Is the SMART risk prediction model ready for real-world implementation?: A validation study in a routine care setting of approximately 380 000 individuals

AIMS: Reliably quantifying event rates in secondary prevention could aid clinical decision-making, including quantifying potential risk reductions of novel, and sometimes expensive, add-on therapies. We aimed to assess whether the SMART risk prediction model performs well in a real-world setting. METHODS AND RESULTS: We conducted a historical open cohort study using UK primary care data from the Clinical Practice Research Datalink (2000-2017) diagnosed with coronary, cerebrovascular, peripheral, and/or aortic atherosclerotic cardiovascular disease (ASCVD). Analyses were undertaken separately for cohorts with established (≥6 months) vs. newly diagnosed ASCVD. The outcome was first post-cohort entry occurrence of myocardial infarction, stroke, or cardiovascular death. Among the cohort with established ASCVD [n = 244 578, 62.1% male, median age 67.3 years, interquartile range (IQR) 59.2-74.0], the calibration and discrimination achieved by the SMART model was not dissimilar to performance at internal validation [Harrell's c-statistic = 0.639, 95% confidence interval (CI) 0.636-0.642, compared with 0.675, 0.642-0.708]. Decision curve analysis indicated that the model outperformed treat all and treat none strategies in the clinically relevant 20-60% predicted risk range. Consistent findings were observed in sensitivity analyses, including complete case analysis (n = 182 482; c = 0.624, 95% CI 0.620-0.627). Among the cohort with newly diagnosed ASCVD (n = 136 445; 61.0% male; median age 66.0 years, IQR 57.7-73.2), model performance was weaker with more exaggerated risk under-prediction and a c-statistic of 0.559, 95% CI 0.556-0.562. CONCLUSIONS: The performance of the SMART model in this validation cohort demonstrates its potential utility in routine healthcare settings in guiding both population and individual-level decision-making for secondary prevention patients

Accuracy of oscillometric blood pressure measurement in atrial fibrillation

OBJECTIVE: The primary aim of this study was to assess the accuracy of automated oscillometry (AO) in outpatients with atrial fibrillation (AF). The secondary aim was to explore whether AO accuracy is influenced by beat-to-beat blood pressure (BP) variability or heart frequency (HF). METHODS: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by AO and beat-to-beat BP using a validated Volume Clamp Method (VCM) technique. AO accuracy was analyzed separately in tertiles of beat-to-beat BP variability and HF. RESULTS: The main study included 58 AF and 38 sinus rhythm (SR) patients in whom the Welch Allyn Spot Vital Signs (WASVS) was used. An auxiliary study in 23 AF patients used the Philips M3002A IntelliVue ×2. For AF and SR patients, respectively, SBP by WASVS deviated by +0.1 (±14.8) mmHg and -7.9 (±15.7) mmHg from VCM. WASVS-DBP was higher than VCM in AF and SR by 6.3 (±9.2) mmHg and 5.0 (±7.7) mmHg, respectively. High beat-to-beat BP variability and high HF decreased WASVS accuracy for both SBP and DBP. SBP and DBP measurements by Philips M3002A IntelliVue ×2 deviated by -6.8 (±13.2) mmHg and 9.4 (±8.1) mmHg, respectively. CONCLUSION: Overall, AO accuracy in AF is limited; in individual patients, AO inaccuracy may be considerable. AO accuracy is especially reduced in patients showing large beat-to-beat BP variability or high HF

