scholarly journals SAT-547 Aldosterone-Potassium Ratio Predicts Primary Aldosteronism Subtype

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Troy Puar ◽  
Wann Jia Loh ◽  
Dawn Shao Ting Lim ◽  
Meifen Zhang ◽  
Roger S Foo ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Predictive Ability ◽  
Cost Effective ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Bilateral Disease ◽  
Unilateral Disease

Abstract Objective Prediction models have been developed to predict either unilateral or bilateral primary aldosteronism, and these have not been validated externally. We aimed to develop a simplified score to predict both subtypes and validate this externally. Methods Our development cohort was taken from 165 patients who underwent adrenal vein sampling (AVS) in two Asian tertiary centres. Unilateral disease was determined using both AVS and post-operative outcome. Multivariable analysis was used to construct prediction models. We validated our tool in a European cohort of 97 patients enrolled in a clinical trial. Previously published prediction models were also tested in our cohorts. Results Backward stepwise logistic regression analysis yielded a final tool using baseline-aldosterone-to-lowest-potassium ratio (APR, ng/dL/mmol/L), with an area under receiver operating characteristic curve of 0.80 (95% CI: 0.70 - 0.89). In the Asian development cohort, probability of bilateral disease was 90.0% (with APR <5) and probability of unilateral disease was 91.4% (with APR >15). Similar results were seen in the European validation cohort. Combining both cohorts, probability of bilateral disease was 76.7% (with APR <5), and probability for unilateral was 91.7% (with APR >15). Other models had similar predictive ability but required more variables, and were less sensitive for identifying bilateral PA. Conclusion The novel aldosterone-potassium ratio (APR) is a convenient score to guide clinicians and patients of various ethnicities on the probability of PA subtype. Using APR to identify patients more likely to benefit from AVS may be a cost-effective strategy to manage this common condition.

Predicting Vaginal Delivery in Nulliparous Women Undergoing Induction of Labor at Term

2017 ◽  
Vol 35 (07) ◽  
pp. 660-668 ◽  
Author(s):  
U. M. Reddy ◽  
C. C. Huang ◽  
T. C. Auguste ◽  
D. Bauer ◽  
R. T. Overcash ◽  
...  
Keyword(s):  
Vaginal Delivery ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Induction Of Labor ◽  
Stepwise Logistic Regression ◽  
Chronic Hypertension ◽  
Stepwise Logistic Regression Analysis ◽  
Maternal Characteristics ◽  
Nulliparous Women

Objective We sought to develop a model to calculate the likelihood of vaginal delivery in nulliparous women undergoing induction at term. Study Design We obtained data from the Consortium on Safe Labor by including nulliparous women with term singleton pregnancies undergoing induction of labor at term. Women with contraindications for vaginal delivery were excluded. A stepwise logistic regression analysis was used to identify the predictors associated with vaginal delivery by considering maternal characteristics and comorbidities and fetal conditions. The receiver operating characteristic curve, with an area under the curve (AUC) was used to assess the accuracy of the model. Results Of 10,591 nulliparous women who underwent induction of labor, 8,202 (77.4%) women had vaginal delivery. Our model identified maternal age, gestational age at delivery, race, maternal height, prepregnancy weight, gestational weight gain, cervical exam on admission (dilation, effacement, and station), chronic hypertension, gestational diabetes, pregestational diabetes, and abruption as significant predictors for successful vaginal delivery. The overall predictive ability of the final model, as measured by the AUC was 0.759 (95% confidence interval, 0.749–0.770). Conclusion We identified independent risk factors that can be used to predict vaginal delivery among nulliparas undergoing induction at term. Our predictor provides women with additional information when considering induction.

scholarly journals A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-Ning Dai ◽  
Wei Zheng ◽  
Qing-Qing Wu ◽  
Tian-Chen Hui ◽  
Nan-Nan Sun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Clinical Manifestations ◽  
Cost Effective ◽  
Rapid Screening ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Screening Model ◽  
Epidemiological Features ◽  
Geographical Regions ◽  
Novel Coronavirus

AbstractNovel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Biomechanical and Anthropometric Factors That Differentiate National- and Regional-Level Judo Players

2021 ◽  
pp. 1-7
Author(s):  
Felipe Guimarães Teixeira ◽  
Paulo Tadeu Cardozo Ribeiro Rosa ◽  
Roger Gomes Tavares Mello ◽  
Jurandir Nadal
Keyword(s):  
Cross Validation ◽  
Center Of Pressure ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Stepwise Logistic Regression ◽  
Mean Velocity ◽  
Bone Width ◽  
Longitudinal Length ◽  
Judo Athletes ◽  
Fold Cross Validation

