scholarly journals Prognostic value and predication model of microvascular invasion in patients with intrahepatic cholangiocarcinoma: a multicenter study from China

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yifan Chen ◽  
Hongzhi Liu ◽  
Jinyu Zhang ◽  
Yijun Wu ◽  
Weiping Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Intrahepatic Cholangiocarcinoma ◽  
Curative Resection ◽  
Survival Curves ◽  
Tumor Number ◽  
Good Consistency ◽  
Before And After ◽  
Preoperative Image ◽  
Independent Risk Factors

Abstract Background At present, hepatectomy is still the most common and effective treatment method for intrahepatic cholangiocarcinoma (ICC) patients. However, the postoperative prognosis is poor. Therefore, the prognostic factors for these patients require further exploration. Whether microvascular invasion (MVI) plays a crucial role in the prognosis of ICC patients is still unclear. Moreover, few studies have focused on preoperative predictions of MVI in ICC patients. Methods Clinicopathological data of 704 ICC patients after curative resection were retrospectively collected from 13 hospitals. Independent risk factors were identified by the Cox or logistic proportional hazards model. In addition, the survival curves of the MVI-positive and MVI-negative groups before and after matching were analyzed. Subsequently, 341 patients from a single center (Eastern Hepatobiliary Hospital) in the above multicenter retrospective cohort were used to construct a nomogram prediction model. Then, the model was evaluated by the index of concordance (C-Index) and the calibration curve. Results After propensity score matching (PSM), Child-Pugh grade and MVI were independent risk factors for overall survival (OS) in ICC patients after curative resection. Major hepatectomy and MVI were independent risk factors for recurrence-free survival (RFS). The survival curves of OS and RFS before and after PSM in the MVI-positive groups were significantly different compared with those in the MVI-negative groups. Multivariate logistic regression results demonstrated that age, gamma-glutamyl transpeptidase (GGT), and preoperative image tumor number were independent risk factors for the occurrence of MVI. Furthermore, the prediction model in the form of a nomogram was constructed, which showed good prediction ability for both the training (C-index = 0.7622) and validation (C-index = 0.7591) groups, and the calibration curve showed good consistency with reality. Conclusion MVI is an independent risk factor for the prognosis of ICC patients after curative resection. Age, GGT, and preoperative image tumor number were independent risk factors for the occurrence of MVI in ICC patients. The prediction model constructed further showed good predictive ability in both the training and validation groups with good consistency with reality.

scholarly journals Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chunnian Ren ◽  
Chun Wu ◽  
Zhengxia Pan ◽  
Quan Wang ◽  
Yonggang Li
Keyword(s):  
Risk Factors ◽  
Cardiopulmonary Bypass ◽  
Prediction Model ◽  
Congenital Heart ◽  
Bypass Surgery ◽  
Pulmonary Infection ◽  
Regression Analyses ◽  
Cardiopulmonary Bypass Surgery ◽  
Independent Risk Factors ◽  
Individualized Prediction

Abstract Objectives The occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis. Methods Patients who underwent CHD surgery from October 2012 to December 2017 in the Children’s Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test. Results The univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability. Conclusions Identified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.

scholarly journals Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bocheng Peng ◽  
Rui Min ◽  
Yiqin Liao ◽  
Aixi Yu
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Prediction Model ◽  
Predictive Factors ◽  
Diabetic Foot ◽  
Univariate Analysis ◽  
Foot Ulcer ◽  
Diabetic Foot Ulcer ◽  
Single Factor ◽  
Independent Risk Factors

