Ahn JY, et al. J Korean Med Sci. 2020 Feb;35(14):e149. https://doi.org/10.3346/jkms.2020.35.e149

Pulmonary tuberculosis (TB) is a known risk factor for lung cancer. However, a detailed analysis of lung cancer type, age, sex, smoking, and TB burden associated with geographic and socioeconomic status has not been performed previously. We systematically appraised relevant observational studies reporting an association between pulmonary TB and lung cancer. All studies were included in the primary analysis, and studies that used robust TB diagnostic methods, such as validated medical diagnostic codes, were included in the secondary analysis. Thirty-two articles were included. The association between the history of pulmonary TB and diagnosis of lung cancer was statistically significant (OR 2.09, 95% CI: 1.62–2.69, p < 0.001). There was a high heterogeneity (I² = 95%), without any publication bias. The analysis indicated a high association in advanced articles describing stringent pulmonary TB diagnosis (OR 2.26, 95% CI: 1.29–3.94, p = 0.004). The subgroup analyses suggested a significant association in countries with medium or high TB burdens, from East Asia and the Pacific region, and upper-middle income countries. Heterogeneity within the subgroups remained high in a majority of the subgroup analyses. A meta-regression analysis revealed that younger patients showed a significantly higher association between TB and lung cancer (regression coefficient = 0.949, p < 0.001). The history of pulmonary TB is an independent risk factor for lung cancer, especially in younger patients diagnosed with pulmonary TB. Clinicians should be aware of this association while treating young patients with a history of pulmonary TB.

Background: The coronavirus disease (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a major global public health issue. SARS-CoV-2 infection is confirmed by the detection of viral RNA using reverse transcription polymerase chain reaction (RT-PCR). Prolonged viral shedding has been reported in patients with SARS-CoV-2 infection, but the presence of viral RNA does not always correlate with infectivity. Therefore, the present study aimed to confirm the presence of viable virus in asymptomatic or mildly symptomatic patients in the later phase of the disease, more than two weeks after diagnosis. Method: Asymptomatic or mildly symptomatic COVID-19 patients who had been diagnosed with the disease at least two weeks previously and admitted to a community treatment center (CTC) from 15 March to 10 April 2020 were enrolled in this study. Nasopharyngeal and salivary swab specimens were collected from each patient. Using these specimens, RT-PCR assay and viral culture were performed. Result: In total, 48 patients were enrolled in this study. There were no significant differences in baseline characteristics between the asymptomatic and mildly symptomatic patient groups. RT-PCR assay and viral culture of SARS-CoV-2 were performed using nasopharyngeal and salivary swabs. The results of RT-PCR performed using salivary swab specimens, in terms of cycle threshold (Ct) values, were similar to those of RT-PCR using nasopharyngeal swab specimens. In addition, no viable virus could be cultured from swab specimens collected from the late-phase COVID-19 patients with prolonged viral RNA shedding. Conclusions: In conclusion, our study suggests that even if viral shedding is sustained in asymptomatic or mildly symptomatic patients with later phase of COVID-19, it can be expected that the transmission risk of the virus is low. In addition, saliva can be used as a reliable specimen for the diagnosis of SARS-CoV-2 infection.

Recently, a new scoring system was developed that uses the red blood cell distribution width (RDW), delta neutrophil index (DNI), and platelet count (PC) to predict mortality in patients with sepsis. We investigated whether a modified simple scoring system based on the RDW, DNI, and mean platelet volume-to-PC (MPV/PC) ratio could predict the mortality of patients with sepsis, and compared it to the previous scoring system. We conducted a retrospective cohort study of 264 adults who had been treated for sepsis in an emergency department between January 2016 and February 2019. Each patient was rated on a scale of 0 to 3 according to the modified scoring system. Point values were assigned based on RDW > 14.5%, DNI > 5.0%, and MPV/PC ratio >10.1. The 28-day mortality rate was 14.4%. Those who died had higher scores than those who survived (mean: 1.55 ± 0.92 vs 0.93 ± 0.78, P < .001). The area under the curve for the new scoring system was higher than that of the previous scoring system (0.685 vs 0.645). The modified scoring system was a good predictor of the 28-day mortality and was more useful than the previous scoring system for predicting mortality in patients with sepsis.

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.

Lee Y, et al. Infect Chemother. 2020 Dec;52(4):503-515. https://doi.org/10.3947/ic.2020.52.4.503