Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores

Author:

Hu Wei‐Huan1ORCID,Lin Shang‐Yi234ORCID,Hu Yuh‐Jyh15,Huang Ho‐Yin67,Lu Po‐Liang289ORCID

Affiliation:

1. College of Computer Science, National Yang Ming Chiao Tung University Hsinchu Taiwan

2. Division of Infectious Diseases, Department of Internal Medicine Kaohsiung Medical University Hospital, Kaohsiung Medical University Kaohsiung Taiwan

3. College of Medicine, Kaohsiung Medical University Kaohsiung Taiwan

4. Department of Laboratory Medicine Kaohsiung Medical University Hospital Kaohsiung Taiwan

5. Institute of Biomedical Engineering, National Yang Ming Chiao Tung University Hsinchu Taiwan

6. Department of Pharmacy Kaohsiung Medical University Hospital, Kaohsiung Medical University Kaohsiung Taiwan

7. School of Pharmacy, College of Pharmacy, Kaohsiung Medical University Kaohsiung Taiwan

8. School of Post‐Baccalaureate Medicine, College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

9. Center for Liquid Biopsy and Cohort Research Kaohsiung Medical University Kaohsiung Taiwan

Abstract

AbstractBackgroundClinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability.ObjectivesUnlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability.MethodsDifferent ML algorithms follow different design philosophies and consequently, they carry different learning biases. We have designed an ensemble meta‐learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances.ResultsIn the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14‐day mortality. The cross‐validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1‐score (0.68, p < .005), Matthews correlation coefficient (0.54, p < .05 vs. SOFA, p < .005 vs. the others) and the area under the curve (AUC; 0.87, p < .005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77.ConclusionsML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.

Funder

Kaohsiung Medical University

National Science and Technology Council

Publisher

Wiley

Subject

Infectious Diseases,Dermatology,General Medicine

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