Objective. To determine the novel proposed nomogram model accuracy in the prediction of the lower-extremity amputations (LEA) risk in diabetic foot ulcer (DFU). Methods and Materials. In this retrospective study, data of 125 patients with diabetic foot ulcer who met the research criteria in Zhongnan Hospital of Wuhan University from January 2015 to December 2019 were collected by filling in the clinical investigation case report form. Firstly, univariate analysis was used to find the primary predictive factors of amputation in patients with diabetic foot ulcer. Secondly, single factor and multiple factor logistic regression analysis were employed to screen the independent influencing factors of amputation introducing the primary predictive factors selected from the univariate analysis. Thirdly, the independent influencing factors were applied to build a prediction model of amputation risk in patients with diabetic foot ulcer by using R4.3; then, the nomogram was established according to the selected variables visually. Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. Results. 7 primary predictive factors were selected by univariate analysis from 21 variables, including the course of diabetes, peripheral angiopathy of diabetic (PAD), glycosylated hemoglobin A1c (HbA1c), white blood cells (WBC), albumin (ALB), blood uric acid (BUA), and fibrinogen (FIB); single factor logistic regression analysis showed that albumin was a protective factor for amputation in patients with diabetic foot ulcer, and the other six factors were risk factors. Multivariate logical regression analysis illustrated that only five factors (the course of diabetes, PAD, HbA1c, WBC, and FIB) were independent risk factors for amputation in patients with diabetic foot ulcer. According to the area under curve (AUC) of ROC was 0.876 and corrected calibration curve of the nomogram displayed good fitting ability, the model established by these 5 independent risk factors exhibited good ability to predict the risk of amputation. The decision analysis curve (DCA) indicated that the nomogram model was more practical and accurate when the risk threshold was between 6% and 91%. Conclusion. Our novel proposed nomogram showed that the course of diabetes, PAD, HbA1c, WBC, and FIB are the independent risk factors of amputation in patients with DFU. This prediction model was well developed and behaved a great accurate value for LEA so as to provide a useful tool for screening LEA risk and preventing DFU from developing into amputation.

scholarly journals A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Joeri W van Straalen ◽  
Gabriella Giancane ◽  
Yasmine Amazrhar ◽  
Nikolay Tzaribachev ◽  
Calin Lazar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Protective Factor ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Information Criterion ◽  
Logistic Regression Models ◽  
Adjusted Risk ◽  
Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

scholarly journals Preoperative leukocytosis and the resection severity index are independent risk factors for survival in patients with intrahepatic cholangiocarcinoma

2020 ◽  
Vol 405 (7) ◽  
pp. 977-988
Author(s):  
Oliver Beetz ◽  
Clara A. Weigle ◽  
Sebastian Cammann ◽  
Florian W. R. Vondran ◽  
Kai Timrott ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prognostic Factors ◽  
Postoperative Complications ◽  
Intrahepatic Cholangiocarcinoma ◽  
Postoperative Morbidity ◽  
Severity Index ◽  
The Body ◽  
Morbidity And Mortality ◽  
Independent Risk Factors ◽  
Risk Factors For Survival

Abstract Purpose The incidence of intrahepatic cholangiocarcinoma is increasing worldwide. Despite advances in surgical and non-surgical treatment, reported outcomes are still poor and surgical resection remains to be the only chance for long-term survival of affected patients. The identification and validation of prognostic factors and scores, such as the recently introduced resection severity index, for postoperative morbidity and mortality are essential to facilitate optimal therapeutic regimens. Methods This is a retrospective analysis of 269 patients undergoing resection of histologically confirmed intrahepatic cholangiocarcinoma between February 1996 and September 2018 at a tertiary referral center for hepatobiliary surgery. Regression analyses were performed to evaluate potential prognostic factors, including the resection severity index. Results Median postoperative follow-up time was 22.93 (0.10–234.39) months. Severe postoperative complications (≥ Clavien-Dindo grade III) were observed in 94 (34.9%) patients. The body mass index (p = 0.035), the resection severity index (ASAT in U/l divided by Quick in % multiplied by the extent of liver resection graded in points; p = 0.006), additional hilar bile duct resection (p = 0.005), and number of packed red blood cells transfused during operation (p = 0.036) were independent risk factors for the onset of severe postoperative complications. Median Kaplan-Meier survival after resection was 27.63 months. Preoperative leukocytosis (p = 0.003), the resection severity index (p = 0.005), multivisceral resection (p = 0.001), and T stage ≥ 3 (p = 0.013) were identified as independent risk factors for survival. Conclusion Preoperative leukocytosis and the resection severity index are useful variables for preoperative risk stratification since they were identified as significant predictors for postoperative morbidity and mortality, respectively.