Purpose: The study aimed to identify the variables that differentiate judo athletes at national and regional levels. Multivariable analysis was applied to biomechanical, anthropometric, and Special Judo Fitness Test (SJFT) data. Method: Forty-two male judo athletes from 2 competitive groups (14 national and 28 state levels) performed the following measurements and tests: (1) skinfold thickness, (2) circumference, (3) bone width, (4) longitudinal length, (5) stabilometric tests, (6) dynamometric tests, and (7) SJFT. The variables with significant differences in the Wilcoxon rank-sum test were used in stepwise logistic regression to select those that better separate the groups. The authors considered models with a maximum of 3 variables to avoid overfitting. They used 7-fold cross validation to calculate optimism-corrected measures of model performance. Results: The 3 variables that best differentiated the groups were the epicondylar humerus width, the total number of throws on the SJFT, and the stabilometric mean velocity of the center of pressure in the mediolateral direction. The area under the receiver-operating-characteristic curve for the model (based on 7-fold cross validation) was 0.95. Conclusion: This study suggests that a reduced set of anthropometric, biomechanical, and SJFT variables can differentiate judo athlete’s levels.

Serum SHBG Is Associated With the Development and Regression of Nonalcoholic Fatty Liver Disease: A Prospective Study

2019 ◽  
Vol 105 (3) ◽  
pp. e791-e804
Author(s):  
Xu Wang ◽  
Jiewen Xie ◽  
Juan Pang ◽  
Hanyue Zhang ◽  
Xu Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Liver Disease ◽  
Fatty Liver ◽  
Nonalcoholic Fatty Liver Disease ◽  
Fatty Liver Disease ◽  
Prediction Models ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Nonalcoholic Fatty Liver ◽  
Potential Biomarker

Abstract Context SHBG, a homodimeric glycoprotein produced by hepatocytes has been shown to be associated with metabolic disorders. Whether circulating SHBG levels are predictive of later risk of nonalcoholic fatty liver disease (NAFLD) remains unknown. In this study, we prospectively investigated the association between SHBG and NAFLD progression through a community-based cohort comprising 3389 Chinese adults. Methods NAFLD was diagnosed using abdominal ultrasonography. Serum SHBG levels were measured by chemiluminescent enzyme immunometric assay, and their relationship with NAFLD development and regression was investigated after a mean follow-up of 3.09 years using multivariable logistic regression. Results Basal SHBG was negatively associated with NAFLD development, with a fully adjusted odds ratio (OR) and its 95% confidence interval (CI) of 0.22 (0.12-0.40) (P < .001). In contrast, basal SHBG was positively associated with NAFLD regression, with a fully adjusted OR of 4.83 (2.38-9.81) (P < .001). Multiple-stepwise logistic regression analysis showed that SHBG concentration was an independent predictor of NAFLD development (OR, 0.28 [0.18-0.45]; P < .001) and regression (OR, 3.89 [2.43-6.22]; P < .001). In addition, the area under the receiver operating characteristic curves were 0.764 (95% CI, 0.740-0.787) and 0.762 (95% CI, 0.738-0.785) for the prediction models of NAFLD development and regression, respectively. Conclusions Serum SHBG concentration is associated with the development and regression of NAFLD; moreover, it can be a potential biomarker for predicting NAFLD progression, and also a novel preventive and therapeutic target for NAFLD.

scholarly journals Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

2020 ◽  
Vol 13 (5) ◽  
pp. 92
Author(s):  
Katarina Valaskova ◽  
Pavol Durana ◽  
Peter Adamko ◽  
Jaroslav Jaros
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Early Warning Systems ◽  
Emerging Countries ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

scholarly journals A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Joeri W van Straalen ◽  
Gabriella Giancane ◽  
Yasmine Amazrhar ◽  
Nikolay Tzaribachev ◽  
Calin Lazar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Protective Factor ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Information Criterion ◽  
Logistic Regression Models ◽  
Adjusted Risk ◽  
Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

scholarly journals External Validation of Prediction Models for Unilateral Primary Aldosteronism

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A287-A287
Author(s):  
Davis Sam ◽  
Gregory A Kline ◽  
Benny So ◽  
Janice L Pasieka ◽  
Adrian Harvey ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Prediction Models ◽  
Validation Cohort ◽  
Large Population ◽  
External Validation ◽  
Poor Performance ◽  
Small Data ◽  
Data Sets ◽  
Recommended Treatment ◽  
Unilateral Disease

