OBJECTIVE -Confirmatory factor analysis (CFA) was used to test the hypothesis that the components of the metabolic syndrome are manifestations of a single common factor. RESEARCH DESIGN AND METHODS-Three different datasets were used to test and validate the model. The Spanish and Mauritian studies included 207 men and 203 women and 1,411 men and 1,650 women, respectively. A third analytical dataset including 847 men was obtained from a previously published CFA of a U.S. population. The one-factor model included the metabolic syndrome core components (central obesity, insulin resistance, blood pressure, and lipid measurements). We also tested an expanded one-factor model that included uric acid and leptin levels. Finally, we used CFA to compare the goodness of fit of one-factor models with the fit of two previously published four-factor models.RESULTS -The simplest one-factor model showed the best goodness-of-fit indexes (comparative fit index 1, root mean-square error of approximation 0.00). Comparisons of one-factor with four-factor models in the three datasets favored the one-factor model structure. The selection of variables to represent the different metabolic syndrome components and model specification explained why previous exploratory and confirmatory factor analysis, respectively, failed to identify a single factor for the metabolic syndrome.CONCLUSIONS -These analyses support the current clinical definition of the metabolic syndrome, as well as the existence of a single factor that links all of the core components. Diabetes Care 29:113-122, 2006T he metabolic syndrome refers to the clustering, within individuals, of several cardiovascular risk factors (1,2). The metabolic syndrome is highly prevalent (3) and is a risk factor for cardiovascular diseases (CVD), chronic kidney disease, and type 2 diabetes (4 -6). Several definitions of the metabolic syndrome have been used, but all include insulin resistance or glucose intolerance, hypertension, dyslipidemia, and central obesity (7-9). Hyperuricemia and hyperleptinemia have also been proposed as components of the metabolic syndrome (1,10,11), and clinical, epidemiological, genetic, and physiologic studies have shown associations between these traits and both the metabolic syndrome components and CVD outcomes (10 -22).A central question in understanding the metabolic syndrome is why these traits cluster in individuals. For example, is there one or are there several factors, such as genetic or lifestyle characteristics, that influence the expression of metabolic syndrome traits in individuals? In an attempt to answer this question, many investigators have used exploratory factor analysis (EFA). This technique is used to analyze the interrelatedness of measured variables, so as to explain their observed correlations in terms of a smaller group of latent (i.e., unmeasured) variables, termed factors. For example, in the field of sociology, education level, income, and job status may all be related, and their relationship may best be explained by the presence of...
Objectives To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. Methods We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. Results There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. Conclusions We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. Key Points • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07957-z.
Objectives To evaluate whether the ultrasound appearance of the deltoid muscle in diabetic patients differs from that in obese nondiabetic patients. Methods Ultrasound images of the deltoid muscle from 137 type 2 diabetic patients (including 13 prediabetic patients) and 49 obese nondiabetic patients were blindly reviewed by 2 musculoskeletal radiologists, and by a third when arbitration was needed, to determine whether the appearance was “normal,” “suspected diabetes,” or “definite diabetes.” Age, sex, race, body mass index (BMI), insulin use, and hemoglobin A1c were analyzed. This retrospective study included patients presenting between October 2005 and November 2017. Statistical analyses included a 2‐sided sample t test or Wilcoxon rank sum test and a χ2 or Fisher exact test. Statistical significance was defined as P < .05. Results The type 2 diabetic patients included 98 women and 39 men aged 29 to 92 years, and the nondiabetic patients included 19 women and 30 men aged 18 to 75 years. A consensus diagnosis of definite diabetes by the musculoskeletal radiologists based on a hyperechoic deltoid was a powerful predictor of diabetes, with a positive predictive value of 89%. A hyperechoic deltoid was also a powerful predictor of prediabetes. Of the 13 prediabetic patients, all had the same hyperechoic appearance of the diabetic deltoid, regardless of BMI. Although obese diabetic patients more often had a diagnosis of definite diabetes, the BMI alone could not explain the increased echogenicity, as obese nondiabetic patients’ deltoid muscles did not appear as hyperechoic and were correctly categorized as not having definite diabetes with 82% specificity. Conclusions The characteristic hyperechoic deltoid appearance is a strong predictor of both diabetes and prediabetes and differs from that of obese nondiabetic patients.
IntroductionGlobal shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit.MethodsAll individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19.ResultsThe study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone.DiscussionUsing a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.
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