In regions where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. During the second wave of COVID-19 in India, healthcare facilities and staff across the country struggled to cope with the surge in the number of cases of COVID-19 due to a shortage of hospital beds for people with severe cases, plus shortages of medicines and limited human resources.
We partnered with teams at CMC, Vellore and AIIMS, Patna to develop tools (clinical prediction models) that could help health workers identify which patients with COVID-19 can be safely cared for in the community. We included over 400 adults with COVID-19 who did not require oxygen therapy at first presentation and determined whether information collected on arrival could be used to predict which patients were most likely to deteriorate.
The final models use three simple measurements – a patient’s age, their sex and their oxygen level at arrival – together with one biomarker measurable using a point-of-care test, to predict which patients are unlikely to get worse and require hospital-based care. By identifying which patients are suitable for outpatient care, the tools could help protect health systems during current and future spikes in infections, especially in locations where health facility capacity is limited.
The PRIORITISE study team would like to hear from groups interested in collaborating to further validate the findings. This is important before the models can be confidently recommended for use. Partners who already have baseline clinical data and stored plasma from patients with moderate COVID-19, alongside data on clinical outcomes over the next 14-28 days, are encouraged to get in touch.Â
Please contact arjun@tropmedres.ac or sakib.burza@barcelona.msf.org if interested.
