Comparison Of Predicted Values Research Articles

The Comparison of Predictive Value Among Chemerin, IL-18 and Hormonal Parameters in Assessing the Risk of Metabolic Syndrome in Men.

Chemerin (CHEM) is a new proinflammatory adipokine involved in the immune, metabolic and reproductive processes. Low–grade state inflammation (LGSI) is a key element in the pathogenesis of metabolic syndrome (MS). Low SHBG is a good marker of male hypogonadism in MS. This study evaluated the prognostic value of selected adipokine, LGSI, and androgenic parameters in predicting the risk of MS among men. One hundred thirty-two random men aged 40 to 70 years old were enrolled. Measurements of anthropometric indices, blood pressure, and laboratory tests were carried out. A total of 62 men (47%) were diagnosed with MS. Chemerin concentrations were higher in men diagnosed with MS compared to healthy: 89.48 (78.12–112.10) vs. 77.9 (65.12–98.64) ng/mL; p = .002. Men diagnosed with MS presented with lower levels of total testosterone: 5.75 (4.00–6.57) vs. 6.40 (5.50–8.40) ng/mL; p = .0014 and SHBG: 46.58 (35.13–66.28) vs. 71.97 (56.1–92.7) nM/L; p < 0.000001. Elevated LGSI indices were demonstrated in men with MS as opposed to healthy [IL–18: 530.64 (409.12–640.56) vs. 418.85 (348.14–496.44) pg/mL; p = .000033 and hs–CRP: 2.15 (0.97–4.26) vs. 1.01 (0.41–2.68) ng/mL; p = .0057)]. In multivariate regression analysis, the highest negative predictive value in assessing the risk of MS was SHBG serum concentration, while the highest positive predictive values were: IL-18, hypertriglyceridemia, and waist circumference. Decreased SHBG levels, combined with elevated IL-18 concentrations in men showing hypertriglyceridemic waist phenotype, significantly increase the risk of MS.

Prediction of left atrial thrombus in patients with nonvalvular atrial fibrillation referred to catheter ablation or cardioversion: comparison of different risk scores

Abstract Funding Acknowledgements Type of funding sources: None. Background. Left atrial thrombus (LAT) is the main source of cardiac emboly in patients with non-valvular atrial fibrillation (NAF). Several risk scores – mostly modified CHADS2 and CHA2DS2-VASc – were offered to predict LAT in patients with NAF. However, their relative predictive value requires further evaluation. Purpose. Compare the ability of different risk scores to predict LAT before catheter ablation or cardioversion in patients with NAF. Methods. In a retrospective single-center study, medical records of 1994 patients with NAF who underwent transesophageal echocardiography before catheter ablation or cardioversion were analyzed. LAT was identified in 33 (1.6%) of them. For the control group 167 patients without LAT were randomly selected from this database. Logistic regression analysis and C-statistic were used for evaluation and comparison of predictive values of CHADS2, R2CHADS2, CHA2DS2-VASc, R-CHA2DS2-VASc, R2CHA2DS2-VASc, CHA2DS2-VASc-RAF, mCHA2DS2-VASc and CHA2DS2-VASc-AFR scores. Results. The mean age of studied patients was 60.3 ± 10.9 years, 110 (55%) of them were males. The mean CHA2DS2-VASc score was 2.54 ± 1.79. Results of univariate analysis and C-statistic for above mentioned risk scores are presented in the table. Each of them was associated with LAT. In comparison with a CHA2DS2-VASc score C-statistic was significantly higher for CHA2DS2-VASc-RAF and CHA2DS2-VASc-AFR scores (p values 0.03 and 0.001 respectively). In multivariate analysis only CHA2DS2-VASc-RAF score was associated with LAT (OR 1.37; 95% CI 1.21-1.55, p &amp;lt; 0.0001). OR for LАT in patients with CHA2DS2-VASc-RAF &amp;gt;3 was 12.8 (95% CI 3.75-43.9; p &amp;lt; 0.0001) with sensitivity, specificity, positive and negative predictive values 90.6%, 57.1%, 33.3% and 58.9% respectively. Conclusion. In a group of patients with NAF and relatively low incidence of LAT all studied scores were associated with LAT and CHA2DS2-VASc-RAF score has appeared the most informative. Predictors of LAT in patients with NAF Risk stratification models OR (95% CI) p-value C-statistic (95% CI) CHADS2 2.12 (1.55-2.91) &amp;lt;0.0001 0.77 (0.68-0.85) R2CHADS2 2.00 (1.53-2.62) &amp;lt;0.0001 0.78 (0.69-0.87) CHA2DS2-VASc 1.65 (1.36-2.05) &amp;lt;0.0001 0.74 (0.65-0.84) R-CHA2DS2-VASc 1.64 (1.34-2.03) &amp;lt;0.0001 0.76 (0.66-0.85) R2CHA2DS2-VASc 1.59 (1.32-1.92) &amp;lt;0.0001 0.76 (0.66-0.85) CHA2DS2-VASc- RAF 1.35 (1.27-1.52) &amp;lt;0.0001 0.84 (0.76-0.91) mCHA2DS2-VASc 1.83 (1.42-2.35) &amp;lt;0.0001 0.75 (0.65-0.85) CHA2DS2-VASc-AFR 1.75 (1.41-2.17) &amp;lt;0.0001 0.80 (0.71-0.88)

