Assessment of Risk of Harm Associated With Intensive Blood Pressure Management Among Patients With Hypertension Who Smoke: A Secondary Analysis of the Systolic Blood Pressure Intervention Trial

Causal effect of video gaming on mental well-being in Japan 2020-2022

The widespread use of video games has raised concerns about their potential negative impact on mental well-being. Nevertheless, the empirical evidence supporting this notion is largely based on correlational studies, warranting further investigation into the causal relationship. Here we identify the causal effect of video gaming on mental well-being in Japan (2020-2022) using game console lotteries as a natural experiment. Employing approaches designed for causal inference on survey data (n = 97,602), we found that game console ownership, along with increased game play, improved mental well-being. The console ownership reduced psychological distress and improved life satisfaction by 0.1-0.6 standard deviations. Furthermore, a causal forest machine learning algorithm revealed divergent impacts between different types of console, with one showing smaller benefits for adolescents and females while the other showed larger benefits for adolescents. These findings highlight the complex impact of digital media on mental well-being and the importance of considering differential screen time effects.

Targeting the Efficacy of Intensive Blood Pressure Treatment in Hypertensive Patients - An Exploratory Analysis of SPRINT

BACKGROUND

Hypertensive patients show highly heterogeneous treatment effects (HTEs) and cardiovascular prognosis, and not all benefit from intensive blood pressure treatment.

METHODS AND RESULTS

We used the causal forest model to identify potential HTEs of patients in the Systolic Blood Pressure Intervention Trial (SPRINT). Cox regression was performed to assess hazard ratios (HRs) for cardiovascular disease (CVD) outcomes and to compare the effects of intensive treatment among groups. The model revealed 3 representative covariates and patients were partitioned into 4 subgroups: Group 1 (baseline body mass index [BMI] ≤28.32 kg/m2and estimated glomerular filtration rate [eGFR] ≤69.53 mL/min/1.73 m2); Group 2 (baseline BMI ≤28.32 kg/m2and eGFR >69.53 mL/min/1.73 m2); Group 3 (baseline BMI >28.32 kg/m2and 10-year CVD risk ≤15.8%); Group 4 (baseline BMI >28.32 kg/m2and 10-year CVD risk >15.8%). Intensive treatment was shown to be beneficial only in Group 2 (HR 0.54, 95% confidence interval [CI] 0.35-0.82; P=0.004) and Group 4 (HR 0.69, 95% CI 0.52-0.91; P=0.009).

CONCLUSIONS

Intensive treatment was effective for patients with high BMI and 10-year CVD risk, or low BMI and normal eGFR, but not for those with low BMI and eGFR, or high BMI and low 10-year CVD risk. Our study could facilitate the categorization of hypertensive patients, ensuring individualized therapy.

Estimated Population Health Benefits of Intensive Systolic Blood Pressure Treatment Among SPRINT-Eligible US Adults

BACKGROUND

The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated an intensive (<120 mm Hg) vs. standard (<140 mm Hg) systolic blood pressure (SBP) goal lowered cardiovascular disease (CVD) risk. Estimating the effect of intensive SBP lowering among SPRINT-eligible adults most likely to benefit can guide implementation efforts.

METHODS

We studied SPRINT participants and SPRINT-eligible participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study and National Health and Nutrition Examination Surveys (NHANES). A published algorithm of predicted CVD benefit with intensive SBP treatment was used to categorize participants into low, medium, or high predicted benefit. CVD event rates were estimated with intensive and standard treatment.

RESULTS

Median age was 67.0, 72.0, and 64.0 years in SPRINT, SPRINT-eligible REGARDS, and SPRINT-eligible NHANES participants, respectively. The proportion with high predicted benefit was 33.0% in SPRINT, 39.0% in SPRINT-eligible REGARDS, and 23.5% in SPRINT-eligible NHANES. The estimated difference in CVD event rate (standard minus intensive) was 7.0 (95% confidence interval [CI] 3.4-10.7), 8.4 (95% CI 8.2-8.5), and 6.1 (95% CI 5.9-6.3) per 1,000 person-years in SPRINT, SPRINT-eligible REGARDS participants, and SPRINT-eligible NHANES participants, respectively (median 3.2-year follow-up). Intensive SBP treatment could prevent 84,300 (95% CI 80,800-87,920) CVD events per year in 14.1 million SPRINT-eligible US adults; 29,400 and 28,600 would be in 7.0 million individuals with medium or high predicted benefit, respectively.

