Dataset Information

The PATRIARCH Study. Using outcome measures for league tables: can a North American prediction of admission score be used in a United Kingdom children's emergency department? PRISA And Triage In A Regional Children's Hospital.

ABSTRACT:

Objectives

The use of league tables has become predominant in the healthcare culture of the United Kingdom. These tables are often based on measures that are viewed with scepticism by clinicians. This study was designed to test the validity of a North American risk of admission score, the PRISA, for use in a United Kingdom population of accident and emergency (A&E) attendees.

Methods

All attendees to a children's A&E department were scored using the PRISA for a single calendar month (November 2000)

Results

701 children were studied in total. The results show that the PRISA applied to this population gives an area under the receiver operator curve of 0.76. Of the 701 patients studied, 206 (29.4%) were admitted. The PRISA predicted a total of 206.10 admissions. Of the 50 patients discharged with the highest PRISA scores (that is, with the highest likelihood of admission), none were admitted in the 48 hours after their original attendance.

Conclusions

These results show that the PRISA is suitable as a measure of paediatric A&E department performance in the United Kingdom and it is highly promising as a future measure of quality.

SUBMITTER: Miles H

PROVIDER: S-EPMC1756318 | biostudies-literature | 2002 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The PATRIARCH Study. Using outcome measures for league tables: can a North American prediction of admission score be used in a United Kingdom children's emergency department? PRISA And Triage In A Regional Children's Hospital.

Miles H H Litton E E Curran A A Goldsworthy L L Sharples P P Henderson A J AJ

Emergency medicine journal : EMJ 20021101 6

<h4>Objectives</h4>The use of league tables has become predominant in the healthcare culture of the United Kingdom. These tables are often based on measures that are viewed with scepticism by clinicians. This study was designed to test the validity of a North American risk of admission score, the PRISA, for use in a United Kingdom population of accident and emergency (A&E) attendees.<h4>Methods</h4>All attendees to a children's A&E department were scored using the PRISA for a single calendar mon ...[more]

PMID: 12421779

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Dataset Information

The PATRIARCH Study. Using outcome measures for league tables: can a North American prediction of admission score be used in a United Kingdom children's emergency department? PRISA And Triage In A Regional Children's Hospital.

Objectives

Methods

Results

Conclusions

Publications

The PATRIARCH Study. Using outcome measures for league tables: can a North American prediction of admission score be used in a United Kingdom children's emergency department? PRISA And Triage In A Regional Children's Hospital.

Similar Datasets

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