Personalized machine learning approach to predict candidemia in medical wards.
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 inte...
| Published in: | Infection Vol. 48; no. 5; pp. 749 - 760 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | algorithm research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146082374&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146082374 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03008126 NXO jtl: Infection issn: 03008126 maglogo: N pubinfo: dt: Oct2020 vid: 48 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146082374 144866208 146082374 146082374 10.1007/s15010-020-01488-3 146082374 ppf: 749 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Personalized machine learning approach to predict candidemia in medical wards. aug: au: Ripoli, Andrea Sozio, Emanuela Sbrana, Francesco Bertolino, Giacomo Pallotto, Carlo Cardinali, Gianluigi Meini, Simone Pieralli, Filippo Azzini, Anna Maria Concia, Ercole Viaggi, Bruno Tascini, Carlo affil: Bioengineering Department, Fondazione Toscana Gabriele Monasterio, Pisa, Italy sug: subj: Individualized Medicine Machine Learning Candidemia Diagnosis Early Diagnosis Candidemia Risk Factors Risk Assessment Prediction Models Random Forest Algorithms Internal Medicine Hospital Units Sensitivity and Specificity Evaluation Human Male Female Middle Age Aged Aged, 80 and Over Multiple Logistic Regression Descriptive Statistics Inpatients Drug Resistance, Microbial Antibiotics Adverse Effects Peripherally Inserted Central Catheters Adverse Effects Total Parenteral Nutrition Adverse Effects Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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