Cardiac implantable electronic devices replacements in patients followed by remote monitoring during COVID-19 lockdown.

Aims: Following coronavirus disease (COVID-19) outbreak, the Italian government adopted strict rules of lockdown and social distancing. The aim of our study was to assess the admission rate for cardiac implantable electronic devices (CIEDs) replacement procedures in Campania, the 3rd-most-populous region of Italy, during COVID-19 lockdown. Methods and results: Data were sourced from 16 referral hospitals in Campania from 10 March to 4 May 2020 (lockdown period) and during the same period in 2019. We retrospectively evaluated consecutive patients hospitalized for CIEDs replacement procedures during the two observational periods. The number and type of CIEDs replacement procedures among patients followed by remote monitoring (RM), the admission rate, and the type of hospital admission between the two observational periods were compared. In total, 270 consecutive patients were hospitalized for CIEDs replacement procedures over the two observation periods. Overall CIEDs replacement procedures showed a reduction rate of 41.2% during COVID-19 lockdown. Patients were equally distributed for sex (P = 0.581), and both age [median 76 years (IQR: 68-83) vs. 79 years (IQR: 68-83); P = 0.497]. Cardiac implantable electronic devices replacement procedures in patients followed by RM significantly increased (IR: +211%; P < 0.001), mainly driven by the remarkable increase rate trend of both PM (IR: +475%; P < 0.001) and implantable cardiac defibrillator replacement procedures (IR: +67%, P = 0.01), during COVID-19 lockdown compared with 2019 timeframe. Conclusions: We showed a significant increase trend rate of replacement procedures among CIEDs patients followed by RM, suggesting the hypothesis of its increased use to closely monitoring and to optimize the hospital admission time during COVID-19 lockdown.

Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models.

Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of coronavirus disease 2019 (COVID-19), they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data affects the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic.

The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers.