Monitoring Procalcitonin in Febrile Neutropenia: What Is Its Utility for Initial Diagnosis of Infection and Reassessment in Persistent Fever?

Background: Management of febrile neutropenic episodes (FE) is challenged by lacking microbiological and clinical documentation of infection. We aimed at evaluating the utility of monitoring blood procalcitonin (PCT) in FE for initial diagnosis of infection and reassessment in persistent fever.Methods: PCT kinetics was prospectively monitored in 194 consecutive FE (1771 blood samples): 65 microbiologically documented infections (MDI, 33.5%; 49 due to non-coagulase-negative staphylococci, non-CNS), 68 clinically documented infections (CDI, 35%; 39 deep-seated), and 61 fever of unexplained origin (FUO, 31.5%).Results: At fever onset median PCT was 190 pg/mL (range 30-26'800), without significant difference among MDI, CDI and FUO. PCT peak occurred on day 2 after onset of fever: non-CNS-MDI/deep-seated-CDI (656, 80-86350) vs. FUO (205, 33-771; p<0.001). PCT >500 pg/mL distinguished non-CNS-MDI/deep-seated-CDI from FUO with 56% sensitivity and 90% specificity. PCT was >500 pg/ml in only 10% of FUO (688, 570-771). A PCT peak >500 pg/mL (1196, 524-11950) occurred beyond 3 days of persistent fever in 17/21 (81%) invasive fungal diseases (IFD). This late PCT peak identified IFD with 81% sensitivity and 57% specificity and preceded diagnosis according to EORTC-MSG criteria in 41% of cases. In IFD responding to therapy, median days to PCT <500 pg/mL and defervescence were 5 (1-23) vs. 10 (3-22; p = 0.026), respectively.Conclusion: While procalcitonin is not useful for diagnosis of infection at onset of neutropenic fever, it may help to distinguish a minority of potentially severe infections among FUOs on day 2 after onset of fever. In persistent fever monitoring procalcitonin contributes to early diagnosis and follow-up of invasive mycose

Practical considerations for measuring the effective reproductive number, Rt

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation