The combination of the neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as a novel predictor of intravenous immunoglobulin resistance in patients with Kawasaki disease: a multicenter study

The clinical value of dynamic monitoring of complete blood count in predicting immunoglobulin resistance in Chinese children with Kawasaki disease

To examine changes in peripheral blood complete blood count (CBC) parameters during acute Kawasaki disease(KD), compare immunoglobulin(IVIG)-sensitive and IVIG-resistant groups, and develop an IVIG resistance model. A retrospective review of clinical and lab data from 282 KD patients (2014-2024) was conducted. CBC parameters were collected at initial, pre-IVIG, and post-IVIG stages. The rank-sum test assessed parameter differences over time. Patients were categorized into IVIG-resistant (n = 29) and IVIG-sensitive (n = 253) groups. Univariate and multivariate logistic regression analyses identified IVIG resistance risk factors, resulting in four predictive models (A, B, C, and D) based on blood changes and clinical experience. The models' effectiveness was evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow test, and decision curve analysis, with the bootstrap(BS) method confirming performance. Significant differences were found in post-IVIG blood parameters, including white blood cell count (WBC), neutrophils, lymphocytes, eosinophils, hemoglobin, platelets, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and mean platelet volume to lymphocyte ratio (MPVLR), compared to pre-IVIG and initial CBC (P < 0.05). In IVIG-resistant patients, NLR, PLR, MPVLR, neutrophil percentage were higher, while lymphocyte percentage was lower than in IVIG-sensitive patients (P < 0.05). The resistant group also showed smaller changes in neutrophil percentages (△N) and lymphocyte percentages (△L). Area under the curve (AUC) values for BS-ROC curves were as follows: model A: 0.758 (95% CI: 0.636-0.878), model B: 0.917 (95% CI: 0.852-0.982), model C: 0.949 (95% CI: 0.909-0.978), and model D (NLR post-IVIG administration combined with △L): 0.910 (95% CI: 0.857-0.963). Hosmer-Lemeshow test P values for all four models were > 0.05. DCA indicated clinical value for all models, especially model C. Blood routine parameters in children with KD vary over time, and IVIG administration alters these parameters. We developed and validated four prediction models for IVIG resistance in KD patients using blood routine data. This indicates that ongoing monitoring of these parameters can predict IVIG resistance and enhance patient outcomes.

Impact of intravenous immunoglobulin treatment on peripheral blood cells in children with Kawasaki disease complicated with coronary artery lesion

BACKGROUND

Kawasaki disease (KD) primarily affects young children and can lead to coronary artery lesions. Intravenous immunoglobulin (IVIG) treatment is essential; however, it may fail in 10-20% of cases, increasing the risk of complications. Complete blood count (CBC) tests can help assess disease severity and predict risks. This study investigated the impact of IVIG on peripheral blood cells, including neutrophil count, platelet-lymphocyte ratio, hemoglobin level, mean platelet volume (MPV), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP), in children with KD complicated by coronary artery lesions (CALs).

METHODS

This retrospective analysis included children diagnosed with typical KD. The neutrophil count, hemoglobin level, platelet-lymphocyte ratio, MPV, ESR, and CRP were compared between those with (CAL+) and without (CAL-) CALs during the acute phase, as well as at 7 days, 1 month, and 2 months after normalizing body temperature with IVIG.

RESULTS

A total of 76 children with KD were analyzed, including 30 with CAL+. CAL+ and CAL- patients exhibited elevated neutrophil counts during the acute phase, with no significant differences (P > 0.05) between groups. After IVIG treatment, CAL- patients demonstrated a more pronounced reduction in neutrophil count (P < 0.05) than CAL+ patients. Hemoglobin levels differed significantly during the acute phase (P < 0.05) but were comparable post-treatment (P > 0.05) between CAL+ and CAL- patients. The platelet-lymphocyte ratio varied significantly between groups during the acute phase and 1-month post-treatment (P < 0.05). Mean ESR and CRP levels were significantly elevated at all time points in the CAL+ group compared with the CAL- group. No significant differences in MPV were observed between groups.

CONCLUSIONS

After IVIG treatment, CAL- patients demonstrated a more important reduction in neutrophil count than CAL+ patients after IVIG. Pediatric patients with KD and CAL+ showed lower hemoglobin and platelet-lymphocyte ratio and higher ESR and CRP compared with CAL-, suggesting that they may serve as indicators for CAL in pediatric patients with KD.

