ReviewCritical care (London, England)2017
The coming era of precision medicine for intensive care.
Review in Critical care (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 62 citations in OpenAlex.
- Description of mitochondrial oxygen tension and its variability in healthy volunteers.PloS one · 2024Trial
- Genetic Determinants of T-Cell Homeostasis in Critical Illness: An Exploratory Analysis of Immune Gene Variants and TREC Dynamics.Journal of personalized medicine · 2026Article
- Artificial Intelligence with Robotics for Metabolic Rehabilitation and Enhanced Patient Recovery in Critical Care.Research (Washington, D.C.) · 2026Review
- Traditional Chinese medicine for sepsis: advancing from evidence to innovative drug discovery.Critical care (London, England) · 2025Review
- Treatment effect of intravenous high-dose selenium in sepsis phenotypes: a retrospective analysis of a large multicenter randomized controlled trial.Journal of intensive care · 2025Article
- Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.BMC medical research methodology · 2025Article
- Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.European journal of epidemiology · 2025Article
- An innovative paradigm of intensive care: hot pot pattern.Frontiers in medicine · 2025Article
- Early serum biomarkers to characterise different phenotypes of primary graft dysfunction after lung transplantation: a systematic scoping review.ERJ open research · 2024Article
- Article
- Sepsis, Management & Advances in Metabolomics.Nanotheranostics · 2024Review
- [Individualized treatment in anesthesiology and intensive care medicine].Die Anaesthesiologie · 2023Review
- The positive impact of COVID-19 on critical care: from unprecedented challenges to transformative changes, from the perspective of young intensivists.Annals of intensive care · 2023Review
- Making machine learning matter to clinicians: model actionability in medical decision-making.NPJ digital medicine · 2023Review
- Pharmacometabolomics identifies candidate predictor metabolites of an L-carnitine treatment mortality benefit in septic shock.Clinical and translational science · 2021Article
- The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care?Critical care (London, England) · 2021Article
- Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Management of Intraoperative Mechanical Ventilation to Prevent Postoperative Complications after General Anesthesia: A Narrative Review.Journal of clinical medicine · 2021Review
- Vitamin D assessment in perioperative medicine and critical care : A prospective observational pilot study.Wiener klinische Wochenschrift · 2021Observational
- DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning.Acta neurochirurgica. Supplement · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in technology and better understanding of mechanisms underlying disease are beginning to enable us to better characterize critically ill patients. Instead of using nonspecific syndromic groupings, such as sepsis or acute respiratory distress syndrome, we can now classify individual patients according to various specific characteristics, such as immune status. This "personalized" medicine approach will enable us to distinguish patients who have similar clinical presentations but different cellular and molecular responses that will influence their need for and responses (both negative and positive) to specific treatments. Treatments will be able to be chosen more accurately for each patient, resulting in more rapid institution of appropriate, effective therapy. We will also increasingly be able to conduct trials in groups of patients specifically selected as being most likely to respond to the intervention in question. This has already begun with, for example, some new interventions being tested only in patients with coagulopathy or immunosuppressive patterns. Ultimately, as we embrace this era of precision medicine, we may be able to offer precision therapies specifically designed to target the molecular set-up of an individual patient, as has begun to be done in cancer therapeutics.
Indexed as
Identifiers
What 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.