Evaluación Bidimensional y Tridimensional de la Vía Aérea Superior

Indexación: Scopus; Scielo.The aim of this study was to validate and correlate the two-dimensional (2D) with the three-dimensional (3D) measures of the upper airway assessment. Lateral cephalograms and cone beam CT of 100 adult subjects were used to perform a 2D and 3D assessment of the upper airway. Spearman correlation coefficient was used to determine whether there was correlation between variables. Additionally, specificity, sensitivity, negative predictive value and positive predictive value was calculated for the 2D assessment of the upper airway. Correlation between all two and three dimensional variables was found. In the nasopharynx and oropharynx, a weak correlation (r <0.51) was found; in the oropharynx a moderate one (0.50 <r <0.76). The validity tests of the 2D assessment resulted in a 73% sensitivity, 45% specificity, 93% negative predictive value and 14% positive predictive value for the nasopharynx; 100% sensitivity, 51% specificity, 100% negative predictive value and 6% positive predictive value in the oropharynx and 100% sensitivity, 71% specificity, 100% negative predictive value and 13% positive predictive value in the hypopharynx. There is a weak correlation between the 2D and 3D assessment of the upper airway. However, the lateral cephalogram has a high sensitivity and high negative predictive value, therefore, an additional complementary examination would not be necessary if the 2D assessment of the upper airway throws a normal result.http://ref.scielo.org/mpkrn

Added predictive value of high-throughput molecular data to clinical data, and its validation

Hundreds of ''molecular signatures'' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set

Applicability of Winthrop Score for the Diagnosis of Influenza A in the Emergency Department of Hospital Pablo Arturo Suárez, January to March of 2018

Introduction: In 2010, the Department of Infectious Diseases at Winthrop University Hospital designed a score system for the diagnosis of Legionella pneumonia. In this study, we applied the score to patients with acute respiratory symptoms suspected of having type A influenza. The identification of patients at medium to high risk of Influenza A allows for early initiation of treatment.Objective: To study the applicability of the Winthrop score for the diagnosis of Influenza A.Methodology: A prospective cohort study was performed in 2018 at Hospital Pablo Arturo Suárez, in Quito, Ecuador. Patients 0 to 100 years old presenting to the emergency department with influenza-like illness in January-March of 2018 were included in the study. Winthrop score results were then compared with the result of the reverse transcription polymerase chain reaction (RT-PCR) for influenza A, the gold standard for diagnosis. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were used to establish the diagnostic performance of this point system for influenza A within the sample at large and in subgroup analyses by age (&lt;5 years, 5-65 years, and &gt;65 years) and comorbidities.Results: 149 patients were enrolled in the study period. The study population included 81 males (54.4%) and the majority of patients were less than 5 years of age (N=85, 57.0%). Furthermore, almost one-third of the patients were less than one year old (N=38, 25.5%). According to the Winthrop point system, 68.5% of the cases had a low probability of having influenza (n = 102), 8.7% of cases had a medium probability (n = 13) and 22.8 % of cases had a high probability (n = 34). The RT-PCR test for influenza was positive for 26.2% of patients (n = 39). The Winthrop point system had a sensitivity of 97.4%, specificity of 91.8%, positive predictive value of 80.8%, negative predictive value of 99.0%, positive likelihood ratio of 11.9, and negative likelihood ratio of 35.8 in the total study population. For children under 5 years, a sensitivity of 100%, specificity of 96.3%, positive predictive value of 77.7%, negative predictive value of 100%, positive likelihood ratio of 27, and negative likelihood ratio of 0. In patients older than 6 years, a sensitivity of 96.9%, specificity of 89%, positive predictive value of 84.21%, negative predictive value of 98%, positive likelihood ratio of 8.8, and negative likelihood ratio of 29.4. Testing in patients over 65 years had a sensitivity of 100%, specificity of 90%, positive predictive value of 87.5%, negative predictive value of 100%, positive likelihood ratio of 10 and negative likelihood ratio of 0. Finally, patients with comorbidities had a sensitivity of 90%, specificity of 88.24%, positive predictive value of 81.82%, negative predictive value of 93.75%, positive likelihood ratio of 7.65, and negative likelihood ratio of 8.82.Conclusions: The Winthrop score performed well in predicting Influenza A in patients with acute respiratory symptoms. This score may be useful in settings were Influenza A PCR testing is unavailable

The shading sign: is it exclusive of endometriomas?

To investigate if the shading sign is an exclusive MRI feature of endometriomas or endometrioid tumors, and to analyze its different patterns. Three hundred and fourty six women with adnexal masses who underwent 1.5/3-T MRI were included in this retrospective, board-approved study. The shading sign was found in 56 patients, but five cases were excluded due to lack of imaging follow-up or histological correlation. The final sample included 51 women. The type of tumor and the pattern of shading were recorded for each case. Thirty endometriomas and five endometrioid carcinomas were found. The remaining 16 cases corresponded to other benign and malignant tumors. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 73%, 93%, 59%, and 96%, respectively. Restricting the analysis to cystic lesions without solid or fat component, sensitivity, specificity, positive predictive value, and negative predictive value were 73%, 96%, 94%, and 80%. Five shading patterns were identified: layering (15.7%), liquid-liquid level (11.8%), homogenous (45.1%), heterogeneous (11.8%), and focal/multifocal shading within a complex mass (19.6%). No significant correlation was found between these patterns and the type of tumor. The shading sign is not exclusive of endometriomas or endometrioid tumors. Homogenous shading was the most prevalent pattern in endometriomas and half of the cases with focal/multifocal shading within a complex mass were endometrioid carcinomas.info:eu-repo/semantics/publishedVersio

Prediction of stocks: a new way to look at it.