Establishment and validation of a nomogram for predicting intravenous immunoglobulin resistance and coronary artery lesion involvement in Kawasaki disease: a retrospective study

OBJECTIVE

We aimed to develop a useful nomogram for early identification of Kawasaki disease (KD) children at a high risk of intravenous immunoglobulin (IVIG) resistance and coronary artery lesion (CAL) complications to improve KD management.

METHODS

Clinical data from 400 patients treated at our hospital between January 1, 2016, and December 31, 2023, were collected. Lasso regression was utilized to screen risk factors for IVIG resistance and CAL involvement. Subsequently, a Logistic regression model incorporating parameters screened by Lasso regression was established and visualized as a nomogram. The discrimination, calibration, clinical applicability, and universality of the model were evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and internal validation.

RESULTS

NEU%, HDL-C, and MHR were identified as predictors of IVIG resistance by Lasso regression, with C-index of the Logistic model being 0.886 for the training set and 0.855 for the validation set. For predicting CAL development, sex, fever date before the first IVIG administration, KD type, and the level of HDL-C and MHR were the optimal variables, yielding C-index of 0.915 and 0.866 for the training and validation set, respectively. Calibration curves for both validation sets performed well, indicating strong predictive abilities of the models.

CONCLUSIONS

We established a nomogram for predicting IVIG resistance that incorporates NEU%, HDL-C, and MHR and a second nomogram for CAL complications incorporating sex, fever date, KD type, and the level of HDL-C and MHR in KD patients, based on the Lasso-Logistic regression model. These nomograms were of guiding significance for screening KD children at high risk of developing IVIG resistance and CAL complications, thereby improving prognosis. Key Points • Two nomograms were established to predict IVIG resistance and CAL complications in KD patients, based on the Lasso-Logistic regression model.

[Development of a predictive scoring model for non-response to intravenous immunoglobulin in Kawasaki disease]

OBJECTIVES

To explore the predictive factors for non-response to intravenous immunoglobulin (IVIG) in children with Kawasaki disease (KD) and to establish an IVIG non-response prediction scoring model for the Sichuan region.

METHODS

A retrospective study was conducted by collecting clinical data from children with KD admitted to four tertiary hospitals in Sichuan Province between 2019 and 2023. Among them, 940 children responded to IVIG, while 74 children did not respond. Multivariate logistic regression analysis was used to identify the predictive factors for non-response to IVIG and to establish a predictive scoring model. The model's effectiveness was assessed using the receiver operating characteristic curve (ROC) and validated with an independent dataset.

RESULTS

Multivariate logistic regression analysis showed that the platelet-to-lymphocyte ratio (PLR), hemoglobin (Hb), serum creatinine, aspartate aminotransferase (AST), and platelet count (PLT) were closely related to non-response to IVIG in children with KD (P<0.05). Based on these indicators, a predictive scoring model was established: PLR > 199, 0.4 points; Hb ≤ 116 g/L, 4 points; AST > 58 U/L, 0.2 points; serum creatinine > 38 µmol/L, 3.9 points; PLT count ≤ 275 × 109/L, 0.3 points. Using this model, children with KD were scored, and a total score greater than 4.3 was considered high risk of non-response to IVIG. The sensitivity of the model in predicting non-response to IVIG was 77.0%, specificity was 65.7%, and the area under the ROC curve was 0.746 (95%CI: 0.688-0.805).

CONCLUSIONS

The predictive scoring model based on PLR, Hb, serum creatinine, AST, and PLT demonstrates good predictive performance for non-response to IVIG in children with KD in the Sichuan region and can serve as a reference for clinical decision-making.

Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning

BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease (KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been employed to develop IVIG non-responder prediction models; however, their validation and reproducibility remain unverified. This study aimed to develop a predictive scoring system for identifying IVIG nonresponders and rigorously test the accuracy and reliability of this system. METHODS: The study included an exposure group of 228 IVIG non-responders and a control group of 997 IVIG responders. Subsequently, a predictive machine learning model was constructed. The Shizuoka score, including variables such as the "initial treatment date" (cutoff: < 4 days), sodium level (cutoff: < 133 mEq/L), total bilirubin level (cutoff: ≥ 0.5 mg/dL), and neutrophil-to-lymphocyte ratio (cutoff: ≥ 2.6), was established. Patients meeting two or more of these criteria were grouped as high-risk IVIG non-responders. Using the Shizuoka score to stratify IVIG responders, propensity score matching was used to analyze 85 patients each for IVIG and IVIG-added prednisolone treatment in the high-risk group. In the IVIG plus prednisolone group, the IVIG non-responder count significantly decreased (p < 0.001), with an odds ratio of 0.192 (95% confidence interval 0.078-0.441). CONCLUSIONS: Intravenous immunoglobulin non-responders were predicted using machine learning models and validated using propensity score matching. The initiation of initial IVIG-added prednisolone treatment in the high-risk group identified by the Shizuoka score, crafted using machine learning models, appears useful for predicting IVIG non-responders.

Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data

BACKGROUND

About 10-20% of Kawasaki disease (KD) patients are resistant to the initial infusion of intravenous immunoglobin (IVIG). The aim of this study was to assess whether IVIG resistance in KD patients could be predicted using standard clinical and laboratory features.

METHODS

Data were from two cohorts: a Korean cohort of 7101 KD patients from 2015 to 2017 and a cohort of 649 KD patients from San Diego enrolled from 1998 to 2021. Features included laboratory values, the worst Z-score from the initial echocardiogram or during hospitalization, and the five clinical KD signs at presentation.

RESULTS

Five machine learning models achieved a maximum median AUC of 0.711 [IQR: 0.706-0.72] in the Korean cohort and 0.696 [IQR: 0.609-0.722] in the San Diego cohort during stratified 10-fold cross-validation using significant laboratory features identified from univariate analysis. Adding the Z-score, KD clinical signs, or both did not considerably improve the median AUC in either cohort.

CONCLUSIONS

Using commonly measured clinical laboratory data alone or in conjunction with echocardiographic findings and clinical features is not sufficient to predict IVIG resistance. Further attempts to predict IVIG resistance will need to incorporate additional data such as transcriptomics, proteomics, and genetics to achieve meaningful predictive utility.

IMPACT

We demonstrated that laboratory, echocardiographic, and clinical findings cannot predict intravenous immunoglobin (IVIG) resistance to a clinically meaningful extent using machine learning in a homogenous Asian or ethnically diverse population of patients with Kawasaki disease (KD). Visualizing these features using uniform manifold approximation and projection (UMAP) is an important step to evaluate predictive utility in a qualitative manner. Further attempts to predict IVIG resistance in KD patients will need to incorporate novel biomarkers or other specialized features such as genetic differences or transcriptomics to be clinically useful.

A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease

INTRODUCTION

In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.

OBJECTIVE

To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model.

METHODS

A retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the Shapley additive explanation results.

RESULTS

Light gradient boosting machine model accurately predicted IVIG resistance (area under the receiver operating characteristic curve (AUC), 0.78; sensitivity, 0.50; specificity, 0.88). Next, using top three influential features (days of illness at initial therapy, serum levels of C-reactive protein, and total cholesterol), we designed a simple scoring system. In spite of its simplicity, it predicted IVIG resistance (AUC, 0.72; sensitivity, 0.49; specificity, 0.82) as accurately as machine learning models. Moreover, accuracy of our scoring system with three clinical features was almost identical to that of Gunma score with seven clinical features (AUC, 0.73; sensitivity, 0.53; specificity, 0.83), a well-known logistic regression scoring model.

CONCLUSION

A simple scoring system based on the findings in machine learning seems to be a useful tool to accurately predict IVIG resistance in KD patients.

Kawasaki disease coronary artery lesions prediction with monocyte-to-high-density lipoprotein ratio

OBJECTIVE

The aim of this study was to evaluate the predictive value of the monocyte-to-high-density lipoprotein ratio (MHR) in Kawasaki disease (KD) complicated with coronary artery lesions (CALs) and to construct a nomogram prediction model.

METHODS

The medical records of KD inpatients diagnosed in the Department of Pediatrics of Lanzhou University Second Hospital from May 2015 to September 2021 were retrospectively analyzed. ROC curves were applied to evaluate the predictive value of MHR in KD complicated with CALs, and logistic regression analysis was used to screen independent risk factors. We constructed a nomogram model and performed internal validation.

RESULTS

A total of 568 KD patients were enrolled in the study. MHR was significantly higher in KD patients complicated with CALs and was identified as an independent risk factor for CALs (OR: 1.604, 95% CI: 1.292-1.990). The area under the ROC curve for MHR in predicting CALs was 0.661. The C-index of the nomogram model constructed by incorporating MHR was 0.725 (95% CI: 0.682-0.768), and the calibration curve revealed good agreement between the predicted and actual probabilities.

CONCLUSIONS

MHR may not be suitable as a single biomarker to predict the occurrence of CALs, but the nomogram model constructed in combination with other independent risk factors had acceptable predictive performance.

IMPACT

The inflammatory response plays an important role in the pathogenesis of Kawasaki disease. The monocyte-to-high-density lipoprotein ratio is a novel systemic inflammation marker. The monocyte-to-high-density lipoprotein ratio is an independent risk factor for Kawasaki disease complicated with coronary artery lesions. The nomogram established by incorporating the monocyte-to-high-density lipoprotein ratio has satisfactory predictive performance for coronary artery lesion formation.

Comparison of IVIG resistance predictive models in Kawasaki disease

BACKGROUND

We aimed to compare the ten different scores (by Kobayashi, Egami, Harada, Formosa, Sano, Piram et al., Wu et al., Yang et al., Tan et al., and Kanai et al.) to assess their performance in predicting IVIG resistance in Turkish children.

METHODS

Complete and incomplete KD patients diagnosed with KD at Hacettepe University between June 2007 and September 2019 were evaluated retrospectively.

RESULTS

A total of 129 patients, 79 boys (61.2%), with a median age 36 (IQR 19.5-57.0) months were evaluated. Sixteen patients (12.4%) had IVIG resistance. Sensitivity was low for all the ten scores. Tan, Sano, and Egami predictive models had the highest specificity (97.3, 89.4, 86.7%, respectively). Almost all scoring systems distinguished the group of patients with low risk for IVIG resistance but could not differentiate IVIG-resistant patients. Multivariate analysis for the laboratory features showed that platelet count <300 × 10⁹/L and GGT serum levels were independent risk factors for IVIG resistance (OR: 3.896; 95% CI: 1.054-14.404; p = 0.042 and OR: 1.008; 95% CI: 1.001-1.015; p = 0.050).

CONCLUSIONS

The current scoring systems had a low sensitivity for predicting the risk for IVIG resistance in Turkish children. On the other hand, increased serum GGT levels and low platelet count were risk factors for predicting IVIG resistance.

IMPACT

Intravenous immunoglobulin (IVIG) resistance may be observed in 10-20% of patients diagnosed with Kawasaki disease. Coronary artery involvement is more frequent in IVIG-resistant patients. It is important to predict the patients who might develop IVIG resistance to improve prognosis. The performance of the IVIG resistance predictive models in Kawasaki disease in our population is limited due to the low sensitivity.

Value of blood inflammatory markers for predicting intravenous immunoglobulin resistance in Kawasaki disease: A systematic review and meta-analysis

BACKGROUND

Previous studies have assessed the diagnostic accuracy of blood inflammatory markers like neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP), and CRP to albumin ratio (CAR) to predict the resistant Kawasaki disease (KD). The aim of the current meta-analysis and systematic review is to compare the prognostic ability of these inflammatory markers to predict the resistance to IVIG in patients with Kawasaki disease.

METHODS

A systematic search of online academic databases and search engines such as EMBASE, PubMed Central, MEDLINE, Cochrane library, Google Scholar, and ScienceDirect was conducted for papers that report the diagnostic accuracy of inflammatory markers for resistant KD. Meta-analysis was performed using STATA software.

RESULTS

Twenty-two studies met the inclusion criteria. Pooled sensitivity and specificity of NLR as a predictor of resistant Kawasaki disease was 72% (95% CI: 62%, 80%) and 71% (95% CI: 63%, 78%), with AUC of 0.77 for PLR was 60% (95% CI: 50%, 69%) and 68% (95% CI: 61%, 75%), with area under the curve (AUC) of 0.69. Pooled sensitivity and specificity of CRP was 75% (95% CI: 68%, 81%) and 66% (95% CI: 55%, 76%), respectively, with an AUC value of 0.78. Pooled sensitivity and specificity of combined NLR and PLR was 58% (95% CI: 46%, 69%) and 73% (95% CI: 65%, 79%), respectively, with an AUC value of 0.72.

CONCLUSION

Our study found that NLR, CRP, PLR, and combined NLR/PLR have a good prognostic value in patients with resistant Kawasaki disease, with moderate to high sensitivity and specificity. More research on the accuracy of these indexes in multiple combinations is needed.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022322165].

