Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections

Background Routine asymptomatic testing using RT-PCR of people who interact with vulnerable populations, such as medical staff in hospitals or care workers in care homes, has been employed to help prevent outbreaks among vulnerable populations. Although the peak sensitivity of RT-PCR can be high, the probability of detecting an infection will vary throughout the course of an infection. The effectiveness of routine asymptomatic testing will therefore depend on testing frequency and how PCR detection varies over time. Methods We fitted a Bayesian statistical model to a dataset of twice weekly PCR tests of UK healthcare workers performed by self-administered nasopharyngeal swab, regardless of symptoms. We jointly estimated times of infection and the probability of a positive PCR test over time following infection; we then compared asymptomatic testing strategies by calculating the probability that a symptomatic infection is detected before symptom onset and the probability that an asymptomatic infection is detected within 7 days of infection. Results We estimated that the probability that the PCR test detected infection peaked at 77% (54–88%) 4 days after infection, decreasing to 50% (38–65%) by 10 days after infection. Our results suggest a substantially higher probability of detecting infections 1–3 days after infection than previously published estimates. We estimated that testing every other day would detect 57% (33–76%) of symptomatic cases prior to onset and 94% (75–99%) of asymptomatic cases within 7 days if test results were returned within a day. Conclusions Our results suggest that routine asymptomatic testing can enable detection of a high proportion of infected individuals early in their infection, provided that the testing is frequent and the time from testing to notification of results is sufficiently fast

Dyslipidaemia in diabetic patients: time for a rethink

Patients with type 2 diabetes have a marked increase in the risk of premature coronary heart disease (CHD). One of the underlying reasons for this increased risk is atherogenic dyslipidaemia, which is common in this patient group and characterized by low plasma levels of high-density lipoprotein cholesterol (HDL-C), increased levels of serum triglycerides, specifically very low-density lipoprotein triglycerides, and an increase in small, dense low-density lipoprotein (LDL) particles. Current management strategies focus on the initial use of statin or fibrate therapy (the latter approach indicated in patients with pronounced hypertriglyceridaemia). Recent treatment guidelines also emphasize the need for reduction in LDL-cholesterol (LDL-C) below 100 mg/dl (2.6 mmol/l) in diabetic patients, as this patient group has a clustering of cardiovascular risk factors that collectively lead to an excess risk of premature mortality. Multidrug lipid-modifying therapy has been proposed to further reduce CHD risk in diabetic patients. Adding nicotinic acid to primary statin therapy would be a logical approach based on the complementary therapeutic benefits of these treatments. Nicotinic acid is the most potent agent currently available for raising HDL-C and is also effective in reducing triglycerides and LDL-C. Moreover, clinical trial data have shown that nicotinic acid can be safely used in diabetic patients. Data from the Arterial Biology for the Investigation of the Treatment Effects of Reducing Cholesterol (ARBITER 2) study in patients with established CHD and low HDL-C (27% of whom had type 2 diabetes) show the atheroprotective effects of nicotinic acid/statin combination therapy. The clinical benefits of this combination therapy are indicated by subgroup analyses from the HDL-Atherosclerosis Treatment Study, which showed 40% reduction in coronary event frequency in patients with impaired glucose tolerance. Together, these data support the proposed strategy of aggressive multidrug treatment of diabetic dyslipidaemia to improve patient outcome

cmmid/pcr-profile

A Github repository containing code to reproduce the figures and analysis from a pre-print titled "Estimating effectiveness of frequent PCR testing at different intervals for detection of SARS-CoV-2 infections"