MIS-C across three SARS-CoV-2 variants: Changes in COVID-19 testing and clinical characteristics in a cohort of U.S. children.

As new variants of SARS-Co-V 2 have emerged over time and Omicron sub-variants have become dominant, the severity of illness from COVID-19 has declined despite greater transmissibility. There are fewer data on how the history, diagnosis, and clinical characteristics of multisystem inflammatory syndrome in children (MIS-C) have changed with evolution in SARS-CoV-2 variants. We conducted a retrospective cohort study of patients hospitalized with MIS-C between April 2020 and July 2022 in a tertiary referral center. Patients were sorted into Alpha, Delta, and Omicron variant cohorts by date of admission and using national and regional data on variant prevalence. Among 108 patients with MIS-C, significantly more patients had a documented history of COVID-19 in the two months before MIS-C during Omicron (74%) than during Alpha (42%) (p = 0.03). Platelet count and absolute lymphocyte count were lowest during Omicron, without significant differences in other laboratory tests. However, markers of clinical severity, including percentage with ICU admission, length of ICU stay, use of inotropes, or left ventricular dysfunction, did not differ across variants. This study is limited by its small, single-center case series design and by classification of patients into era of variant by admission date rather than genomic testing of SARS- CoV-2 samples.     Conclusion: Antecedent COVID-19 was more often documented in the Omicron than Alpha or Delta eras, but clinical severity of MIS-C was similar across variant eras. What is Known: • There has been a decrease in incidence of MIS-C in children despite widespread infection with new variants of COVID-19. • Data has varied on if the severity of MIS-C has changed over time across different variant infections. What is New: • MIS-C patients were significantly more likely to report a known prior infection with SARS-CoV-2 during Omicron than during Alpha. • There was no difference in severity of MIS-C between the Alpha, Delta, and Omicron cohorts in our patient population.

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.

A Novel Combination Treatment with Honokiol and Rapamycin Effectively Restricts c-Met-Induced Growth of Renal Cancer Cells, and also Inhibits the Expression of Tumor Cell PD-L1 Involved in Immune Escape.

The mTOR inhibitor Rapamycin has tumor inhibitory properties; and it is also used as an immunosuppressive agent after organ transplantation. However, prolonged Rapamycin treatment re-activates Akt and can promote cancer growth. Honokiol is a natural compound with both anti-tumorigenic and anti-inflammatory properties. Here, we assessed the anti-tumor effects of Rapamycin and Honokiol combination in renal cell carcinoma (RCC). Receptor tyrosine kinase c-Met-mediated signaling plays a major role in RCC growth. We observed that compared with Rapamycin alone, Rapamycin + Honokiol combination can effectively down-regulate c-Met-induced Akt phosphorylation in renal cancer cells; and it markedly inhibited Ras activation and cell proliferation and promoted G1 phase cell cycle arrest. The combination treatment significantly induced ROS generation and cancer cell apoptosis even when c-Met is activated. Importantly, Honokiol, but not Rapamycin, decreased c-Met-induced expression of the co-inhibitory molecule PD-L1, implied in the immune escape of renal cancer cells. In mouse renal cancer cells and Balb/c splenocytes co-culture assay, Rapamycin + Honokiol markedly potentiated immune-cell-mediated killing of cancer cells, possibly through the down-regulation of PD-L1. Together, Honokiol can effectively overcome the limitation of Rapamycin treatment alone; and the combination treatment can markedly restrict the growth of RCC, with particular importance to post-transplantation renal cancer.