ArticleCanadian journal of anaesthesia = Journal canadien d'anesthesie2020
External validation of a prognostic model for intensive care unit mortality: a retrospective study using the Ontario Critical Care Information System.
Article in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Development and initial testing of an illness perception questionnaire for substitute decision makers of critically ill patients: a mixed methods study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Observational
- The burden of COVID-19 care in community and academic intensive care units in Ontario, Canada: a retrospective cohort study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Article
- Comparison of risk-adjusted cumulative quality control charts compared with standardized mortality ratios in critical care.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Article
- Functional mass spectrometry indicates anti-protease and complement activity increase with COVID-19 severity.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Automated APACHE II and SOFA score calculation using real-world electronic medical record data in a single center.Journal of clinical monitoring and computing · 2023Article
- Forecasting ICU Census by Combining Time Series and Survival Models.Critical care explorations · 2023Article
- Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.Molecular medicine (Cambridge, Mass.) · 2023Article
- Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism.Molecular medicine (Cambridge, Mass.) · 2022Article
- Critically Ill COVID-19 Patients Exhibit Anti-SARS-CoV-2 Serological Responses.Pathophysiology : the official journal of the International Society for Pathophysiology · 2021Article
- Detection and Profiling of Human Coronavirus Immunoglobulins in Critically Ill Coronavirus Disease 2019 Patients.Critical care explorations · 2021Article
- Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers.Critical care explorations · 2020Article
- Endothelial Injury and Glycocalyx Degradation in Critically Ill Coronavirus Disease 2019 Patients: Implications for Microvascular Platelet Aggregation.Critical care explorations · 2020Article
- Novel Outcome Biomarkers Identified With Targeted Proteomic Analyses of Plasma From Critically Ill Coronavirus Disease 2019 Patients.Critical care explorations · 2020Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo externally validate an intensive care unit (ICU) mortality prediction model that was created using the Ontario Critical Care Information System (CCIS), which includes the Multiple Organ Dysfunction Score (MODS).
methodsWe applied the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations to a prospective longitudinal cohort of patients discharged between 1 July 2015 and 31 December 31 2016 from 90 adult level-3 critical care units in Ontario. We used multivariable logistic regression with measures of discrimination, calibration-in-the-large, calibration slope, and flexible calibration plots to compare prediction model performance of the entire data set and for each ICU subtype.
resultsAmong 121,201 CCIS records with ICU mortality of 11.3%, the C-statistic for the validation data set was 0.805. The C-statistic ranged from 0.775 to 0.846 among the ICU subtypes. After intercept recalibration to adjust the baseline risk, the mean predicted risk of death matched actual ICU mortality. The calibration slope was close to 1 with all CCIS data and ICU subtypes of cardiovascular and community hospitals with low ventilation rates. Calibration slopes significantly less than 1 were found for ICUs in teaching hospitals and community hospitals with high ventilation rates whereas coronary care units had a calibration slope significantly higher than 1. Calibration plots revealed over-prediction in high risk groups to a varying degree across all cohorts.
conclusionsA risk prediction model primarily based on the MODS shows reproducibility and transportability after intercept recalibration. Risk adjusting models that use existing and feasible data collection can support performance measurement at the individual ICU level.
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