ReviewNature reviews. Cardiology2013
Personalized cardiovascular medicine: concepts and methodological considerations.
Review in Nature reviews. Cardiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Next-Generation Antioxidants in Cardiovascular Disease: Mechanistic Insights and Emerging Therapeutic Strategies.Antioxidants (Basel, Switzerland) · 2026Review
- Machine learning-enhanced prediction model for atrial fibrillation development in patients with concurrent type 2 diabetes and obstructive sleep apnea syndrome: a comorbidity perspective.Nutrition & metabolism · 2025Article
- Brain‑heart axis: Neurostimulation techniques in ischemic heart disease (Review).International journal of molecular medicine · 2025Review
- Data cleaning and harmonization of clinical trial data: Medication-assisted treatment for opioid use disorder.PloS one · 2024Article
- Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129.Cardiovascular research · 2022Article
- Echocardiographic phenotypes of Chinese patients with type 2 diabetes may indicate early diabetic myocardial disease.ESC heart failure · 2022Article
- Phenotypes of heart failure with preserved ejection fraction and effect of spironolactone treatment.ESC heart failure · 2022Article
- Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice.Journal of personalized medicine · 2020Review
- Whole-Transcriptome Sequencing: a Powerful Tool for Vascular Tissue Engineering and Endothelial Mechanobiology.High-throughput · 2018Review
- Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.BMC systems biology · 2017Article
- Multiple sclerosis: clinical profiling and data collection as prerequisite for personalized medicine approach.BMC neurology · 2016Review
- 3D Printed Anatomical Nerve Regeneration Pathways.Advanced functional materials · 2015Article
- Genotype-based clinical trials in cardiovascular disease.Nature reviews. Cardiology · 2015Review
- The continuum of personalized cardiovascular medicine: a position paper of the European Society of Cardiology.European heart journal · 2014Article
- Cohort profile: Greifswald approach to individualized medicine (GANI_MED).Journal of translational medicine · 2014Article
- Therapeutic target database update 2014: a resource for targeted therapeutics.Nucleic acids research · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The primary goals of personalized medicine are to optimize diagnostic and treatment strategies by tailoring them to the specific characteristics of an individual patient. In this Review, we summarize basic concepts and methods of personalizing cardiovascular medicine. In-depth characterization of study participants and patients in general practice using standardized methods is a pivotal component of study design in personalized medicine. Standardization and quality assurance of clinical data are similarly important, but in daily practice imprecise definitions of clinical variables can reduce power and introduce bias, which limits the validity of the data obtained as well as their potential clinical applicability. Changes in statistical methods with personalized medicine include a shift from dichotomous outcomes towards continuously measured variables, predictive modelling, and individualized medical decisions, subgroup analyses, and data-mining strategies. A variety of approaches to personalized medicine exist in cardiovascular research and clinical practice that might have the potential to individualize diagnostic and therapeutic procedures. For some of the emerging methods, such as data mining, the most-efficient way to use these tools is not yet fully understood. In addition, the predictive models-although promising-are far from mature, and are likely to be greatly improved by using available large-scale data sets.
Indexed as
Identifiers
23528962What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.