Incidence and Severity of Kawasaki Disease Among Vietnamese Children.

BACKGROUND: Kawasaki disease (KD) disproportionately affects children of Asian descent. San Diego is home to a large Vietnamese population but no previous study has addressed the outcome of KD in this group. METHODS: We performed a retrospective review of Vietnamese patients seen at Rady Children's Hospital San Diego from 2001 to 2019. Non-Vietnamese Asian and non-Asian KD patients were matched (2:1) based on date of onset and age with Vietnamese patients. Demographic, clinical, and echocardiographic data were compared. Interviews with cardiologists at the Children's Hospital 1 in Ho Chi Minh City, Vietnam, explored local practices in the diagnosis and management of KD patients. KD publications in Vietnamese were translated and summarized. RESULTS: Of 978 KD patients for whom both parents had the same ethnicity, 20 were Vietnamese (2.1%), 168 (17%) were non-Vietnamese Asian, and 789 (81%) were non-Asian. Vietnamese and non-Vietnamese Asians had an earlier median day of diagnosis at day 6 (interquartile range [IQR] 5-6) and 5.5 (IQR 4-6.75), respectively, compared with non-Asians (day 7, IQR 5-8.75, P = 0.02). Prominent cervical lymphadenopathy at diagnosis was more common in both Vietnamese and non-Vietnamese Asians (20% and 40%, respectively) compared with non-Asians (12.5%, P = 0.01). Importantly, Vietnamese KD patients had a higher rate of coronary artery aneurysms (60% vs. 27.5%) compared to non-Asians (P = 0.024). Vietnamese literature review and structured interviews suggested a high incidence and severity of KD in Vietnamese children. CONCLUSIONS: Physicians should be aware that Vietnamese children may be disproportionately affected by KD and have worse coronary artery outcomes.

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.

Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. METHODS: We developed KIDMATCH ( K awasak I D isease vs M ultisystem Infl A mma T ory syndrome in CH ildren), a deep learning algorithm for screening patients for MIS-C, KD, or other febrile illness, using age, the five classical clinical KD signs, and 17 laboratory measurements prospectively collected within 24 hours of admission to the emergency department from 1448 patients diagnosed with KD or other febrile illness between January 1, 2009 and December 31, 2019 at Rady Children's Hospital. For MIS-C patients, the same data was collected from 131 patients between May 14, 2020 to June 18, 2021 at Rady Children's Hospital, Connecticut Children's Hospital, and Children's Hospital Los Angeles. We trained a two-stage model consisting of feedforward neural networks to distinguish between MIS-C and non MIS-C patients and then KD and other febrile illness. After internally validating the algorithm using 10-fold cross validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts, enhancing the model generalizability and confidence by flagging unfamiliar cases as indeterminate instead of making spurious predictions. We externally validated KIDMATCH on 175 MIS-C patients from 16 hospitals across the United States. FINDINGS: KIDMATCH achieved a high median area under the curve in the 10-fold cross validation of 0.988 [IQR: 0.98-0.993] in the first stage and 0.96 [IQR: 0.956-0.972] in the second stage using thresholds set at 95% sensitivity to detect positive MIS-C and KD cases respectively during training. External validation of KIDMATCH on MIS-C patients correctly classified 76/83 (2 rejected) patients from the CHARMS consortium, 47/50 (1 rejected) patients from Boston Children's Hospital, and 36/42 (2 rejected) patients from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid frontline clinicians with distinguishing between MIS-C, KD, and similar febrile illnesses in a timely manner to allow prompt treatment and prevent severe complications. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Heart, Lung, and Blood Institute, Patient-Centered Outcomes Research Institute, National Library of Medicine.

Kawasaki Disease Outcomes and Response to Therapy in a Multiethnic Community: A 10-Year Experience.

OBJECTIVES: To describe the epidemiology, response to therapy, and outcomes of Kawasaki disease in a multiethnic community with a large Hispanic and Asian population. STUDY DESIGN: We analyzed prospectively collected data from 788 unselected patients with Kawasaki disease diagnosed and treated at a single medical center over a 10-year period. RESULTS: The average incidence of Kawasaki disease in children <5 years in San Diego County over the 10 years from 2006 to 2015 was 25 per 100 000 children, with the greatest incidence (50 per 100 000) for Asian/Pacific Islanders. Compared with other race/ethnicities, Asian/Pacific Islander patients with Kawasaki disease were younger, were diagnosed earlier in the course of their fever, had higher levels of inflammatory markers, and were more likely to develop aneurysms. There was no difference across race/ethnicity groups in response to intravenous immunoglobulin therapy. Filipino children had the highest recurrence rates (9.1%; 95% CI, 3.0%-22.6%) and 12 of 788 patients (1.5%) had a first- or second-degree relative with a history of Kawasaki disease. After correcting for age of onset, sex, and illness day at diagnosis, Asian/Pacific Islander children had an increased risk of developing aneurysms (aOR, 2.37; 95% CI, 1.37-4.11; P = .002). Overall, 180 of 788 patients (22.8%) had a maximal Z score of 2.5-10.0 and 14 of the 788 patients (1.8%) had a maximal Z score ≥10.0 despite 84% of these patients being treated within 10 days of fever onset. CONCLUSIONS: Our data provide new insights into the natural history of treated Kawasaki disease in a multiethnic population. Patient race/ethnicity influenced susceptibility to Kawasaki disease, timing of diagnosis, coronary artery outcome, and recurrence rates.