Antonio Palazón-Bru

Is the Physician's Behavior in Dyslipidemia Diagnosis in Accordance with Guidelines? Cross-Sectional Escarval Study

Background Clinical inertia has been defined as mistakes by the physician in starting or intensifying treatment when indicated. Inertia, therefore, can affect other stages in the healthcare process, like diagnosis. The diagnosis of dyslipidemia requires ≥2 high lipid values, but inappropriate behavior in the diagnosis of dyslipidemia has only previously been analyzed using just total cholesterol (TC). Objectives To determine clinical inertia in the dyslipidemia diagnosis using both TC and high-density lipoprotein cholesterol (HDL-c) and its associated factors. Design Cross-sectional. Setting All health center visits in the second half of 2010 in the Valencian Community (Spain). Patients 11,386 nondyslipidemic individuals aged ≥20 years with ≥2 lipid determinations. Measurement Variables Gender, atrial fibrillation, hypertension, diabetes, cardiovascular disease, age, and ESCARVAL training course. Lipid groups: normal (TC<5.17 mmol/L and normal HDL-c [≥1.03 mmol/L in men and ≥1.29 mmol/L in women], TC inertia (TC≥5.17 mmol/L and normal HDL-c), HDL-c inertia (TC<5.17 mmol/L and low HDL-c), and combined inertia (TC≥5.17 mmol/L and low HDL-c). Results TC inertia: 38.0% (95% CI: 37.2–38.9%); HDL-c inertia: 17.7% (95% CI: 17.0–18.4%); and combined inertia: 9.6% (95% CI: 9.1–10.2%). The profile associated with TC inertia was: female, no cardiovascular risk factors, no cardiovascular disease, middle or advanced age; for HDL-c inertia: female, cardiovascular risk factors and cardiovascular disease; and for combined inertia: female, hypertension and middle age. Limitations Cross-sectional study, under-reporting, no analysis of some cardiovascular risk factors or other lipid parameters. Conclusions A more proactive attitude should be adopted, focusing on the full diagnosis of dyslipidemia in clinical practice. Special emphasis should be placed on patients with low HDL-c levels and an increased cardiovascular risk.

Predictive models for all‐cause and cardiovascular mortality in type 2 diabetic inpatients. A cohort study

Many authors have analysed premature mortality in cohorts of type 2 diabetic patients, but no analyses have assessed mortality in hospitalised diabetic patients.

Prognostic Value of Obesity on Both Overall Mortality and Cardiovascular Disease in the General Population

Background Obesity represents an important health problem and its association with cardiovascular risk factors is well-known. The aim of this work was to assess the correlation between obesity and mortality (both, all-cause mortality and the combined variable of all-cause mortality plus the appearance of a non-fatal first cardiovascular event) in a general population sample from the south-east of Spain. Materials and Methods This prospective cohort study used stratified and randomized two-stage sampling. Obesity [body mass index (BMI) ≥30 kg/m2] as a predictive variable of mortality and cardiovascular events was assessed after controlling for age, sex, cardiovascular disease history, high blood pressure, diabetes mellitus, hypercholesterolemia, high-density lipoprotein/triglycerides ratio, total cholesterol and smoking with the Cox regression model. Results The mean follow-up time of the 1,248 participants was 10.6 years. The incidence of all-cause mortality during this period was 97 deaths for every 10,000 person/years (95% CI: 80–113) and the incidence of all-cause mortality+cardiovascular morbidity was 143 cases for every 10,000 person/years (95% CI: 124–163). A BMI ≥35 kg/m2 yielded a hazard ratio for all-cause mortality of 1.94 (95% CI: 1.11–3.42) in comparison to non-obese subjects (BMI <30 kg/m2). For the combination of cardiovascular morbidity plus all-cause mortality, a BMI ≥35 kg/m2 had a hazard ratio of 1.84 (95% CI: 1.15–2.93) compared to non-obese subjects. Conclusions A BMI ≥35 kg/m2 is an important predictor of both overall mortality and of the combination of cardiovascular morbidity plus all-cause mortality.

Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

The existing models to predict mortality in intensive care units (ICU) present difficulties in clinical practice.

Association between diabetes mellitus and active tuberculosis: A systematic review and meta-analysis.

The burgeoning epidemic of diabetes mellitus (DM) is one of the major global health challenges. We systematically reviewed the published literature to provide a summary estimate of the association between DM and active tuberculosis (TB). We searched Medline and EMBASE databases for studies reporting adjusted estimates on the TB–DM association published before December 22, 2015, with no restrictions on region and language. In the meta-analysis, adjusted estimates were pooled using a DerSimonian-Laird random-effects model, according to study design. Risk of bias assessment and sensitivity analyses were conducted. 44 eligible studies were included, which consisted of 58,468,404 subjects from 16 countries. Compared with non-DM patients, DM patients had 3.59–fold (95% confidence interval (CI) 2.25–5.73), 1.55–fold (95% CI 1.39–1.72), and 2.09–fold (95% CI 1.71–2.55) increased risk of active TB in four prospective, 16 retrospective, and 17 case-control studies, respectively. Country income level (3.16–fold in low/middle–vs. 1.73–fold in high–income countries), background TB incidence (2.05–fold in countries with >50 vs. 1.89–fold in countries with ≤50 TB cases per 100,000 person-year), and geographical region (2.44–fold in Asia vs. 1.71–fold in Europe and 1.73–fold in USA/Canada) affected appreciably the estimated association, but potential risk of bias, type of population (general versus clinical), and potential for duplicate data, did not. Microbiological ascertainment for TB (3.03–fold) and/or blood testing for DM (3.10–fold), as well as uncontrolled DM (3.30–fold), resulted in stronger estimated association. DM is associated with a two- to four-fold increased risk of active TB. The association was stronger when ascertainment was based on biological testing rather than medical records or self-report. The burgeoning DM epidemic could impact upon the achievements of the WHO “End TB Strategy” for reducing TB incidence.

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.

Sample size calculation to externally validate scoring systems based on logistic regression models

Background A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model. Objective The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study. Methods The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units. Results In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature. Conclusion An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application

The most described techniques used to detect diabetic retinopathy and diabetic macular edema have to be interpreted correctly, such that a person not specialized in ophthalmology, as is usually the case of a primary care physician, may experience difficulties with their interpretation; therefore we constructed, validated and implemented as a mobile app a new tool to detect diabetic retinopathy or diabetic macular edema (DRDME) using simple objective variables. We undertook a cross-sectional, observational study of a sample of 142 eyes from Spanish diabetic patients suspected of having DRDME in 2012–2013. Our outcome was DRDME and the secondary variables were: type of diabetes, gender, age, glycated hemoglobin (HbA1c), foveal thickness and visual acuity (best corrected). The sample was divided into two parts: 80% to construct the tool and 20% to validate it. A binary logistic regression model was used to predict DRDME. The resulting model was transformed into a scoring system. The area under the ROC curve (AUC) was calculated and risk groups established. The tool was validated by calculating the AUC and comparing expected events with observed events. The construction sample (n = 106) had 35 DRDME (95% CI [24.1–42.0]), and the validation sample (n = 36) had 12 DRDME (95% CI [17.9–48.7]). Factors associated with DRDME were: HbA1c (per 1%) (OR = 1.36, 95% CI [0.93–1.98], p = 0.113), foveal thickness (per 1 µm) (OR = 1.03, 95% CI [1.01–1.04], p < 0.001) and visual acuity (per unit) (OR = 0.14, 95% CI [0.00–0.16], p < 0.001). AUC for the validation: 0.90 (95% CI [0.75–1.00], p < 0.001). No significant differences were found between the expected and the observed outcomes (p = 0.422). In conclusion, we constructed and validated a simple rapid tool to determine whether a diabetic patient suspected of having DRDME really has it. This tool has been implemented on a mobile app. Further validation studies are required in the general diabetic population.

Construction and validation of a model to predict nonadherence to guidelines for prescribing antiplatelet therapy to hypertensive patients.

Abstract Objective: To construct and validate a model to predict nonadherence to guidelines for prescribing antiplatelet therapy (NGAT) to hypertensive patients. Methods: This 3 month prospective study was undertaken in 2007–2009 to determine whether 712 hypertensive patients were or were not being prescribed antiplatelet therapy. Outcome: NGAT according to clinical guidelines (just for patients in secondary prevention or with Systematic COronary Risk Evaluation (SCORE) ≥10%). Secondary variables: Duration of hypertension (years), blood pressure (BP), age, gender, smoking, diabetes, dyslipidemia, cardiovascular disease, lipid parameters, SCORE. Of the whole sample 80% was used to construct the model and 20% to validate it. To construct the model, we performed a multivariate logistic regression model which was adapted to be a scoring system with risk groups. The adjusted odds ratios (ORs) were obtained through the model. To validate the model we calculated the area under the ROC curve (AUC) and then compared the expected and the observed NGAT. The final model was adapted for use as a mobile application. Results: NGAT: 18.5%, construction; 17.9%, validation. Factors: higher duration of hypertension diagnosis, higher systolic BP, older age, male gender, smoking, diabetes, dyslipidemia and cardiovascular disease. Validation: AUC = 0.82 (95% CI: 0.74–0.90, p < 0.001), with no differences between the observed and the expected NGAT (p = 0.334). Conclusion: A tool was constructed and validated to predict NGAT. The associated factors were related with a greater cardiovascular risk. The scoring system has to be validated in other areas.

Scoring System for Mortality in Patients Diagnosed with and Treated Surgically for Differentiated Thyroid Carcinoma with a 20-Year Follow-Up.

Background Differentiated thyroid carcinoma (DTC) is associated with an increased mortality. Few studies have constructed predictive models of all-cause mortality with a high discriminating power for patients with this disease that would enable us to determine which patients are more likely to die. Objective To construct a predictive model of all-cause mortality at 5, 10, 15 and 20 years for patients diagnosed with and treated surgically for DTC for use as a mobile application. Design We undertook a retrospective cohort study using data from 1984 to 2013. Setting All patients diagnosed with and treated surgically for DTC at a general university hospital covering a population of around 200,000 inhabitants in Spain. Participants The study involved 201 patients diagnosed with and treated surgically for DTC (174, papillary; 27, follicular). Exposures Age, gender, town, family history, type of surgery, type of cancer, histological subtype, microcarcinoma, multicentricity, TNM staging system, diagnostic stage, permanent post-operative complications, local and regional tumor persistence, distant metastasis, and radioiodine therapy. Main outcome measure All-cause mortality. Methods A Cox multivariate regression model was constructed to determine which variables at diagnosis were associated with mortality. Using the model a risk table was constructed based on the sum of all points to estimate the likelihood of death. This was then incorporated into a mobile application. Results The mean follow-up was 8.8±6.7 years. All-cause mortality was 12.9% (95% confidence interval [CI]: 8.3–17.6%). Predictive variables: older age, local tumor persistence and distant metastasis. The area under the ROC curve was 0.81 (95% CI: 0.72–0.91, p<0.001). Conclusion This study provides a practical clinical tool giving a simple and rapid indication (via a mobile application) of which patients with DTC are at risk of dying in 5, 10, 15 or 20 years. Nonetheless, caution should be exercised until validation studies have corroborated our results.