Corynebacterium pseudodiphtheriticum as a Pathogen in Bacterial Co-Infection in COVID-19 Patients on Mechanical Ventilation.

Many studies have been conducted on ventilator-associated complications (VACs) in patients with coronavirus 2019 (COVID-19). However, in these studies, the causative organisms were similar, and there were no reports on VAC corresponding with Corynebacteria. Coryneforms are frequently cultured in cases of polymicrobial infections and are usually considered contaminants in respiratory specimens. However, Corynebacterium pseudodiphtheriticum or C. striatum is known to be a pathogen in lower respiratory tract infections. We report three cases of VAC, probably due to C. pseudodiphtheriticum, in patients with COVID-19. If purulent lower respiratory tract specimens showed coryneform predominantly upon Gram staining, empirical therapy should be started. Furthermore, species identification and drug susceptibility testing should be performed.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.

Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care.

Since the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

[A Case of COVID-19 Pneumonia in an 83-year-old Woman Living with a Suspected SARS-CoV-2 Carrier, Who Repeatedly Tested Negative by the PCR Test]

The patient, an 83-year-old woman, lived with her daughter, at whose workplace, a person had been diagnosed as having COVID-19. The daughter was admitted to the hospital for pneumonia, however, the results of the PCR test for SARS-CoV-2 performed twice were negative. The patient developed fever a few days later, and visited an outpatient clinic for patients with fever and a history of travel abroad. The result of a nasal swab PCR test was negative, and antibiotics were prescribed. While the fever gradually subsided, the patient began to experience dyspnea. Therefore, she visited the outpatient clinic again for a repeat nasal swab test. Meanwhile, the dyspnea became severe and she was transported to our hospital. Immediately after admission, she was intubated and initiated on mechanical ventilation. A nasal swab and a specimen of lower respiratory tract secretions were submitted for COVID-19 testing by PCR, and while the nasal swab test result was negative again, the lower respiratory tract specimen yielded a positive result�E�EThe possibility of false-negative results of PCR testing for SARS-CoV-2 should be borne in mind in close contacts or strongly suspected cases of COVID-19. PCR testing of specimens of lower respiratory tract secretions might be necessary for suspected cases of COVID-19 pneumonia.