Syndrome métabolique chez les patients hypertendus dans le service cardiologie du CHU Yalgado Ouedraogo de Ouagadougou, Burkina Faso

Introduction: le syndrome métabolique constitue de nos jours un véritable problème de santé publique. Le syndrome métabolique est le moteur d’une double épidémie mondiale de diabète type II et de maladies cardiovasculaires. L’objectif de notre étude est de décrire les aspects épidémiologiques, cliniques, para cliniques et évolutifs chez les hypertendus dans le service de Cardiologie du CHU Yalgado Ouédraogo. Méthodes: il s’agissait d’une étude rétrospective sur une période de deux ans dans le service de cardiologie chez les patients hypertendus ayant un syndrome métabolique. Résultats: la fréquence du syndrome métabolique était de 17,5 % des patients hypertendus. Le sex ratio était de 1,2. L’âge moyen des patients étaient de 56,1 ±10,7 ans. Les patients connus hypertendus étaient de 92,1% avec une durée moyenne d’évolution de l’HTA qui était de 8,7 ± 5,9 ans. Le suivi était irrégulier dans 60% cas et une rupture du traitement dans 37,1% des cas. La dyslipidémie était notée dans 84,2 % des cas et le diabète dans 60,5 % des cas. La PAS moyenne était de 184,3 ± 47,3 mmHg et la PAD moyenne était de 110,7 ± 27,7 mmHg. L’HTA était sévère dans 63,2% des cas. La glycémie moyenne était de 8,3 ± 4,3 mmol/L, le LDL cholestérol moyen était de 3,5 ± 1,0 mmol/L et le taux des triglycérides moyen était de 1,6 ± 1,1 mmol/L. L’HVG électrique était notée chez 76,3 % des patients et échographique dans 58,8 % des cas. Les atteintes viscérales étaient neurologique dans 44,5 %, rénale dans 55,3 % et cardiaque dans 31,2 % des cas. Le nombre moyen d’antihypertenseurs était de 3,0 ± 1,0 et 76,3 % ont reçu au moins une trithérapie antihypertensive. Le taux de mortalité était de 5,3%. Conclusion: le syndrome métabolique est une pathologie qui pose la problématique de la définition qui n’est pas consensuelle d’une part et d’autre part du contrôle de ses éléments constitutifs surtout l’HTA

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient