Dataset Information

Predictive Modelling of Lung Function using Emphysematous Density Distribution.

ABSTRACT: Target lung tissue selection remains a challenging task to perform for treating severe emphysema with lung volume reduction (LVR). In order to target the treatment candidate, the percentage of low attenuation volume (LAV%) representing the proportion of emphysema volume to whole lung volume is measured using computed tomography (CT) images. Although LAV% have shown to have a correlation with lung function in patients with chronic obstructive pulmonary disease (COPD), similar measurements of LAV% in whole lung or lobes may have large variations in lung function due to emphysema heterogeneity. The functional information of regional emphysema destruction is required for supporting the choice of optimal target. The purpose of this study is to develop an emphysema heterogeneity descriptor for the three-dimensional emphysematous bullae according to the size variations of emphysematous density (ED) and their spatial distribution. The second purpose is to derive a predictive model of airflow limitation based on the regional emphysema heterogeneity. Deriving the bullous representation and grouping them into four scales in the upper and lower lobes, a predictive model is computed using the linear model fitting to estimate the severity of lung function. A total of 99 subjects, 87 patients with mild to very severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I~IV) and 12 control participants with normal lung functions (forced expiratory volume in one second (FEV₁)/forced vital capacity (FVC)?>?0.7) were evaluated. The final model was trained with stratified cross-validation on randomly selected 75% of the dataset (n?=?76) and tested on the remaining dataset (n?=?23). The dispersed cases of LAV% inconsistent with their lung function outcome were evaluated, and the correlation study suggests that comparing to LAV of larger bullae, the widely spread smaller bullae with equivalent LAV has a larger impact on lung function. The testing dataset has the correlation of r?=?-0.76 (p?1/FVC, whereas using two ED % of scales and location-dependent variables to predict the emphysema-associated FEV₁/FVC, the results shows their correlation of 0.82 (p?1/FVC.

SUBMITTER: Lor KL

PROVIDER: S-EPMC6930211 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Predictive Modelling of Lung Function using Emphysematous Density Distribution.

Lor Kuo-Lung KL Liu Cheng-Pei CP Chang Yeun-Chung YC Yu Chong-Jen CJ Wang Cheng-Yi CY Chung Ming-Jui MJ Lin Fan-Ya FY Chen Chung-Ming CM

Scientific reports 20191224 1

Target lung tissue selection remains a challenging task to perform for treating severe emphysema with lung volume reduction (LVR). In order to target the treatment candidate, the percentage of low attenuation volume (LAV%) representing the proportion of emphysema volume to whole lung volume is measured using computed tomography (CT) images. Although LAV% have shown to have a correlation with lung function in patients with chronic obstructive pulmonary disease (COPD), similar measurements of LAV% ...[more]

PMID: 31875053

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Project description:IntroductionEmphysematous pyelonephritis (EPN) is a severe form of life-threatening renal infection. Conservative treatment represents the gold standard in the management of EPN, but nephrectomy remains appropriate in certain situations.ObjectiveThe aim of our study was to report our experience in the conservative management of emphysematous pyelonephritis and to identify the predictive factors of failure of conservative treatment.Patients and methodsThis is a retrospective study including all patients treated for emphysematous pyelonephritis in our department between January 2015 and December 2020. The first-line treatment was conservative based on antibiotic therapy and drainage in case of an obstructive cause. A nephrectomy was performed in case of failure of the conservative approach. Epidemiological, clinical, biological, therapeutic, and evolutionary data were collected from the patients' files. Statistical analysis was made using SPSS version 28.Results41 patients were included in our study. The mean age was 64.4 years old [28-91] with gender ratio of 0.46 (13H/28F). Diabetes mellitus was present in 75.6% of cases. The mean presentation delay was 3.28 days (Kaiser and Fournier, 2005; Kapoor et al., 2010; Aswathaman et al., 2008; Agha et al., 2020; Huang and Tseng, 2000; Falagas et al., 2007; Dutta et al., 2007; Dutta et al., 2007; Deoraj et al., 2018 Sep; Rahim et al., 2021 Mar; Maheshwari, 2021 Jul-Sep) [1-11]. In CT scan, 21 patients had class 1 EPN, 9 had class 2 EPN, 8 had class 3 EPN and 3 had class 4 EPN. The obstructive origin was found in 24 cases. Initially, 25 patients (60.9%) presented with severe sepsis and 7 patients (17.07%) developed a septic shock. Seven patients required nephrectomy with a mean delay of 2.12 days (Kapoor et al., 2010; Aswathaman et al., 2008; Agha et al., 2020; Huang and Tseng, 2000; Falagas et al., 2007) [2-6]. Five patients with septic shock refractory to conservative treatment and two patients whose evolution was marked by the occurrence of secondary septic shock. In the univariate analysis, thrombocytopenia, initially septic shock, and the need for hemodialysis were the predictive factors of failure of conservative management in patients with emphysematous pyelonephritis.ConclusionEmphysematous pyelonephritis is a serious condition with significant mortality. The optimal management is based on conservative treatment in most cases. However, patients requiring hemodialysis and with thrombocytopenia and initially septic shock should be considered candidates for emergency nephrectomy.

Project description:Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.

Project description:BackgroundStatistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/principal findingsWe developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/significanceWe demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

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Predictive Modelling of Lung Function using Emphysematous Density Distribution.

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Predictive Modelling of Lung Function using Emphysematous Density Distribution.

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