Comparison of predictive value of NT-proBNP, sST2 and MMPs in heart failure patients with different ejection fractions

BackgroundThis study sought to compare the predictive value of NT-proBNP, sST2 and MMPs in HF with different ejection fractions from a population in southern China.MethodsA cross-sectional study was conducted on 113 HF patients admitted to Fujian Provincial Hospital from December 2016 to March 2018.The patients were divided into three subgroups: 60 cases in HFpEF group (LVEF≥50%), 28 cases in HFmrEF group (41% ≤ LVEF≤49%) and 25 cases in HFrEF group (LVEF≤40%). ELISA method was applied to detect the concentrations of sST2, MMP-2 and MMP-9. Electrochemical luminescence immunoassay was applied to detect the concentration of plasma NT-proBNP. Univariate and multivariate Cox and logistic regression models were used to analyze the diagnostic significance of these plasma biomarkers in HF patients. Kaplan–Meier survival curves were used to assess the prognostic value of sST2 in the incidence of long-term adverse events during study.ResultsThis study showed that plasma sST2 levels in HFrEF or HFmrEF patients were significantly higher than in HFpEF patients. Plasma levels of MMP-2 and MMP-9 in HFrEF patients were apparently higher than in HFpEF or HFmrEF patients. For the diagnosis of HFpEF, the AUC of NT-proBNP was higher than that of sST2, MMP-2 and MMP-9, which were 0.881, 0.717, 0.705 and 0.597, respectively. For the diagnosis of HFmrEF, the AUC of plasma sST2 was higher than that of MMP-2, MMP-9 and NT-proBNP, which were 0.799, 0.678, 0.676 and 0.793, respectively. For the diagnosis of HFrEF, the AUC of plasma NT-proBNP, sST2, MMP-2, and MMP-9 were 0.945, 0.820, 0.814, and 0.774 respectively. Spearman correlation analysis showed that plasma sST2 levels were significantly correlated with plasma MMP-2, MMP-9 and NT-proBNP levels. Further logistic regression analysis showed that except MMP-9, the biomarkers sST2 (OR = 1.960), MMP-2 (OR = 0.805) and NT-proBNP (OR = 0.002) were all independent risk factors for patients with heart failure. Survival analysis results suggested that for patients with HFmrEF, a higher level of plasma sST2 (≥ 0.332 ng/ml at admission) may predict a higher risk of endpoint events and a lower survival rate (P < 0.025).ConclusionsThe circulating biomarkers sST2, MMP-2 and NT-proBNP were all independent risk factors for patients with heart failure. The sST2 can be a useful biomarker with both diagnostic and prognostic value in patients with HFmrEF. The higher sST2 level in patients with heart failure was related to a higher incidence of combined endpoint outcome.

