Assessment of Risk of Harm Associated With Intensive Blood Pressure Management Among Patients With Hypertension Who Smoke

Scarpa, Joseph R.; Bruzelius, Emilie; Doupé, Patrick; Le, Matthew; Faghmous, James H.; Baum, Aaron

doi:10.1001/jamanetworkopen.2019.0005

Cited by 26 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yet, the question of whether this treatment strategy is beneficial in distinct subgroups of children [e.g., atopic children with rhinovirus-induced wheezing ( 46 )] remains unclear. Recently, machine learning approaches [e.g., random forest ( 47 ) ( Table 3 , Figure 2 )] have been applied to health data to (1) identify subgroups with different treatment effects, and (2) estimate individual (heterogeneous) treatment effects for subgroups in various disease conditions (e.g., diabetes) ( 48 , 49 ). An integration of these algorithms, careful interpretation (e.g., covariate balance between the derived subgroups, false discoveries) and prospective validation will help precision medicine realize preventive and treatment strategies tailored to patients with a unique set of clinical characteristics.…”

Section: Major Causal Inference Toolsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Major Causal Inference Toolsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Second, the generalizability of treatment effect estimates from causal forest methodologies has yet to be widely documented. These machine learning models for computation of heterogenous treatment effects have only begun to be utilized in medicine [ 6 , 13 , 14 ] and we were unable to find any studies using these methods in the field of respiratory infections. This study also used a composite outcome, combining several clinical outcomes: failure to reach clinical improvement within 7 days, transfer to intensive care 24 h after admission, or rehospitalization or death within 30 days.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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

show abstract

“…На самом деле нет точного ответа на вопрос, насколько такое мнение обосновано. Недавно выполнен анализ данных об участниках исследования SPRINT, которые курили (n=466) [36]. Учитывая ограниченность стандартных методов анализа в подгруппах для выявления гетерогенных эффектов, анализ выполняли с использованием усовершенствованного статистического метода машинного обучения, применение которого позволяет выявлять гетерогенные эффекты лечения в условиях большого числа ковариат.…”

Section: каковы научные основания для рекомендаций европейских экспертов по целевому уровню ад у пациентов с сахарным диабетом?unclassified

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

Gilyarevsky¹,

Бенделиани

Голшмид³

et al. 2020

Kardiologiia

View full text Add to dashboard Cite

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.

show abstract

Assessment of Risk of Harm Associated With Intensive Blood Pressure Management Among Patients With Hypertension Who Smoke

Abstract: This secondary analysis of the Systolic Blood Pressure Intervention Trial uses a random forest–based analysis to assess whether clinically important heterogeneity exists in the risk of harm associated with intensive blood pressure treatment among adults with hypertension who smoke.

Cited by 26 publications

References 28 publications

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

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

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

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

Contact Info

Product

Resources

About