CTSI’s resources enable clinical discoveries, researcher says

HERSHEY, Pa. — Paddy Ssentongo, an infectious disease fellow at Penn State Health Milton S. Hershey Medical Center, is working to better understand tick-borne babesiosis, a condition often referred to as “American malaria.” Despite upticks in infections, Ssentongo said, his work was limited by the lack of real-world data on co-infections and mortality outcomes in large, diverse populations.

“Babesiosis can mimic many other infections, making diagnosis and treatment challenging,” Ssentongo said. “The parasite infects and multiplies within red blood cells, causing their lysis, or breaking apart, which can result in severe anemia and end-organ damage. Because its symptoms overlap with other febrile illnesses, a high degree of clinical suspicion is required. If left untreated, babesiosis can be life-threatening. Treatment strategies also vary depending on the severity of the disease, underscoring the importance of accurate and timely diagnosis. That’s why continued research is essential.”

According to Ssentongo, he was able to conduct babesiosis research thanks to support from Penn State Clinical and Translational Science Institute (CTSI). The institute, of which Ssentongo is an affiliate, provides access to a number of externally managed data platforms, including one called TriNetX. It’s a global platform with a network of academic institutions, pharmaceutical companies and more than a hundred health care organizations across 30 countries that aims to facilitate collaborations across health care stakeholders. Members share their data and, in exchange, can access a wide array of de-identified clinical data from others and employ analytical tools to determine potential connections, as well as connect with other members for possible collaborations.

Searching TriNetX for patients meeting specified criteria, Ssentongo and his team accessed more than 3,000 anonymized cases of babesiosis from over 10 years across the United States. With this large dataset, Ssentongo's team could study not only babesiosis but also its co-occurrence with other tick-borne infections.

“From the TriNetX data, we saw that four in every 10 patients with babesiosis also had Lyme disease,” he said. “That’s a high rate of co-infection. This is significant because the treatments for these diseases are very different.”

In addition to this connection, the researchers also found that the number of babesiosis cases had increased 9% per year between 2015 and 2022. They published their findings — which made news headlines, including in Time — in Open Forum Infectious Diseases last year. The journal named the paper an editor’s choice and included it in its top 10 list for 2024.

Their analysis also revealed that patients with multiple infections actually had better survival outcomes than those with only babesiosis, which Ssentongo and his team said they found strange. Why would multiples infections result in better outcomes? The reasons still are not fully understood, but the researchers theorized that it is possible the double infection could better activate the immune system or that the main treatment for Lyme disease — doxycycline — might resolve the Lyme symptoms and reveal babesiosis-specific symptoms earlier. They also found that co-infected patients were more likely to be treated with doxycycline compared with those who had babesiosis alone.  Additionally, patients already diagnosed with an infection may also be more vigilant about identifying symptoms of another infection.  

Other authors on the study from Penn State include Vernon Chinchilli, distinguished professor of public health sciences; Djibril Ba, assistant professor of public health sciences; Natasha Venugopal, internal medicine resident at Penn State Health Milton S. Hershey Medical Center; and Yue Zhang, epidemiology doctoral student.

Ssentongo is continuing the work, which he said is significantly bolstered by the access to TriNetX. His projects include predictive modeling efforts that combine clinical data with artificial intelligence (AI) to forecast disease risks. Without CTSI’s data infrastructure and staff to support TriNetX access, these discoveries would have taken far longer and been far more expensive, Ssentongo said.

“Without CTSI and TriNetX, this study simply wouldn’t have been possible,” Ssentongo said. “Collecting data like this from dozens of hospitals would have taken years and immense resources. TriNetX gave us access to a clean, comprehensive dataset, immediately.”

He credited the hands-on support from the CTSI team, including Avnish Katoch, senior project manager; Masayo Mesler, senior analyst; and Vasant Honavar, co-lead for CTSI's informatics core. The team guides data extraction and query design.

“Many of my colleagues don’t even know this support exists,” he said. “They think it’s too technical or difficult. But we have people in place to help us.”

With support from CTSI’s machine learning experts and colleagues in computational health, he’s developing AI tools to forecast disease risks.

“AI can help us personalize treatment, predict outcomes and target interventions more precisely — and we have the tools here at Penn State to lead that effort,” Ssentongo said, noting that those tools are supported by the expertise of his colleagues in CTSI and across the University. “TriNetX is just one example of how CTSI’s support can transform health care and public health. These resources are a game-changer for clinical researchers.”