Construction and Validation of a 14-Year Cardiovascular Risk Score for Use in the General Population: The Puras-GEVA Chart

Prognosis of Systolic Pressure 130 to 139 According to Risk. A Prospective Cohort Study Between 1992 and 2019

BACKGROUND

Guidelines recommend pharmacological treatment for systolic blood pressure (SBP) of 130 to 139 mm Hg in secondary prevention. However, uncertainty persists in primary prevention in low cardiovascular risk patients (CVR).

METHODS

Cohort study representative of the general population of Albacete/Southeast Spain. We examined 1029 participants with untreated blood pressure and free of cardiovascular disease, followed-up during 1992 to 2019. Cox regression modeled the association of SBP with cardiovascular morbidity and mortality (outcome-1) and cardiovascular morbidity and all-cause mortality (outcome-2).

RESULTS

Participants' mean age was 44.8 years (53.8%, women; 77.1% at low-CVR); 20.3% had SBP 120 to 129; 13.0% 130 to 139 at low-CVR and 3.4% at high-CVR; and 27.4% ≥140 mm Hg. After a 25.7-year median follow-up, 218 outcome-1 and 302 outcome-2 cases occurred. Unadjusted hazard ratios of outcome-1 for these increasing SBP categories (versus <120) were 2.72, 2.27, 11.54, and 7.52, respectively; and 2.69, 2.32, 10.55, and 7.34 for outcome-2 (all P<0.01). After adjustment for other risk factors, hazard ratio (95% CI) of outcome-1 were 1.49 (0.91-2.44), 1.65 (0.94-2.91, P=0.08), 1.36 (0.72-2.57), and 1.82 (1.15-2.88), respectively, and 1.39 (0.91-2.11), 1.69 (1.05-2.73), 1.09 (0.63-1.88), and 1.64 (1.11-2.41) for outcome-2. Compared with 130 to 139 at low-CVR, hazard ratio for 130 to 139 at high-CVR was 4.85 for outcome-1 (P<0.001) and 4.43 for outcome-2 (P<0.001).

CONCLUSIONS

In this primary prevention population of relatively young average age, untreated SBP of 130 to 139 mm Hg at low-CVR had long-term prognostic value and might benefit from stricter SBP targets. High-CVR patients had nonsignificant higher risk (limited sample size) but 4-fold greater risk when compared with low-CVR. Overall, results indicate the importance of risk stratification, supporting risk-based decision-making.

Effect of Physical Activity on Cardiovascular Event Risk in a Population-Based Cohort of Patients with Type 2 Diabetes

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among patients with type 2 diabetes (T2D). Physical activity (PA) is one of the few modifiable factors that can reduce this risk. The aim of this study was to estimate to what extent PA can contribute to reducing CVD risk and all-cause mortality in patients with T2D. Information from a population-based cohort including 26,587 patients with T2D from the Navarre Health System who were followed for five years was gathered from electronic clinical records. Multivariate Cox regression models were fitted to estimate the effect of PA on CVD risk and all-cause mortality, and the approach was complemented using conditional logistic regression models within a matched nested case–control design. A total of 5111 (19.2%) patients died during follow-up, which corresponds to 37.8% of the inactive group, 23.9% of the partially active group and 12.4% of the active group. CVD events occurred in 2362 (8.9%) patients, which corresponds to 11.6%, 10.1% and 7.6% of these groups. Compared with patients in the inactive group, and after matching and adjusting for confounders, the OR of having a CVD event was 0.84 (95% CI: 0.66–1.07) for the partially active group and 0.71 (95% CI: 0.56–0.91) for the active group. A slightly more pronounced gradient was obtained when focused on all-cause mortality, with ORs equal to 0.72 (95% CI: 0.61–0.85) and 0.50 (95% CI: 0.42–0.59), respectively. This study provides further evidence that physically active patients with T2D may have a reduced risk of CVD-related complications and all-cause mortality.

Comparison Between Non-High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model

BACKGROUND

Although studies exist comparing low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (HDL-C) in the development of cardiovascular disease (CVD), most have limitations in the mathematical models used to evaluate their prognostic power adjusted for the other risk factors (cardiovascular risk).

