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Personalized machine learning approach to predict candidemia in medical wards

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Abstract

Purpose

Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs).

Methods

A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer–Lemeshow test.

Results

The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer–Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer–Lemeshow statistics = 38.182 ± 15.983, p < 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy.

Conclusion

RF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription.

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Acknowledgements

None.

Funding

The project was partially funded with Digital Health Program 2017 funds—Gilead Sciences Srl.

Author information

Authors and Affiliations

  1. Bioengineering Department, Fondazione Toscana Gabriele Monasterio, Pisa, Italy

    Andrea Ripoli

  2. Emergency Department, North-West District, Tuscany Health Care, Spedali Riuniti Livorno, Livorno, Italy

    Emanuela Sozio

  3. U.O. Lipoapheresis and Center for Inherited Dyslipidemias, Fondazione Toscana Gabriele Monasterio, Via Moruzzi,1, 56124, Pisa, Italy

    Francesco Sbrana

  4. Pharmaceutical Department, ASSL Cagliari, Cagliari, Italy

    Giacomo Bertolino

  5. Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy

    Giacomo Bertolino

  6. UOC Malattie Infettive, Ospedale San Donato Arezzo, Sud-Est District, Tuscany Health Care, Arezzo, Italy

    Carlo Pallotto

  7. Sezione Di Malattie Infettive, Dipartimento Di Medicina, Università Di Perugia, Perugia, Italy

    Carlo Pallotto

  8. Department of Pharmaceutical Sciences-Microbiology, University of Perugia, Perugia, Italy

    Gianluigi Cardinali

  9. CEMIN, Centre of Excellence On Nanostructured Innovative Materials, Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

    Gianluigi Cardinali

  10. Internal Medicine Unit, Santa Maria Annunziata Hospital, Florence, Italy

    Simone Meini

  11. Intermediate Care Unit, Azienda Ospedaliera Universitaria Careggi, Florence, Italy

    Filippo Pieralli

  12. Dipartimento Di Diagnostica E Sanità Pubblica, Sezione Di Malattie Infettive, Università Di Verona, Verona, Italy

    Anna Maria Azzini & Ercole Concia

  13. Department of Anesthesia, Neuro Intensive Care Unit, Careggi Universital Hospital, Florence, Italy

    Bruno Viaggi

  14. First Division of Infectious Diseases, Cotugno Hospital, Azienda Ospedaliera Dei Colli, Napoli, Italy

    Carlo Tascini

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  1. Andrea Ripoli
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  2. Emanuela Sozio
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  3. Francesco Sbrana
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  4. Giacomo Bertolino
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  5. Carlo Pallotto
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  6. Gianluigi Cardinali
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  9. Anna Maria Azzini
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  11. Bruno Viaggi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by CT, AR, ES, GB, FS and GC. The first draft of the manuscript was written by AR and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Francesco Sbrana.

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Conflict of interest

C.T. has received funds for speaking at symposia organized on behalf of Pfizer, Novartis, Merck, Angelini, Zambon, Thermofischer, Biotest, Gilead, Hikma, Biomerieux and Astellas. G.C. received a prize from GILEAD. No Conflict of Interest for the other authors.

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Ripoli, A., Sozio, E., Sbrana, F. et al. Personalized machine learning approach to predict candidemia in medical wards. Infection 48, 749–759 (2020). https://doi.org/10.1007/s15010-020-01488-3

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  • DOI: https://doi.org/10.1007/s15010-020-01488-3

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