To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical ...setting.
The performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information ˗ diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naïve approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics.
C-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82–0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74–0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age).
Validity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing.
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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
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.
Aim
To quantify the magnitude and specific contributions of known cardiovascular risk factors leading to higher cardiovascular risk and all‐cause mortality caused by type 2 diabetes (T2D).
Methods
...Mediation analysis was performed to assess the relative contributions of known classical risk factors for vascular disease in T2D (insulin resistance, systolic blood pressure, renal function, LDL‐cholesterol, triglycerides and micro‐albuminuria), and what proportion of the effect of T2D on cardiovascular events and all‐cause mortality these factors mediate in the Second Manifestations of ARTerial disease (SMART) cohort consisting of 1910 T2D patients.
Results
Only 35% (95% CI 15‐71%) of the excess cardiovascular risk caused by T2D is mediated by the classical cardiovascular risk factors. The largest mediated effect was through insulin resistance proportion of mediated effect (PME) 18%, 95% CI 3‐37%, followed by elevated triglycerides (PME 8%, 95% CI 4‐14%) and micro‐albuminuria (PME 7%, 95% CI 3‐17%). Only 42% (95% CI 18‐73%) of the excess mortality risk was mediated by the classical risk factors considered. The largest mediated effect was by micro‐albuminuria (PME 18%, 95% CI 10‐29%) followed by insulin resistance (PME 15%, 95% CI 1‐33%).
Conclusion
A substantial amount of the increased cardiovascular risk and all‐cause mortality caused by T2D cannot be explained by traditional vascular risk factors. Future research should focus on identifying non‐classical pathways that might further explain the increased cardiovascular and mortality risk caused by T2D.
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.
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 100person-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 6.5years (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 100person-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.
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.
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.
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.
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.
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 HF.
To test the hypothesis that treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when ...based on estimated risk reduction over 10 years.
A microsimulation model was constructed for 10,000 patients with stable cardiovascular disease (CVD). Costs and quality-adjusted life years (QALYs) due to recurrent cardiovascular events and (non)vascular death were estimated for lifetime benefit-based compared to 10-year risk-based treatment, with PCSK9 inhibition as an illustration example. Lifetime benefit in months gained and 10-year absolute risk reduction were estimated using the SMART-REACH model, including an individualized treatment effect of PCSK9 inhibitors based on baseline low-density lipoprotein cholesterol. For the different numbers of patients treated (i.e. the 5%, 10%, and 20% of patients with the highest estimated benefit of both strategies), cost-effectiveness was assessed using the incremental cost-effectiveness ratio (ICER), indicating additional costs per QALY gain.
Lifetime benefit-based treatment of 5%, 10%, and 20% of patients with the highest estimated benefit resulted in an ICER of €36,440/QALY, €39,650/QALY, or €41,426/QALY. Ten-year risk-based treatment decisions of 5%, 10%, and 20% of patients with the highest estimated risk reduction resulted in an ICER of €48,187/QALY, €53,368/QALY, or €52,390/QALY.
Treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when based on estimated risk reduction over 10 years.
SPRINT trial: It's not just the blood pressure Berkelmans, Gijs Fn; Visseren, Frank Lj; Jaspers, Nicole Em ...
European journal of preventive cardiology
24, Številka:
14
Journal Article
Recenzirano
Odprti dostop
Background The SPRINT trial showed a beneficial effect of systolic blood pressure treatment targets of 120 mmHg on cardiovascular risk compared to targets of 140 mmHg. However, differences in ...medication use, most importantly diuretics, are suggested as an alternative explanation. This post-hoc analysis aimed to determine whether the reduced event rate can be attributed to changes in systolic blood pressure (ΔSBP) . Methods Analyses were based on all 9361 participants of the SPRINT trial. ΔSBP was defined as the change between baseline and 6-month follow-up systolic blood pressure. Major cardiovascular events were myocardial infarction, other acute coronary syndromes, stroke, heart failure, or cardiovascular death. Cox regression was used to describe the relation between ΔSBP and major cardiovascular events. Analyses were performed separately for patients in the lowest tertile of baseline systolic blood pressure, as the SPRINT trial reported the highest treatment effect in this subgroup. Results The relation between ΔSBP and major cardiovascular events was a hazard ratio per 10 mmHg decrease of 0.93 (95% confidence interval 0.89-0.98). Similar results were found within the lowest tertile of baseline systolic blood pressure: hazard ratio per 10 mmHg decrease 0.91 (95% confidence interval 0.82-1.01). Conclusion Our results show that lowering blood pressure prevents cardiovascular disease. However, not all the positive effects in the SPRINT trial could be explained by ΔSBP. Alternative explanations, such as differences in medication use, should be considered for the positive findings of the SPRINT trial.
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