OBJECTIVE

The aim of this study was to compare LDL-C and non-HDL-C in patients with CVD to determine whether both parameters predict CVD similarly.

METHODS

A cohort of 1322 subjects drawn from the general population of a Spanish region was followed between 1992 and 2006. The outcome was time to CVD. Secondary variables were gender, age, hypertension, diabetes, personal history of CVD, current smoker, body mass index, LDL-C, and non-HDL-C. Two CVD prediction models were constructed with the secondary variables, with only the lipid parameter varying (non-HDL-C or LDL-C). In the construction of the models, the following were considered: multiple imputation, events per variable of 10 or more, and continuous predictors as powers. The validation was conducted by bootstrapping obtaining the distribution of the C statistic (discrimination) and the probabilities observed by smooth curves. These results were compared in both models using graphical and analytical testing.

RESULTS

There were a total of 137 CVD events. The models showed no differences in the distributions of the C statistic (discrimination, P = .536) or in the calibration plot.

CONCLUSIONS

In our population, LDL-C and non-HDL-C were equivalent at predicting CVD. More studies using this methodology are needed to confirm these results.

Evaluation of non-HDL cholesterol as a predictor of non-fatal cardiovascular events in a prospective population cohort

INTRODUCTION

Non-HDL cholesterol (non-HDL-C) is becoming relevant both in its participation in cardiovascular risk assessment and as a therapeutic target. The objective of the present study was to assess the independent predictive capacity of both non-HDL-C and LDL-C (the main priority in dyslipidemias to reduce cardiovascular risk), in cardiovascular morbidity in a population-based sample.

METHODS

A prospective cohort study involving 1186 individuals in the non-HDL-C group and 1177 in the LDL-C group, followed for 10.7years (SD=2.2), who had not had any previous cardiovascular event. The predictor variables included in the adjustment were: gender, age, arterial hypertension, diabetes mellitus, smoker status and non-HDL-C in one group. In the other group, consisting of patients presenting TG levels of 400mg/dL, non-HDL-C was replaced by LDL-C. Survival curves (Kaplan-Meier) were calculated and two Cox regression models were applied, one for each group.

RESULTS

Non-HDL-C group presented 6.2% of non-fatal cardiovascular episodes during follow-up and the LDL-C group 6.0%. After adjustment, for each 30mg/dL increase in non-HDL-C, the incidence of new non-fatal cardiovascular events increased by 31% (HR=1.31, 95%CI: 1.06-1.61; P=.018) and in the LDL-C group by 27% (HR=1.27, 95%CI: 0.97-1.61, P=.068).

CONCLUSIONS

After a follow-up of 10.7years, non-HDL-C has been shown in our population as a prognostic factor of non-fatal cardiovascular disease, but not LDL-C, although its HR is close to statistical significance.

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

BACKGROUND

Other studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.

OBJECTIVES

To construct and internally validate a predictive model for nonadherence to PPIs.

METHODS

This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).

RESULTS

The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83-0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70-0.87]) and a specificity of 0.82 (95% CI [0.76-0.87]). The three parameters were very similar in the bootstrap validation.

CONCLUSIONS

A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

A method to construct a points system to predict cardiovascular disease considering repeated measures of risk factors

Current predictive models for cardiovascular disease based on points systems use the baseline situation of the risk factors as independent variables. These models do not take into account the variability of the risk factors over time. Predictive models for other types of disease also exist that do consider the temporal variability of a single biological marker in addition to the baseline variables. However, due to their complexity these other models are not used in daily clinical practice. Bearing in mind the clinical relevance of these issues and that cardiovascular diseases are the leading cause of death worldwide we show the properties and viability of a new methodological alternative for constructing cardiovascular risk scores to make predictions of cardiovascular disease with repeated measures of the risk factors and retaining the simplicity of the points systems so often used in clinical practice (construction, statistical validation by simulation and explanation of potential utilization). We have also applied the system clinically upon a set of simulated data solely to help readers understand the procedure constructed.