Background Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. Methods CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals.Findings In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0•92 (95% CI 0•89-0•94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0•93 (95% CI 0•88-0•96), sensitivity 83•0% (95% CI 74•0-91•1), and specificity 88•3% (95% CI 83•1-92•8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0•88 (95% CI 0•85-0•91). When radiographs annotated as equivocal were excluded, the AUROC was 0•93 (0•92-0•95).Interpretation A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research.
Klebsiella is a leading cause of health care-associated infections. Patients who are intestinally colonized with Klebsiella are at a significantly increased risk of subsequent infection, but only a subset of colonized patients progress to disease.
Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19.Objective: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. Methods:The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions.
Background In patients with Clostridioides difficile infection (CDI), the relationship between clinical, microbial, and temporal/epidemiological trends relate and disease severity and adverse outcomes is incompletely understood. Here, in a follow-up to our study conducted in 2010–2013, we evaluate stool toxin levels and C. difficile PCR ribotypes. We hypothesized that elevated stool toxins and infection with ribotype 027 associate with severe disease and adverse outcomes. Methods In a cohort of 565 subjects at the University of Michigan with CDI diagnosed by positive testing for toxins A/B by EIA or PCR for the tcdB gene, we quantified stool toxin levels via a modified cell cytotoxicity assay, isolated C. difficile by anaerobic culture, and performed PCR ribotyping. Severe CDI was defined by IDSA criteria, and primary outcomes were all-cause 30-day mortality and a composite of colectomy, ICU admission, and/or death attributable to CDI within 30 days. Analyses included bivariable tests and adjusted logistic regression. Results 199 samples were diagnosed by EIA and 447 were diagnosed by PCR. Toxin positivity associated with IDSA severity, but not primary outcomes. In 2016, compared to 2010–2013, ribotype 106 newly emerged, accounting for 10.6% of strains, ribotype 027 fell from 16.5% to 9.3%, and ribotype 014-027 remained stable at 18.9%. Ribotype 014-020 associated with IDSA severity and 30-day mortality (P=.001). Conclusion Toxin positivity by EIA and CCA associated with IDSA severity, but not with subsequent adverse outcomes. The molecular epidemiology of C. difficile has shifted, and this may have implications for the optimal diagnostic strategy for and clinical severity of CDI.
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