Point-of-care multiplex molecular diagnosis coupled with procalcitonin-guided algorithm for antibiotic stewardship in lower respiratory tract infection: a randomized controlled trial.

OBJECTIVE: We aimed to show that coupling molecular syndromic respiratory panel (RP) testing with procalcitonin (PCT) measurement in the emergency department improves antibiotic (ATB) stewardship in lower respiratory tract infection. METHODS: Open-label, prospective, randomized interventional trial, conducted from 2019 to 2022 in an adult emergency department. Patients with a suspicion of lower respiratory tract infection were randomized into an intervention arm (PCT measurement and point-of-care BIOFIRE RP2.1 plus testing, accompanied by a recommended ATB algorithm) or a standard of care (SOC) arm (PCT allowed as current practice). The primary endpoint was the duration of antibiotic exposure. RESULTS: Four hundred fifty-one patients were randomized, median age 65 years (Q1-Q3: 49-77), the hospitalization rate was 59.9% (270/451), the median length of stay 5 days (Q1-Q3: 3 - 12), and the 28-day mortality rate 5.3% (23/451). The median duration of ATB exposure was 6 days (Q1-Q3: 0-9) and 5 days (Q1-Q3: 0-9) in the SOC and interventional arm respectively (p = 0.71). ATB was started in 29.6 % (67/226) and 33.8% (76/225) respectively (p = 0.54). The BIOFIRE RP2.1 plus identified at least one viral species in 112/225 patients (49.8%) of intervention arm. Two hundred twelve out of two hundred twenty-six (93.8%) SOC patients had PCT measurement. The adherence rate to algorithm in the intervention arm was 93.3 % (210/225). CONCLUSION: Displaying PCT and real-time RP results to emergency physicians failed to significantly reduce ATB exposure in lower respiratory tract infection suspicions. However, the median ATB duration and rate of initiation were already low in the SOC arm using PCT measurement routinely.

Stabilizing cumulative incidence estimation of pregnancy outcome with delayed entries.

A pregnancy may end up with (at least) three possible events: live birth, spontaneous abortion, or elective termination, yielding a competing risks issue when studying an association between a risk factor and a pregnancy outcome. Cumulative incidences (probabilities to end up with the different outcomes depending on gestational age) can be estimated via the Aalen-Johansen estimate. Another issue is that women are usually not entering such an observational study from the first day of pregnancy, resulting in delayed entries. As in traditional survival analysis, this can be solved by considering "at risk" at a given gestational age only for those women who entered the study before that age. However, the number of women at risk at an early gestational age might be extremely low, such that the estimates of cumulative incidence may increase exaggeratedly at that age because of a single event. One solution to reduce the problem has been recently proposed in the literature, which is to ignore simply those early events, creating a small mean bias but enhancing stability of estimates. In the present paper, we propose an alternative computationally simple approach to tackle this problem that consists to postpone to later gestational ages (rather than to ignore) those early events. The two approaches are compared with respect to bias, stability, and sensitivity on the smoothing parameter via simulations reproducing realistic pregnancy scenarios, and are illustrated with data from a study on the effects of statins on pregnancy outcomes. We also outline that all three approaches are asymptotically equivalent.