For example, the RECODe model derived its risk equations from ACCORD (2001C2009) clinical trial data [18]. in the Ochsner (being the failure (event) probability, that is, the His-Pro chance of an event occurring in the interval (0, is the baseline survival of the three cardiovascular outcomes, x is the corresponding value of each variable in each model, is the corresponding mean of the cohorts characteristics for each continuous variable in each model, and is 0 for each categorical variable for the reference group in the model[18]. C-statistics were calculated by using as probability and event status (i.e., whether the patient had cardiovascular event). A logistic regression model was used His-Pro to assess the calibration of risk models. The outcome probability, and are identically 0 and 1, respectively. The model calibration was assessed with three tests at time (%)1284 (20.6)?Blood pressure, mmHg??Systolic133.0 (18.0)??Diastolic78.2 (10.9)?LDL cholesterol, mg/dl109.4 (36.8)?HDL cholesterol, mg/dl44.1 (12.4)?Total cholesterol, mg/dl182.6 (44.8)?Triglycerides, mg/dl145.6 (87.2)?Estimated GFR, ml/min/1.73 m251.5 (36.6)?Medical history (type 2 diabetes mellitus; body mass index; low-density lipoprotein; high-density lipoprotein; glomerular filtration rate; glucagon-like peptide-1; sodium-glucose cotransporter 2 Table ?Table22 provides the coefficients of the Ochsner risk equations for each of cardiovascular outcomes. The LASSO regularization method revealed His-Pro that common variables in Ochsner models include age, BMI, systolic blood pressure, HbA1c and eGFR. The other significant predictors were medical histories, such as CHD, HF and hypertension, followed by medication prescription histories and race. Table 2 Coefficients of the Ochsner models for calculating 5-year risk of CHD, HF and stroke is the coefficient, and x is the covariate vector for an individual patient within the Ochsner cohort. for the CHD, HF and stroke are 0.898, 0.928 and 0.975, respectively. For example, a 65-year-old white man with BMI 34.9?kg/m2, systolic blood pressure 143?mmHg, HbA1c 8.4%, LDL cholesterol 110?mg/dl, HDL cholesterol 45?mg/dl, triglycerides 145?mg/dl and estimated GFR 52?ml/min/1.73 m2 and with hypertension history, without CHD/HF/stroke history, and currently using antidiabetic and antihypertensive drugs, would have a 5-year CHD risk of 1 ? 0.898^exp (0.02143??65 ? 0.00959??34.9?+?0.13083??1?+?0.00213??143?+?0.05607??8.4 ? 0.00078??110 ? 0.00847??45?+?0.00292??52?+?0.55760??1?+?0.05846??1 ? 0.20982??1 ? 1.37)?=?0.192 or 19.2% 5-year risk, where 1.37 is the mean coronary heart disease; heart failure; body mass index; low-density lipoprotein; high-density lipoprotein; glomerular filtration rate Among factors identified as statistically significant in the Ochsner (type 2 Diabetes Mellitus; coronary heart disease Table ?Table44 shows the logistic regression results of the prognostic index on having CHD in the Ochsner T2DM cohort. The estimate of the intercept in the Ochsner model suggested that the predicted risk of having CHD at 5?years is about exp(?3.829)?=?0.021 higher that a perfect calibration, indicating that the Ochsner model overestimated the 5-year CHD risk. Along with the joint test results, a miscalibration for the Ochsner model was common with all the other models (Supplementary Material Table S2). Among the four models, the Ochsner model equations had a relatively high internal calibration. Table 4 Logistic regression results of PI on having CHD in the Ochsner T2DM cohort value /th th align=”left” colspan=”2″ rowspan=”1″ 95% Confidence interval /th /thead OchsnerPI0.8820.0450.0000.7940.970Intercept? 3.8290.0960.000? 4.018? 3.641RECODePI1.2880.0810.0001.1291.447Intercept? 2.8450.0600.000? 2.962? 2.727QRISK3PI0.0650.1430.651? 0.2160.346Intercept? 2.7440.2200.000? 3.175? 2.313AS-CVDPI0.4730.0600.0000.3550.591Intercept? 3.0580.0960.000? 3.246? 2.869 Open in a separate window Discussion In an era of learning health systems during which health policy changes are driving population health management to improve the quality, cost and experience of healthcare, health systems need reliable, reproducible predictive analytic tools that account for the diverse characteristics of populations they serve and allow for better patient care. Our results show a significantly better performance in the locally fitted Ochsner model than the other three models for predicting incident cardiovascular disease in the Ochsner population. These findings suggest locally fitted models may provide more useful predictive analytics compared to existing broader models. Although we only compared the number of significant predictors of CHD among the four models, this study found that the Ochsner model required fewer predictors, implying future efficiencies in data extraction and mapping. We did not compare the number of significant predictors of HF and stroke Rabbit Polyclonal to OR2B6 since neither QRISK3 nor AS-CVD predicted the incident risk of developing HF or His-Pro stroke. This study also found that the Ochsner model showed the best discrimination of predicting cardiovascular risk in the Ochsner T2DM cohort. The.

For example, the RECODe model derived its risk equations from ACCORD (2001C2009) clinical trial data [18]