scholarly journals Development and Validation of a Practical Machine-learning Triage Algorithm for the Detection of Patients in Need of Critical Care in the Emergency Department

2021 ◽  
Author(s):  
Yecheng Liu ◽  
Jiandong Gao ◽  
Jihai Liu ◽  
Joseph Harold Walline ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cohort Study ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Characteristic Curve ◽  
Learning System ◽  
Validation Dataset ◽  
Development And Validation ◽  
Triage Algorithm

Abstract Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 20,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training. We then conducted a prospective randomized cohort study of ED patients between March and April 2020. The area under the receiver operating characteristic curve (AUC) was 0.86 in a retrospective validation dataset of 2,000 randomized database cases. In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.

scholarly journals Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yecheng Liu ◽  
Jiandong Gao ◽  
Jihai Liu ◽  
Joseph Harold Walline ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Reference Model ◽  
Characteristic Curve ◽  
Learning System ◽  
Development And Validation ◽  
Triage Algorithm ◽  
Mls Method

AbstractIdentifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Association of delayed time in the emergency department with the clinical outcomes for critically ill patients

QJM ◽  
2020 ◽  
Author(s):  
S Lin ◽  
S Ge ◽  
W He ◽  
M Zeng
Keyword(s):  
Emergency Department ◽  
Cohort Study ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Waiting Time ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Stratified Analysis ◽  
Delayed Time

Summary Background Previous studies have shown the association of waiting time in the emergency department with the prognosis of critically ill patients, but these studies linking the waiting time to clinical outcomes have been inconsistent and limited by small sample size. Aim To determine the relationship between the waiting time in the emergency department and the clinical outcomes for critically ill patients in a large sample population. Design A retrospective cohort study of 13 634 patients. Methods We used the Medical Information Mart for Intensive Care III database. Multivariable logistic regression was used to determine the independent relationships of the in-hospital mortality rate with the delayed time and different groups. Interaction and stratified analysis were conducted to test whether the effect of delayed time differed across various subgroups. Results After adjustments, the in-hospital mortality in the ≥6 h group increased by 38.1% (OR 1.381, 95% CI 1.221–1.562). Moreover, each delayed hour was associated independently with a 1.0% increase in the risk of in-hospital mortality (OR 1.010, 95% CI 1.008–1.010). In the stratified analysis, intensive care unit (ICU) types, length of hospital stay, length of ICU stay, simplified acute physiology score II and diagnostic category were found to have interactions with ≥6 h group in in-hospital mortality. Conclusions In this large retrospective cohort study, every delayed hour was associated with an increase in mortality. Furthermore, clinicians should be cautious of patients diagnosed with sepsis, liver/renal/metabolic diseases, internal hemorrhage and cardiovascular disease, and if conditions permit, they should give priority to transferring to the corresponding ICUs.

scholarly journals ACEI/ARB Medication During ICU Stay Decrease All-Cause In-hospital Mortality in Critically Ill Patients With Hypertension: A Retrospective Cohort Study Based on Machine Learning

2022 ◽  
Vol 8 ◽  
Author(s):  
Boshen Yang ◽  
Sixuan Xu ◽  
Di Wang ◽  
Yu Chen ◽  
Zhenfa Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Learning Models ◽  
Hyperparameter Optimization ◽  
Icu Stay ◽  
Machine Learning Models

Background: Hypertension is a rather common comorbidity among critically ill patients and hospital mortality might be higher among critically ill patients with hypertension (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg). This study aimed to explore the association between ACEI/ARB medication during ICU stay and all-cause in-hospital mortality in these patients.Methods: A retrospective cohort study was conducted based on data from Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which consisted of more than 40,000 patients in ICU between 2008 and 2019 at Beth Israel Deaconess Medical Center. Adults diagnosed with hypertension on admission and those had high blood pressure (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg) during ICU stay were included. The primary outcome was all-cause in-hospital mortality. Patients were divided into ACEI/ARB treated and non-treated group during ICU stay. Propensity score matching (PSM) was used to adjust potential confounders. Nine machine learning models were developed and validated based on 37 clinical and laboratory features of all patients. The model with the best performance was selected based on area under the receiver operating characteristic curve (AUC) followed by 5-fold cross-validation. After hyperparameter optimization using Grid and random hyperparameter search, a final LightGBM model was developed, and Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. The features closely associated with hospital mortality were presented as significant features.Results: A total of 15,352 patients were enrolled in this study, among whom 5,193 (33.8%) patients were treated with ACEI/ARB. A significantly lower all-cause in-hospital mortality was observed among patients treated with ACEI/ARB (3.9 vs. 12.7%) as well as a lower 28-day mortality (3.6 vs. 12.2%). The outcome remained consistent after propensity score matching. Among nine machine learning models, the LightGBM model had the highest AUC = 0.9935. The SHAP plot was employed to make the model interpretable based on LightGBM model after hyperparameter optimization, showing that ACEI/ARB use was among the top five significant features, which were associated with hospital mortality.Conclusions: The use of ACEI/ARB in critically ill patients with hypertension during ICU stay is related to lower all-cause in-hospital mortality, which was independently associated with increased survival in a large and heterogeneous cohort of critically ill hypertensive patients with or without kidney dysfunction.

