ArticleIntensive care medicine2019
ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.
Article in Intensive care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06288724 (Potentially MOdifiable Factors To ImproVe Outcomes of Mechanically Ventilated Patients in ICUs in a Low-income Country), which is not on this map. Cited by 34 papers.
What it found
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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.
Potentially MOdifiable Factors To ImproVe Outcomes of Mechanically Ventilated Patients in ICUs in a Low-income Country: Rationale and Protocol for a Registry-embedded Prospective Observational Study
Who cites it
34 citing papers in PubMed, 88 citations in OpenAlex.
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- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
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- Interprofessional Team Staffing in U.S. Intensive Care Units.Annals of the American Thoracic Society · 2025Article
- Potentially MOdifiable factors To ImproVe outcomes of mechanically Ventilated patients in a low-income country Intensive Care Units (MOTIVATE-ICU): rationale and protocol for a registry-embedded prospective observational study.Critical care science · 2025Article
- Beyond the abscess:Frontiers in cellular and infection microbiology · 2025Article
- The relationship between nursing workload and quality of care in intensive care units.Critical care science · 2025Article
- Using machine learning to predict neurologic injury in venovenous extracorporeal membrane oxygenation recipients: An ELSO Registry analysis.JTCVS open · 2024Article
- Interprofessional Staffing Pattern Clusters in U.S. ICUs.Critical care explorations · 2024Article
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- Data and ICU registries to improve care delivery in low-resource settings.Intensive care medicine · 2024Article
- Update on the Epimed Monitor Adult ICU Database: 15 years of its use in national registries, quality improvement initiatives and clinical research.Critical care science · 2024Review
- A decade of the ORCHESTRA study: organizational characteristics, patient outcomes, performance and efficiency in critical care.Critical care science · 2024Article
- Unmasking the hidden aftermath: postintensive care unit sequelae, discharge preparedness, and long-term follow-up.Critical care science · 2024Review
- Scientific output and organizational characteristics in Brazilian intensive care units: a multicenter cross-sectional study.Critical care science · 2024Article
- Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment.BMC medical informatics and decision making · 2023Article
- High-value care for critically ill oncohematological patients: what do we know thus far?Critical care science · 2023Review
- Article
- Clinical characteristics and outcomes of patients with COVID-19 admitted to the intensive care unit during the first and second waves of the pandemic in Brazil: a single-center retrospective cohort study.Einstein (Sao Paulo, Brazil) · 2023Article
- Clustering phosphate and iron-related markers and prognosis in dialysis patients.Clinical kidney journal · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 9 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
methodsThe following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression.
resultsAnalysis included data from 129,680 patients admitted to 93 ICUs (2014-2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87-0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22-1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54-1.69)]. Cluster 1 had the worst outcomes.
conclusionPatients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.
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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.