A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Bilateral downregulation of Nav1.8 in dorsal root ganglia of rats with bone cancer pain induced by inoculation with Walker 256 breast tumor cells

<p>Abstract</p> <p>Background</p> <p>Rapid and effective treatment of cancer-induced bone pain remains a clinical challenge and patients with bone metastasis are more likely to experience severe pain. The voltage-gated sodium channel Nav1.8 plays a critical role in many aspects of nociceptor function. Therefore, we characterized a rat model of cancer pain and investigated the potential role of Nav1.8.</p> <p>Methods</p> <p>Adult female Wistar rats were used for the study. Cancer pain was induced by inoculation of Walker 256 breast carcinosarcoma cells into the tibia. After surgery, mechanical and thermal hyperalgesia and ambulation scores were evaluated to identify pain-related behavior. We used real-time RT-PCR to determine Nav1.8 mRNA expression in bilateral L4/L5 dorsal root ganglia (DRG) at 16-19 days after surgery. Western blotting and immunofluorescence were used to compare the expression and distribution of Nav1.8 in L4/L5 DRG between tumor-bearing and sham rats. Antisense oligodeoxynucleotides (ODNs) against Nav1.8 were administered intrathecally at 14-16 days after surgery to knock down Nav1.8 protein expression and changes in pain-related behavior were observed.</p> <p>Results</p> <p>Tumor-bearing rats exhibited mechanical hyperalgesia and ambulatory-evoked pain from day 7 after inoculation of Walker 256 cells. In the advanced stage of cancer pain (days 16-19 after surgery), normalized Nav1.8 mRNA levels assessed by real-time RT-PCR were significantly lower in ipsilateral L4/L5 DRG of tumor-bearing rats compared with the sham group. Western-blot showed that the total expression of Nav1.8 protein significantly decreased bilaterally in DRG of tumor-bearing rats. Furthermore, as revealed by immunofluorescence, only the expression of Nav1.8 protein in small neurons down regulated significantly in bilateral DRG of cancer pain rats. After administration of antisense ODNs against Nav1.8, Nav1.8 protein expression decreased significantly and tumor-bearing rats showed alleviated mechanical hyperalgesia and ambulatory-evoked pain.</p> <p>Conclusions</p> <p>These findings suggest that Nav1.8 plays a role in the development and maintenance of bone cancer pain.</p

249 TP53 mutation has high prevalence and is correlated with larger and poorly differentiated HCC in Brazilian patients

<p>Abstract</p> <p>Background</p> <p>Ser-249 TP53 mutation (249^Ser) is a molecular evidence for aflatoxin-related carcinogenesis in Hepatocellular Carcinoma (HCC) and it is frequent in some African and Asian regions, but it is unusual in Western countries. HBV has been claimed to add a synergic effect on genesis of this particular mutation with aflatoxin. The aim of this study was to investigate the frequency of 249^Sermutation in HCC from patients in Brazil.</p> <p>Methods</p> <p>We studied 74 HCC formalin fixed paraffin blocks samples of patients whom underwent surgical resection in Brazil. 249^Sermutation was analyzed by RFLP and DNA sequencing. HBV DNA presence was determined by Real-Time PCR.</p> <p>Results</p> <p>249^Sermutation was found in 21/74 (28%) samples while HBV DNA was detected in 13/74 (16%). 249^Sermutation was detected in 21/74 samples by RFLP assay, of which 14 were confirmed by 249^Sermutant-specific PCR, and 12 by nucleic acid sequencing. All HCC cases with p53-249ser mutation displayed also wild-type p53 sequences. Poorly differentiated HCC was more likely to have 249^Sermutation (OR = 2.415, 95% CI = 1.001 – 5.824, p = 0.05). The mean size of 249^SerHCC tumor was 9.4 cm versus 5.5 cm on wild type HCC (p = 0.012). HBV DNA detection was not related to 249^Sermutation.</p> <p>Conclusion</p> <p>Our results indicate that 249^Sermutation is a HCC important factor of carcinogenesis in Brazil and it is associated to large and poorly differentiated tumors.</p

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Classification of cassava starch films by physicochemical properties and water vapor permeability quantification by FTIR and PLS

Cassava starches are widely used in the production of biodegradable films, but their resistance to humidity migration is very low. In this work, commercial cassava starch films were studied and classified according to their physicochemical properties. A nondestructive method for water vapor permeability determination, which combines with infrared spectroscopy and multivariate calibration, is also presented. The following commercial cassava starches were studied: pregelatinized (amidomax 3550), carboxymethylated starch (CMA) of low and high viscosities, and esterified starches. To make the films, 2 different starch concentrations were evaluated, consisting of water suspensions with 3% and 5% starch. The filmogenic solutions were dried and characterized for their thickness, grammage, water vapor permeability, water activity, tensile strength (deformation force), water solubility, and puncture strength (deformation). The minimum thicknesses were 0.5 to 0.6 mm in pregelatinized starch films. The results were treated by means of the following chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. PCA analysis on the physicochemical properties of the films showed that the differences in concentration of the dried material (3% and 5% starch) and also in the type of starch modification were mainly related to the following properties: permeability, solubility, and thickness. IR spectra collected in the region of 4000 to 600 cm(-1) were used to build a PLS model with good predictive power for water vapor permeability determination, with mean relative errors of 10.0% for cross-validation and 7.8% for the prediction set.724E184E18