ArticleSurgery2013
Predicting the need for urgent intubation in a surgical/trauma intensive care unit.
Article in Surgery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 48 citations in OpenAlex.
- Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.Journal of clinical monitoring and computing · 2014Trial
- Predicting pulmonary hemorrhage in very low birth weight infants.Pediatric research · 2026Article
- Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.Journal of biomedical informatics · 2025Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
- Risk Prediction Models for Invasive Mechanical Ventilation in Patients with Autoimmune Encephalitis: A Retrospective Cohort Study.Journal of immunology research · 2023Article
- Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.Learning health systems · 2023Article
- Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial.Clinical and experimental emergency medicine · 2022Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.PLOS digital health · 2022Article
- Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study.Frontiers in pediatrics · 2022Article
- Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.International journal of nursing studies advances · 2021Article
- External validation of a novel signature of illness in continuous cardiorespiratory monitoring to detect early respiratory deterioration of ICU patients.Physiological measurement · 2021Article
- Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.Applied clinical informatics · 2021Article
- Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2021Article
- Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System.Critical care explorations · 2020Article
- Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.Scientific reports · 2020Article
- Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.Journal of clinical monitoring and computing · 2019Article
- Data Science for Child Health.The Journal of pediatrics · 2019Review
- Predictive modeling in urgent care: a comparative study of machine learning approaches.JAMIA open · 2018Article
- Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.Critical care medicine · 2016Observational
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAnalysis and modeling of data monitoring vital signs and waveforms in patients in a surgical/trauma intensive care unit (STICU) may allow for early identification and treatment of patients with evolving respiratory failure.
methodsBetween February 2011 and March 2012, data of vital signs and waveforms for STICU patients were collected. Every-15-minute calculations (n = 172,326) of means and standard deviations of heart rate (HR), respiratory rate (RR), pulse-oxygen saturation (SpO2), cross-correlation coefficients, and cross-sample entropy for HR-RR, RR-SpO2, and HR-SpO2, and cardiorespiratory coupling were calculated. Urgent intubations were recorded. Univariate analyses were performed for the periods <24 and ≥24 hours before intubation. Multivariate predictive models for the risk of unplanned intubation were developed and validated internally by subsequent sample and bootstrapping techniques.
resultsFifty unplanned intubations (41 patients) were identified from 798 STICU patients. The optimal multivariate predictive model (HR, RR, and SpO2 means, and RR-SpO2 correlation coefficient) had a receiving operating characteristic (ROC) area of 0.770 (95% confidence interval [CI], 0.712-0.841). For this model, relative risks of intubation in the next 24 hours for the lowest and highest quintiles were 0.20 and 2.95, respectively (15-fold increase, baseline risk 1.46%). Adding age and days since previous extubation to this model increased ROC area to 0.865 (95 % CI, 0.821-0.910).
conclusionAmong STICU patients, a multivariate model predicted increases in risk of intubation in the following 24 hours based on vital sign data available currently on bedside monitors. Further refinement could allow for earlier detection of respiratory decompensation and intervention to decrease preventable morbidity and mortality in surgical/trauma patients.
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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.