Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model

2001 ◽  
Vol 10 (5) ◽  
pp. 313-319 ◽  
Author(s):  
L Copeland-Fields ◽  
T Griffin ◽  
T Jenkins ◽  
M Buckley ◽  
LC Wise
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Clinical Judgment ◽  
Mortality Prediction ◽  
Survival Outcomes ◽  
Emergency Physicians ◽  
Nursing Experience ◽  
Mortality Prediction Model ◽  
And Function

BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit nurses at the time of admission to the unit between February and September 1995 for 235 consecutive adult nontrauma patients. Scores on the Mortality Prediction Model were calculated on admission. Data on 85 of the 235 patients were analyzed by using descriptive, chi 2, and correlational statistics. Nurses' predictions of function were compared with patients' actual outcomes 6 months after admission. RESULTS: Nurses' predictions of survival were comparable to those of emergency physicians and superior to those obtained by using the objective tool. Years of nursing experience had no relationship to attitudes toward aggressiveness of care. Nurses accurately predicted functional outcomes in 52% of the followed-up cases. Intensive care physicians were more accurate than nurses and emergency physicians in predicting survival. All predictions made by clinicians were superior to those obtained by using the model. CONCLUSIONS: Nurses can predict survival outcomes as accurately as physicians do. Greater sensitivity and specificity are necessary before clinical judgment or predictive tools can be considered as screens for determining aggressiveness of care.

scholarly journals Development of a Machine Learning Model for the Prediction of the Real Time Mortality in Patients in the Intensive Care Unit

2021 ◽  
Author(s):  
Jaeyoung Yang ◽  
Hong-Gook Lim ◽  
Wonhyeong Park ◽  
Dongseok Kim ◽  
Jin Sun Yoon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Learning Model ◽  
Mortality Prediction ◽  
Machine Learning Model ◽  
Mortality Prediction Model

Abstract BackgroundPrediction of mortality in intensive care units is very important. Thus, various mortality prediction models have been developed for this purpose. However, they do not accurately reflect the changing condition of the patient in real time. The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using four easy-to-collect vital signs.MethodsTwo independent retrospective observational cohorts were included in this study. The primary training cohort included the data of 1968 patients admitted to the intensive care unit at the Veterans Health Service Medical Center, Seoul, South Korea, from January 2018 to March 2019. The external validation cohort comprised the records of 409 patients admitted to the medical intensive care unit at Seoul National University Hospital, Seoul, South Korea, from January 2019 to December 2019. Datasets of four vital signs (heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation [SpO2]) measured every hour for 10 h were used for the development of the machine learning model. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset.ResultsThe machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. Thus, to investigate the importance of variables that influence the performance of the machine learning model, machine learning models were generated for each observation time or vital sign using the RF algorithm. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89). ConclusionsThe mortality prediction model developed in this study using data from only four types of commonly recorded vital signs is simpler than any existing mortality prediction model. This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.

A novel mortality prediction model for the current population in an adult intensive care unit

Heart & Lung ◽  
2018 ◽  
Vol 47 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Sofia Fika ◽  
Serafeim Nanas ◽  
Georgios Baltopoulos ◽  
Efstratia Charitidou ◽  
Pavlos Myrianthefs
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Mortality Prediction ◽  
Adult Intensive Care Unit ◽  
Current Population ◽  
Mortality Prediction Model

scholarly journals Lung ultrasound presentation of COVID-19 patients: phenotypes and correlations

2021 ◽  
Author(s):  
Gianmarco Secco ◽  
◽  
Marzia Delorenzo ◽  
Francesco Salinaro ◽  
Caterina Zattera ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Lung Ultrasound ◽  
Structure And Function ◽  
General Ward ◽  
Prognostic Role ◽  
Interstitial Syndrome ◽  
Lung Structure ◽  
And Function ◽  
The Relationship

AbstractBedside lung ultrasound (LUS) can play a role in the setting of the SarsCoV2 pneumonia pandemic. To evaluate the clinical and LUS features of COVID-19 in the ED and their potential prognostic role, a cohort of laboratory-confirmed COVID-19 patients underwent LUS upon admission in the ED. LUS score was derived from 12 fields. A prevalent LUS pattern was assigned depending on the presence of interstitial syndrome only (Interstitial Pattern), or evidence of subpleural consolidations in at least two fields (Consolidation Pattern). The endpoint was 30-day mortality. The relationship between hemogasanalysis parameters and LUS score was also evaluated. Out of 312 patients, only 36 (11.5%) did not present lung involvment, as defined by LUS score < 1. The majority of patients were admitted either in a general ward (53.8%) or in intensive care unit (9.6%), whereas 106 patients (33.9%) were discharged from the ED. In-hospital mortality was 25.3%, and 30-day survival was 67.6%. A LUS score > 13 had a 77.2% sensitivity and a 71.5% specificity (AUC 0.814; p < 0.001) in predicting mortality. LUS alterations were more frequent (64%) in the posterior lower fields. LUS score was related with P/F (R2 0.68; p < 0.0001) and P/F at FiO2 = 21% (R2 0.59; p < 0.0001). The correlation between LUS score and P/F was not influenced by the prevalent ultrasound pattern. LUS represents an effective tool in both defining diagnosis and stratifying prognosis of COVID-19 pneumonia. The correlation between LUS and hemogasanalysis parameters underscores its role in evaluating lung structure and function.

Incorporating initial treatments improves the performance of a mortality prediction model based on administrative data

2012 ◽  
Vol 40 (7) ◽  
pp. 2268-2269 ◽  
Author(s):  
Tara Lagu ◽  
Thomas L. Higgins ◽  
Brian H. Nathanson ◽  
Peter K. Lindenauer
Keyword(s):  
Prediction Model ◽  
Administrative Data ◽  
Mortality Prediction ◽  
Model Based ◽  
Mortality Prediction Model
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