Understanding the predictive value of continuous markers for censored survival data using a likelihood ratio approach

BackgroundThe likelihood ratio function (LR), the ratio of conditional probabilities of obtaining a specific marker value among those with the event of interest over those without, provides an easily interpretable way to quantify the update of the risk prediction due to the knowledge of the marker value. The LR has been explored for both binary and continuous markers for binary events (e.g., diseased or not), however the use of the LR in censored data has not been fully explored.MethodsWe extend the concept of LR to a time-dependent LR (TD-LR) for survival outcomes that are subject to censoring. Estimation for the TD-LR is done using Kaplan-Meier estimation and a univariate Cox proportional hazards (PH) model. A “scale invariant” approach based on marker quantiles is provided to allow comparison of predictive values between markers with different scales. Relationships to time-dependent receiver-operator characteristic (ROC) curves, area under the curve (AUC), and optimal cut-off values are considered.ResultsThe proposed methods were applied to data from a bladder cancer clinical trial to determine whether the neutrophil-to-lymphocyte ratio (NLR) is a valuable biomarker for predicting overall survival following surgery or combined chemotherapy and surgery. The TD-LR method yielded results consistent with the original findings while providing an easily interpretable three-dimensional surface display of how NLR related to the likelihood of event in the trial data.ConclusionsThe TD-LR provides a more nuanced understanding of the relationship between continuous markers and the likelihood of events in censored survival data. This method also allows more straightforward communication with a clinical audience through graphical presentation.

Is prediction of clinical response to methotrexate in individual rheumatoid arthritis patients possible? A systematic literature review.

To identify, by a systematic literature review, predictors of clinical response to methotrexate treatment in rheumatoid arthritis patients, which would facilitate personalised treatment. PubMed and Embase databases were searched for original articles. Additionally, congress abstracts of European League Against Rheumatism and American College of Rheumatology annual meetings of the past 2 years were screened. Articles describing predictors of clinical response to methotrexate after 3 to 6 months were included, since this reflects the time span used to determine treatment effectiveness and decide on treatment changes in treat-to-target recommendations. Thirty articles were included, containing 100 different predictors and 11 predictive models. Nineteen predictors and 2 predictive models were studied in multiple cohorts. Female gender was found to be a predictor of non-response in two studies (odds ratios 0.55 and 0.54), but these findings could not be replicated in two other studies. In two studies, smoking predicted non-response (adjusted odds ratios 0.35 and 0.60), although this was inconsistent over all response criteria assessed. Rheumatoid factor positivity predicted non-response in two studies (adjusted hazard ratio 0.61, adjusted odds ratio 0.4), but this was not found in three other studies. Heterogeneity in studies prohibited further comparison of predictive values between studies. Additionally, a validated epigenetic model was found (area under the curve 0.90 and 0.91). No predictors were identified reliably predicting clinical response to methotrexate after 3 to 6 months in the individual patient: clinical predictors were weak. However, a promising epigenetic model was found that needs further validation.

Comparison of predictive value between two kinds of score system in hospital mortality of pneumonia patients

Objective To compare the value in predicting hospital mortality of pneumonia patients between Charlson’s weighted index of comorbidities (WIC) and chronic health score (CHS), in Intensive Care Units (ICU). Methods The clinical data of pneumonia patients collected from September 2013 to September 2014 in ICU of two centers were retrospectively analyzed . The data included sex, age, comorbidites, past history about diseases contracted, presence or absence of severe sepsis. WIC and the acute physiology score (APS), age, chronic health evaluation Ⅱ(APACHEⅡ) score including CHS, APS, age scores, and sepsis related organ failure assessment (SOFA) were calculated. Results Of 293 patients, there are 195 (66.6%) males and 98 (33.4%) females; 197 (67.2%) survivors and 114(33.7%) non-survivors. The average age was (62.5±17.0) years. Compared with survivors, WIC scores were higher in non-survivors. However, the difference in CHS between two groups was not statistically significant. Along with the increase of WIC and CHS score, the patients mortality was increased. The univariate analysis showed that the variables including age, APACHEⅡ score, SOFA, APS, CHS, WIC, and presence or absence of severe sepsis, diabetes, stroke and congestive cardiac failure were related with patientss’ prognosis in 28 days. The univariate logistic regression of CHS (model 1) or WIC (model 2) combine with age, APS, presence or absence of severe sepsis and main comorbidities, such as diabetes, stroke and congestive cardiac failure indicated that risk of death depended significantly on age, APS, CHS, WIC, presence or absence of severe sepsis. The area under the receiver operating characteristics curve (ROC) in predicting mortality was 0.557 95%CI: (0.49-0.623)for CHS, it was lower than the 0.649 95%CI: (0.586-0.712)for WIC (P=0.206, Z=2.315). The AUC of multivariable equations was significantly better than that of APACHEⅡ score (P<0.01). Conclusions The WIC and CHS were independent risk factors for in-hospital mortality of pneumonia patients in ICU. WIC was better than CHS in evaluating comorbidity. Key words: WIC; CHS; APACHE Ⅱ; SOFA; Sepsis; Diabetes; Pneumonia; Outcome

