Browsing by Author "Mandl, Kenneth D."

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    Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents
    (Elsevier, 2021-08-31) Geva, Alon; Patel, Manish M.; Geva, Alon; Patel, Manish M.; Newhams, Margaret M.; Young, Cameron C.; Son, Mary Beth F.; Kong, Michele; Maddux, Aline B.; Hall, Mark W.; Riggs, Becky J.; Singh, Aalok R.; Giuliano, John S.; Hobbs, Charlotte V.; Loftis, Laura L.; McLaughlin, Gwenn E.; Schwartz, Stephanie P.; Schuster, Jennifer E.; Babbitt, Christopher J.; Halasa, Natasha B.; Gertz, Shira J.; Doymaz, Sule; Hume, Janet R.; Bradford, Tamara T.; Irby, Katherine; Carroll, Christopher L.; McGuire, John K.; Tarquinio, Keiko M.; Rowan, Courtney M.; Mack, Elizabeth H.; Cvijanovich, Natalie Z.; Fitzgerald, Julie C.; Spinella, Philip C.; Staat, Mary A.; Clouser, Katharine N.; Soma, Vijaya L.; Dapul, Heda; Maamari, Mia; Bowens, Cindy; Havlin, Kevin M.; Mourani, Peter M.; Heidemann, Sabrina M.; Horwitz, Steven M.; Feldstein, Leora R.; Tenforde, Mark W.; Newburger, Jane W.; Mandl, Kenneth D.; Randolph, Adrienne G.; Overcoming COVID-19 Investigators; Pediatrics, School of Medicine
    Background Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. Methods We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients. Findings Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from cluster 1 patients. Interpretation Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C.
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    Real world performance of the 21st Century Cures Act population-level application programming interface
    (Oxford University Press, 2024) Jones, James R.; Gottlieb, Daniel; McMurry, Andrew J.; Atreja, Ashish; Desai, Pankaja M.; Dixon, Brian E.; Payne, Philip R. O.; Saldanha, Anil J.; Shankar, Prabhu; Solad, Yauheni; Wilcox, Adam B.; Ali, Momeena S.; Kang, Eugene; Martin, Andrew M.; Sprouse, Elizabeth; Taylor, David E.; Terry, Michael; Ignatov, Vladimir; Mandl, Kenneth D.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Objective: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. Materials and methods: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. Results: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. Discussion: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.