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Development and validation of a federated learning framework for detection of subphenotypes of multisystem
Naimin Jing1,2, Xiaokang Liu1, Qiong Wu1
1Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA.
Insights
Researchers identified three distinct subphenotypes of multisystem inflammatory syndrome in children (MIS-C) using a novel distributed approach. This classification aids in personalized treatment strategies for MIS-C, a severe post-COVID-19 condition.
Area of Science:
- Pediatric critical care medicine
- Infectious diseases
- Computational biology
Background:
- Multisystem inflammatory syndrome in children (MIS-C) is a serious condition following SARS-CoV-2 infection with diverse clinical presentations.
- Characterizing MIS-C by subphenotypes is crucial for accurate diagnosis and tailored treatment, but multi-site identification is challenging.
Approach:
- A distributed multi-site latent class analysis (dMLCA) approach was developed to jointly identify MIS-C subphenotypes across multiple institutions.
- Data from electronic health records of 864 MIS-C patients across nine U.S. children's hospitals were analyzed.
- The dMLCA algorithm identified three distinct MIS-C subphenotypes based on clinical conditions, lab results, and procedures.
Key Points:
- Subphenotype 1 (46.1%): Mild MIS-C, no ICU, minimal cardiac involvement.
- Subphenotype 2 (25.3%): High risk of shock, cardiac/renal involvement, moderate respiratory risk.
- Subphenotype 3 (28.6%): ICU admission, high risk of shock, cardiac involvement, and >4 organ systems affected.
Conclusions:
- The identified MIS-C subphenotypes offer critical insights for developing personalized treatment strategies and improving patient outcomes.
- The dMLCA algorithm enables federated subphenotyping, effectively accounting for inter-hospital heterogeneity in MIS-C presentation.
Background:
Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection. The highly diverse clinical features of MIS-C necessities characterizing its features by subphenotypes for improved recognition and treatment. However, jointly identifying subphenotypes in multi-site settings can be challenging. We propose a distributed multi-site latent class analysis (dMLCA) approach to jointly learn MIS-C subphenotypes using data across multiple institutions.
Methods:
We used data from the electronic health records (EHR) systems across nine U.S. children's hospitals. Among the 3,549,894 patients, we extracted 864 patients < 21 years of age who had received a diagnosis of MIS-C during an inpatient stay or up to one day before admission. Using MIS-C conditions, laboratory results, and procedure information as input features for the patients, we applied our dMLCA algorithm and identified three MIS-C subphenotypes. As validation, we characterized and compared more granular features across subphenotypes. To evaluate the specificity of the identified subphenotypes, we further compared them with the general subphenotypes identified in the COVID-19 infected patients.
Findings:
Subphenotype 1 (46.1%) represents patients with a mild manifestation of MIS-C not requiring intensive care, with minimal cardiac involvement. Subphenotype 2 (25.3%) is associated with a high risk of shock, cardiac and renal involvement, and an intermediate risk of respiratory symptoms. Subphenotype 3 (28.6%) represents patients requiring intensive care, with a high risk of shock and cardiac involvement, accompanied by a high risk of >4 organ system being impacted. Importantly, for hospital-specific clinical decision-making, our algorithm also revealed a substantial heterogeneity in relative proportions of these three subtypes across hospitals. Properly accounting for such heterogeneity can lead to accurate characterization of the subphenotypes at the patient-level.
Interpretation:
Our identified three MIS-C subphenotypes have profound implications for personalized treatment strategies, potentially influencing clinical outcomes. Further, the proposed algorithm facilitates federated subphenotyping while accounting for the heterogeneity across hospitals.
