Dataset Information

Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance.

ABSTRACT:

Objective

Markers of illness severity are increasingly captured in emergency department (ED) electronic systems, but their value for surveillance is not known. We assessed the value of age, triage score, and disposition data from ED electronic records for predicting influenza-related hospitalizations.

Materials and methods

From June 2006 to January 2011, weekly counts of pneumonia and influenza (P&I) hospitalizations from five Montreal hospitals were modeled using negative binomial regression. Over lead times of 0-5 weeks, we assessed the predictive ability of weekly counts of 1) total ED visits, 2) ED visits with influenza-like illness (ILI), and 3) ED visits with ILI stratified by age, triage score, or disposition. Models were adjusted for secular trends, seasonality, and autocorrelation. Model fit was assessed using Akaike information criterion, and predictive accuracy using the mean absolute scaled error (MASE).

Results

Predictive accuracy for P&I hospitalizations during non-pandemic years was improved when models included visits from patients ?65 years old and visits resulting in admission/transfer/death (MASE of 0.64, 95% confidence interval (95% CI) 0.54-0.80) compared to overall ILI visits (0.89, 95% CI 0.69-1.10). During the H1N1 pandemic year, including visits from patients <18 years old, visits with high priority triage scores, or visits resulting in admission/transfer/death resulted in the best model fit.

Discussion

Age and disposition data improved model fit and moderately reduced the prediction error for P&I hospitalizations; triage score improved model fit only during the pandemic year.

Conclusion

Incorporation of age and severity measures available in ED records can improve ILI surveillance algorithms.

SUBMITTER: Savard N

PROVIDER: S-EPMC7784300 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Publications

Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance.

Savard Noémie N Bédard Lucie L Allard Robert R Buckeridge David L DL

Journal of the American Medical Informatics Association : JAMIA 20150226 3

<h4>Objective</h4>Markers of illness severity are increasingly captured in emergency department (ED) electronic systems, but their value for surveillance is not known. We assessed the value of age, triage score, and disposition data from ED electronic records for predicting influenza-related hospitalizations.<h4>Materials and methods</h4>From June 2006 to January 2011, weekly counts of pneumonia and influenza (P&I) hospitalizations from five Montreal hospitals were modeled using negative binomia ...[more]

PMID: 25725005

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Project description:BackgroundEmergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.ObjectiveEvaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED.DesignAn institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality.Key resultsOverall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.ConclusionThe gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Project description:BackgroundThe number of older adults with a high risk of frailty and severe illness continues to increase. Moreover, physiological change and multiple comorbidities are challenging to triage in geriatrics. Therefore, we aimed to evaluate variables to predict 28-day mortality and develop a screening tool to predict mortality and lifesaving intervention among geriatric patients in the emergency department (ED).MethodsThis study was a retrospective, single-center, observational study at the ED of Ramathibodi Hospital, Bangkok. Patients aged ≥ 65 years who visited the ED between January 2018 and December 2019 were enrolled. In the development cohort, univariable logistic regression was used to identify predictors of 28-day mortality in older patients. A predictive model for mortality and the need for lifesaving intervention was developed by multivariable logistic regression. In addition, the score was validated with internal validation and compared between development and validation set by chi-square.ResultsWe enrolled 1393 patients. In the development cohort, among these 1002 patients, 103 (10.3%) of whom died within 28 days. Malignancy, shock index (SI), systolic blood pressure (SBP) < 100 mmHg, and altered mentation were independent risk factors of 28-day mortality. We developed new screening tools named the S-TRIAGE score, which has the respiratory rate (< 11, > 22 breaths/min), the ratio of pulse oximetric saturation to the fraction of inspired oxygen (< 420, 420-450), SI (> 1, 0.6-0.99), SBP < 100 mmHg, body temperature (< 36, > 37.5 °C), and mental change. The area under a receiver operating characteristic (ROC) curve of the S-TRIAGE score in the validation cohort was 0.826 [95% confidence interval (95%CI) 0.773-0.879] in predicting mortality and lifesaving intervention, and the clinical score classified patients into five groups.ConclusionThis study showed malignancy, hypotension, increased SI, and mental status change were predictive factors for 28-day mortality in older adults in the ED. The screening tool risk score for geriatrics used in this study is potentially a good predictor of mortality and lifesaving intervention in high-risk older patients in the ED.

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Dataset Information

Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance.

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