Prognostic Value of the Systemic Immune-Inflammation Index and Prognostic Nutritional Index in Patients With Medulloblastoma Undergoing Surgical Resection

A Low Prognostic Nutritional Index Level, an Independent Predictor for Postoperative Intra-abdominal Abscess After Laparoscopic Appendectomy

BACKGROUND

Intra-abdominal abscess (IAA) is a very common postoperative complication after laparoscopic appendectomy and is associated with poor prognosis.

MATERIALS AND METHODS

This study retrospectively analyzed elderly patients who underwent laparoscopic appendectomy from 2015 to 2024, examining demographic, clinical, and laboratory data to identify IAA risk factors. Independent risk factors for IAA were screened and finally identified by multivariate logistic regression analysis.

RESULTS

A total of 37 patients developed IAA within postoperative 30 days, with an incidence of 9.6% (37/385). Receiver operating characteristic (ROC) curve analysis indicated white blood cell (cut-off value: 13.35, sensitivity: 54.60%, specificity: 70.27%, P=0.003) and Prognostic Nutritional Index (PNI) (cut-off value: 41.95, sensitivity: 72.41%, specificity: 59.46%, P<0.0001) were 2 predictors for IAA with an area under the curve of 0.649 and 0.727, respectively. Based on the multivariate analysis, perforated appendicitis (odds ratio: 2.48, 95% CI: 1.15-5.32, P=0.021) and a lower PNI score (odds ratio: 3.44, 95% CI: 1.49-7.58, P=0.002) were 2 independent risk factors for IAA in elderly patients after laparoscopic appendectomy.

CONCLUSIONS

Elderly patients with perforated appendicitis and a PNI score <41.95 have higher risks for IAA after laparoscopic appendectomy.

A novel nomogram based on inflammatory-nutritional biomarkers for gallbladder cancer after surgical resection

PURPOSE

Systemic inflammation and nutrition are vital for tumor progression. This study aimed to identify prognostic inflammation nutrition markers and develop a predictive nomogram for gallbladder cancer (GBC).

METHODS

A total of 123 patients with GBC who underwent surgical resection at the First Affiliated Hospital of Soochow University and Suzhou Kowloon Hospital were included in our study. The final prognostic variables were identified using univariate and multivariate analyses. A nomogram model was then established, and the consistency index (C-index), calibration curves, and Kaplan-Meier analysis were performed to evaluate the accuracy and discrimination of the nomogram. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) suggested that our nomogram had better predictive ability and clinical feasibility than a published model.

RESULTS

The cox regression analysis showed that carcinoembryonic antigen (CEA) > 4.580, albumin-bilirubin (ALBI) > -2.091, geriatric nutritional risk index (GNRI) < 90.83, T3-T4, and N2 are independent prognostic factors. A predictive nomogram was constructed with a C-index of 0.793. In the calibration curves, the nomogram-predicted 1-, 3-, and 5-year survival matched well with the actual survival. Kaplan-Meier analysis showed that the high-risk group had worse survival than the low-risk group (P < 0.001). Finally, our nomogram achieved better 1-, 3- and 5-year AUCs than an established model (0.871, 0.844, and 0.781 vs. 0.753, 0.750, and 0.693). DCA also confirmed that our model outperformed the established model.

CONCLUSIONS

In conclusion, our study revealed that CEA > 4.580, GNRI < 90.83, ALBI > -2.091, T3-T4 stage, and N2 were related to clinical outcomes of patients with GBC after surgical resection. The constructed nomogram has superior predictive ability and clinical practicality.

Association of systemic immune-inflammation index with malnutrition among Chinese hospitalized patients: a nationwide, multicenter, cross-sectional study

Background

Systemic immune-inflammation index (SII) is associated with increased risk in a wide range of illnesses. However, few studies have explored the associations between SII and the risk of malnutrition. Therefore, this study aimed to investigate the association between SII and malnutrition in a nationwide, multicenter, cross-sectional study involving Chinese hospitalized patients.

