Marcel Kopp/ Ulrike Grittner, Charité - Universitätsmedizin Berlin, Department of Experimental Neurology, Berlin, Germany

PRECISION-SCI – PREdicting Central nervous system Injury-associated Systemic InfectiONs after Spinal Cord Injury

Funded in: 2020, 2021, 2022

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Problem: Respiratory and urinary tract infections are serious complications
Target: Statistical models able to relate immune parameter changes to the occurrence and timing of infections
Goal: Support decisions on the means to prevent or treat infections

Respiratory and urinary tract infections are very frequent and serious complications of spinal cord injury (SCI). Pneumonia is potentially life-threatening and is also associated with poor functional recovery. To prevent or treat infections as early as possible it is essential to accurately identify individuals who are likely to develop relevant infections.

Problem statement
Certain clinical conditions are known risk factors for infections. For example, mechanical ventilation can facilitate the development of pneumonia. In addition, increased susceptibility to infections refers to the so-called Spinal Cord Injury-induced Immune Deficiency Syndrome (SCI-IDS). The severity of the SCI-IDS, which is related to the height and severity of the spinal cord lesion, can be defined in more depths by specific immune parameters in the patient’s blood. Nevertheless, these parameters and their association to infections reveal relevant changes over time.

The researchers will develop statistical models able to relate immune parameter changes to the occurrence and timing of infections after SCI. The models are built in an ‘original’ dataset of a previous study in order to determine the value of both clinical and laboratory information for diagnosis or prediction of infections. In a next step these models will be challenged in a ‘new’ dataset gathered in an upcoming prospective study.

Expected results
The models will serve to better predict or diagnose specific types of infections at different stages of SCI. The researchers expect to identify a number of key predictors that can be used to create tools for estimating the risk of infection at the individual patient level.

Potential application
The results can be applied in the clinical care to support decisions on the means to prevent or treat infections. In addition, it is about to be used for individual benefit-risk assessment in clinical research on specific interventions (immune therapies) with the target to consolidate the patient's immune system after SCI.