Anti-inflammatory Treatment of Kawasaki Disease: Comparison of Current Guidelines and Perspectives

Kawasaki disease (KD), an acute, generalized vasculitis, is associated with an increased risk of coronary heart disease and is the most common cause of acquired heart disease in childhood. The incidence of KD is increasing worldwide. There are numerous international treatment guidelines. Our study aims to perform the first one so far comparison of them. While the gold standard therapy remains still the same (intravenous immunoglobulins and aspirin), there is currently a lack of evidence for choosing optimal treatment for high-risk patients and refractory KD. In this review, we also discuss the treatment of complications of KD and Kawasaki-like phenotypes, present an anti-inflammatory treatment in the light of new scientific data, and present novel potential therapeutic targets for KD.

The immune landscape of SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Children (MIS-C) from acute disease to recovery

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Deep immune profiling showed acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells, with increased frequencies of B-cell plasmablasts and double-negative B-cells. Post treatment samples from the same patients, taken during symptom resolution, identified recovery-associated immune features including increased monocyte CD163 levels, emergence of a new population of immature neutrophils and, in some patients, transiently increased plasma arginase. Plasma profiling identified multiple features shared by MIS-C, Kawasaki Disease and COVID-19 and that therapeutic inhibition of IL-6 may be preferable to IL-1 or TNF-α. We identified several potential mechanisms of action for IVIG, the most commonly used drug to treat MIS-C. Finally, we showed systemic complement activation with high plasma C5b-9 levels is common in MIS-C suggesting complement inhibitors could be used to treat the disease.

Prognostic value of pretreatment prognostic nutritional index in intravenous immunoglobulin-resistant Kawasaki disease

The aim of the present study was to investigate the potential predictive significance of pretreatment prognostic nutritional index (PNI) in intravenous immunoglobulin (IVIG) resistant Kawasaki disease (KD). The PNI, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were analyzed in 1257 eligible patients with KD. Receiver operating curve analysis was used to explore the prediction accuracy for IVIG-resistant KD. The optimal cut-off values were identified as 49.5 for PNI, 3.58 for NLR and 164.00 for PLR, respectively. Lower pretreatment PNI (< 49.5) was demonstrated to be associated with lower age, serum sodium levels and platelet counts, and with a higher incidence of IVIG resistance and higher C-reactive protein levels. There was a significantly negative association between the PNI and NLR, and PLR. Univariate and multivariate analyses revealed that PNI, NLR and PLR were independent predictive factors for IVIG resistance. The discriminatory ability of PNI was not inferior to NLR, PLR and their combination (NLR > 3.58 and PLR > 164) for predicting IVIG resistance, respectively. Pretreatment PNI may serve as a novel surrogate independent predictor for IVIG-resistant KD.

Utilizing Network Pharmacology to Explore the Possible Mechanism of Coptidis Rhizoma in Kawasaki Disease

Background: The purpose of the research is to identify the main active ingredients in Coptidis Rhizoma (CR) and explore the possible molecular mechanisms in the treatment of Kawasaki disease (KD). Materials and Methods: A total of 58 children with KD were randomly divided into a control group and a Berberine treatment group. The therapeutic indicators of the two groups before and after treatment were compared. Then, compounds and drug targets of CR from the TCMSP, SWISS, SEA, and the STITCH were collected, and targeted KD genes were retrieved from the DisGeNET, DrugBank, and GeneCards databases. The network pharmacology approach involved network construction, target prediction, and module analysis. GO and KEGG enrichment analysis were performed to investigate the possible pathways related to CR for KD treatments. Finally, protein expression was determined to verify the core targets using Western blotting in the cell experiment. Results: In total, nine compounds, 369 relative drug targets, and 624 KD target genes were collected in the above database. The network analysis revealed that 41 targets might be the therapeutic targets of CR on KD. GO and KEGG enrichment analysis revealed that the biological processes, namely, response to hormone, response to inorganic substance, and enzyme-linked receptor protein signaling pathway, and Pathways in cancer, Toll-like receptor signaling pathway, and Pancreatic cancer are the most significant. Protein expression of CASP3, PTGS2, and SRC was upregulated and AKT1 and ERK were downregulated. Conclusion: We provided useful resources to understand the molecular mechanism and the potential targets for novel therapy of KD.