While the traditional $R^{2}$ value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples. In this paper we introduce a validated $R_{V}^{2}$ value that is Taylor made for prediction. Based on data from the Danish stock market, using this measure we find that the dividend-price ratio has good predictive power for time horizons between one year and five years. We explain how the $R_{S}^{2}$ s for different time horizons could be compared, respectively, how they must not be interpreted. For our data we can conclude that the quality of prediction is almost the same for the five different time horizons. This is in contradiction to earlier studies based on the traditional $R^{2}$ value, where it has been argued that the predictive power increases with the time horizon up to a horizon of about five or six years. Furthermore, we find that while inflation and interest rate do not add to the predictive power of the dividend-price ratio then last years excess stock return does

Approximating Cross-validatory Predictive P-values with Integrated IS for Disease Mapping Models

An important statistical task in disease mapping problems is to identify out- lier/divergent regions with unusually high or low residual risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is a gold standard for computing predictive p-value that can flag such outliers. However, actual LOOCV is time-consuming because one needs to re-simulate a Markov chain for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called iIS, for approximating LOOCV with only Markov chain samples simulated from a posterior based on a full data set. iIS is based on importance sampling (IS). iIS integrates the p-value and the likelihood of the test observation with respect to the distribution of the latent variable without reference to the actual observation. The predictive p-values computed with iIS can be proved to be equivalent to the LOOCV predictive p-values, following the general theory for IS. We com- pare iIS and other three existing methods in the literature with a lip cancer dataset collected in Scotland. Our empirical results show that iIS provides predictive p-values that are al- most identical to the actual LOOCV predictive p-values and outperforms the existing three methods, including the recently proposed ghosting method by Marshall and Spiegelhalter (2007).Comment: 21 page

Predictive value of hematological and phenotypical parameters on postchemotherapy leukocyte recovery

Background: Grade IV chemotherapy toxicity is defined as absolute neutrophil count &lt;500/μL. The nadir is considered as the lowest neutrophil number following chemotherapy, and generally is not expected before the 7th day from the start of chemotherapy. The usual prophylactic dose of rHu-G-CSF (Filgrastim) is 300 μg/day, starting 24-48 h after chemotherapy until hematological recovery. However, individual patient response is largely variable, so that rHu-G-CSF doses can be different. The aim of this study was to verify if peripheral blood automated flow cytochemistry and flow cytometry analysis may be helpful in predicting the individual response and saving rHu-G-CSF. Methods: During Grade IV neutropenia, blood counts from 30 cancer patients were analyzed daily by ADVIA 120 automated flow cytochemistry analyzer and by Facscalibur flow cytometer till the nadir. "Large unstained cells" (LUCs), myeloperoxidase index (MPXI), blasts, and various cell subpopulations in the peripheral blood were studied. At nadir rHu-G-CSF was started and 81 chemotherapy cycles were analyzed. Cycles were stratified according to their number and to two dose-levels of rHuG-CSF needed to recovery (300-600 vs. 900-1200 μg) and analyzed in relation to mean values of MPXI and mean absolute number of LUCs in the nadir phase. The linear regressions of LUCs % over time in relation to two dose-levels of rHu-G-CSF and uni-multivariate analysis of lymphocyte subpopulations, CD34+ cells, MPXI, and blasts were also performed. Results: In the nadir phase, the increase of MPXI above the upper limit of normality (&gt;10; median 27.7), characterized a slow hematological recovery. MPXI levels were directly related to the cycle number and inversely related to the absolute number of LUCs and CD34 +/CD45+ cells. A faster hematological recovery was associated with a higher LUC increase per day (0.56% vs. 0.25%), higher blast (median 36.7/μL vs. 19.5/μL) and CD34+/CD45+ cell (median 2.2/μL vs. 0.82/μL) counts. Conclusions: Our study showed that some biological indicators such as MPXI, LUCs, blasts, and CD34 +/CD45+ cells may be of clinical relevance in predicting individual hematological response to rHu-G-CSF. Special attention should be paid when nadir MPXI exceeds the upper limit of normality because the hematological recovery may be delayed. © 2009 Clinical Cytometry Society

Prior-predictive value from fast growth simulations

Building on a variant of the Jarzynski equation we propose a new method to numerically determine the prior-predictive value in a Bayesian inference problem. The method generalizes thermodynamic integration and is not hampered by equilibration problems. We demonstrate its operation by applying it to two simple examples and elucidate its performance. In the case of multi-modal posterior distributions the performance is superior to thermodynamic integration.Comment: 8 pages, 11 figure