scholarly journals Performance of intensive care unit severity scoring systems across different ethnicities

2021 ◽  
Author(s):  
Rahuldeb Sarkar ◽  
Christopher Martin ◽  
Heather Mattie ◽  
Judy Wawira Gichoya ◽  
David J. Stone ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
African Americans ◽  
Ethnic Groups ◽  
Medical Information ◽  
Scoring Systems ◽  
Illness Severity ◽  
Research Database ◽  
Severity Scoring ◽  
Severity Scores

ABSTRACTBackgroundDespite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources such as mechanical ventilation during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias.MethodData from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups.FindingsWhile measurements of discrimination -area under the receiver operating characteristic curve (AUROC) -were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration -standardised mortality ratio (SMR) -indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics.InterpretationThe systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality.FundingLAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.

scholarly journals Neutrophil-to-lymphocyte ratio as a predictor of mortality in intensive care unit patients: a retrospective analysis of the Medical Information Mart for Intensive Care III Database

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e053548
Author(s):  
Xie Wu ◽  
Qipeng Luo ◽  
Zhanhao Su ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Inflammatory Markers ◽  
Predictive Power ◽  
Medical Information ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Mortality Rates ◽  
Simplified Acute Physiology Score ◽  
Icu Patients

ObjectivesIdentifying high-risk patients in the intensive care unit (ICU) is important given the high mortality rate. However, existing scoring systems lack easily accessible, low-cost and effective inflammatory markers. We aimed to identify inflammatory markers in routine blood tests to predict mortality in ICU patients and evaluate their predictive power.DesignRetrospective case–control study.SettingSingle secondary care centre.ParticipantsWe analysed data from the Medical Information Mart for Intensive Care III database. A total of 21 822 ICU patients were enrolled and divided into survival and death groups based on in-hospital mortality.Primary and secondary outcome measuresThe predictive values of potential inflammatory markers were evaluated and compared using receiver operating characteristic curve analysis. After identifying the neutrophil-to-lymphocyte ratio (NLR) as having the best predictive ability, patients were redivided into low (≤1), medium (1–6) and high (>6) NLR groups. Univariate and multivariate logistic regression analyses were performed to evaluate the association between the NLR and mortality. The area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess whether incorporating the NLR could improve the predictive power of existing scoring systems.ResultsThe NLR had the best predictive ability (AUC: 0.609; p<0.001). In-hospital mortality rates were significantly higher in the low (OR (OR): 2.09; 95% CI 1.64 to 2.66) and high (OR 1.64; 95% CI 1.50 to 1.80) NLR groups than in the medium NLR group. Adding the NLR to the Simplified Acute Physiology Score II improved the AUC from 0.789 to 0.798, with an NRI and IDI of 16.64% and 0.27%, respectively.ConclusionsThe NLR predicted mortality in ICU patients well. Both low and high NLRs were associated with elevated mortality rates, including the NLR may improve the predictive power of the Simplified Acute Physiology Score II.

scholarly journals Performance of Intensive Care Unit Severity Scoring Systems Across Different Ethnicities

2020 ◽  
Author(s):  
Rahuldeb Sarkar ◽  
Christopher Martin ◽  
Heather Mattie ◽  
Judy Wawira Gichoya ◽  
David J. Stone ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Scoring Systems ◽  
Severity Scoring

scholarly journals Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding

2021 ◽  
Vol 28 (1) ◽  
pp. e100245
Author(s):  
Riccardo Levi ◽  
Francesco Carli ◽  
Aldo Robles Arévalo ◽  
Yuksel Altinel ◽  
Daniel J Stein ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Learning Algorithm ◽  
Observation Time ◽  
Research Database ◽  
Gi Bleeding ◽  
Healthcare Applications

ObjectiveGastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.MethodsA machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.ResultsThe optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.ConclusionsThe potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

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