In smokers, the pulmonary compartment has a number of macrophage-specific immune impairments that provide some mechanistic explanations whereby cigarette smoking renders a patient susceptible to tuberculosis infection and disease.

Mathematical models can predict solute clearances and solute concentrations during renal replacement therapy. At present, however, most nephrologists cannot use these models because they require mathematical software. In this report, we describe models of solute transport by convection and diffusion adapted to run on the commonly available software program Excel for Macintosh computers and PCs running Windows. Two programs have been created that can be downloaded from http://www.stanford.edu/~twmeyer/ or http://dev.satellitehealth.com/research/journal.asp. The first, called 'Dr Addis Clearance Calculator', calculates clearance values from inputs including the blood flow Q(b), the hematocrit, the ultrafiltration rate Q(f), the dialysate flow rate Q(d), the reflection coefficient sigma and the mass transfer area coefficient K(o)A for the solute of interest, and the free fraction f if the solute is protein bound. Solute concentration profiles along the length of the artificial kidney are displayed graphically. The second program, called 'Dr Coplon Dialysis Simulator', calculates plasma solute concentrations from the clearance values obtained by the first program and from additional input values including the number of treatments per week, the duration of the treatments, and the solute's production rate and volumes of distribution. The program calculates the time-averaged solute concentration and provides a graphic display of the solute concentration profile through a week-long interval.

No abstract

Introduction: The National Lung Screening Trial (NLST) identified individuals at high risk for lung cancer and showed that serial low-dose helical computer tomographic scans (CT) were able to identify lung cancer at an earlier stage and also demonstrated mortality reduction. However, there has been little evidence regarding the effectiveness of the Lung Cancer Screening Criteria in the Asian population. Methods: To determine lung cancer patients who miss out on Lung Cancer screening criteria, we performed a retrospective audit from January to December 2018 in our hospital, and describe the characteristics of our patients diagnosed with lung cancer. Results: We found that only 38.1% of the patients in our cohort who were diagnosed with lung cancer in 2018 fitted into NLST Criteria strictly by age and smoking criteria. However, those who fitted the inclusion criteria of lung cancer screening would derive significant benefits, as 85.4% presented at advanced stage and 54.6% did not survive one year. We explored using the United States Preventive Services Task Force criteria, which increased sensitivity to 58.7% of identifying our patients with diagnosed lung cancer. 15.5% of females with lung cancer in our cohort fitted into NLST Criteria, but their low smoking quantity is a significant contributing factor for females being excluded. Conclusion: Majority of Singapore patients diagnosed with lung cancer would not have been picked up by NLST Criteria, especially female patients. However, those who fitted the inclusion criteria would derive significant benefit, while expanding to an older limit may yield benefits with improved sensitivity.

Introduction: Singapore’s enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. Methods: This was a single-centre retrospective observational study. Patients admitted to our institution’s respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. Results: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. Conclusion: Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation.