Vital Signs as Predictor Factors of Intravenous Immunoglobulin Resistance in Patients With Kawasaki Disease

Gámez-González, Luisa Berenise; Hamada, Hiromichi; Castolo, Martín Cisneros; Honda, Takafumi; Yasukawa, Kumi; Takanashi, Jun‐ichi

doi:10.1177/0009922818759320

Cited by 8 publications

(8 citation statements)

References 31 publications

(34 reference statements)

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“…In 1 previous study, a temperature .38.8°C was used before IVIg infusion in the predictive model; however, the authors defined IVIg nonresponse as a temperature $37.5°C within 48 hours of IVIg treatment start, which may overestimate the rate of nonresponse. 10 Antipyretic IVIg premedication at our institution limited the utility of pre-IVIg temperatures in our population. Canadian researchers found significant differences in temperature patterns after IVIg treatment in patients by level of nonresponse (complete or partial nonresponse versus complete response), 35 supporting our findings.…”

Section: Results From Each Sensitivity Analysis (Supplementalmentioning

confidence: 97%

“…For example, in many studies, IVIg nonresponse is defined as a temperature $37.5°C, which is below the typical US threshold of 38.0°C to 38.5°C. 5,6,9,10,12 This difference may lead to an overestimation of nonresponse and limits applicability of risk prediction models to US patients.…”

Section: Results From Each Sensitivity Analysis (Supplementalmentioning

confidence: 99%

“…Authors of several studies have attempted to predict the risk of IVIg nonresponse for an individual patient with models using a combination of laboratory results, clinical information (eg, number of days of illness), and demographic features (eg, age and sex). [5][6][7][8][9][10][11][12][13][14] Predictive models in populations in Asian countries have had low sensitivity and/or specificity for identifying IVIg nonresponse among patients in the United States. 7,8 Furthermore, US studies have been limited by small sample size or lack of internal or external validation.…”

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confidence: 99%

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Identifying Patients With Kawasaki Disease Safe for Early Discharge: Development of a Risk Prediction Model at a US Children’s Hospital

Hester¹,

Watson

Nickel

et al. 2019

Hospital Pediatrics

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To develop a model to predict risk of intravenous immunoglobulin (IVIg) nonresponse in patients with Kawasaki disease (KD) to assist in early discharge decision-making. METHODS: Retrospective cohort study of 430 patients 0 to 18 years old discharged from a US children's hospital January 1, 2010, through July 31, 2017 with a diagnosis of KD. IVIg nonresponse was defined as at least 1 of the following: temperature $38.0°C between 36 hours and 7 days after initial IVIg dose, receipt of a second IVIg dose after a temperature $38.0°C at least 20 hours after initial IVIg dose, or readmission within 7 days with administration of a second IVIg dose. Backward stepwise logistic regression was used to select a predictive model. RESULTS: IVIg nonresponse occurred in 19% (81 of 430) of patients. We identified a multivariate model (which included white blood cell count, hemoglobin level, platelet count, aspartate aminotransferase level, sodium level, albumin level, temperature within 6 hours of first IVIg dose, and incomplete KD) with good predictive ability (optimism-adjusted concordance index: 0.700) for IVIg nonresponse. Stratifying into 2 groups by a predictive probability cutoff of 0.10, we identified 26% of patients at low risk for IVIg nonresponse, with a sensitivity and specificity of 90% and 30%, respectively, and a negative predictive value of 93%. CONCLUSIONS: We developed a model with good predictive value for identifying risk of IVIg nonresponse in patients with KD at a US children's hospital. Patients at lower risk may be considered for early discharge by using shared decision-making. Our model may be used to inform implementation of electronic health record tools and future risk prediction research.