CONCLUSIONS

Most of the population health benefit from intensive SBP goals could be achieved by treating those characterized by a previously published algorithm as having medium or high predicted benefit.

Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects

"Heterogeneous treatment effects" is a term which refers to conditional average treatment effects (i.e., CATEs) that vary across population subgroups. Epidemiologists are often interested in estimating such effects because they can help detect populations that may particularly benefit from or be harmed by a treatment. However, standard regression approaches for estimating heterogeneous effects are limited by preexisting hypotheses, test a single effect modifier at a time, and are subject to the multiple-comparisons problem. In this article, we aim to offer a practical guide to honest causal forests, an ensemble tree-based learning method which can discover as well as estimate heterogeneous treatment effects using a data-driven approach. We discuss the fundamentals of tree-based methods, describe how honest causal forests can identify and estimate heterogeneous effects, and demonstrate an implementation of this method using simulated data. Our implementation highlights the steps required to simulate data sets, build honest causal forests, and assess model performance across a variety of simulation scenarios. Overall, this paper is intended for epidemiologists and other population health researchers who lack an extensive background in machine learning yet are interested in utilizing an emerging method for identifying and estimating heterogeneous treatment effects.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

OBJECTIVE

Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.

METHODS

Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink).

RESULTS

Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1).

CONCLUSIONS

Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

Machine learning-based causal models for predicting the response of individual patients to dexamethasone treatment as prophylactic antiemetic

Risk-based strategies are widely used for decision making in the prophylaxis of postoperative nausea and vomiting (PONV), a major complication of general anesthesia. However, whether risk is associated with individual treatment effect remains uncertain. Here, we used machine learning-based algorithms for estimating the conditional average treatment effect (CATE) (double machine learning [DML], doubly robust [DR] learner, forest DML, and generalized random forest) to predict the treatment response heterogeneity of dexamethasone, the first choice for prophylactic antiemetics. Electronic health record data of 2026 adult patients who underwent general anesthesia from January to June 2020 were analyzed. The results indicated that only a small subset of patients respond to dexamethasone treatment, and many patients may be non-responders. Estimated CATE did not correlate with predicted risk, suggesting that risk may not be associated with individual treatment responses. The current study suggests that predicting treatment responders by CATE models may be more appropriate for clinical decision making than conventional risk-based strategy.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

The association of genetic susceptibility to smoking with cardiovascular disease mortality and the benefits of adhering to a DASH diet: The Singapore Chinese Health Study

BACKGROUND

Understanding the genetic predisposition to cardiovascular disease (CVD) may help to improve clinical intervention strategies. Lifestyle factors, such as diet, may differ among ethnic groups and may, in turn, modify individuals' risks to diseases.

OBJECTIVES

We examined the genetic predisposition to ever smoking in relation to CVD mortality and assessed whether such an association could be modified by dietary intake.

METHODS

A total of 23,760 Chinese adults from the Singapore Chinese Heath Study who were free of cancer and CVD at recruitment (1993-1998) were included in the study. A weighted genetic risk score (wGRS) was calculated to define the genetically determined regular smoking behavior (never or ever). Multivariable-adjusted Cox regression models were used to assess the association between the wGRS and CVD mortality. We also conducted a 1-sample Mendelian randomization analysis for ever smoking and CVD mortality.

RESULTS

Over a mean of 22.6 years of follow-up, 2301 CVD deaths were identified. A genetic predisposition to ever smoking was significantly associated with CVD mortality; the multivariable-adjusted HR of CVD mortality was 1.07 (95% CI: 1.03-1.12), with a per-SD increment in the wGRS. However, the Mendelian randomization analysis did not support a causal relationship between ever smoking and CVD mortality (OR, 1.13; 95% CI: 0.87-1.45). Additionally, the Dietary Approaches to Stop Hypertension (DASH) score significantly modified the association between the smoking wGRS and CVD mortality; the association between a genetic predisposition to smoking and CVD mortality was only observed among individuals with a low DASH score (P-interaction = 0.004).

CONCLUSIONS

A genetic predisposition to smoking was associated with CVD mortality in the Chinese population. In addition, we detected a significant interaction showing higher CVD mortality related to genetically determined smoking among those with lower DASH scores.

Heterogeneity of Treatment Effects for Intensive Blood Pressure Therapy by Individual Components of FRS: An Unsupervised Data-Driven Subgroup Analysis in SPRINT and ACCORD

Background

Few studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control.

Methods

A self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control.

Results

We identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = −1.19%; 95% CI: −2.08, −0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = −2.32%; 95% CI: −4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01–3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD.

Conclusion

Similar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.

Harrell Jr F, Moons KGM, Debray TPA, Reitsma JB. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.

Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection

BACKGROUND

Influenza is associated with excess morbidity and mortality of individuals each year. Few therapies exist for treatment of influenza infection, and each require initiation as early as possible in the course of infection, making efficacy difficult to estimate in the hospitalized patient with lower respiratory tract infection. Using causal machine learning methods, we re-analyze data from a randomized trial of oseltamivir versus standard of care aimed at reducing clinical failure in hospitalized patients with lower respiratory tract infection during the influenza season.

METHODS

This was a secondary analysis of the Rapid Empiric Treatment with Oseltamivir Study (RETOS). Conditional average treatment effects (CATE) and 95% confidence intervals were computed from causal forest including 85 clinical and demographic variables. RETOS was a multicenter, randomized, unblinded, trial of adult patients hospitalized with lower respiratory tract infections in Kentucky from 2009 through 2012. Adult hospitalized patients with lower respiratory tract infection were randomized to standard of care or standard of care plus oseltamivir as early as possible after hospital admission but within 24 h of enrollment. After randomization, oseltamivir was initiated in the treatment arm per package insert. The primary outcome was clinical failure, a composite measure including failure to reach clinical improvement within 7 days, transfer to intensive care 24 h after admission, or rehospitalization or death within 30 days.

RESULTS

A total of 691 hospitalized patients with lower respiratory tract infections were included in the study. The only subgroup of patients with a statistically significant CATE was those with laboratory-confirmed influenza infection with a 26% lower risk of clinical failure when treated with oseltamivir (95% CI 3.2-48.0%).

CONCLUSIONS

This study suggests that addition of oseltamivir to standard of care may decrease clinical failure in hospitalized patients with influenza-associated lower respiratory tract infection versus standard of care alone. These results are supportive of current recommendations to initiate antiviral treatment in hospitalized patients with confirmed or suspected influenza as soon as possible after admission. Trial registration Original trial: Clinical Trials.Gov; Rapid Empiric Treatment With Oseltamivir Study (RETOS) (RETOS); ClinicalTrials.gov Identifier: NCT01248715 https://clinicaltrials.gov/ct2/show/NCT01248715.

Big Data, Data Science, and Causal Inference: A Primer for Clinicians

Clinicians handle a growing amount of clinical, biometric, and biomarker data. In this “big data” era, there is an emerging faith that the answer to all clinical and scientific questions reside in “big data” and that data will transform medicine into precision medicine. However, data by themselves are useless. It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity to re-think this data-centric view. For example, while precision medicine seeks to provide the right prevention and treatment strategy to the right patients at the right time, its realization cannot be achieved by algorithms that operate exclusively in data-driven prediction modes, as do most machine learning algorithms. Better understanding of data science and its tasks is vital to interpret findings and translate new discoveries into clinical practice. In this review, we first discuss the principles and major tasks of data science by organizing it into three defining tasks: (1) association and prediction, (2) intervention, and (3) counterfactual causal inference. Second, we review commonly-used data science tools with examples in the medical literature. Lastly, we outline current challenges and future directions in the fields of medicine, elaborating on how data science can enhance clinical effectiveness and inform medical practice. As machine learning algorithms become ubiquitous tools to handle quantitatively “big data,” their integration with causal reasoning and domain knowledge is instrumental to qualitatively transform medicine, which will, in turn, improve health outcomes of patients.

