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  • 1.
    Blom, Mathias Carl
    et al.
    Department of Clinical Sciences, Lund University, Lund, Sweden.
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Halland Hospital, Region Halland, Halmstad, Sweden.
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Anderson, Philip D.
    Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA & Harvard Medical School, Boston, Massachusetts, USA.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Department of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study2019Inngår i: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, nr 8, artikkel-id e028015Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power.

    Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed.

    Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set.

    Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection.

    Trial registration number: Not applicable.

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