scholarly journals Comparative study of scoring systems in ICU and emergency department in predicting mortality of critically ill

2017 ◽  
Vol 5 (4) ◽  
pp. 1352 ◽  
Author(s):  
Sasi Sekhar T. V. D. ◽  
Anjani Kumar C. ◽  
Bhavya Ch. ◽  
Sameera B. ◽  
Rama Devi Ch.
Keyword(s):  
Emergency Department ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Laboratory Data ◽  
Scoring Systems ◽  
Apache Ii ◽  
Early Warning Score ◽  
Saps Ii

Background: Scoring systems can be used to define critically ill patients, estimate their prognosis, help in clinical decision making, and guide the allocation of resources and to estimate the quality of care.  It remains unclear whether the additional data needed to compute ICU scores improves mortality prediction for critically ill patients compared to the simpler ED scores.Methods: We have done a prospective observational study of consecutively admitted 400 critically ill patients to ICU directly from Emergency Department in Dr PSIMS and RF over a period of 2 years. Clinical and laboratory data conforming to the modified early warning score (MEWS), rapid emergency medicine score (REMS), acute physiology and chronic health evaluation (APACHE II), and simplified acute physiology score (SAPS II) were recorded for all patients. A comparison was made between ED scoring systems MEWS, REMS and ICU scoring systems APACHE II, SAPSII. The outcome was recorded in two categories: survived and non-survived with a primary end point of 30-day mortality. Discrimination was evaluated using receiver operating characteristic (ROC) curves.Results: The ICU scores outperformed the ED scores with more area under curve values. The predicted mortality percentage of ICU based scoring systems is high compared to emergency scores (predicted mortality % of SAPS II-63%, APACHE II-33.3%, MEWS-18.5%, REMS-14.8%).Conclusions: ICU scores showed more predictive accuracy than ED scores in prognosticating the outcomes in critically ill patients. This difference is seemed more due to complexity of ICU scores.

scholarly journals A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

10.2196/24246 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e24246 ◽  
Author(s):  
Siavash Bolourani ◽  
Max Brenner ◽  
Ping Wang ◽  
Thomas McGinn ◽  
Jamie S Hirsch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Respiratory Failure ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Invasive Mechanical Ventilation ◽  
Laboratory Data ◽  
Early Warning Score ◽  
Learning Approaches

Background Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

scholarly journals A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Siavash Bolourani ◽  
Max Brenner ◽  
Ping Wang ◽  
Thomas McGinn ◽  
Jamie S Hirsch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Respiratory Failure ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Invasive Mechanical Ventilation ◽  
Laboratory Data ◽  
Early Warning Score ◽  
Learning Approaches

BACKGROUND Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

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.

scholarly journals Investigate predictive capacity of in-hospital mortality of four severity score systems on critically ill patients with acute kidney injury

2019 ◽  
Vol 67 (8) ◽  
pp. 1103-1109 ◽  
Author(s):  
Yu Gong ◽  
Feng Ding ◽  
Fen Zhang ◽  
Yong Gu
Keyword(s):  
Acute Kidney Injury ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Mortality Prediction ◽  
Apache Ii ◽  
University Teaching ◽  
Predictive Capacity ◽  
Saps Ii

Although significant improvements have been achieved in the renal replacement therapy of acute kidney injury (AKI), the mortality of patients with AKI remains high. The aim of this study is to prospectively investigate the capacity of Acute Physiology and Chronic Health Evaluation version II (APACHE II), Simplified Acute Physiology Score version II (SAPS II), Sepsis-related Organ Failure Assessment (SOFA) and Acute Tubular Necrosis Individual Severity Index (ATN-ISI) to predict in-hospital mortality of critically ill patients with AKI. A prospective observational study was conducted in a university teaching hospital. 189 consecutive critically ill patients with AKI were selected according Risk, Injury, Failure, Loss, or End-stage kidney disease criteria. APACHE II, SAPS II, SOFA and ATN-ISI counts were obtained within the first 24 hours following admission. Receiver operating characteristic analyses (ROCs) were applied. Area under the ROC curve (AUC) was calculated. Sensitivity and specificity of in-hospital mortality prediction were calculated. In this study, the in-hospital mortality of critically ill patients with AKI was 37.04% (70/189). AUC of APACHE II, SAPS II, SOFA and ATN-ISI was 0.903 (95% CI 0.856 to 0.950), 0.893 (95% CI 0.847 to 0.940), 0.908 (95% CI 0.866 to 0.950) and 0.889 (95% CI 0.841 to 0.937) and sensitivity was 90.76%, 89.92%, 90.76% and 89.08% and specificity was 77.14%, 70.00%, 71.43% and 71.43%, respectively. In this study, it was found APACHE II, SAPS II, SOFA and ATN-ISI are reliable in-hospital mortality predictors of critically ill patients with AKI. Trial registration number: NCT00953992.

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