Risk factors of hospital mortality after initial hepatic resection among patients with primary hepatocellular carcinoma.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 352-352
Author(s):  
Hong-Gui Qin ◽  
Jian-Hong Zhong ◽  
Yan-Yan Wang ◽  
Shi-Dong Lu ◽  
Bang-De Xiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Multivariate Analysis ◽  
Portal Hypertension ◽  
Major Hepatectomy ◽  
Postoperative Mortality ◽  
Primary Hepatocellular Carcinoma ◽  
Model Parameters ◽  
Tumor Number ◽  
Independent Risk Factors

352 Background: Hepatectomy is widely used to treat patients with hepatocellular carcinoma (HCC), even those with intermediate and advanced disease. Despite its well-demonstrated clinical efficacy in many patients, postoperative mortality is an inevitable problem. This study aims to investigate the risk factors of mortality after hepatectomy. Methods: A consecutive sample of 1518 patients with HCC who underwent initial hepatectomy from January 1, 2004 to October 31, 2013 were retrospective analyzed. Multivariate analysis to identify independent risk factors of postoperative mortality was carried out using the Cox proportional hazards model. Parameters for multivariate analyses included age, gender, tumor size, tumor number, preoperative serum albumin, alanine aminotransferase, total bilirubin, α-fetoprotein, prothrombin time, tumor capsule, macrovascular invasion, portal hypertension, diabetes mellitus, ascites, major hepatectomy, surgical time, blood loss, blood transfusion, and clamping portal hepatis time. Results: A total of 18 (1.19%) and 45 (2.96%) patients died within 30 and 90 days after hepatectomy, respectively. Multivariate analysis revealed that tumor number ( ≥ 4), macrovascular invasion, and major hepatectomy were independent risk factors of 30 and 90 days mortality, while portal hypertension was also an independent risk factor of 90 days mortality. Conclusions: Among HCC patients with tumor number equal or more than four, macrovascular invasion, portal hypertension, or underwent major hepatectomy, intensive postoperative care management are in particular.

scholarly journals Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection

2021 ◽  
Vol 8 ◽  
Author(s):  
Luming Zhang ◽  
Feng Zhang ◽  
Fengshuo Xu ◽  
Zichen Wang ◽  
Yinlong Ren ◽  
...  
Keyword(s):  
Risk Factors ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Prediction Model ◽  
Stepwise Logistic Regression ◽  
Distribution Width ◽  
Sepsis Risk ◽  
Lemeshow Test ◽  
Independent Risk Factors

Background: Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients.Methods: Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).Results: This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability.Conclusion: This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.

scholarly journals Features for deep vein thrombosis in the uninjured limbs of lower extremity fractures: a retrospective study 1454 patients

2020 ◽  
Author(s):  
Shuang-Wei Qu ◽  
Yu-Xuan Cong ◽  
Peng-Fei Wang ◽  
Chen Fei ◽  
Zhi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lower Extremity ◽  
Doppler Ultrasonography ◽  
Vein Thrombosis ◽  
Before And After ◽  
Independent Risk Factors ◽  
And Gender ◽  
Deep Vein ◽  
Actual Incidence ◽  
D Dimer

Abstract Objective: The purpose of this study was to investigate the incidence of and independent risk factors for deep venous thrombosis (DVT) in the uninjured limb, before and after operation, in patients with lower extremity fractures.Methods: We collected the clinical data of patients with lower extremities fractures who presented at Xi’an Honghui Hospital between 1 July, 2015 and 31 October, 2017. Doppler ultrasonography was used to diagnose the DVT. Patients were examined pre- and postoperatively. The patients were then divided into a thrombosis group and a no thrombosis group according to the preoperative and postoperative ultrasonography results. The thrombosis group was defined as patients admitted to our hospital with DVT in the uninjured limb and the no thrombosis group was defined as patients without DVT in the uninjured limb.Results: This study enrolled 1454 patients who met the inclusion criteria. The incidence of preoperative DVT in the uninjured limb was 9.63% whereas the postoperative incidence was 20.29%. Age (OR=0.965, 95 CI%: 0.954 - 0.977; P=0.000) and gender (OR=0.667, 95% CI: 0.451-0.986, P=0.042) were independent risk factors for preoperative DVT in the uninjured limb. Blood loss (OR=0.997, 95 CI%: 0.995-1.000; P=0.020), D-dimer levels at admission (OR=0.941, 95 CI%: 0.887-0.999; P=0.045), and postoperative day 5 D-dimer levels (OR=0.889, 95 CI%: 0.819-0.965; P=0.005), were independent risk factors for postoperative DVT in the uninjured limb.Conclusion: In conclusion, the actual incidence of DVT in the uninjured lower extremity after fracture may currently be underestimated and should be pay more attention.