Subgroup discovery in non-inferiority trials

Approaches and guidelines for performing subgroup analysis to assess heterogeneity of treatment effect in clinical trials have been the topic of numerous papers in the statistical and clinical literature, but have been discussed predominantly in the context of conventional superiority trials. Concerns about treatment heterogeneity are the same if not greater in non-inferiority (NI) trials, especially since overall similarity between two treatment arms in a successful NI trial could be due to the existence of qualitative interactions that are more likely when comparing two active therapies. Even in unsuccessful NI trials, subgroup analyses can yield important insights about the potential reasons for failure to demonstrate non-inferiority of the experimental therapy. Recent NI trials have performed a priori subgroup analyses using standard statistical tests for interaction, but there is increasing interest in more flexible machine learning approaches for post-hoc subgroup discovery. The performance and practical application of such methods in NI trials have not been systematically explored, however. We considered the Virtual Twin method for the NI setting, an algorithm for subgroup identification that combines random forest with classification and regression trees, and conducted extensive simulation studies to examine its performance under different NI trial conditions and to devise decision rules for selecting the final subgroups. We illustrate the utility of the method with data from a NI trial that was conducted to compare two acupuncture treatments for chronic musculoskeletal pain.

Associations Between a New York City Paid Sick Leave Mandate and Health Care Utilization Among Medicaid Beneficiaries in New York City and New York State

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Question

Findings

Meaning

Patient Selection for Intensive Blood Pressure Management Based on Benefit and Adverse Events

BACKGROUND

Intensive systolic blood pressure (SBP) treatment prevents cardiovascular disease (CVD) events in patients with high CVD risk on average, though benefits likely vary among patients.

OBJECTIVES

The aim of this study was to predict the magnitude of benefit (reduced CVD and all-cause mortality risk) along with adverse event (AE) risk from intensive versus standard SBP treatment.

METHODS

This was a secondary analysis of SPRINT (Systolic Blood Pressure Intervention Trial). Separate benefit outcomes were the first occurrence of: 1) a CVD composite of acute myocardial infarction or other acute coronary syndrome, stroke, heart failure, or CVD death; and 2) all-cause mortality. Treatment-related AEs of interest included hypotension, syncope, bradycardia, electrolyte abnormalities, injurious falls, and acute kidney injury. Modified elastic net Cox regression was used to predict absolute risk for each outcome and absolute risk differences on the basis of 36 baseline variables available at the point of care with intensive versus standard treatment.

RESULTS

Among 8,828 SPRINT participants (mean age 67.9 years, 35% women), 600 CVD composite events, 363 all-cause deaths, and 481 treatment-related AEs occurred over a median follow-up period of 3.26 years. Individual participant risks were predicted for the CVD composite (C index = 0.71), all-cause mortality (C index = 0.75), and treatment-related AEs (C index = 0.69). Higher baseline CVD risk was associated with greater benefit (i.e., larger absolute CVD risk reduction). Predicted CVD benefit and predicted increased treatment-related AE risk were correlated (Spearman correlation coefficient = -0.72), and 95% of participants who fell into the highest tertile of predicted benefit also had high or moderate predicted increases in treatment-related AE risk. Few were predicted as high benefit with low AE risk (1.8%) or low benefit with high AE risk (1.5%). Similar results were obtained for all-cause mortality.

CONCLUSIONS

SPRINT participants with higher baseline predicted CVD risk gained greater absolute benefit from intensive treatment. Participants with high predicted benefit were also most likely to experience treatment-related AEs, but AEs were generally mild and transient. Patients should be prioritized for intensive SBP treatment on the basis of higher predicted benefit. (Systolic Blood Pressure Intervention Trial [SPRINT]; NCT01206062).

