Posterior Oropharyngeal Saliva for the Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has put tremendous pressure on the healthcare system worldwide. Diagnostic testing remained one of the limiting factors for early identification and isolation of infected patients. This study aimed to evaluate posterior oropharyngeal saliva (POPS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection among patients with confirmed or suspected COVID-19. METHODS: The laboratory information system was searched retrospectively for all respiratory specimens and POPS requested for SARS-CoV-2 RNA detection between 1 February 2020 and 15 April 2020. The agreement and diagnostic performance of POPS against NPsp were evaluated. RESULTS: A total of 13772 specimens were identified during the study period, including 2130 POPS and 8438 nasopharyngeal specimens (NPsp). Two hundred and twenty-nine same-day POPS-NPsp paired were identified with POPS and NPsp positivity of 61.5% (95% confidence interval [CI] 55.1-67.6%) and 53.3% (95% CI 46.8-59.6%). The overall, negative and positive percent agreement were 76.0% (95% CI 70.2-80.9%), 65.4% (95% CI 55.5-74.2%), 85.2% (95% CI 77.4-90.8%). Better positive percent agreement was observed in POPS-NPsp obtained within 7 days (96.6%, 95% CI 87.3-99.4%) compared with after 7 days of symptom onset (75.0%, 95% CI 61.4-85.2%). Among the 104 positive pairs, the mean difference in Cp value was 0.26 (range: 12.63 to -14.74), with an overall higher Cp value in NPsp (Pearson coefficient 0.579). No significant temporal variation was noted between the 2 specimen types. CONCLUSIONS: POPS is an acceptable alternative specimen to nasopharyngeal specimen for the detection of SARS-CoV-2.

Predicting antibiotic susceptibility in urinary tract infection with artificial intelligence-model performance in a multi-centre cohort.

Objective: To develop an artificial intelligence model to predict an antimicrobial susceptibility pattern in patients with urinary tract infection (UTI). Materials and methods: 26 087 adult patients with culture-proven UTI during 2015-2020 from a university teaching hospital and three community hospitals in Hong Kong were included. Cases with asymptomatic bacteriuria (absence of diagnosis code of UTI, or absence of leucocytes in urine microscopy) were excluded. Patients from 2015 to 2019 were included in the training set, while patients from the year 2020 were included as the test set.Three first-line antibiotics were chosen for prediction of susceptibility in the bacterial isolates causing UTI: namely nitrofurantoin, ciprofloxacin and amoxicillin-clavulanate. Baseline epidemiological factors, previous antimicrobial consumption, medical history and previous culture results were included as features. Logistic regression and random forest were applied to the dataset. Models were evaluated by F1-score and area under the curve-receiver operating characteristic (AUC-ROC). Results: Random forest was the best algorithm in predicting susceptibility of the three antibiotics (nitrofurantoin, amoxicillin-clavulanate and ciprofloxacin). The AUC-ROC values were 0.941, 0.939 and 0.937, respectively. The F1 scores were 0.938, 0.928 and 0.906 respectively. Conclusions: Random forest model may aid judicious empirical antibiotics use in UTI. Given the reasonable performance and accuracy, these accurate models may aid clinicians in choosing between different first-line antibiotics for UTI.