MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care ...hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL ...applications in the critical care setting.
This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models.
We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included.
We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application.
RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.
Over the last decade, a massive increase in data collection and analysis has occurred in many fields. In the health sector, however, there has been relatively little progress in data analysis and ...application despite a rapid rise in data production. Given adequate governance, improvements in the quality, quantity, storage and analysis of health data could lead to substantial improvements in many health outcomes. In low- and middle-income countries in particular, the creation of an information feedback mechanism can move health-care delivery towards results-based practice and improve the effective use of scarce resources. We review the evolving definition of big data and the possible advantages of - and problems in - using such data to improve health-care delivery in low- and middle-income countries. The collection of big data as mobile-phone based services improve may mean that development phases required elsewhere can be skipped. However, poor infrastructure may prevent interoperability and the safe use of patient data. An appropriate governance framework must be developed and enforced to protect individuals and ensure that health-care delivery is tailored to the characteristics and values of the target communities.
The neutrophil-to-lymphocyte ratio (NLR) is a biological marker that has been shown to be associated with outcomes in patients with a number of different malignancies. The objective of this study was ...to assess the relationship between NLR and mortality in a population of adult critically ill patients.
We performed an observational cohort study of unselected intensive care unit (ICU) patients based on records in a large clinical database. We computed individual patient NLR and categorized patients by quartile of this ratio. The association of NLR quartiles and 28-day mortality was assessed using multivariable logistic regression. Secondary outcomes included mortality in the ICU, in-hospital mortality and 1-year mortality. An a priori subgroup analysis of patients with versus without sepsis was performed to assess any differences in the relationship between the NLR and outcomes in these cohorts.
A total of 5,056 patients were included. Their 28-day mortality rate was 19%. The median age of the cohort was 65 years, and 47% were female. The median NLR for the entire cohort was 8.9 (interquartile range, 4.99 to 16.21). Following multivariable adjustments, there was a stepwise increase in mortality with increasing quartiles of NLR (first quartile: reference category; second quartile odds ratio (OR) = 1.32; 95% confidence interval (CI), 1.03 to 1.71; third quartile OR = 1.43; 95% CI, 1.12 to 1.83; 4th quartile OR = 1.71; 95% CI, 1.35 to 2.16). A similar stepwise relationship was identified in the subgroup of patients who presented without sepsis. The NLR was not associated with 28-day mortality in patients with sepsis. Increasing quartile of NLR was statistically significantly associated with secondary outcome.
The NLR is associated with outcomes in unselected critically ill patients. In patients with sepsis, there was no statistically significant relationship between NLR and mortality. Further investigation is required to increase understanding of the pathophysiology of this relationship and to validate these findings with data collected prospectively.
Purpose
While the use of transthoracic echocardiography (TTE) in the ICU is rapidly expanding, the contribution of TTE to altering patient outcomes among ICU patients with sepsis has not been ...examined. This study was designed to examine the association of TTE with 28-day mortality specifically in that population.
Methods and results
The MIMIC-III database was employed to identify patients with sepsis who had and had not received TTE. The statistical approaches utilized included multivariate regression, propensity score analysis, doubly robust estimation, the gradient boosted model, and an inverse probability-weighting model to ensure the robustness of our findings. Significant benefit in terms of 28-day mortality was observed among the TTE patients compared to the control (no TTE) group (odds ratio = 0.78, 95% CI 0.68–0.90,
p
< 0.001). The amount of fluid administered (2.5 vs. 2.1 L on day 1,
p
< 0.001), use of dobutamine (2% vs. 1%,
p
= 0.007), and the maximum dose of norepinephrine (1.4 vs. 1 mg/min,
p
= 0.001) were significantly higher for the TTE patients. Importantly, the TTE patients were weaned off vasopressors more quickly than those in the no TTE group (vasopressor-free days on day 28 of 21 vs. 19,
p
= 0.004).
Conclusion
In a general population of critically ill patients with sepsis, use of TTE is associated with an improvement in 28-day mortality.
To 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.
Retrospective ...cohort study.
All ICUs at a tertiary care hospital.
Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data.
None.
We 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).
Hospital 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.
Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at ...this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.
Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential ...growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval CI: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.
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