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Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome.

ABSTRACT: Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS). Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms-XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)-and a logistic regression (LR) model to predict patients with candidaemia. Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value. Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

SUBMITTER: Yuan S

PROVIDER: S-EPMC8416760 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS).MethodsA prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected based off of the lowest postestimation deviance between the training and testing samples. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0, with 1.0 being perfect discrimination and 0.5 indicating the model is no better than chance. A log-likelihood χ2 test was used to evaluate the goodness-of-fit of the logistic regression model.ResultsA total of 2113 patients were included. Inference of minimum Bayes information criteria model indicated that male sex (odds ratio [OR] 1.82, P = .028), non-white race (African American OR 2.41, P = .013; other non-white OR 1.42, P = .042), smoking (OR 1.07, P = .021), and failure to provide a phone number (OR 1.78, P = .032) increased the risk for being lost to follow-up. Furthermore, greater preoperative International Hip Outcome Tool 12-item component questionnaire (OR 1.03, P = .004), and modified Harris Hip Score (OR 1.05, P = .014) scores increased the risk of being lost to follow-up. The c-statistic was 0.76 (95% confidence interval 0.701-0.848). The log-likelihood indicated that the regression model as a whole was statistically significant (P = .002).ConclusionsPatients who are male, non-white, smokers, fail to provide a telephone number, and have greater preoperative modified Harris Hip Score and International Hip Outcome Tool 12-item component questionnaire scores are at an increased risk for being lost to follow-up 2 years after hip arthroscopy for FAIS.Level of evidenceLevel III, case control study.

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Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome.

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