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Section: Results From Each Sensitivity Analysis (Supplementalmentioning

confidence: 97%

Section: Results From Each Sensitivity Analysis (Supplementalmentioning

confidence: 99%

mentioning

confidence: 99%

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Identifying Patients With Kawasaki Disease Safe for Early Discharge: Development of a Risk Prediction Model at a US Children’s Hospital

Hester¹,

Watson

Nickel

et al. 2019

Hospital Pediatrics

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“…Therefore, finding biomarkers that can predict IVIG resistance is an important area of current KD research. Neutrophils [14,[77][78][79][80][81], B cells [23], CD8+ T cells [82], and Th17 cells [32,33] in KD peripheral blood have all been reported to predict IVIG resistance, but the types of immune cells supported by different studies are not consistent. Our research did not identify immune cells that can accurately predict IVIG response in multiple datasets.…”

Section: Discussionmentioning

confidence: 99%

Atlas of Circulating Immune Cells in Kawasaki Disease

Z¹,

Huang²,

X³

et al. 2021

Preprint

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Increased evidence shows that the pathogenesis of Kawasaki disease (KD) is caused by abnormal and unbalanced innate and adaptive immune responses. However, the changes in and functions of adaptive immune cells in KD peripheral blood are still controversial. In this study, three different methods, CIBERSORT, Immune Cell Abundance Identifier (ImmuCellAI), and immune cell markers, were used to evaluate the proportions and abundances of immune cells in eight KD datasets (GSE9863, GSE9864, GSE18606, GSE63881, GSE68004, GSE73461, GSE73463, and GSE64486; a total of 1,251 samples). Compared with those in normal controls and convalescent KD samples, the proportions and abundances of innate immune cells such as neutrophils, monocytes, and macrophages in acute KD peripheral blood samples were significantly increased, while those of adaptive immune cells such as B and T cells were significantly decreased. The change tendency of these immune cells was similar to that seen in other febrile illnesses but more dramatic. However, in the coronary artery tissue of KD patients, adaptive immune cells, especially B cells and CD8+ T cell subsets, were significantly increased. This result suggests that adaptive immune cells are selectively recruited from peripheral blood into the coronary arteries. In addition, we found that elevated neutrophils in peripheral blood could be used as a biomarker to assist in the differential diagnosis of KD, but we did not find immune cells that can accurately predict intravenous immunoglobulin (IVIG) response in multiple datasets.

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“…18 Five studies reported that patients with poorly documented or unclassified clinical data during hospitalization were excluded. 17,[25][26][27][28] (Supplementary Table 2).…”

Section: Characteristics Of the Modelsmentioning

confidence: 99%

Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST

et al. 2023

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Background The prediction model of intravenous immunoglobulin (IVIG) resistance in Kawasaki disease can calculate the probability of IVIG resistance and provide a basis for clinical decision-making. We aim to assess the quality of these models developed in the children with Kawasaki disease. Methods Studies of prediction models for IVIG-resistant Kawasaki disease were identified through searches in the PubMed, Web of Science, and Embase databases. Two investigators independently performed literature screening, data extraction, quality evaluation, and discrepancies were settled by a statistician. The checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) was used for data extraction, and the prediction models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results Seventeen studies meeting the selection criteria were included in the qualitative analysis. The top three predictors were neutrophil measurements (peripheral neutrophil count and neutrophil %), serum albumin level, and C-reactive protein (CRP) level. The reported area under the curve (AUC) values for the developed models ranged from 0.672 (95% confidence interval [CI]: 0.631–0.712) to 0.891 (95% CI: 0.837–0.945); The studies showed a high risk of bias (ROB) for modeling techniques, yielding a high overall ROB. Conclusion IVIG resistance models for Kawasaki disease showed high ROB. An emphasis on improving their quality can provide high-quality evidence for clinical practice. Impact statement This study systematically evaluated the risk of bias (ROB) of existing prediction models for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease to provide guidance for future model development meeting clinical expectations. This is the first study to systematically evaluate the ROB of IVIG resistance in Kawasaki disease by using PROBAST. ROB may reduce model performance in different populations. Future prediction models should account for this problem, and PROBAST can help improve the methodological quality and applicability of prediction model development.

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Vital Signs as Predictor Factors of Intravenous Immunoglobulin Resistance in Patients With Kawasaki Disease

Cited by 8 publications

References 31 publications

Identifying Patients With Kawasaki Disease Safe for Early Discharge: Development of a Risk Prediction Model at a US Children’s Hospital

Identifying Patients With Kawasaki Disease Safe for Early Discharge: Development of a Risk Prediction Model at a US Children’s Hospital

Atlas of Circulating Immune Cells in Kawasaki Disease

Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST

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