Fighting covid-19

IDOTCOVID is an open-source platform hosting information about transplanted patients who tested positive to Covid-19, providing a holistic perspective on the influences of the pandemic on donations and transplantations’ outcomes. The database displays 135 different variables and regroups statics about patients’ status, recoveries, immunosuppressive treatments and Covid-19 treatments.

The goal of the database is to develop a machine learning algorithm to assist in the decision-making process - treatment alternatives and expected outcomes - of the population at the study.

The IDOTCOVID database uses data provided by 4 international registries - SEN, ELITA, CENATRA, and INCUCAI -, as well as 43 distinct centers. The collaborating registries / centers have been entering data in two ways: individually by an official representative of the registry / centers or automatically from the Registry of the Spanish Society of Nephrology as of August 2020.

To date, IDOTCOVID has registered 1363 SOTr COVID-19 + from 78 transplant centers in 15 countries. A pre-analysis identified most of the cases are middle-aged men (59.2 years), kidney transplants 72% with 26% liver transplantation and 20 cases of transplants of other organs. Fever (78%), cough (63%), and dyspnea (41%) as most prevalent symptoms. A mortality rate of 24% was detected during follow-up, with significant discrepancies according to transplanted organ (Kidney 25%, liver 15%, heart 20%, lung 50%; p=.006). Age at diagnosis was associated with an increased risk of death (OR 1.065 CI95 [1.049-1.081]), a mortality of 62% in those with >65yo (n=341), with an increased risk x12 superior to those <35yo (OR 12.96 CI95 [3.09-54.3]). On a multivariate analysis liver transplant risk remained with lower (OR 0.556 CI95 [0.378-0.824]) regardless of age.

During the next months, we will be applying different multivariate logistic regression and machine learning algorithms to develop a DSA to support the clinical decision making; that allows individualization of patient treatment, with focus on clinical management (outpatient vs. hospital admission) and of IS (suspension vs reduction).

Thanks to your donations we can create the most complete and diverse database possible in order to achieve the goal of this project: to create a decision support algorithm (DSA).