scholarly journals Diagnostic Accuracy of Biomarkers for Early Multiple Organ Dysfunction in Critically Ill Patients: A Multicenter Prospective Observational Study

2020 ◽  
Author(s):  
Shigeto Ishikawa ◽  
Yuto Teshima ◽  
Hiroki Otsubo ◽  
Takashi Shimazui ◽  
Taka-aki Nakada ◽  
...  
Keyword(s):  
Emergency Department ◽  
Observational Study ◽  
Critically Ill ◽  
Organ Dysfunction ◽  
Critically Ill Patients ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Multiple Organ ◽  
Multiple Organ Dysfunction ◽  
Qsofa Score

Abstract BackgroundShock and organ damage occur in critically ill patients in the emergency department because of biological responses to invasion, and cytokines play an important role in their development. It is important to predict early multiple organ dysfunction (MOD) because it is useful in predicting patient outcomes and selecting treatment strategies. This study examined the accuracy of biomarkers, including interleukin (IL)-6, in predicting early MOD in critically ill patients compared with that of quick sequential organ failure assessment (qSOFA).MethodsThis observational study was conducted at five universities from 2016 to 2018. Data of adult patients with systemic inflammatory response syndrome who presented to the emergency department or were admitted to the intensive care unit were prospectively evaluated. qSOFA score and each biomarker (IL-6, IL-8, IL-10, tumor necrosis factor-α, C-reactive protein, and procalcitonin [PCT]) level were assessed on Days 0, 1, and 2. The primary outcome was set as MOD on Day 2, and the area under the receiver operating characteristic curve (AUROC) was analyzed to evaluate qSOFA scores and biomarker levels.ResultsOf 199 patients, 38 were excluded and 161 were included. Patients with MOD on Day 2 had significantly higher qSOFA, SOFA, and Acute Physiology and Chronic Health Evaluation II scores and a trend toward worse prognosis, including mortality. The AUROC for qSOFA score (Day 0) that predicted MOD (Day 2) was 0.728 (95% confidence interval [CI]: 0.651–0.794). IL-6 (Day 1) showed the highest AUC among all biomarkers (0.790 [95% CI: 0.711–852]). The combination of qSOFA (Day 0) and IL-6 (Day 1) showed improved prediction accuracy (0.859 [95% CI: 0.792–0.907]). The combination model using qSOFA (Day 0) and IL-6 (Day 1) also showed a higher AUROC (0.889 [95% CI: 0.828–0.929]).ConclusionsThe addition of serum IL-6 level to qSOFA scores improved the accuracy of early MOD prediction.

scholarly journals Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation

2021 ◽  
Vol 11 (11) ◽  
pp. 1055
Author(s):  
Pei-Chen Lin ◽  
Kuo-Tai Chen ◽  
Huan-Chieh Chen ◽  
Md. Mohaimenul Islam ◽  
Ming-Chin Lin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Performance ◽  
Learning Model ◽  
Gradient Boosting ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting ◽  
Development And Validation

Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.

scholarly journals Prognostic impact of elevated lactate levels on mortality in critically ill patients with and without preadmission metformin treatment: a Danish registry-based cohort study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rene A. Posma ◽  
Trine Frøslev ◽  
Bente Jespersen ◽  
Iwan C. C. van der Horst ◽  
Daan J. Touw ◽  
...  
Keyword(s):  
Cohort Study ◽  
Mortality Rate ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Regression ◽  
Lower Mortality ◽  
Prognostic Impact ◽  
Icu Admission ◽  
Lactate Levels ◽  
Restricted Cubic Splines

Abstract Background Lactate is a robust prognostic marker for the outcome of critically ill patients. Several small studies reported that metformin users have higher lactate levels at ICU admission without a concomitant increase in mortality. However, this has not been investigated in a larger cohort. We aimed to determine whether the association between lactate levels around ICU admission and mortality is different in metformin users compared to metformin nonusers. Methods This cohort study included patients admitted to ICUs in northern Denmark between January 2010 and August 2017 with any circulating lactate measured around ICU admission, which was defined as 12 h before until 6 h after admission. The association between the mean of the lactate levels measured during this period and 30-day mortality was determined for metformin users and nonusers by modelling restricted cubic splines obtained from a Cox regression model. Results Of 37,293 included patients, 3183 (9%) used metformin. The median (interquartile range) lactate level was 1.8 (1.2–3.2) in metformin users and 1.6 (1.0–2.7) mmol/L in metformin nonusers. Lactate levels were strongly associated with mortality for both metformin users and nonusers. However, the association of lactate with mortality was different for metformin users, with a lower mortality rate in metformin users than in nonusers when admitted with similar lactate levels. This was observed over the whole range of lactate levels, and consequently, the relation of lactate with mortality was shifted rightwards for metformin users. Conclusion In this large observational cohort of critically ill patients, early lactate levels were strongly associated with mortality. Irrespective of the degree of hyperlactataemia, similar lactate levels were associated with a lower mortality rate in metformin users compared with metformin nonusers. Therefore, lactate levels around ICU admission should be interpreted according to metformin use.

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