Abstract Primary aldosteronism (PA) is the most common cause of remediable hypertension. Treatment is informed by establishing whether disease is unilateral (localized to one adrenal gland) or bilateral. Adrenalectomy is the guideline-recommended treatment of choice for unilateral PA. However, the currently recommended subtyping test, adrenal vein sampling (AVS), is often limited in accessibility. Thus, prediction models have been developed to diagnose unilateral PA and therefore bypass AVS. However, their generalizability remains unknown. In this retrospective study, we aimed to externally validate the performance of prediction models for unilateral PA in a large population of PA patients at a Canadian referral center who underwent AVS during 2006–2018. The presence of unilateral disease was indicated by a lateralization index of >3 on AVS. We identified 6 clinical prediction models from the literature. The discrimination and calibration of each model were systematically evaluated. For the original models, the derivation cohorts were based out of Japan, France, Italy, and England, with mean age between 46–54 years and 43–56% being male. The derivation cohorts were generally small, with 4 of the 6 studies reporting less than 50 people with unilateral PA. Common variables reported to be predictive of unilateral PA included male sex, hypokalemia, elevated aldosterone-renin ratio, and the presence of a unilateral adrenal nodule on imaging. The validation cohort included 342 PA patients who underwent successful AVS (average age, 52.1 years; 58.8% male). Among them, 186 (54.4%) demonstrated unilateral disease, and the remaining 156 (45.6%) were considered to have bilateral disease. The baseline characteristics of the validation cohort were broadly similar to those of the derivation cohorts, except for potential differences in ethnicity. When applying the models to the validation cohort, subjects were excluded if any candidate variables were missing. All 6 models demonstrated poor discrimination in the validation set (C-statistics; range, 0.59–0.72), representing a marked decrease compared to the derivation sets where they were reported (range, 0.80–0.87). Assessment of calibration by comparing observed and predicted probabilities of the unilateral subtype revealed significant miscalibration. Calibration-in-the-large for every model was >0 (range, 0.36–2.23), signifying systematic underprediction of unilateral PA. Calibration slopes were all <1 (range, 0.35–0.85), indicating poor performance at the extremes of risk. These results suggest that the original models were optimistic due to overfitting in the derivation cohorts and therefore lack generalizability. This is primarily because these models were developed in small data sets. In conclusion, clinical assessment with prediction models for unilateral PA cannot be readily used to bypass AVS in the general PA population.

scholarly journals OR34-04 Efficiency of Adrenal Venous Sampling in the Treatment Choice of Primary Aldosteronism (AVSTAT Study)

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Mitsuhide Naruse ◽  
Felix Beuschlein ◽  
Mirko Parasiliti Caprino ◽  
Jaap Deinum ◽  
William Drake ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Clinical Decision ◽  
Pressure Control ◽  
Primary Objective ◽  
Adrenal Venous Sampling ◽  
Venous Sampling ◽  
Bilateral Disease ◽  
Good Blood Pressure Control ◽  
Number Of Patients ◽  
Unilateral Disease

Abstract BACKGROUND: Adrenal venous sampling (AVS) is strongly recommended for a subtype diagnosis of primary aldosteronism (PA) if adrenalectomy (ADX) is desired by the patient. Given various issues related to AVS such as technical demand, invasive nature, expensive cost and radiation exposure, AVS is expected to lead efficiently to the subtype diagnosis and ADX. Aim: Primary objective was to assess the performance of AVS to determine treatment of PA by investigating the ratio of unilateral disease and rate of ADX following AVS in patients with unilateral disease. Methods: Sixteen major referral centers in ENS@T (n=10) and Japan (n=6) participated in the study. Study period was from 2006 to 2018. Data on total number of PA patients, AVS (total number and number of successful procedures), number of patients with unilateral diseases, and number of patients that underwent ADX were collected by a questionnaire-based survey. In addition, reasons for not proceeding to ADX in patients with a unilateral diagnosis were investigated. The diagnosis of PA was based on the positive case detection and at least one positive result in confirmatory testing. Results: Total number of confirmed PA patients and conducted AVS showed a dramatic increase during the past decade (PA: 1061 pts/ 2006–2011 to 3718 pts/ 2012–2018; AVS: 720/ 2006–2011 to 2448/ 2012–2018). Success rate of AVS was improved from 79.0% (2006–2011) to 92.5% (2012–2018). Both rate of unilateral PA and ADX of successful procedures decreased from 42.7% (2006–2011) to 37.3% (2012–2018) and from 40.8% (2006–2011) to 34.9% (2012–2018), respectively. Of the patients with successful AVS, bilateral disease was diagnosed in 63.5% (1812/2854 pts). Of the unilateral PA patients, 11.9% (125/ 1054 pts) were not subjected to ADX. The rate of the patients not subjected to ADX was significantly higher in Japan than in ENS@T centers both in patients with successful AVS (75.8% vs. 53.4%) and with unilateral disease (19.9% vs. 8.6%). Clinical decision against ADX in unilateral disease was made by the physicians in 33.3%, the patients in 33.3%, and both in 33.3%. Medical factors for Dr.’s decision against ADX in unilateral disease included good blood pressure control, normokalemia, comorbidities (e.g. DM, CKD), non-lateralized CT findings (e.g. no tumor, contralateral tumor), and discordant results among different criteria of AVS. Conclusions: High prevalence of bilateral disease and change of treatment policy after implementation affected the efficiency of AVS as an essential diagnostic procedure prior to ADX. Development of non-invasive procedures to exclude bilateral PA and more strict indication of AVS are warranted.

