Residual cardiovascular risk reduction guided by lifetime benefit estimation in patients with symptomatic atherosclerotic disease: effectiveness and cost-effectiveness.

AIMS: To determine the (cost)-effectiveness of blood pressure lowering, lipid-lowering, and antithrombotic therapy guided by predicted lifetime benefit compared to risk factor levels in patients with symptomatic atherosclerotic disease. METHODS AND RESULTS: For all patients with symptomatic atherosclerotic disease in the UCC-SMART cohort (1996-2018; n = 7697) two treatment strategies were compared. The lifetime benefit-guided strategy was based on individual estimation of gain in cardiovascular disease (CVD)-free life with the SMART-REACH model. In the risk factor-based strategy, all patients were treated the following: low-density lipoprotein cholesterol (LDL-c) < 1.8 mmol/L, systolic blood pressure <140 mmHg, and antithrombotic medication. Outcomes were evaluated for the total cohort using a microsimulation model. Effectiveness was evaluated as total gain in CVD-free life and events avoided, cost-effectiveness as incremental cost-effectivity ratio (ICER). In comparison to baseline treatment, treatment according to lifetime benefit would lead to an increase of 24 243 CVD-free life years [95% confidence interval (CI) 19 980-29 909] and would avoid 940 (95% CI 742-1140) events in the next 10 years. For risk-factor based treatment, this would be an increase of 18 564 CVD-free life years (95% CI 14 225-20 456) and decrease of 857 (95% CI 661-1057) events. The ICER of lifetime benefit-based treatment with a treatment threshold of ≥1 year additional CVD-free life per therapy was €15 092/QALY gained and of risk factor-based treatment €9933/QALY gained. In a direct comparison, lifetime benefit-based treatment compared to risk factor-based treatment results in 1871 additional QALYs for the price of €36 538/QALY gained. CONCLUSION: Residual risk reduction guided by lifetime benefit estimation results in more CVD-free life years and more CVD events avoided compared to the conventional risk factor-based strategy. Lifetime benefit-based treatment is an effective and potentially cost-effective strategy for reducing residual CVD risk in patients with clinical manifest vascular disease.

[Development of an algorithm to predict the incidence of major depression among primary care consultants]. / Desarrollo y validación de un algoritmo para predecir riesgo de depresión en consultantes de atención primaria en Chile.

BACKGROUND: The reduction of major depression incidence is a public health challenge. AIM: To develop an algorithm to estimate the risk of occurrence of major depression in patients attending primary health centers (PHC). MATERIAL AND METHODS: Prospective cohort study of a random sample of 2832 patients attending PHC centers in Concepción, Chile, with evaluations at baseline, six and twelve months. Thirty nine known risk factors for depression were measured to build a model, using a logistic regression. The algorithm was developed in 2,133 patients not depressed at baseline and compared with risk algorithms developed in a sample of 5,216 European primary care attenders. The main outcome was the incidence of major depression in the follow-up period. RESULTS: The cumulative incidence of depression during the 12 months follow up in Chile was 12%. Eight variables were identified. Four corresponded to the patient (gender, age, depression background and educational level) and four to patients' current situation (physical and mental health, satisfaction with their situation at home and satisfaction with the relationship with their partner). The C-Index, used to assess the discriminating power of the final model, was 0.746 (95% confidence intervals (CI = 0,707-0,785), slightly lower than the equation obtained in European (0.790 95% CI = 0.767-0.813) and Spanish attenders (0.82; 95% CI = 0.79-0.84). CONCLUSIONS: Four of the factors identified in the risk algorithm are not modifiable. The other two factors are directly associated with the primary support network (family and partner). This risk algorithm for the incidence of major depression provides a tool that can guide efforts towards design, implementation and evaluation of effectiveness of interventions to prevent major depression.

Desarrollo y validación de un algoritmo para predecir riesgo de depresión en consultantes de atención primaria en Chile / Development of an algorithm to predict the incidence of major depression among primary care consultants

Background: The reduction of major depression incidence is a public health challenge. Aim: To develop an algorithm to estimate the risk of occurrence of major depression in patients attending primary health centers (PHC). Material and Methods: Prospective cohort study of a random sample of 2832 patients attending PHC centers in Concepción, Chile, with evaluations at baseline, six and twelve months. Thirty nine known risk factors for depression were measured to build a model, using a logistic regression. The algorithm was developed in 2,133 patients not depressed at baseline and compared with risk algorithms developed in a sample of 5,216 European primary care attenders. The main outcome was the incidence of major depression in the follow-up period. Results: The cumulative incidence of depression during the 12 months follow up in Chile was 12%. Eight variables were identified. Four corresponded to the patient (gender, age, depression background and educational level) and four to patients' current situation (physical and mental health, satisfaction with their situation at home and satisfaction with the relationship with their partner). The C-Index, used to assess the discriminating power of the final model, was 0.746 (95% confidence intervals (CI = 0,707-0,785), slightly lower than the equation obtained in European (0.790 95% CI = 0.767-0.813) and Spanish attenders (0.82; 95% CI = 0.79-0.84). Conclusions: Four of the factors identified in the risk algorithm are not modifiable. The other two factors are directly associated with the primary support network (family and partner). This risk algorithm for the incidence of major depression provides a tool that can guide efforts towards design, implementation and evaluation of effectiveness of interventions to prevent major depression.

Subfields of the hippocampal formation at 7 T MRI: in vivo volumetric assessment.

Animal and human autopsy studies suggest that subfields of the hippocampal formation are differentially affected by neuropsychiatric diseases. Therefore, subfield volumes may be more sensitive to effects of disease processes. The few human studies that segmented subfields of the hippocampal formation in vivo either assessed the subfields only in the body of the hippocampus, assessed only three subfields, or did not take the differential angulation of the head of the hippocampus into account. We developed a protocol using 7 Tesla MRI with isotropic voxels to reliably delineate the entorhinal cortex (ERC), subiculum (SUB), CA1, CA2, CA3, dentate gyrus (DG)&CA4 along the full-length of the hippocampus. Fourteen subjects (aged 54-74 years, 2 men and 12 women) were scanned with a 3D turbo spin echo (TSE) sequence with isotropic voxels of 0.7 × 0.7 × 0.7 mm(3) on a 7 T MRI whole body scanner. Based on previous protocols and extensive anatomic atlases, a new protocol for segmentation of subfields of the hippocampal formation was formulated. ERC, SUB, CA1, CA2, CA3 and DG&CA4 were manually segmented twice by one rater from coronal MR images. Good-to-excellent consistency was found for all subfields (Intraclass Correlation Coefficient's (ICC) varying from 0.74 to 0.98). Accuracy as measured with the Dice Similarity Index (DSI) was above 0.82 for all subfields, with the exception of the smaller subfield CA3 (0.68-0.70). In conclusion, this study shows that it is possible to delineate the main subfields of the hippocampal formation along its full-length in vivo at 7 T MRI. Our data give evidence that this can be done in a reliable manner. Segmentation of subfields in the full-length of the hippocampus may bolster the study of the etiology neuropsychiatric diseases.