Category: Statistics

J Vet Intern Med. 2021 Feb 26. doi: 10.1111/jvim.16067. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiac magnetic resonance imaging (CMR) allows for detection of fibrosis in hypertrophic cardiomyopathy (HCM) by quantification of the extracellular volume fraction (ECV).

HYPOTHESIS/OBJECTIVES: To quantify native T1 mapping and ECV in cats. We hypothesize that native T1 mapping and ECV will be significantly increased in HCM cats compared with healthy cats.

ANIMALS: Seventeen healthy and 12 preclinical HCM, age-matched, client-owned cats.

METHODS: Prospective observational study. Tests performed included indirect blood pressure, CBC, biochemical analysis including total thyroid, urinalysis, transthoracic echocardiogram, and CMR. Cats were considered healthy if all tests were within normal limits and a diagnosis of HCM was determined by the presence of left ventricular concentric hypertrophy ≥6 mm on echocardiography.

RESULTS: There were statistically significant differences in LV mass (healthy = 5.87 g, HCM = 10.3 g, P < .0001), native T1 mapping (healthy = 1122 ms, HCM = 1209 ms, P = .004), and ECV (healthy = 26.0%, HCM = 32.6%, P < .0001). Variables of diastolic function including deceleration time of early diastolic transmitral flow (DTE), ratio between peak velocity of early diastolic transmitral flow and peak velocity of late diastolic transmitral flow (E : A), and peak velocity of late diastolic transmitral flow (A wave) were significantly correlated with ECV (DTE; r = 0.73 P = .007, E : A; r = -0.75 P = .004, A wave; r = 0.76 P = .004).

CONCLUSIONS AND CLINICAL IMPORTANCE: Quantitative assessment of cardiac ECV is feasible and can provide additional information not available using echocardiography.

PMID:33634479 | DOI:10.1111/jvim.16067

J Digit Imaging. 2021 Feb 25. doi: 10.1007/s10278-021-00431-8. Online ahead of print.

ABSTRACT

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.

PMID:33634413 | DOI:10.1007/s10278-021-00431-8

J Digit Imaging. 2021 Feb 25. doi: 10.1007/s10278-021-00433-6. Online ahead of print.

ABSTRACT

The Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider’s first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories (“usually appropriate” [green], “may be appropriate” [yellow], “usually not appropriate” [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), -0.8% (95% CI -0.5% to -1.1%), and -3.0% (95% CI 3.3% to -2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI -1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.

PMID:33634414 | DOI:10.1007/s10278-021-00433-6

J Digit Imaging. 2021 Feb 25. doi: 10.1007/s10278-021-00432-7. Online ahead of print.

ABSTRACT

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with special equipment is a common method that is used to identify the total serum bilirubin level in patients. Fully automated multi-class recognition of jaundice combines two key issues: (1) the critical difficulties in multi-class recognition of jaundice approaches contrasting with the binary class and (2) the subtle difficulties in multi-class recognition of jaundice represent extensive individuals variability of high-resolution photos of subjects, huge coherency between healthy controls and occult jaundice, as well as broadly inhomogeneous color distribution. We introduce a novel approach for multi-class recognition of jaundice to detect occult jaundice, obvious jaundice and healthy controls. First, region annotation network is developed and trained to propose eye candidates. Subsequently, an efficient jaundice recognizer is proposed to learn similarities, context, localization features and globalization characteristics on photos of subjects. Finally, both networks are unified by using shared convolutional layer. Evaluation of the structured model in a comparative study resulted in a significant performance boost (categorical accuracy for mean 91.38%) over the independent human observer. Our work was exceeded against the state-of-the-art convolutional neural network (96.85% and 90.06% for training and validation subset, respectively) and showed a remarkable categorical result for mean 95.33% on testing subset. The proposed network makes a performance better than physicians. This work demonstrates the strength of our proposal to help bringing an efficient tool for multi-class recognition of jaundice into clinical practice.

PMID:33634415 | DOI:10.1007/s10278-021-00432-7

Behav Res Methods. 2021 Feb 25. doi: 10.3758/s13428-020-01520-2. Online ahead of print.

ABSTRACT

For decades, statisticians and methodologists have insisted researchers utilize graphical analysis much more heavily. Despite cogent and passionate recommendations, there has been no graphical revolution. Instead, researchers rely heavily on misleading graphics that violate visual processing heuristics. Perhaps the main reason for the persistence of deceptive graphics is software; most software familiar to psychological researchers suffer from poor defaults and limited capabilities. Also, visualization is ancillary to statistical analysis, providing an incentive to not produce graphics at all. In this paper, we argue that every statistical analysis must have an accompanying graphic, and we introduce the point-and-click software Flexplot, available both in JASP and Jamovi. We then present the theoretical framework that guides Flexplot, as well as show how to perform the most common statistical analyses in psychological literature.

PMID:33634423 | DOI:10.3758/s13428-020-01520-2

Eur J Epidemiol. 2021 Feb 25. doi: 10.1007/s10654-021-00727-7. Online ahead of print.

ABSTRACT

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

PMID:33634345 | DOI:10.1007/s10654-021-00727-7

Clin Exp Metastasis. 2021 Feb 25. doi: 10.1007/s10585-021-10079-x. Online ahead of print.

ABSTRACT

Implementation of COVID-19 measures may have induced concerns about access and quality of health care for cancer patients with bone metastases, and it may have affected their quality of life. In this study, we evaluated the effect of the first COVID-19 lockdown on quality of life and emotional functioning of patients with stage IV cancer treated for painful bone metastases in the UMC Utrecht, the Netherlands. A COVID-19 specific questionnaire was sent to active participants in the Prospective Evaluation of interventional StudiEs on boNe meTastases (PRESENT) cohort, consisting of patients irradiated for metastatic bone disease. Patient reported outcomes (PROs) were compared with the last two PROs collected within the PRESENT cohort before the COVID-19 lockdown in the Netherlands on the 16th of March. For the 169 (53%) responders, median age at start of lockdown was 68 years (range 38-92) and 62% were male. Patients reported a statistically significant decrease in emotional functioning (83.6 to 79.2, P = 0.004) and in general quality of life score during the COVID-19 lockdown (72.4 to 68.7, P = 0.007). A steep increase in feeling isolated was reported (18% before and 67% during lockdown). This study has shown a strong increase in the experience of isolation and a decrease of emotional functioning and general quality of life during the COVID-19 lockdown in cancer patients with bone metastases. Due to the nature of the treatment of this patient population, efforts should be made to minimize these changes during future lockdowns.

PMID:33634347 | DOI:10.1007/s10585-021-10079-x

Adv Exp Med Biol. 2021 Feb 26. doi: 10.1007/5584_2021_621. Online ahead of print.

ABSTRACT

Type 2 diabetes is one of the most common noncommunicable diseases in the world. Recent studies suggest a link between type 2 diabetes and microbiota, as well as the ability to treat and prevent it using personalized approaches to nutrition. In this work, we conducted clinical studies on the effects of a personalized diet on 56 female patients. Biochemical, physical, and immunological parameters were measured by standard methods on days 1 and 18 of the experiment. Gut and oral microbiota studies were performed in dynamics on days 1, 7, 11, and 18 using real-time polymerase chain reaction. With the help of the developed information system, a personalized diet was developed for each participant of the experiment. In the group of patients following personalized diets a statistically significant decreasing levels of glucose, thymol test, creatinine, very low-density lipoprotein, urea, secretory IgA, and tumour necrosis factor-α, and improvement in all physical parameters were observed. There was a statistically significant increase in uric acid, sodium, and magnesium. Statistically significant changes in gut microbiota were observed in Enterococcus faecalis, Escherichia coli (lac+, lac-), Lactobacillus spp., and Candida spp. Such microorganisms of oral microbiota as E. faecalis, Lactobacillus spp., Pseudomonas aeruginosa, and Candida spp. demonstrated statistically significant changes. All these changes indicate an improvement in the patients’ condition in the experimental group compared to the control group. Our algorithm used for the development of personalized diets for patients with diabetes type 2 demonstrated clinical efficacy of its implementation.

PMID:33634376 | DOI:10.1007/5584_2021_621

Integr Psychol Behav Sci. 2021 Feb 25. doi: 10.1007/s12124-020-09598-1. Online ahead of print.

ABSTRACT

Statistical methods to test hypotheses about direct and indirect effects from a person-oriented research perspective are scarce. For categorical variables, previously suggested approaches use configural frequency analysis (CFA) to detect extreme patterns (CFA Types/Antitypes) that are responsible for the observed direct and indirect effects. Existing methods rest on complex (log-linear) model comparison strategies and may perform poorly with respect to Type I error protection and statistical power. We, therefore, propose a simplified configural approach to answer the question “What carries a mediation process?” This simplified approach is based on two log-linear models that are needed to estimate (variable-oriented) direct and indirect effects. The first model identifies extreme patterns for the predictor-mediator path, the second model searches for extreme cells in the mediator-outcome path. Joint significance testing can be used to test the presence of mediation. Definitions of Mediation Types/Antitypes are given based on possible Type/Antitype patterns for the binary simple mediation model. In two Monte-Carlo simulation experiments, we evaluate the performance of the simplified approach in a homogenous population (i.e., where all individuals develop homogenously along a variable-oriented mediation mechanism) and a heterogenous population (i.e., where specific configurations, instead of a variable-oriented effect, drive the mediation process). Results suggest that the presented approach performs acceptably with respect to Type I error protection and statistical power. In general, larger sample sizes are preferable to reliably detect mediation-generating configurations. An empirical example is given for illustrative purposes and extensions and limitations of the proposed method are discussed.

PMID:33634410 | DOI:10.1007/s12124-020-09598-1

Microb Ecol. 2021 Feb 26. doi: 10.1007/s00248-021-01711-0. Online ahead of print.

ABSTRACT

The present investigation is aiming to report the oral bacterial composition of smokeless tobacco (SLT) users and to determine the influence of SLT products on the healthy Indian population. With the aid of the V3 hypervariable region of the 16S rRNA gene, a total of 8,080,889 high-quality reads were clustered into 15 phyla and 180 genera in the oral cavity of the SLT users. Comparative analysis revealed a more diverse microbiome where two phyla and sixteen genera were significantly different among the SLT users as compared to the control group (p-value < 0.05). The prevalence of Fusobacteria-, Porphyromonas-, Desulfobulbus-, Enterococcus-, and Parvimonas-like genera among SLT users indicates altered bacterial communities among SLT users. Besides, the depletion of health-compatible bacteria such as Lactobacillus and Haemophilus also suggests poor oral health. Here, the majority of the altered genera belong to Gram-negative anaerobes that have been reported for assisting biofilm formation that leads in the progression of several oral diseases. The PICRUSt analysis further supports the hypothesis where a significant increase in the count of the genes involved in the metabolism of nitrogen, amino acids, and nicotinate/nicotinamide was observed among tobacco chewers. Moreover, this study has a high significance in Indian prospects where the SLT consumers are prevalent but we are deficient in information on their oral microbiome.

PMID:33634334 | DOI:10.1007/s00248-021-01711-0