Implementation of KIDMATCH: A Clinical Decision Support Tool for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease

Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic that may lead to cardiac dysfunction or death in pediatric patients. Early detection of MIS-C remains a challenge given the lack of a diagnostic test and its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. We developed and validated the KawasakI Disease vs Multisystem InflAmmaTory syndrome in CHildren (KIDMATCH) clinical decision support tool for screening patients for MIS-C, KD, or other febrile illnesses. Here we describe the implementation and iterative refinement of KIDMATCH with provider feedback as a web calculator in the clinical workflow within Rady Children's Hospital. Our findings demonstrate KIDMATCH and its underlying artificial intelligence model have clinical utility in aiding clinicians at the time of initial evaluation within the hospital setting to distinguish patients who have MIS-C, KD, or other febrile illnesses.

A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting. METHODS: In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Children) using patient age, the five classic clinical Kawasaki disease signs, and 17 laboratory measurements. All features were prospectively collected at the time of initial evaluation from patients diagnosed with Kawasaki disease or other febrile illness between Jan 1, 2009, and Dec 31, 2019, at Rady Children's Hospital in San Diego (CA, USA). For patients with MIS-C, the same data were collected from patients between May 7, 2020, and July 20, 2021, at Rady Children's Hospital, Connecticut Children's Medical Center in Hartford (CT, USA), and Children's Hospital Los Angeles (CA, USA). We trained a two-stage model consisting of feedforward neural networks to distinguish between patients with MIS-C and those without and then those with Kawasaki disease and other febrile illnesses. After internally validating the algorithm using stratified tenfold cross-validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts. We finally externally validated KIDMATCH on patients with MIS-C enrolled between April 22, 2020, and July 21, 2021, from Boston Children's Hospital (MA, USA), Children's National Hospital (Washington, DC, USA), and the CHARMS Study Group consortium of 14 US hospitals. FINDINGS: 1517 patients diagnosed at Rady Children's Hospital between Jan 1, 2009, and June 7, 2021, with MIS-C (n=69), Kawasaki disease (n=775), or other febrile illnesses (n=673) were identified for internal validation, with an additional 16 patients with MIS-C included from Connecticut Children's Medical Center and 50 from Children's Hospital Los Angeles between May 7, 2020, and July 20, 2021. KIDMATCH achieved a median area under the receiver operating characteristic curve during internal validation of 98·8% (IQR 98·0-99·3) in the first stage and 96·0% (95·6-97·2) in the second stage. We externally validated KIDMATCH on 175 patients with MIS-C from Boston Children's Hospital (n=50), Children's National Hospital (n=42), and the CHARMS Study Group consortium of 14 US hospitals (n=83). External validation of KIDMATCH on patients with MIS-C correctly classified 76 of 81 patients (94% accuracy, two rejected by conformal prediction) from 14 hospitals in the CHARMS Study Group consortium, 47 of 49 patients (96% accuracy, one rejected by conformal prediction) from Boston Children's Hospital, and 36 of 40 patients (90% accuracy, two rejected by conformal prediction) from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid front-line clinicians to distinguish between MIS-C, Kawasaki disease, and other similar febrile illnesses to allow prompt treatment and prevent severe complications. FUNDING: US Eunice Kennedy Shriver National Institute of Child Health and Human Development, US National Heart, Lung, and Blood Institute, US Patient-Centered Outcomes Research Institute, US National Library of Medicine, the McCance Foundation, and the Gordon and Marilyn Macklin Foundation.

Immune response to intravenous immunoglobulin in patients with Kawasaki disease and MIS-C.

BACKGROUNDMultisystem inflammatory syndrome in children (MIS-C) is a rare but potentially severe illness that follows exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Kawasaki disease (KD) shares several clinical features with MIS-C, which prompted the use of intravenous immunoglobulin (IVIG), a mainstay therapy for KD. Both diseases share a robust activation of the innate immune system, including the IL-1 signaling pathway, and IL-1 blockade has been used for the treatment of both MIS-C and KD. The mechanism of action of IVIG in these 2 diseases and the cellular source of IL-1ß have not been defined.METHODSThe effects of IVIG on peripheral blood leukocyte populations from patients with MIS-C and KD were examined using flow cytometry and mass cytometry (CyTOF) and live-cell imaging.RESULTSCirculating neutrophils were highly activated in patients with KD and MIS-C and were a major source of IL-1ß. Following IVIG treatment, activated IL-1ß+ neutrophils were reduced in the circulation. In vitro, IVIG was a potent activator of neutrophil cell death via PI3K and NADPH oxidase, but independently of caspase activation.CONCLUSIONSActivated neutrophils expressing IL-1ß can be targeted by IVIG, supporting its use in both KD and MIS-C to ameliorate inflammation.FUNDINGPatient Centered Outcomes Research Institute; NIH; American Asthma Foundation; American Heart Association; Novo Nordisk Foundation; NIGMS; American Academy of Allergy, Asthma and Immunology Foundation.