Predictors of failure with high-flow nasal oxygen therapy in COVID-19 patients with acute respiratory failure: a multicenter observational study.

PURPOSE: We aimed to describe the use of high-flow nasal oxygen (HFNO) in patients with COVID-19 acute respiratory failure and factors associated with a shift to invasive mechanical ventilation. METHODS: This is a multicenter, observational study from a prospectively collected database of consecutive COVID-19 patients admitted to 36 Spanish and Andorran intensive care units (ICUs) who received HFNO on ICU admission during a 22-week period (March 12-August 13, 2020). Outcomes of interest were factors on the day of ICU admission associated with the need for endotracheal intubation. We used multivariable logistic regression and mixed effects models. A predictive model for endotracheal intubation in patients treated with HFNO was derived and internally validated. RESULTS: From a total of 259 patients initially treated with HFNO, 140 patients (54%) required invasive mechanical ventilation. Baseline non-respiratory Sequential Organ Failure Assessment (SOFA) score [odds ratio (OR) 1.78; 95% confidence interval (CI) 1.41-2.35], and the ROX index calculated as the ratio of partial pressure of arterial oxygen to inspired oxygen fraction divided by respiratory rate (OR 0.53; 95% CI: 0.37-0.72), and pH (OR 0.47; 95% CI: 0.24-0.86) were associated with intubation. Hospital site explained 1% of the variability in the likelihood of intubation after initial treatment with HFNO. A predictive model including non-respiratory SOFA score and the ROX index showed excellent performance (AUC 0.88, 95% CI 0.80-0.96). CONCLUSIONS: Among adult critically ill patients with COVID-19 initially treated with HFNO, the SOFA score and the ROX index may help to identify patients with higher likelihood of intubation.

Two lymphomas in the same patient: A case report.

INTRODUCCIÓN: La asociación de forma sincrónica de un linfoma Hodgkin y un linfoma no Hodgkin en un mismo paciente es muy rara. CASO CLÍNICO: Varón de 68 años que comienza su padecimiento con adenomegalias cervicales y axilares. Se realiza biopsia escisional y se encuentra linfoma de Hodgkin de tipo celularidad mixta, por lo que se inicia quimioterapia. Durante el transcurso del tratamiento se observa la continuidad de lesiones refractarias en la piel y el músculo, por lo que el servicio de radiooncología decide hacer una nueva biopsia y se demuestra que se trata de una nueva afección, un linfoma no Hodgkin difuso de células B grandes. CONCLUSIONES: Este caso ejemplifica que ante un paciente con un cuadro clínico típico y con una evolución tórpida hay que replantearse el diagnóstico e incluso repetir los estudios realizados para descartar diagnósticos alternos que puedan alterar el curso de la enfermedad. BACKGROUND: The synchronous association of Hodgkin lymphoma and non-Hodgkin lymphoma in the same patient is very rare. CASE REPORT: A 68 years old male patient who began his disease with cervical and axillary adenomegaly, it was performed an excisional biopsy and a mixed cell-type Hodgkin lymphoma was found. Chemotherapy with partial response was initiated. During the course of the treatment, refractory lesions were observed in skin and muscle. Radiation oncology service decided to make a second biopsy which confirms the presence of a large diffuse B cell non-Hodgkin ­lymphoma. CONCLUSIONS: This case is an example of how when you have a patient with a typical clinical picture of bad prognosis, the diagnosis should be reconsidered and laboratory studies previously done must be repeated to rule out alternative diagnoses that may change the course of the disease.

Implementation of a Piezo-diagnostics Approach for Damage Detection Based on PCA in a Linux-Based Embedded Platform.

The implementation of damage-detection methods for continuously assessing structural integrity entails systems with attractive features such as storage capabilities, memory capacity, computational complexity and time-consuming processing. In this sense, embedded hardware platforms are a promising technology for developing integrated solutions in Structural Health Monitoring. In this paper, design, test, and specifications for a standalone inspection prototype are presented, which take advantage of piezo-diagnostics principle, statistical processing via Principal Component Analysis (PCA) and embedded systems. The equipment corresponds to a piezoelectric active system with the capability to detect defects in structures, by using a PCA-based algorithm embedded in the Odroid-U3 ARM Linux platform. The operation of the equipment consists of applying, at one side of the structure, wide guided waves by means of piezoelectric devices operated in actuation mode and to record the wave response in another side of the structure by using the same kind of piezoelectric devices operated in sensor mode. Based on the nominal response of the guide wave (no damages), represented by means of a PCA statistical model, the system can detect damages between the actuated/sensed points through squared prediction error (Q-statistical index). The system performance was evaluated in a pipe test bench where two kinds of damages were studied: first, a mass is added to the pipe surface, and then leaks are provoked to the pipe structure by means of a drill tool. The experiments were conducted on two lab structures: (i) a meter carbon-steel pipe section and (ii) a pipe loop structure. The wave response was recorded between the instrumented points for two conditions: (i) The pipe in nominal conditions, where several repetitions will be applied to build the nominal statistical model and (ii) when damage is caused to the pipe (mass adding or leak). Damage conditions were graphically recognized through the Q-statistic chart. Thus, the feasibility to implement an automated real-time diagnostic system is demonstrated with minimum processing resources and hardware flexibility.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment.

