med. 2021, 10, 2885

The authors wish to make the following corrections to this paper [...]

Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain : Large-Scale Epidemiological Study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD-Crohn's disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)-during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100,000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31-56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery

Estrategias docentes para la integración de estudiantes chinos y para el desarrollo de competencias relacionadas con la atención a la diversidad y el respeto a los Derechos Humanos

Memoria ID-0075. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2017-2018