You are here

  • Home
  • Online First
  • Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19

Article Text

Article menu

other Versions

Download PDFPDF
Original research
Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19
  1. Prathamesh Parchure1,
  2. Himanshu Joshi1,2,
  3. Kavita Dharmarajan1,3,4,
  4. Robert Freeman1,5,
  5. David L Reich5,6,
  6. Madhu Mazumdar1,2,
  7. Prem Timsina1 and
  8. Arash Kia1
  1. 1Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  2. 2Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  3. 3Department of Geriatrics and Palliative Care, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  4. 4Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  5. 5Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  6. 6Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  1. Correspondence to Dr Madhu Mazumdar; madhu.mazumdar{at}mountsinai.org

Abstract

Objectives To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.

Methods A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20–84 hours from the time of prediction. Input features included patients’ vital signs, laboratory data and ECG results.

Results Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3–23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).

Conclusions Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

  • end of life care
  • hospital care
  • prognosis
  • supportive care
  • terminal care

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

https://bmj.com/coronavirus/usage

https://doi.org/10.1136/bmjspcare-2020-002602

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Footnotes

    • Twitter @RFreeman_RN

    • PT and AK contributed equally.

    • Contributors AK and PT conceived the study and supervision. PP and PT contributed to data curation. PP, PT, AK and HJ contributed to formal analysis. HJ and KD contributed to formal validation of data and results. HJ and MM contributed to drafting of the original manuscript. All authors contributed to critical revision of the manuscript for important intellectual content.

    • Funding This study was funded by National Institute of Aging (P30AG028741), Division of Cancer Prevention, National Cancer Institute (P30CA196521).

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request. Raw data were generated at the Mount Sinai Health System. Derived data supporting the findings of this study are available from the corresponding author (MM) on request.