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.

Support Stiffness Monitoring of Cylindrical Structures Using Magnetostrictive Transducers and the Torsional Mode T(0,1).

In this paper, a support stiffness monitoring scheme based on torsional guided waves for detecting loss of rigidity in a support of cylindrical structures is presented. Poor support performance in cylindrical specimens such as a pipeline setup located in a sloping terrain may produce a risky operation condition in terms of the installation integrity and the possibility of human casualties. The effects of changing the contact forces between support and the waveguide have been investigated by considering variations in the load between them. Fundamental torsional T ( 0 , 1 ) mode is produced and launched by a magnetostrictive collar in a pitch-catch configuration to study the support effect in the wavepacket propagation. Several scenarios are studied by emulating an abnormal condition in the support of a dedicated test bench. Numerical results revealed T ( 0 , 1 ) ultrasonic energy leakage in the form of S H 0 bulk waves when a mechanical coupling between the cylindrical waveguide and support is yielded. Experimental results showed that the rate of ultrasonic energy leakage depends on the magnitude of the reaction forces between pipe and support; so different levels of attenuation of T ( 0 , 1 ) mode will be produced with different mechanical contact conditions. Thus, it is possible to relate a measured attenuation to variations in the supports condition. Results of each scenarios are presented and discussed demonstrating the feasibility and potential of tracking of the amplitude of the T ( 0 , 1 ) as an indicator of abnormal conditions in simple supports.

PCA Based Stress Monitoring of Cylindrical Specimens Using PZTs and Guided Waves.

Since mechanical stress in structures affects issues such as strength, expected operational life and dimensional stability, a continuous stress monitoring scheme is necessary for a complete integrity assessment. Consequently, this paper proposes a stress monitoring scheme for cylindrical specimens, which are widely used in structures such as pipelines, wind turbines or bridges. The approach consists of tracking guided wave variations due to load changes, by comparing wave statistical patterns via Principal Component Analysis (PCA). Each load scenario is projected to the PCA space by means of a baseline model and represented using the Q-statistical indices. Experimental validation of the proposed methodology is conducted on two specimens: (i) a 12.7 mm ( 1 / 2 " ) diameter, 0.4 m length, AISI 1020 steel rod, and (ii) a 25.4 mm ( 1 " ) diameter, 6m length, schedule 40, A-106, hollow cylinder. Specimen 1 was subjected to axial loads, meanwhile specimen 2 to flexion. In both cases, simultaneous longitudinal and flexural guided waves were generated via piezoelectric devices (PZTs) in a pitch-catch configuration. Experimental results show the feasibility of the approach and its potential use as in-situ continuous stress monitoring application.

Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis.

OBJECTIVES: The objective was the development of a method for the automatic recognition of different types of atypical lymphoid cells. METHODS: In the method development, a training set (TS) of 1,500 lymphoid cell images from peripheral blood was used. To segment the images, we used clustering of color components and watershed transformation. In total, 113 features were extracted for lymphocyte recognition by linear discriminant analysis (LDA) with a 10-fold cross-validation over the TS. Then, a new validation set (VS) of 150 images was used, performing two steps: (1) tuning the LDA classifier using the TS and (2) classifying the VS in the different lymphoid cell types. RESULTS: The segmentation algorithm was very effective in separating the cytoplasm, nucleus, and peripheral zone around the cell. From them, descriptive features were extracted and used to recognize the different lymphoid cells. The accuracy for the classification in the TS was 98.07%. The precision, sensitivity, and specificity values were above 99.7%, 97.5%, and 98.6%, respectively. The accuracy of the classification in the VS was 85.33%. CONCLUSIONS: The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.

Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes.

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.

Situación de salud en la localidad 13, Teusaquillo, 1998 / Health situation in locality 13, Teusaquillo, 1998

Estudio cuyo objetivo fue establecer el estado de salud y sus condicionantes en los habitantes de la localidad de Teusaquillo. Se realizó una encuesta de salud de Teusaquillo a través de una encuesta de hogares por muestreo para medir la morbilidad sentida, los conocimientos, actitudes y prácticas en enfermedades degenerativas, enfermedades de la infancia, accidentes, violencia y variables asociadas, demanda y utilización de servicios y afiliación a la seguridad social. Se describe cada uno de los tópicos contemplados en esta encuesta, los cuales fueron de gran importancia para la comprensión de los problemas de la localidad en materia de salud, la complementación del diagnóstico local y la orientación de las políticas propias para esta comunidad.

The objective of this study was to establish the state of health and its determinants in the inhabitants of Teusaquillo. A health survey of Teusaquillo was conducted through a sample household survey to measure felt morbidity, knowledge, attitudes and practices in degenerative diseases, childhood diseases, accidents, violence and associated variables, demand and use of services and affiliation to social security. Each of the topics contemplated in this survey is described, which were of great importance for the understanding of the local health problems, the complementation of the local diagnosis and the orientation of the policies for this community.