scholarly journals Deep Vein Thrombosis in the Uninjured Limbs of Lower Extremity Fractures: A Retrospective Study of 1454 Patients

2020 ◽  
Author(s):  
Shuang-Wei Qu ◽  
Yu-Xuan Cong ◽  
Peng-Fei Wang ◽  
Chen Fei ◽  
Zhi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lower Extremity ◽  
Doppler Ultrasonography ◽  
Vein Thrombosis ◽  
Before And After ◽  
Independent Risk Factors ◽  
And Gender ◽  
Deep Vein ◽  
Actual Incidence ◽  
D Dimer

Abstract Objective: The purpose of this study was to investigate the incidence of deep venous thrombosis (DVT) in the uninjured limb, before and after operation, in patients with lower extremity fractures.Methods: We collected the clinical data of patients with lower extremities fractures who presented at Xi’an Honghui Hospital between 1 July, 2015 and 31 October, 2017. Doppler ultrasonography was used to diagnose the DVT. Patients were examined pre- and postoperatively. The patients were then divided into a thrombosis group and a no thrombosis group according to the preoperative and postoperative ultrasonography results. The thrombosis group was defined as patients admitted to our hospital with DVT in the uninjured limb and the no thrombosis group was defined as patients without DVT in the uninjured limb. Results: This study enrolled 1454 patients who met the inclusion criteria. The incidence of preoperative DVT in the uninjured limb was 9.63% whereas the postoperative incidence was 20.29%. Age (OR=0.965, 95 CI%: 0.954 - 0.977; P≤0.001) and gender (OR=0.667, 95% CI: 0.451-0.986, P=0.042) were independent risk factors for preoperative DVT in the uninjured limb. Blood loss (OR=0.997, 95 CI%: 0.995-1.000; P=0.020), D-dimer levels at admission (OR=0.941, 95 CI%: 0.887-0.999; P=0.045), and postoperative day 5 D-dimer levels (OR=0.889, 95 CI%: 0.819-0.965; P=0.005), were independent risk factors for postoperative DVT in the uninjured limb.Conclusion: In conclusion, the actual incidence of DVT in the uninjured lower extremity after fracture may currently be underestimated and should be pay more attention.

scholarly journals The Role of Lymph Node Dissection in Intrahepatic Cholangiocarcinoma: A Multicenter Retrospective Study. 

2021 ◽  
Author(s):  
Hanjie Hu ◽  
Gang Xu ◽  
Shunda Du ◽  
Zhiwen Luo ◽  
Hong Zhao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Lymph Node Dissection ◽  
Intrahepatic Cholangiocarcinoma ◽  
Surgical Margin ◽  
Disease Free Survival ◽  
Positive Surgical Margin ◽  
Node Dissection ◽  
Independent Risk Factors

Abstract BackgroundLymph node dissection (LND) is of great significance in intrahepatic cholangiocarcinoma (ICC). Although the National Comprehensive Cancer Network (NCCN) guidelines recommend routine LND in ICC, the effects of LND remains controversial. This study aimed to explore the role and application of LND in ICC.MethodsPatients were identified in two Chinese academic centers. Inverse probability of treatment weighting (IPTW) was used to reduce bias. Kaplan–Meier curves and Cox proportional hazards models were used to compare overall survival (OS) and disease-free survival (DFS).ResultsOf 232 patients, 177 (76.3%) underwent LND, and 71 (40.1%) had metastatic lymph nodes. A minimum of 6 lymph nodes were dissected in 66 patients (37.3%). LND did not improve the prognosis of ICC. LNM >3 may have worse OS and DFS than LNM 1-3, especially in the LND >=6 group. For nLND patients, the adjuvant treatment group had better OS and DFS.ConclusionsCA 19-9, CEA, operative time, positive surgical margin, and T stage were independent risk factors for OS; CEA and differentiation were independent risk factors for DFS. LND has no definite predictive effect on prognosis. Patients with 4 or more LNMs may have a worse prognosis than patients with 1-3 LNMs. Adjuvant therapy may benefit patients of nLND.

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

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.

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