A cardiovascular risk score for hypertensive patients previously admitted to hospital.

BACKGROUND: Cardiovascular risk scales in hypertensive populations have limitations for clinical practice. AIMS: To develop and internally validate a predictive model to estimate one-year cardiovascular risk for hypertensive patients admitted to hospital. METHODS: Cohort study of 303 hypertensive patients admitted through the Emergency Department in a Spanish region in 2015-2017. The main variable was the onset of cardiovascular disease during follow-up. The secondary variables were: gender, age, educational level, family history of cardiovascular disease, Charlson score and its individual conditions, living alone, quality of life, smoking, blood pressure, physical activity and adherence to the Mediterranean diet. A Cox regression model was constructed to predict cardiovascular disease one year after admission. This was then adapted to a points system, externally validated by bootstrapping (discrimination and calibration) and implemented in a mobile application for Android. RESULTS: A total of 93 patients developed cardiovascular disease (30.7%) over a mean period of 1.68 years. The predictors in the points system were: gender, age, myocardial infarction, heart failure, peripheral arterial disease and daily activity (quality of life). The internal validation by bootstrapping was satisfactory. CONCLUSION: A novel points system was developed to predict short-term cardiovascular disease in hypertensive patients after hospital admission. External validation studies are needed to corroborate the results obtained.

A one-year risk score to predict all-cause mortality in hypertensive inpatients.

The aim of this study was to construct and internally validate a scoring system to estimate the probability of death in hypertensive inpatients. Existing predictive models do not meet all the indications for clinical application because they were constructed in patients enrolled in clinical trials and did not use the recommended statistical methodology. This cohort study comprised 302 hypertensive patients hospitalized between 2015 and 2017 in Spain. The main variable was time-to-death (all-cause mortality). Secondary variables (potential predictors of the model) were: age, gender, smoking, blood pressure, Charlson Comorbidity Index (CCI), physical activity, diet and quality of life. A Cox model was constructed and adapted to a points system to predict mortality one year from admission. The model was internally validated by bootstrapping, assessing both discrimination and calibration. The system was integrated into a mobile application for Android. During the study, 63 patients died (20.9%). The points system prognostic variables were: gender, CCI, personal care and daily activities. Internal validation showed good discrimination (mean C statistic of 0.76) and calibration (observed probabilities adjusted to predicted probabilities). In conclusion, a points system was developed to determine the one-year mortality risk for hypertensive inpatients. This system is very simple to use and has been internally validated. Clinically, we could monitor more closely those patients with a higher risk of mortality to improve their prognosis and quality of life. However, the system must be externally validated to be applied in other geographic areas.

An inferential study of the phenotype for the chromosome 15q24 microdeletion syndrome: a bootstrap analysis.

In January 2012, a review of the cases of chromosome 15q24 microdeletion syndrome was published. However, this study did not include inferential statistics. The aims of the present study were to update the literature search and calculate confidence intervals for the prevalence of each phenotype using bootstrap methodology. Published case reports of patients with the syndrome that included detailed information about breakpoints and phenotype were sought and 36 were included. Deletions in megabase (Mb) pairs were determined to calculate the size of the interstitial deletion of the phenotypes studied in 2012. To determine confidence intervals for the prevalence of the phenotype and the interstitial loss, we used bootstrap methodology. Using the bootstrap percentiles method, we found wide variability in the prevalence of the different phenotypes (3-100%). The mean interstitial deletion size was 2.72 Mb (95% CI [2.35-3.10 Mb]). In comparison with our work, which expanded the literature search by 45 months, there were differences in the prevalence of 17% of the phenotypes, indicating that more studies are needed to analyze this rare disease.

Diagnostic inertia in dyslipidaemia: results of a preventative programme in Spain.

Others have analysed the relationship between inadequate behaviour by healthcare professionals in the diagnosis of dyslipidaemia (diagnostic inertia) and the history of cardiovascular risk factors. However, since no study has assessed cardiovascular risk scores as associated factors, we carried out a study to quantify diagnostic inertia in dyslipidaemia and to determine if cardiovascular risk scores are associated with this inertia. In the Valencian Community (Spain), a preventive programme (cardiovascular, gynaecologic and vaccination) was started in 2003 inviting persons aged ≥40 years to undergo a health check-up at their health centre. This cross-sectional study examined persons with no known dyslipidaemia seen during the first six months of the programme (n = 16, 905) but whose total cholesterol (TC) was ≥5.17 mmol/L. Diagnostic inertia was defined as lack of follow-up to confirm/discard the dyslipidaemia diagnosis. Other variables included in the analysis were gender, history of cardiovascular risk factors/cardiovascular disease, counselling (diet/exercise), body mass index (BMI), age, blood pressure, fasting blood glucose and lipids. TC was grouped as ≥/<6.20 mmol/L. In patients without cardiovascular disease and <75/≤65 years (n = 15, 778/13, 597), the REGICOR (REgistre GIroní del COr)/SCORE (Systematic COronary Risk Evaluation) cardiovascular risk functions were used to classify risk (high/low). Inertia was quantified and the adjusted odds ratios calculated from multivariate models. In the overall sample, the rate of diagnostic inertia was 52% (95% CI [51.2-52.7]); associated factors were TC ≥ 6.20 mmol/L, high or "not measured" BMI, hypertension, smoking and higher values of fasting blood glucose, systolic blood pressure and TC. In the REGICOR sample, the rate of diagnostic inertia was 51.9% (95% CI [51.1-52.7]); associated factors were REGICOR high and high or "not measured" BMI. In the SCORE sample the rate of diagnostic inertia was 51.7% (95% CI [50.9-52.5]); associated factors were SCORE high and high or "not measured" BMI. Diagnostic inertia existed in over half the patients and was associated with a greater cardiovascular risk.

Diagnostic inertia in obesity and the impact on cardiovascular risk in primary care: a cross-sectional study.

BACKGROUND: Prevalence of diagnostic inertia (DI), defined as a failure to diagnose disease, has not been analysed in patients with obesity. AIM: To quantify DI for cardiovascular risk factors (CVRF) in patients with obesity, and determine its association with the cardiovascular risk score. DESIGN AND SETTING: Cross-sectional study of people ≥40 years attending a preventive programme in primary healthcare centres in Spain in 2003-2004. METHOD: All patients with obesity attending during the first 6 months of the preventive programme were analysed. Participants had to be free of CVD (myocardial ischaemia or stroke) and aged 40-65 years; the criteria used to measure SCORE (Systematic COronary Risk Evaluation). Three subgroups of patients with obesity with no personal history of CVRF but with poor control of risk factors were established. Outcome variable was DI, defined as poor control of risk factors and no action taken by the physician. Secondary variables were diabetes, fasting blood glucose (FBG), body mass index (BMI), and SCORE. Adjusted odds ratios (OR) was determined using multivariate logistic regression models. RESULTS: Of 8687 patients with obesity in the programme, 6230 fulfilled SCORE criteria. Prevalence of DI in the three subgroups was: hypertension, 1275/1816 (70.2%) patients affected (95% CI = 68.1 to 72.3%); diabetes, 335/359 (93.3%) patients affected (95% CI = 90.7 to 95.9%); dyslipidaemia subgroup, 1796/3341 (53.8%) patients affected (95% CI = 52.1 to 55.4%. Factors associated with DI for each subgroup were: for hypertension, absence of diabetes, higher BMI, and greater cardiovascular risk; for dyslipidaemia, diabetes, higher BMI, and greater cardiovascular risk (SCORE); and for diabetes, lower FBG levels, lower BMI, and greater cardiovascular risk. CONCLUSION: This study quantified DI in patients with obesity and determined that it was associated with a greater cardiovascular risk.

A four-year cardiovascular risk score for type 2 diabetic inpatients.

As cardiovascular risk tables currently in use were constructed using data from the general population, the cardiovascular risk of patients admitted via the hospital emergency department may be underestimated. Accordingly, we constructed a predictive model for the appearance of cardiovascular diseases in patients with type 2 diabetes admitted via the emergency department. We undertook a four-year follow-up of a cohort of 112 adult patients with type 2 diabetes admitted via the emergency department for any cause except patients admitted with acute myocardial infarction, stroke, cancer, or a palliative status. The sample was selected randomly between 2010 and 2012. The primary outcome was time to cardiovascular disease. Other variables (at baseline) were gender, age, heart failure, renal failure, depression, asthma/chronic obstructive pulmonary disease, hypertension, dyslipidaemia, insulin, smoking, admission for cardiovascular causes, pills per day, walking habit, fasting blood glucose and creatinine. A cardiovascular risk table was constructed based on the score to estimate the likelihood of cardiovascular disease. Risk groups were established and the c-statistic was calculated. Over a mean follow-up of 2.31 years, 39 patients had cardiovascular disease (34.8%, 95% CI [26.0-43.6%]). Predictive factors were gender, age, hypertension, renal failure, insulin, admission due to cardiovascular reasons and walking habit. The c-statistic was 0.734 (standard error: 0.049). After validation, this study will provide a tool for the primary health care services to enable the short-term prediction of cardiovascular disease after hospital discharge in patients with type 2 diabetes admitted via the emergency department.

Diabetes screening: a pending issue in hypertense/obese patients.

The literature about possible cardiovascular consequences of diagnostic inertia in diabetes is scarce. We examined the influence of undetected high fasting blood glucose (FBG) levels on the cardiovascular risk and poor control of cardiovascular risk factors in hypertensive or obese patients, with no previous diagnosis of diabetes mellitus (i.e., diagnostic inertia). A cross-sectional study during a preventive program in a Spanish region was performed in 2003-2004. The participants were aged ≥40 years and did not have diabetes but were hypertensive (n = 5, 347) or obese (n = 7, 833). The outcomes were high cardiovascular risk (SCORE ≥5%), poor control of the blood pressure (≥140/90 mmHg) and class II obesity. The relationship was examined between FBG and the main parameters, calculating the adjusted odd ratios with multivariate models. Higher values of FBG were associated with all the outcomes. A more proactive attitude towards the diagnosis of diabetes mellitus in the hypertensive and obese population should be adopted.