ABSTRACT
The challenge of treating severely ill COVID-19 patients is particularly great due to the need to simultaneously manage oxygenation and the inflammatory state without compromising viral clearance. Currently, there are many tools to aid in oxygen management and in monitoring viral replication. However, predictive biomarkers for monitoring the host immune response across COVID-19 disease stages and specifically, for titrating immunomodulatory therapy are lacking. We utilized a recently cleared platform (MeMed Key) that enables rapid and easy serial measurement of IP-10, a host protein implicated in lung injury due to viral-induced hyperinflammation. A dynamic clinical decision support protocol was employed for managing SARS-CoV-2 positive patients admitted to a COVID-19 dedicated medical center run by Clalit Health Services. This is the first protocol to include real-time measurements of IP-10 as a potential aid for regulating inflammation. Overall, 502 serial real-time IP-10 measurements were performed on 52 patients recruited between 7th April 2020 to 10th May 2020, with 12 patients admitted to the intensive care unit (ICU). IP-10 levels correlated with increased COVID-19 severity score and ICU admission. Within the ICU admitted patients, the number of days with IP-10 measurements >1,000 pg/ml was associated with mortality. Upon administration of corticosteroid immunomodulatory therapy, a significant decrease in IP-10 levels was observed. Real-time IP-10 monitoring represents a new tool to aid in management and therapeutic decisions relating to the inflammatory status of COVID-19 patients.
ABSTRACT
Facing the rapidly spreading novel coronavirus disease (COVID-19), evidence to inform decision-making at both the clinical and policy-making level is highly needed. Based on the results of a study by Petrilli et al, we have developed a calculator using patient data at admission to predict the risk of critical illness (intensive care unit admission, use of mechanical ventilation, discharge to hospice, or death). We report a retrospective validation of the risk calculator on 145 consecutive patients admitted with COVID-19 to a single hospital in Israel. Of the 18 patients with critical illness, 17 were correctly identified by the model(sensitivity: 94.4%, 95% CI, 72.7% to 99.9%; specificity: 81.9%, 95% CI, 74.1% to 88.2%). Of the 127 patients with non-critical illness, 104 were correctly identified. This, despite considerable differences between the original and validation study populations. Our results show that data from published knowledge can be used to provide reliable, patient level, automated risk assessment, potentially reducing the cognitive burden on physicians and helping policy makers better prepare for future needs.
