ReviewCritical care (London, England)2017
A path to precision in the ICU.
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 48 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
48 citing papers in PubMed.
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO.Information (Basel) · 2026Article
- Building the future of ICU care: Is our digital foundation strong enough? A multicentre survey of Australian and New Zealand intensive care units.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- Prediction of pharmacist medication interventions using medication regimen complexity.JAMIA open · 2025Article
- PharmacyGPT: exploration of artificial intelligence for medication management in the intensive care unit.BMC medical informatics and decision making · 2025Article
- Association between hemoglobin glycation index and mortality in critically ill patients: a retrospective cohort study.Journal of health, population, and nutrition · 2025Article
- A Personalized Predictive Model That Jointly Optimizes Discrimination and Calibration.Statistics in medicine · 2025Article
- Exploring intensive care unit nurses' acceptance of clinical decision support systems and use of volumetric pump data: A qualitative description study.Nursing in critical care · 2025Article
- Impact of different intravenous bolus rates on fluid and electrolyte balance and mortality in critically ill patients.World journal of critical care medicine · 2024Review
- Clinical audit of cases and outcomes of patients admitted to the intensive care unit at Kamuzu Central Hospital, Lilongwe, Malawi.Scientific reports · 2024Article
- Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study.Antimicrobial resistance and infection control · 2024Article
- Artificial intelligence as a further step in the detection of dyspnea in the critically ill mechanically ventilated patient.Intensive care medicine · 2024Article
- 21st century critical care medicine: An overview.World journal of critical care medicine · 2024Review
- Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis.Scientific reports · 2024Article
- Development and application of a nomogram model for the prediction of carbapenem-resistantMicrobiology spectrum · 2024Article
- An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients.Hospital pharmacy · 2023Article
- Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.Scientific reports · 2023Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.Critical care (London, England) · 2023Observational
- [Individualized treatment in anesthesiology and intensive care medicine].Die Anaesthesiologie · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a "one size fits all" approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges.First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the "omics" technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.
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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.