Experience with tumour necrosis factor-alpha inhibitors for the treatment of cardiac sarcoidosis in a UK medical centre

P217 Table 1Case series outcomes table Case no. IFX dose& regime Follow-up duration (months) Change in prednisolone dose Change in FDG-PET uptake Change in LV systolic function Change in arrythmia burden Adverse events Composite Endpoint Dose pre-IFX(mg) Dose post-IFX(mg) Pre-IFX Post-IFX LVEF pre-IFX (%) LVEF post-IFX(%) Pre-IFX Post-IFX Infections Heart failure VT/VF (requiring device) All-cause mortality Aborted SCD (device) Cardiac Transplant Case 1 IFX 3 mg/kg every 8 weeks (break after 10th dose due to covid pandemic;restarted 3 months later) 29 20 15 Active CS(SUVmax 11.1) Improvement(SUVmax3.5) 58 59 N/A N/A 0 0 0 0 0 0 Case 2 IFX 3 mg/kg every 8 weeks. Stopped after 15.6 months due to resolution 15.6 10 10 Active CS(SUVmax 10.2) Improvement(SUVmax 2.65) 55 62 VA 0 1 (chest infection) 0 0 0 0 0 Case 2Relapse (7 months after stopping IFX) due to VT and FDG uptake IFX 3 mg/kg every 8 weeks 12 10 10 Active CS(SUVmax 3.3) Improvement(no uptake) N/A 50 VA 0 0 Mild LVSD 0 0 0 0 Case 3 IFX 3 mg/kg0 weeks and 4 weeks;missed 8 weeks’ appointment due to COVID-19 10 20 20 Active CS(SUVmax 11.3) N/A 55 N/A 0 N/A 1 (Covid-19) 0 0 0 0 0 Case 4 IFX 3 mg/kg at 0, 2 and 8 weeks afterwards 16 20 10 Active CS(SUVmax 13) Improved(SUVmax 3.4) 45 66 VA N/A 0 0 0 1 (PFO and shunt/complications) 0 0 Case 5 IFX 3 mg/kg 0, 2, 6 and every 8 weeks 8.5 30 15 Active CS(SUVmax 11.3) Improved(no uptake) 46 51 VA 0 0 0 0 0 0 0 Totals 6 IFX 3 mg/kg Mean=15.2 Mean=18.3 Mean=13.3 All had active CS 5 improved;1 data not available Mean=51.8 Mean=57.6 4 had VAs;1 data not available None had VA;3 had data not available 2 1 0 1 0 0 CS = cardiac sarcoidosis;FDG-PET =fluorodeoxyglucose positron emission tomography;IFX = Infliximab;LV = left ventricular;LVEF = left ventricular ejection fraction;LVSD = left ventricular systolic dysfunction;N/A = data not available;PFO= patent foramen ovale;

Analysis and Prediction of COVID-19 Pandemic in India

In this paper, we have analysed the COVID-19 progression in India and the three most affected Indian states (viz. Maharashtra, Tamil Nadu and Andhra Pradesh) as of 29-Aug-20 and developed a prediction model to forecast the behaviour of COVID-19 spread in the future months. We used time series data for India and applied the Susceptible-Infective-Removed (SIR) model and the FbProphet model to predict the peak infectives and peak infective date for India and the three most affected states. In this paper, we further performed the comparative analysis of the prediction results from SIR and FbProphet models. From this study, we concluded that with the assumption that a total 5% of India's population might be infected by the pandemic, the countrywide spread is forecasted to reach its peak by the end of Nov-20. And till the time there is no vaccination, for the states that have already reached their peak and with festivals around the corner, there are high chances of resurgence in the number of cases if the social distancing and other control measures are not followed diligently in the coming months. © 2020 IEEE.