Clinical inertia in diagnosis and treatment of hypertension in primary care: quantification and associated factors.

OBJECTIVES: The objective of the present study was to quantify both diagnostic and therapeutic inertia in hypertension and to identify patient-associated variables. PATIENTS AND METHODS: Cross-sectional, multicenter study of 35 424 subjects carried out in 428 health centers and/or primary care clinics in the Valencian Community, Spain, in a preventive activity conducted during 2003 and 2004. Diagnostic inertia was identified when a patient without known hypertension had high blood pressure (BP) but was labeled "normal" by the medical staff, and therapeutic inertia when treatment was not modified for a hypertensive patient on the presence of high BP values. Bivariate and multivariate statistical analyses were performed to identify patient's characteristics associated with inertia. RESULTS: Diagnostic inertia was present in 32.5% (95% CI 31.4-33.6) and therapeutic inertia in 37.0% (95% CI 35.6-38.5) of the cases. Both were more frequent in cases of isolated systolic or diastolic high BP. In the multivariate models, the factors associated with diagnostic inertia were type-2 diabetes (p=0.041), non-smoking (p=0.004), previous coronary heart disease (p=0.001), BP values (p<0.001) and body mass index (p=0.031), whereas for therapeutic inertia they were type-2 diabetes (p=0.003), previous coronary heart disease (p=0.016) or stroke (p<0.001) and BP values (p<0.001). CONCLUSIONS: Clinical inertia, either diagnostic or therapeutic, was present in one of every three cases of high BP. The most frequent factors associated with clinical inertia were the presence of associated conditions, which requires lower BP goals and the BP values.

[Agreement between REGICOR and SCORE scales in identifying high cardiovascular risk in the Spanish population]. / Concordancia de las escalas REGICOR y SCORE para la identificación del riesgo cardiovascular alto en la población española.

INTRODUCTION: The aims of this study were to evaluate the consistency between the SCORE (Systematic Coronary Risk Evaluation) and REGICOR (Registre Gironí del cor) scales in identifying high cardiovascular risk and to describe the characteristics of those individuals for whom scale results were discrepant. METHODS: This cross-sectional study involved 8942 subjects aged 40-65 years who had an indication for a complete lipid profile. The agreement between SCORE (for low-risk countries) and Framingham-REGICOR (with a high risk threshold of 10%) scales in classifying patients as high risk was evaluated using the kappa statistic. Subjects for whom there was a discrepancy between classifications were identified and variables associated with this discrepancy were determined by multivariate analysis involving binary logistic regression. RESULTS: The REGICOR scale classified 6.7% of subjects (95% confidence interval [CI], 6.2%-7.3%) as high-risk, while SCORE classified 12.5% (95% CI 11.8%-13.2%) as high-risk. Discrepant findings were observed in 10.2% of the total population (8% had a high risk on SCORE but not REGICOR, and 2.2% had a high risk on REGICOR but not SCORE; kappa=0.420; P< .001). The best agreement was observed between SCORE and REGICOR with a high-risk threshold of 8% (kappa=0.463). Multivariate analysis showed that a high risk on SCORE but not REGICOR was associated with lower age, female sex, a high fasting glucose level, and raised diastolic blood pressure, and a high risk on REGICOR but not SCORE, with male sex, smoking, and a low high-density lipoprotein (HDL) cholesterol level. These variables accounted for the extent of the discrepancy in 93.2% of cases. CONCLUSIONS: The SCORE and REGICOR (threshold 10%) scales identified different populations as being at a high risk, though the agreement between them was reasonably good. The concurrence of a number of factors (e.g., male sex, low HDL-cholesterol, and smoking) in a subject with a low risk on the SCORE scale should be regarded as increasing the cardiovascular risk.

Concordancia de las escalas REGICOR y SCORE para la identificación del riesgo cardiovascular alto en la población española / Agreement between REGICOR and SCORE scales in identifying high cardiovascular risk in the spanish population

Introducción y objetivos. Los objetivos de este estudio fueron valorar la concordancia entre las escalas SCORE y REGICOR para la identificación de riesgo cardiovascular alto, y describir los perfiles en los que las escalas discrepan. Métodos. Estudio transversal en 8.942 sujetos de 40-65 años con indicación de perfil lipídico completo. Se valoró la concordancia en la clasificación de riesgo alto entre las escalas SCORE (para países de bajo riesgo) y Framingham-REGICOR (umbral de riesgo alto, 10%) mediante el índice kappa. Se identificó a los sujetos con discrepancia en la clasificación y se realizó un análisis multivariable por regresión logística binaria para identificar las variables relacionadas. Resultados. REGICOR clasificó como alto riesgo al 6,7% (intervalo de confianza del 95%, 6,2%-7,3%) y SCORE al 12,5% (11,8%-13,2%). El 10,2% mostró discrepancias (el 8% riesgo SCORE alto y REGICOR no alto, y el 2,2% REGICOR alto y SCORE no alto; κ = 0,420; p < 0,001). La concordancia más elevada fue entre SCORE y REGICOR con umbral de alto riesgo del 8% (κ = 0,463). En un análisis multivariable, SCORE alto con REGICOR no alto se relacionó con edad inferior, sexo femenino, glucosa basal elevada y presión arterial diastólica elevada, y SCORE no alto con REGICOR alto, con sexo masculino, tabaquismo y colesterol de las lipoproteínas de alta densidad bajo. Estas variables explicaron la variabilidad en las discrepancias en un 93,2%. Conclusiones. SCORE y REGICOR (umbral, 10%) identificaron poblaciones de riesgo alto diferentes, y la concordancia fue discreta. Se podría considerar que la confluencia de algunas variables (sexo varón, colesterol de las lipoproteínas de alta densidad bajo, tabaquismo) y riesgo SCORE no alto incrementa el riesgo cardiovascular (AU)

Introduction. The aims of this study were to evaluate the consistency between the SCORE (Systematic Coronary Risk Evaluation) and REGICOR (Registre Gironí del cor) scales in identifying high cardiovascular risk and to describe the characteristics of those individuals for whom scale results were discrepant. Methods. This cross-sectional study involved 8942 subjects aged 40­65 years who had an indication for a complete lipid profile. The agreement between SCORE (for low-risk countries) and Framingham-REGICOR (with a high risk threshold of 10%) scales in classifying patients as high risk was evaluated using the kappa statistic. Subjects for whom there was a discrepancy between classifications were identified and variables associated with this discrepancy were determined by multivariate analysis involving binary logistic regression. Results. The REGICOR scale classified 6.7% of subjects (95% confidence interval [CI], 6.2%­7.3%) as high-risk, while SCORE classified 12.5% (95% CI 11.8%­13.2%) as high-risk. Discrepant findings were observed in 10.2% of the total population (8% had a high risk on SCORE but not REGICOR, and 2.2% had a high risk on REGICOR but not SCORE; κ=0.420; P<.001). The best agreement was observed between SCORE and REGICOR with a high-risk threshold of 8% (κ=0.463). Multivariate analysis showed that a high risk on SCORE but not REGICOR was associated with lower age, female sex, a high fasting glucose level, and raised diastolic blood pressure, and a high risk on REGICOR but not SCORE, with male sex, smoking, and a low high-density lipoprotein (HDL) cholesterol level. These variables accounted for the extent of the discrepancy in 93.2% of cases. Conclusions. The SCORE and REGICOR (threshold 10%) scales identified different populations as being at a high risk, though the agreement between them was reasonably good. The concurrence of a number of factors (e.g., male sex, low HDL-cholesterol, and smoking) in a subject with a low risk on the SCORE scale should be regarded as increasing the cardiovascular risk (AU)