ArticleCritical care medicine2013
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension.
Article in Critical care medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Development and validation of novel sepsis subphenotypes using trajectories of vital signs.Intensive care medicine · 2022Trial
- Correlation between sub-phenotypes of coagulopathy and prognosis in sepsis patients in ICU using longitudinal group trajectory modeling: a retrospective analysis.European journal of medical research · 2026Article
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- Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests.Scientific reports · 2024Article
- WAVES - The Lucile Packard Children's Hospital Pediatric Physiological Waveforms Dataset.Scientific data · 2023Article
- Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis.Sensors (Basel, Switzerland) · 2022Article
- Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature.JMIR medical informatics · 2022Review
- Acute kidney injury detection using refined and physiological-feature augmented urine output.Scientific reports · 2021Article
- Leveraging hybrid biomarkers in clinical endpoint prediction.BMC medical informatics and decision making · 2020Article
- A machine learning approach for mortality prediction only using non-invasive parameters.Medical & biological engineering & computing · 2020Review
- Precision medicine in anesthesiology.International anesthesiology clinics · 2020Article
- Applying machine learning to continuously monitored physiological data.Journal of clinical monitoring and computing · 2019Review
- Developing a Registry of Healthcare-Associated Infections at Intensive Care Units in West China: Study Rationale and Patient Characteristics.Clinical epidemiology · 2019Article
- Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG.Brain : a journal of neurology · 2018Article
- An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.Critical care medicine · 2018Observational
- Predictive modeling of inpatient mortality in departments of internal medicine.Internal and emergency medicine · 2018Article
- A dual boundary classifier for predicting acute hypotensive episodes in critical care.PloS one · 2018Article
- Serial Daily Organ Failure Assessment Beyond ICU Day 5 Does Not Independently Add Precision to ICU Risk-of-Death Prediction.Critical care medicine · 2017Article
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6 authors.
Funding
Abstract
objectivesTo determine if a prediction rule for hospital mortality using dynamic variables in response to treatment of hypotension in patients with sepsis performs better than current models.
designRetrospective cohort study.
settingAll ICUs at a tertiary care hospital. PATIENTS: Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe developed a prediction algorithm for hospital mortality in patients with sepsis and hypotension requiring medical intervention using data from the Multiparameter Intelligent Monitoring in Intensive Care II. We extracted 189 candidate variables, including treatments, physiologic variables and laboratory values collected before, during, and after a hypotensive episode. Thirty predictors were identified using a genetic algorithm on a training set (n=1500) and validated with a logistic regression model on an independent validation set (n=613). The final prediction algorithm used included dynamic information and had good discrimination (area under the receiver operating curve=82.0%) and calibration (Hosmer-Lemeshow C statistic=10.43, p=0.06). This model was compared with Acute Physiology and Chronic Health Evaluation IV using reclassification indices and was found to be superior with an Net Reclassification Improvement of 0.19 (p<0.001) and an Integrated Discrimination Improvement of 0.09 (p<0.001).
conclusionsHospital mortality predictions based on dynamic variables surrounding a hypotensive event is a new approach to predicting prognosis. A model using these variables has good discrimination and calibration and offers additional predictive prognostic information beyond established ones.
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