Browsing by Author "Xie, Feng"

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    Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.
    (JAMA network open, 2021-08-02) Xie, Feng; Ong, Marcus Eng Hock; Liew, Johannes Nathaniel Min Hui; Tan, Kenneth Boon Kiat; Ho, Andrew Fu Wah; Nadarajan, Gayathri Devi; Low, Lian Leng; Kwan, Yu Heng; Goldstein, Benjamin Alan; Matchar, David Bruce; Chakraborty, Bibhas; Liu, Nan

    Importance

    Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations.

    Objectives

    To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores.

    Design, setting, and participants

    This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021.

    Main outcomes and measures

    Three SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively. Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curve in the receiver operating characteristic analysis.

    Results

    The study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates in the training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores.

    Conclusions and relevance

    In this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.
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    ItemOpen Access
    Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study.
    (BMJ open, 2019-09-26) Xie, Feng; Liu, Nan; Wu, Stella Xinzi; Ang, Yukai; Low, Lian Leng; Ho, Andrew Fu Wah; Lam, Sean Shao Wei; Matchar, David Bruce; Ong, Marcus Eng Hock; Chakraborty, Bibhas
    OBJECTIVES:To identify risk factors for inpatient mortality after patients' emergency admission and to create a novel model predicting inpatient mortality risk. DESIGN:This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split into a derivation set and a validation set. The stepwise model selection was employed. We compared our model with one of the current clinical scores, Cardiac Arrest Risk Triage (CART) score. SETTING:A single tertiary hospital in Singapore. PARTICIPANTS:All adult hospitalised patients, admitted via emergency department (ED) from 1 January 2008 to 31 October 2017 (n=433 187 by admission episodes). MAIN OUTCOME MEASURE:The primary outcome of interest was inpatient mortality following this admission episode. The area under the curve (AUC) of the receiver operating characteristic curve of the predictive model with sensitivity and specificity for optimised cut-offs. RESULTS:15 758 (3.64%) of the episodes were observed inpatient mortality. 19 variables were observed as significant predictors and were included in our final regression model. Our predictive model outperformed the CART score in terms of predictive power. The AUC of CART score and our final model was 0.705 (95% CI 0.697 to 0.714) and 0.817 (95% CI 0.810 to 0.824), respectively. CONCLUSION:We developed and validated a model for inpatient mortality using EHR data collected in the ED. The performance of our model was more accurate than the CART score. Implementation of our model in the hospital can potentially predict imminent adverse events and institute appropriate clinical management.