Accuracy of oscillometric blood pressure measurement in atrial fibrillation

The primary aim of this study was to assess the accuracy of automated oscillometry (AO) in outpatients with atrial fibrillation (AF). The secondary aim was to explore whether AO accuracy is influenced by beat-to-beat blood pressure (BP) variability or heart frequency (HF). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by AO and beat-to-beat BP using a validated Volume Clamp Method (VCM) technique. AO accuracy was analyzed separately in tertiles of beat-to-beat BP variability and HF. The main study included 58 AF and 38 sinus rhythm (SR) patients in whom the Welch Allyn Spot Vital Signs (WASVS) was used. An auxiliary study in 23 AF patients used the Philips M3002A IntelliVue ×2. For AF and SR patients, respectively, SBP by WASVS deviated by +0.1 (±14.8) mmHg and -7.9 (±15.7) mmHg from VCM. WASVS-DBP was higher than VCM in AF and SR by 6.3 (±9.2) mmHg and 5.0 (±7.7) mmHg, respectively. High beat-to-beat BP variability and high HF decreased WASVS accuracy for both SBP and DBP. SBP and DBP measurements by Philips M3002A IntelliVue ×2 deviated by -6.8 (±13.2) mmHg and 9.4 (±8.1) mmHg, respectively. Overall, AO accuracy in AF is limited; in individual patients, AO inaccuracy may be considerable. AO accuracy is especially reduced in patients showing large beat-to-beat BP variability or high H

Decline in risk of recurrent cardiovascular events in the period 1996 to 2014 partly explained by better treatment of risk factors and less subclinical atherosclerosis

Background To quantify the decline in recurrent major cardiovascular events (MCVE) risk in patients with clinically manifest vascular disease between 1996 and 2014 and to assess whether the improvements in recurrent MCVE-risk can be explained by reduced prevalence of risk factors, more medication use and less subclinical atherosclerosis. Methods and results The study was conducted in the Second Manifestations of ARTerial disease (SMART) cohort in patients entering the cohort in the period 1996–2014. The prevalence of risk factors and subclinical atherosclerosis was measured at baseline. Incidence rates per 100 person-years for recurrent MCVE (including stroke, myocardial infarction, retinal bleeding, retinal infarction, terminal heart failure, sudden death, fatal rupture of abdominal aneurysm) were calculated, stratified by the year of study enrolment. For the attributable risk of changes in risk factors, risk factor treatment, and subclinical atherosclerosis on the incidence rates of recurrent MCVE, adjusted rate ratios were estimated with Poisson regression. 7216 patients had a median follow-up of 6.5 years (IQR 3.4–9.9). The crude incidence of recurrent MCVE declined by 53% between 1996 and 2014 (from 3.68 to 1.73 events per 100 person-years) and by 75% adjusted for age and sex. This improvement in vascular prognosis was 36% explained by changes in risk factors, medication use and subclinical atherosclerosis. Conclusion The risk of recurrent MCVE in patients with clinically manifest vascular disease has strongly declined in the period between 1996 and 2014. This is only partly attributable to lower prevalence of risk factors, improved medication use and less subclinical atherosclerosis

Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease

Background and aims The predictive value of traditional risk factors for vascular events in patients with manifest vascular disease is limited, underscoring the need for novel biomarkers to improve risk stratification. Since hematological parameters are routinely assessed in clinical practice, they are readily available candidates. Methods We used data from 3,922 vascular patients, who participated in the Second Manifestations of ARTerial Disease (SMART) study. We first investigated associations between recurrent vascular events and 22 hematological parameters, obtained from the Utrecht Patient Oriented Database (UPOD), and then assessed whether parameters associated with outcome improved risk prediction. Results After adjustment for all SMART risk score (SRS) variables, lymphocyte %, neutrophil count, neutrophil % and red cell distribution width (RDW) were significantly associated with vascular events. When individually added to the SRS, lymphocyte % improved prediction of recurrent vascular events with a continuous net reclassification improvement (cNRI) of 17.4% [95% CI: 2.1, 32.1%] and an increase in c-statistic of 0.011 [0.000, 0.022]. The combination of lymphocyte % and neutrophil count resulted in a cNRI of 22.2% [3.2, 33.4%] and improved c-statistic by 0.011 [95% CI: 0.000, 0.022]. Lymphocyte % and RDW yielded a cNRI of 18.7% [3.3, 31.9%] and improved c-statistic by 0.016 [0.004, 0.028]. However, the addition of hematological parameters only modestly increased risk estimates for patients with an event during follow-up. Conclusions Several hematological parameters were independently associated with recurrent vascular events. Lymphocyte % alone and in combination with other parameters enhanced discrimination and reclassification. However, the incremental value for patients with a recurrent event was limited

Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with Type 2 diabetes mellitus

Aims: Although group-level effectiveness of lipid, blood pressure, glucose, and aspirin treatment for prevention of cardiovascular disease (CVD) has been proven by trials, important differences in absolute effectiveness exist between individuals. We aim to develop and validate a prediction tool for individualizing lifelong CVD prevention in people with Type 2 diabetes mellitus (T2DM) predicting life-years gained without myocardial infarction or stroke. Methods and results: We developed and validated the Diabetes Lifetime-perspective prediction (DIAL) model, consisting of two complementary competing risk adjusted Cox proportional hazards functions using data from people with T2DM registered in the Swedish National Diabetes Registry (n = 389 366). Competing outcomes were (i) CVD events (vascular mortality, myocardial infarction, or stroke) and (ii) non-vascular mortality. Predictors were age, sex, smoking, systolic blood pressure, body mass index, haemoglobin A1c, estimated glomerular filtration rate, non- high-density lipoprotein cholesterol, albuminuria, T2DM duration, insulin treatment, and history of CVD. External validation was performed using data from the ADVANCE, ACCORD, ASCOT and ALLHAT-LLT-trials, the SMART and EPIC-NL cohorts, and the Scottish diabetes register (total n = 197 785). Predicted and observed CVD-free survival showed good agreement in all validation sets. C-statistics for prediction of CVD were 0.83 (95% confidence interval: 0.83-0.84) and 0.64-0.65 for internal and external validation, respectively. We provide an interactive calculator at www.U-Prevent.com that combines model predictions with relative treatment effects from trials to predict individual benefit from preventive treatment. Conclusion: Cardiovascular disease-free life expectancy and effects of lifelong prevention in terms of CVD-free life-years gained can be estimated for people with T2DM using readily available clinical characteristics. Predictions of individual-level treatment effects facilitate translation of trial results to individual patients

Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with Type 2 diabetes mellitus.

Although group-level effectiveness of lipid, blood pressure, glucose, and aspirin treatment for prevention of cardiovascular disease (CVD) has been proven by trials, important differences in absolute effectiveness exist between individuals. We aim to develop and validate a prediction tool for individualizing lifelong CVD prevention in people with Type 2 diabetes mellitus (T2DM) predicting life-years gained without myocardial infarction or stroke. We developed and validated the Diabetes Lifetime-perspective prediction (DIAL) model, consisting of two complementary competing risk adjusted Cox proportional hazards functions using data from people with T2DM registered in the Swedish National Diabetes Registry (n = 389 366). Competing outcomes were (i) CVD events (vascular mortality, myocardial infarction, or stroke) and (ii) non-vascular mortality. Predictors were age, sex, smoking, systolic blood pressure, body mass index, haemoglobin A1c, estimated glomerular filtration rate, non- high-density lipoprotein cholesterol, albuminuria, T2DM duration, insulin treatment, and history of CVD. External validation was performed using data from the ADVANCE, ACCORD, ASCOT and ALLHAT-LLT-trials, the SMART and EPIC-NL cohorts, and the Scottish diabetes register (total n = 197 785). Predicted and observed CVD-free survival showed good agreement in all validation sets. C-statistics for prediction of CVD were 0.83 (95% confidence interval: 0.83-0.84) and 0.64-0.65 for internal and external validation, respectively. We provide an interactive calculator at www.U-Prevent.com that combines model predictions with relative treatment effects from trials to predict individual benefit from preventive treatment. Cardiovascular disease-free life expectancy and effects of lifelong prevention in terms of CVD-free life-years gained can be estimated for people with T2DM using readily available clinical characteristics. Predictions of individual-level treatment effects facilitate translation of trial results to individual patients