scholarly journals Predictors of Clinical Success After Surgery for Primary Aldosteronism in the Japanese Nationwide Cohort

2019 ◽  
Vol 3 (11) ◽  
pp. 2012-2022 ◽  
Author(s):  
Mitsuha Morisaki ◽  
Isao Kurihara ◽  
Hiroshi Itoh ◽  
Mitsuhide Naruse ◽  
Yoshiyu Takeda ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Prediction Models ◽  
Outcome Measure ◽  
Clinical Success ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
University Hospitals ◽  
Factors Associated ◽  
History Of

Abstract Context Aldosterone-producing adenomas are a curable subtype of primary aldosteronism (PA); however, hypertension persists in some patients after adrenalectomy. Objective To identify factors associated with, and develop prediction models for, blood pressure (BP) normalization or improvement after adrenalectomy. Design Retrospective analysis of patients treated between 2006 and 2018, with a 6-month follow-up. Setting A nationwide, 29-center Japanese registry encompassing 15 university hospitals and 14 city hospitals. Patients We categorized 574 participants in the Japan Primary Aldosteronism Study, who were diagnosed with PA and underwent adrenalectomy, as BP normalized or improved, on the basis of their presentations at 6 months postsurgery. Main Outcome Measure The rate of complete, partial, and absent clinical success. Predictive factors related to BP outcomes after PA surgery were also evaluated. Results Complete clinical success was achieved in 32.6% and partial clinical success was achieved in 53.0% of the patients at 6 months postsurgery. The following five variables were independent predictors for BP normalization: ≤7 years of hypertension, body mass index ≤25 kg/m2, no more than one antihypertensive medication, absence of medical history of diabetes, and female sex. The area under the receiver operator characteristic curve was 0.797 in the BP normalization model. Conclusion We established models that predicted postoperative BP normalization in patients with PA. These should be useful for shared decision-making regarding adrenalectomy for PA.

scholarly journals Serum Biomarkers for Prediction of Response to Methotrexate Monotherapy in Early Rheumatoid Arthritis: Results from the SWEFOT Trial

2019 ◽  
Vol 46 (6) ◽  
pp. 555-563 ◽  
Author(s):  
Karen Hambardzumyan ◽  
Rebecca J. Bolce ◽  
Johan K. Wallman ◽  
Ronald F. van Vollenhoven ◽  
Saedis Saevarsdottir
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Disease Activity ◽  
Early Rheumatoid Arthritis ◽  
Characteristic Curve ◽  
Serum Biomarkers ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Baseline Serum ◽  
Baseline Levels

Objective.To investigate baseline levels of 12 serum biomarkers that constitute a multibiomarker disease activity test, as predictors of response to methotrexate (MTX) in patients with early rheumatoid arthritis (eRA).Methods.In 298 patients from the Swedish Pharmacotherapy (SWEFOT) clinical trial, baseline serum levels of 12 proteins were analyzed for association with disease activity based on the 28-joint count Disease Activity Score (DAS28) after 3 months of MTX monotherapy using uni-/multivariate logistic regression. Primary outcome was low disease activity (LDA; DAS28 ≤ 3.2).Results.Of 298 patients, 104 achieved LDA after 3 months on MTX. Four of the 12 biomarkers [C-reactive protein (CRP), leptin, tumor necrosis factor receptor I (TNF-RI), and vascular cell adhesion molecule 1 (VCAM-1)] significantly predicted LDA based on stepwise logistic regression analysis. Dichotomization of patients using receiver-operating characteristic curve analysis-based cutoffs for these biomarkers showed significantly higher proportions with LDA among patients with lower versus higher levels of CRP or leptin (40% vs 23%, p = 0.004, and 40% vs 25%, p = 0.011, respectively), as well as among those with higher versus lower levels of TNF-RI or VCAM-1 (43% vs 27%, p = 0.004, and 41% vs 25%, p = 0.004, respectively). Combined score based on these biomarkers, adjusted for known predictors of LDA (smoking, sex, and age), associated with decreased chance of LDA (adjusted OR 0.45, 95% CI 0.32–0.62).Conclusion.Low baseline levels of CRP and leptin, and high baseline levels of TNF-RI and VCAM-1 were associated with LDA after 3 months of MTX therapy in patients with eRA. Combination of these 4 biomarkers increased accuracy of prediction. [Trial registration number: NCT00764725]

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