Value of detecting HEARTS3 Score to improve risk stratification in patients with non-ST segment elevation chest pain in emergency department

Objective To investigate the value of detecting HEART score and HEARTS3 score in risk stratification and prognosis of acute coronary syndrome (ACS) in patients with non-ST segment elevation chest pain in emergency department (ED). Methods Clinical data of case-control retrospective study of 775 patients with non-ST segment elevation chest pain in ED were collected from July 2011 to March 2015. The patients were estimated and risk stratification was made with HEART score and HEARTS3 score. After follow-up visiting by telephone for 30 days, outcomes were found to be ACS and myocardial infarction (MI). And the patients were categorized with score into low, intermediate and high risk groups .The correlation between the ACS and risk score in three groups was analyzed. Comparison of capability of performance in predicting 30-day ACS between the HEART score and HEARTS3 risk score. Statistical analyses were performed using SPSS13.0. Enumeration variables were expressed as percentage. For comparison of predictive value of the two sets of scores, area under the receiver operating curve (auROC) was calculated and compared by Z test. Results There were 92 cases with 30-day ACS. The rate of ACS had a trend of increase with increase in HEART score and HEARTS3 score. The patients with higher scores of HEART and HEARTS3, higher incidence of ACS in 30 days .Especially, the high-risk patients with score≥7 of HEART score and≥8 of HEARTS3 score had higher rate of ACS. And there was significant difference in predicting high-risk patients between two sets of scoring (P<0.05). The HEARTS3 score outperformed the HEART score as determined by comparison of areas under the ROC curve for MI (0.952 vs 0.813; P=0.028), 30-day ACS (0.913 vs. 0.815; P=0.034). Conclusions HEART score and HEARTS3 score both can be used to evaluate and perform risk stratification for non-ST segment elevation chest pain patients in ED .But HEARTS3 score can more precisely stratify high-risk patients with chest pain for 30-day ACS. Key words: HEART score; HEARTS3 score; Chest pain; Risk Stratification; Acute Coronary Syndrome(ACS); Prognosis

The Analysis of Ballast Settlement Data Based on Time Series

The stability of ballast is the key to guarantee the traffic safety. This paper is on the back -ground of ballast settlement monitoring of a bridge constructed by jacking method. And the bridge is located at the 1094 kilometers in Jingha railway line. Monitoring data is analyzed by the time series method. Then we build an ARIMA model for these data. Through the AIC principle, the orders of the model are obtained. Then we predict the settlement of the ballast. By the comparison of predictive value and the true monitoring data, it shows that using time series to analyze the settlement of ballast is reasonable and reliable. Therefore it is a new method to analyze the settlement of ballast. At the same time, this method only relay on the monitoring dates but not on other conditions.

Global hypothesis test to simultaneously compare the predictive values of two binary diagnostic tests

The positive and negative predictive values of a binary diagnostic test are measures of the clinical accuracy of the diagnostic test, which depend on the sensitivity and specificity of the diagnostic test and the disease prevalence, and therefore they are two interdependent parameters. The comparisons of predictive values in paired designs do not consider the dependence between predictive values. A global hypothesis test has been studied in order to simultaneously compare the predictive values of two or more binary diagnostic tests when the binary tests and the gold standard are applied to all of the individuals in a random sample. This global hypothesis test is an asymptotic hypothesis test based on the chi-square distribution. Simulation experiments have been carried out in order to study the type I error and the power of the global hypothesis test when comparing the predictive values of two and three binary diagnostic tests, respectively. From the results of the simulation experiments, a method has been proposed to simultaneously compare the predictive values of two or more binary diagnostic tests. The results have been applied to the diagnosis of coronary disease.

Author's reply to the preceding discussion [by G. N. Alexander on ‘An empirical comparison of predictive values of three extreme value procedures’

Author's reply to the preceding discussion [by G. N. Alexander on ‘An empirical comparison of predictive values of three extreme value procedures’