PREDICTING PATIENT OUTCOMES USING ONE-HOUR ADULT EARLY WARNING SCORE (AEWS)

Early Warning Scores (EWS) are widely utilized tools to predict patient outcomes, particularly in critical care settings and emergency departments (ED). However, while Adult Early Warning Scores (AEWS) have been employed for general patient outcome prediction, the specific utility of a one-hour AEWS in the ED has not been thoroughly explored. Accurate early prediction of patient disposition could enhance clinical decision-making, resource allocation, and patient safety. Objective: This study aims to evaluate the effectiveness of one-hour Adult Early Warning Scores (AEWS) in predicting patient disposition (discharge, floor admission, or ICU admission) in the emergency department. Methods: A prospective observational study was conducted on 227 patients aged 16 years and above who presented to the ED of Shifa International Hospital, Islamabad, from January 2024 to June 2024. Vital signs, including heart rate, respiratory rate, blood pressure, oxygen saturation, and temperature, were recorded at the one-hour mark post-admission, and the one-hour AEWS was calculated. Patient outcomes were categorized into three groups: discharge, admission to the general floor, or admission to the ICU. Logistic regression was used to determine the relationship between one-hour AEWS and patient disposition. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the predictive power of the AEWS for determining patient outcomes. Results: There was a statistically significant correlation between the one-hour AEWS and patient disposition (p<0.001). The ROC curve analysis showed an Area Under the Curve (AUC) of 0.753, demonstrating good predictive accuracy. A cutoff AEWS value of 2.5 was identified, with patients scoring below this threshold being more likely to be discharged, while those scoring above 2.5 were more likely to require hospital admission or ICU care. The sensitivity and specificity of AEWS at this cutoff were 71% and 68%, respectively. Conclusion: The one-hour AEWS has proven to be a valuable tool in predicting patient disposition in the emergency department. Its ability to distinguish between patients who can be safely discharged and those requiring further care highlights its potential utility in improving patient triage, optimizing resource use, and enhancing clinical decision-making. Further multicenter studies are needed to validate these findings and assess the integration of AEWS into routine ED practice.

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Publication Details

Journal
Biological and Clinical Sciences Research Journal
Published
2024-10-26
DOI
https://doi.org/10.54112/bcsrj.v2024i1.1206
Primary Topic
Machine Learning in Healthcare
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article
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article

PREDICTING PATIENT OUTCOMES USING ONE-HOUR ADULT EARLY WARNING SCORE (AEWS)

Asmat Ullah Malik, Abid Hossain Khan, M. A. Shah, Hamza M. Kamal et al.
Biological and Clinical Sciences Research Journal
Machine Learning in Healthcare
article

PREDICTING PATIENT OUTCOMES USING ONE-HOUR ADULT EARLY WARNING SCORE (AEWS)

Asmat Ullah Malik, Abid Hossain Khan, M. A. Shah, Hamza M. Kamal, Abdul Waheed, HW SAQIB
article en

Abstract

Early Warning Scores (EWS) are widely utilized tools to predict patient outcomes, particularly in critical care settings and emergency departments (ED). However, while Adult Early Warning Scores (AEWS) have been employed for general patient outcome prediction, the specific utility of a one-hour AEWS in the ED has not been thoroughly explored. Accurate early prediction of patient disposition could enhance clinical decision-making, resource allocation, and patient safety. Objective: This study aims to evaluate the effectiveness of one-hour Adult Early Warning Scores (AEWS) in predicting patient disposition (discharge, floor admission, or ICU admission) in the emergency department. Methods: A prospective observational study was conducted on 227 patients aged 16 years and above who presented to the ED of Shifa International Hospital, Islamabad, from January 2024 to June 2024. Vital signs, including heart rate, respiratory rate, blood pressure, oxygen saturation, and temperature, were recorded at the one-hour mark post-admission, and the one-hour AEWS was calculated. Patient outcomes were categorized into three groups: discharge, admission to the general floor, or admission to the ICU. Logistic regression was used to determine the relationship between one-hour AEWS and patient disposition. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the predictive power of the AEWS for determining patient outcomes. Results: There was a statistically significant correlation between the one-hour AEWS and patient disposition (p<0.001). The ROC curve analysis showed an Area Under the Curve (AUC) of 0.753, demonstrating good predictive accuracy. A cutoff AEWS value of 2.5 was identified, with patients scoring below this threshold being more likely to be discharged, while those scoring above 2.5 were more likely to require hospital admission or ICU care. The sensitivity and specificity of AEWS at this cutoff were 71% and 68%, respectively. Conclusion: The one-hour AEWS has proven to be a valuable tool in predicting patient disposition in the emergency department. Its ability to distinguish between patients who can be safely discharged and those requiring further care highlights its potential utility in improving patient triage, optimizing resource use, and enhancing clinical decision-making. Further multicenter studies are needed to validate these findings and assess the integration of AEWS into routine ED practice.

Biological and Clinical Sciences Research JournalVol. 2024(1)
International Islamic University, Islamabad (PK), Riphah International University (PK), Shifa International Hospital (PK)
Openalex Percentile: Top 83%
Machine Learning in Healthcare
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