Dataset Information

Predictive model based on gene and laboratory data for intravenous immunoglobulin resistance in Kawasaki disease in a Chinese population.

ABSTRACT:

Background

Here, we investigated the predictive efficiency of a newly developed model based on single nucleotide polymorphisms (SNPs) and laboratory data for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) in a Chinese population.

Methods

Data relating to children with KD were acquired from a single center between December 2015 and August 2019 and used to screen target SNPs. We then developed a predictive model of IVIG resistance using previous laboratory parameters. We then validated our model using data acquired from children with KD attending a second center between January and December 2019.

Results

Analysis showed that rs10056474 GG, rs746994GG, rs76863441GT, rs16944 (CT/TT), and rs1143627 (CT/CC), increased the risk of IVIG-resistance in KD patients (odds ratio, OR > 1). The new predictive model, which combined SNP data with a previous model derived from laboratory data, significantly increased the area under the receiver-operator-characteristic curves (AUC) (0.832, 95% CI: 0.776-0.878 vs 0.793, 95%CI:0.734-0.844, P < 0.05) in the development dataset, and (0.820, 95% CI: 0.730-0.889 vs 0.749, 95% CI: 0.652-0.830, P < 0.05) in the validation dataset. The sensitivity and specificity of the new assay were 65.33% (95% CI: 53.5-76.0%) and 86.67% (95% CI: 80.2-91.7%) in the development dataset and 77.14% (95% CI: 59.9-89.6%) and 86.15% (95% CI: 75.3-93.5%) in the validation dataset.

Conclusion

Analysis showed that rs10056474 and rs746994 in the SMAD5 gene, rs76863441 in the PLA2G7 gene, and rs16944 or rs1143627 in the interleukin (IL)-1B gene, were associated with IVIG resistant KD in a Chinese population. The new model combined SNPs with laboratory data and improved the predictve efficiency of IVIG-resistant KD.

SUBMITTER: Meng L

PROVIDER: S-EPMC8236184 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAbout 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.MethodsData 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.ResultsFive 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.ConclusionsUsing 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.ImpactWe 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.

Project description:Background: The prediction of intravenous immunoglobulin (IVIG) resistance and cardiovascular complications are critically clinical issues in Kawasaki disease (KD). This prospective study firstly aimed to determine the predictive ability of the systemic immune inflammation index (SII) for IVIG resistance and cardiovascular complications and compare the prognostic accuracy of SII with that of neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR). Methods: Patients with KD were divided into different groups according to the presence of IVIG resistance or cardiovascular complications (coronary artery lesions, valve regurgitation, myocarditis, pericardial effusion, and Kawasaki disease shock syndrome [KDSS]). The clinical and laboratory parameters were compared. Further analysis stratified by platelet level was performed. Multivariate logistic regression analysis was used to identify predictors for IVIG resistance and cardiovascular complications. The receiver operating characteristic (ROC) curve was applied to assess and compare the ability of SII, NLR, and PLR for predicting IVIG resistance and cardiovascular complications. Results: SII was significantly higher in KD patients with IVIG-resistance, myocarditis, valve regurgitation, and KDSS. It was identified as an independent risk factor for IVIG resistance, myocarditis, and valve regurgitation. For KD patients with thrombocytopenia, there were no significant differences in SII between KD patients with IVIG resistance/cardiovascular complications and those without. The best cutoff values of SII for IVIG resistance, myocarditis, valve regurgitation, and KDSS prediction in the whole cohort were 1331.4 × 109, 1368.6 × 109, 1002.4 × 109, and 1485.4 × 109, with a corresponding sensitivity of 0.525, 0.614, 0.754, and 0.670, a specificity of 0.711, 0.723, 0.584, and 0.730, respectively. The predictive value of SII for both IVIG resistance and cardiovascular complications were not superior to that of NLR. Conclusion: Although the parameter of SII may predict IVIG resistance, myocarditis, valve regurgitation, and KDSS in KD as a single parameter, its predictive ability was not good enough and not superior to NLR. SII might not be applicable in patients with KD having thrombocytopenia.

Project description:Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms. Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models. Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067-1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270-1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008-1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996-1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575). Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.