Analysis of Some Behavioral Risk Factors in Relation to Acute Coronary Events

The association of acute coronary events and behavioral risk factors is already known. Of these, smoking and alcohol consumption are the behavioral risk factors with the most intense impact in the occurrence of these events. The correct knowledge of the dynamics and their involvement in the evolution of acute coronary events remains of overwhelming importance in the light of current data. To achieve the purpose of this study data from three family medicine practices from the period November 2018 to May 2019 were corroborated. Anonymous questionnaires were applyed to the subjects. For this study, questions related to the habit of smoking and consuming alcohol were selected. The study aimed to analyze the associative relationships between acute coronary events and two of the most common behavioral risk factors, smoking and alcohol consumption. The highest prevalence of acute coronary events was observed in current smokers and in former smokers. The period of exposure to smoking showed that this is one of the variables most strongly associated with an increased risk of acute coronary events. Moderate consumption of wine or beer seems to have a weak association with acute coronary events, even weaker than those who do not consume at all suggesting a protective effect.

[Evidence-Based Information Which Could Influence Arterial Hypertension Treatment Approach after Publication of SPRINT Trial Results]

The article discusses results of secondary analysis of the data obtained in the SPRINT study and published in recent years. Unresolved issues in the tactics of managing patients with arterial hypertension are discussed. One of such issues is choosing an optimum level of blood pressure (BP) for a subgroup of patients with certain characteristics, including elderly and senile patients, patients with chronic kidney disease, and patients with arterial hypertension who continue smoking. The article discusses calculation of a threshold of risk for complications of cardiovascular diseases, at which a maximum advantage of intensified regimens of antihypertensive therapy could be achieved. In addition, the article addresses approaches to selection of antihypertensive drugs in the current conditions. The authors discussed the role of candesartan in the treatment of arterial hypertension, a sartan most studied in a broad range of patients. The issue of a rapid increase in BP without a damage to target organs is addressed; evidence for the role of captopril in such clinical situation is provided.

The Magnitude of Blood Pressure Reduction Predicts Poor In-Hospital Outcome in Acute Intracerebral Hemorrhage

BACKGROUND

Early systolic blood pressure (SBP) reduction is believed to improve outcome after spontaneous intracerebral hemorrhage (ICH), but there has been a limited assessment of SBP trajectories in individual patients. We aimed to determine the prognostic significance of SBP trajectories in ICH.

METHODS

We collected routine data on spontaneous ICH patients from two healthcare systems over 10 years. Unsupervised functional principal components analysis (FPCA) was used to characterize SBP trajectories over first 24 h and their relationship to the primary outcome of unfavorable shift on modified Rankin scale (mRS) at hospital discharge, categorized as an ordinal trichotomous variable (mRS 0-2, 3-4, and 5-6 defined as good, poor, and severe, respectively). Ordinal logistic regression models adjusted for baseline SBP and ICH volume were used to determine the prognostic significance of SBP trajectories.

RESULTS

The 757 patients included in the study were 65 ± 23 years old, 56% were men, with a median (IQR) Glasgow come scale of 14 (8). FPCA revealed that mean SBP over 24 h and SBP reduction within the first 6 h accounted for 76.8% of the variation in SBP trajectories. An increase in SBP reduction (per 10 mmHg) was significantly associated with unfavorable outcomes defined as mRS > 2 (adjusted-OR = 1.134; 95% CI 1.044-1.233, P = 0.003). Compared with SBP reduction < 20 mmHg, worse outcomes were observed for SBP reduction = 40-60 mmHg (adjusted-OR = 1.940, 95% CI 1.129-3.353, P = 0.017) and > 60 mmHg, (adjusted-OR = 1.965, 95% CI 1.011, 3.846, P = 0.047). Furthermore, the association of SBP reduction and outcome varied according to initial hematoma volume. Smaller SBP reduction was associated with good outcome (mRS 0-2) in small (< 7.42 mL) and medium-size (≥ 7.42 and < 30.47 mL) hematomas. Furthermore, while the likelihood of good outcome was low in those with large hematomas (≥ 30.47 mL), smaller SBP reduction was associated with decreasing probability of severe outcome (mRS 5-6).

CONCLUSION

Our analyses suggest that in the first 6 h SBP reduction is significantly associated with the in-hospital outcome that varies with initial hematoma volume, and early SBP reduction > 40 mmHg may be harmful in ICH patients. For early SBP reduction to have an effective therapeutic effect, both target levels and optimum SBP reduction goals vis-à-vis hematoma volume should be considered.