Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project

Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48–72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum β-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.</p

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p p p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics</p