F carotid plaque (Table 2).NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA

F carotid plaque (Table 2).NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptUsing Salford Predictive Modeling software, the most significant independent predictors and cutoff points were as follows: piHDL function 0.94 FU, leptin levels 34 ng/ml, sTWEAK levels 373 pg/ml, homocysteine levels 12 moles/liter, age 48 years, and history of diabetes. These variables were designated the Predictors of Risk for Elevated Flares, Damage Progression, and Increased Cardiovascular Disease in Patients with SLE, or PREDICTS. Logistic regression analysis determined which variables were most consistently associated with any carotid plaque at baseline or followup in SLE patients. The model included individual PREDICTS variables, other significant predictors on univariate analysis, and traditional cardiac and SLE-associated factors that are known potential confounders for our biomarkers of interest (e.g, hypertension, BMI, tobacco use, and statin use). IndependentArthritis Rheumatol. Author manuscript; available in PMC 2014 July 22.McMahon et al.Pagepredictors of carotid plaque included age 48 years, history of diabetes, high piHDL function, increased plasma leptin levels, and increased plasma sTWEAK levels (Table 3).NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptBetter predictive capacity of a high-risk PREDICTS score for ATH in SLE patients, when compared with individual biomarkers or traditional cardiac risk factors The overall profile for the prediction of any longitudinal carotid plaque was evaluated for each cardiac risk factor and biomarker by calculating the positive predictive value (PPV) and negative predictive value (NPV), the specificity, and the sensitivity (Table 4).Triamcinolone For example, history of diabetes had a specificity of 98 for the presence of plaque; however, the sensitivity was only 13 .Peresolimab A combination of 3 traditional cardiac risk factors also had good specificity but low sensitivity. High piHDL function and increased sTWEAK levels individually had high NPV but low PPV. When the PREDICTS markers were grouped together to classify a high-risk profile, defined as 3 positive biomarkers or 1 biomarker plus a history of diabetes, it had the best overall predictive profile of any of the variables tested (Table 4). The high-risk PREDICTS profile also had the best overall predictive profile for incident plaque, with a sensitivity of 81 , specificity of 79.2 , PPV of 40.4 , NPV of 95.4 , and area under the receiver operating characteristic curve of 0.80 (95 confidence interval [95 CI] 0.PMID:25818744 71.90) (results available from the corresponding author upon request). Multivariate analysis using PREDICTS as a single variable showed that SLE patients with the high-risk PREDICTS profile had 27.7-fold increased odds (P 0.001) for the presence of any plaque on the baseline or followup carotid ultrasound (Table 5). In a separate logistic regression analysis in control subjects, the high-risk PREDICTS profile conferred 8.1-fold (95 CI 1.86.4) increased odds for the presence of carotid plaque (P = 0.006). A history of hypertension was also a significant independent predictor in control subjects (OR 9.4, 95 CI 1.50.4, P = 0.019). Association of high-risk PREDICTS profile with a higher rate of ATH progression over time A high PREDICTS score was also associated with progression of subclinical ATH over time (Table 6). In univariate analyses, SLE patients with a high PREDICTS score were significantly more likely to ha.