Machine-Learning Model for Mortality Prediction in Patients With Community-Acquired Pneumonia Development and Validation Study

Background: Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP. Research question: Is a CPN model able to predict mortality in patients with CAP better than the commonly used severity scores? Study design and methods: This was a derivation-validation retrospective study conducted in two Spanish university hospitals. The ability of a CPN designed to predict mortality in sepsis (SepsisFinder [SeF]), and adapted for CAP (SeF-ML), to predict 30-day mortality was assessed and compared with other scoring systems (Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick Sequential Organ Failure Assessment [qSOFA], and CURB-65 criteria [confusion, urea, respiratory rate, BP, age ≥ 65 years]). The SeF models are proprietary software. Differences between receiver operating characteristic curves were assessed by the DeLong method for correlated receiver operating characteristic curves. Results: The derivation cohort comprised 4,531 patients, and the validation cohort consisted of 1,034 patients. In the derivation cohort, the areas under the curve (AUCs) of SeF-ML, CURB-65, SOFA, PSI, and qSOFA were 0.801, 0.759, 0.671, 0.799, and 0.642, respectively, for 30-day mortality prediction. In the validation study, the AUC of SeF-ML was 0.826, concordant with the AUC (0.801) in the derivation data (P = .51). The AUC of SeF-ML was significantly higher than those of CURB-65 (0.764; P = .03) and qSOFA (0.729, P = .005). However, it did not differ significantly from those of PSI (0.830; P = .92) and SOFA (0.771; P = .14). Interpretation: SeF-ML shows potential for improving mortality prediction among patients with CAP, using structured health data. Additional external validation studies should be conducted to support generalizability

Using image processing and automated classification models to classify microscopic gram stain images

Background and Objective: Fast and correct classification of bacterial samples are important for accurate diagnostics and treatment. Manual microscopic interpretation of Gram stain samples is both time consuming and operator dependent. The aim of this study was to investigate the potential for developing an automated algorithm for the classification of microscopic Gram stain images. Methods: We developed and tested two algorithms (using image processing an Casual Probabilistic Network (CPN) and a Random Forest (RF) classification) for the automated classification of Gram stain images. A dataset of 660 images including 33 microbial species (32 bacteria and one fungus) was split into training, validation, and test sets. The algorithms were evaluated based on their ability to correctly classify samples and general characteristics such as aggregation and morphology. Results: The CPN correctly classified 633/792 images to achieve an overall accuracy of 80% compared to the RF which correctly classified 782/792 images to achieve an overall accuracy of 99% (p < 0.001). The CPN performed well when distinguishing between GN and GP, with an accuracy of 95% (731/768). The RF also performed well in distinguishing between GN and GP, achieving an accuracy of 99% (767/768) (p < 0.001). Conclusions: The findings from this study show promising results regarding the potential for an automated algorithm for the classification of microscopic Gram stain images