Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies.

BACKGROUND: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. METHOD: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. RESULTS: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. CONCLUSIONS: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting.

Direct Cost Related to Stroke: A Longitudinal Analysis of Survivors After Discharge From a Rehabilitation Hospital.

BACKGROUND: After discharge from a rehabilitation hospital, stroke survivors and their families may face considerable stroke-related direct costs. The total amount could be ascribed to the costs of formal and informal care and to the equipment or materials needed for care. OBJECTIVES: This study aims to describe the direct costs incurred after a stroke by survivors during their first poststroke year and to analyze the basic predictors of these costs. METHODS: Stroke survivors (N = 415) were enrolled for this study during discharge from rehabilitation hospitals (baseline) and interviewed at 3, 6, 9, and 12 months after discharge for a longitudinal study. The trend of the direct costs incurred during the follow-up (from T1 to T4; n = 239) was evaluated using a linear mixed-effects model. The mixed-effects model was used to identify the baseline predictors of the incurred direct costs from the stroke survivors. RESULTS: During the first year after discharge, stroke survivors spent approximately $3700 on stroke-related direct (ie, medical and nonmedical) costs. The highest direct costs occurred during the first 6 months, although there was not a significant change over time. The higher direct costs incurred were predicted by the linear effect of time, by the educational level (higher vs low), and by the lower Barthel Index score, whereas a higher perceived cost was predicted only by the linear effect of time and by the lower Barthel Index score. CONCLUSION: In the first poststroke year, direct costs have remained stable over time and can be predicted by the level of education and physical functioning. The identification of specific direct cost predictors would be helpful for developing more socially and economically tailored interventions for stroke survivors in their first year after their stroke.

Structural decomposition of decadal climate prediction errors: A Bayesian approach.

Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.