Design

From August 2020 to August 2021, a total of 40,379 hospitalized patients met the inclusion and exclusion criteria. Detailed demographic data, diagnoses, as well as physical and laboratory examination results were recorded. The diagnosis of malnutrition was used with two distinct methods: the Malnutrition Screening Tool 2002 (NRS 2002) + Global Leaders Initiative on Malnutrition (GLIM) criteria and the controlling nutritional status (CONUT) score. The risk factors for malnutrition were analyzed using binary logistic regression and multiple logistic regression to obtain odds ratios (OR) and 95% confidence intervals (CI). Restricted cubic spline (RCS), linear spline, and receiver operating characteristic (ROC) analysis were also used.

Results

The prevalence of malnutrition diagnosed by the two methods was 13.4% and 14.9%, respectively. In the NRS 2002 + GLIM diagnostic model, lnSII showed statistical significance between the malnutrition and non-malnutrition group (6.28 ± 0.78 vs. 6.63 ± 0.97, p < 0.001). A positive association was observed between higher SII and the risk of malnutrition in both before and after adjustment models compared to the first quartile (Q3 vs. Q1, OR = 1.27, 95%CI: 1.15-1.40; Q4 vs. Q1, OR = 1.83, 95%CI: 1.67-2.00). However, a significant reduction in prevalence was observed when SII was in the second quartile (Q2 vs. Q1, OR < 1), as indicated by a restricted cubic spline with a U trend (p for nonlinear <0.001). According to the CONUT score, the prevalence of individuals with normal nutritional status decreased with increasing SII, while the occurrence of three different degrees of malnutrition generally increased. The Kappa value between the two diagnostic methods was 0.23, and the merged data observed an area under the ROC curve of 0.73 (95%CI: 0.714-0.742).

Conclusion

The U-shaped association between SII and the prevalence of malnutrition was observed. Both lower and higher SII levels (either continuous or categorical variable) were significantly associated with an increased risk of malnutrition.

A nomogram model based on SII, AFR, and NLR to predict infectious complications of laparoscopic hysterectomy for cervical cancer

BACKGROUND

This study aimed to investigate the potential risk factors associated with postoperative infectious complications following laparoscopic hysterectomy for cervical cancer and to develop a prediction model based on these factors.

METHODS

This study enrolled patients who underwent selective laparoscopic hysterectomy for cervical cancer between 2019 and 2024. A multivariate regression analysis was performed to identify independent risk factors associated with postoperative infectious complications. A nomogram prediction model was subsequently constructed and evaluated using R software.

RESULTS

Out of 301 patients were enrolled and 38 patients (12.6%) experienced infectious complications within one month postoperatively. Six variables were independent risk factors for postoperative infectious complications: age ≥ 60 (OR: 3.06, 95% confidence interval (CI): 1.06-8.79, P = 0.038), body mass index (BMI) ≥ 24.0 (OR: 3.70, 95%CI: 1.4-9.26, P = 0.005), diabetes (OR: 2.91, 95% CI: 1.10-7.73, P = 0.032), systemic immune-inflammation index (SII) ≥ 830 (OR: 6.95, 95% CI: 2.53-19.07, P < 0.001), albumin-to-fibrinogen ratio (AFR) < 9.25 (OR: 4.94, 95% CI: 2.02-12.07, P < 0.001), and neutrophil-to-lymphocyte ratio (NLR) ≥ 3.45 (OR: 7.53, 95% CI: 3.04-18.62, P < 0.001). Receiver operator characteristic (ROC) curve analysis indicated an area under the curve (AUC) of this nomogram model of 0.928, a sensitivity of 81.0%, and a specificity of 92.1%.

CONCLUSIONS

The nomogram model, incorporating age, BMI, diabetes, SII, AFR, and NLR, demonstrated strong predictive capabilities for postoperative infectious complications following laparoscopic hysterectomy for cervical cancer.

Prognostic nutritional index and modified frailty index, independent risk factors for recompression in elderly patients with osteoporotic vertebral compression fractures

BACKGROUND

To identify some clinical and laboratory independent risk factors for postoperative recompression among elderly osteoporotic vertebral compression fractures (OVCF) patients.

METHODS

A retrospective analysis was conducted on 287 elderly OVCF patients after percutaneous vertebroplasty (PVP). Relevant risk factors for recompression were screened and further analyzed through multivariate logistic regression.

RESULTS

Within postoperative 1 year, recompression had occurred in 72 patients, with an incidence of 25.1% (72/287). Multivariate logistic analysis indicated that mean spinal BMD <  - 2.85 (OR: 4.55, 95%CI 2.22-9.31, P < 0.001), ODI ≥ 68.05% (OR: 6.78, 95%CI 3.16-14.55, P < 0.001), PNI score < 43.1 (OR: 2.81, 95%CI 1.34-5.82, P = 0.005), and mFI score ≥ 0.225 (OR: 8.30, 95%CI 3.14-21.95, P < 0.001) were four distinct risk factors that independently contributed to postoperative recompression.

CONCLUSIONS

Spinal BMD, ODI, PNI and mFI independently predict recompression in OVCF patients after PVP treatment.

Systemic immune inflammation index and gastric cancer prognosis: A systematic review and meta‑analysis

The present study aimed to pool the available data on the associations between the systemic immune inflammation index (SII) and overall survival (OS) or recurrence-free survival (RFS) in patients with gastric cancer (GC). A systematic search was conducted in the PubMed, EMBASE and Scopus databases for observational studies, and a random effects model was used to conduct the statistical analysis. Pooled effect sizes were reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CI). Data from 30 studies (24 conducted in China) with follow-ups ranging between 15.5 and 65.6 months were analyzed. Patients with GC and high SII levels had poor OS (HR, 1.53; 95% CI, 1.34-1.75) and recurrence free survival (HR, 1.41; 95% CI, 1.17-1.70). These increased risks were present irrespective of the treatment strategy (surgical or non-surgical management), the sample size (<500 and ≥500) and the cut-off used to define high and low SII (<600 and ≥600 x109 cells/l). The results of this meta-analysis suggest that high pretreatment SII levels were associated with poor OS and RFS in patients with GC.

Evaluation of the Easy Albumin-Bilirubin Score as a Prognostic Tool for Mortality in Adult Trauma Patients in the Intensive Care Unit: A Retrospective Study

The easy albumin-bilirubin (EZ-ALBI) score is derived using the following equation: total bilirubin (mg/dL) - 9 × albumin (g/dL). This study aimed to determine whether the EZ-ALBI score predicted mortality risk in adult trauma patients in an intensive care unit (ICU). Data from a hospital's trauma database were retrospectively evaluated for 1083 adult trauma ICU patients (139 deaths and 944 survivors) between 1 January 2016 and 31 December 2021. Patients were classified based on the ideal EZ-ALBI cut-off of -26.5, which was determined via receiver operating characteristic curve analysis. The deceased patients' EZ-ALBI scores were higher than those of the surviving patients (-26.8 ± 6.5 vs. -30.3 ± 5.9, p = 0.001). Multivariate logistic analysis revealed that, in addition to age, the presence of end-stage renal disease, Glasgow Coma Scale scores, and injury severity scores, the EZ-ALBI score is an independent risk factor for mortality (odds ratio (OR), 1.10; 95% confidence interval (CI): 1.06-1.14; p = 0.001)). Compared with patients with EZ-ALBI scores < -26.5, those with scores ≥ -26.5 had a 2.1-fold higher adjusted mortality rate (adjusted OR, 2.14; 95% CI: 1.43-3.19, p = 0.001). In conclusion, the EZ-ALBI score is a substantial and independent predictor of mortality and can be screened to stratify mortality risk in adult trauma ICU patients.

Prognostic evaluation of system immune-inflammatory index and prognostic nutritional index in double expressor diffuse large B-cell lymphoma

Predicting MYC and BCL2 double-expressor lymphoma prognosis using the system immune-inflammatory index (SII) and prognostic nutritional index (PNI) (DEL). From January 2015 to December 2021, 281 diffuse large B-cell lymphoma (DLBCL) wax blocks were used to make tissue chips. Screening double expressor lymphoma (DEL) instances involved immunocytochemistry and fluorescence in situ hybridization. Academic analysis used clinicopathological characteristics and follow-up data. SII, PNI, and DEL prognosis were correlated using univariate and multivariate cox regression analysis. The median age of 78 DEL patients is 60 (range: 43-74). SII and PNI cut-off values of 603.5, 3.07, and 144 predict PFS and OS well. Lower SII is associated with longer PFS (HR for SII = 0.34, 95% CI 0.15-0.76, P = 0.006; HR for NLR = 0.46, 95% CI 0.22-0.99, P = 0.048; HR for PLR = 0.39, 95% CI 0.17-0.94, P = 0.025; LMR = 0.39, 95%, CI 0.17-0.94, P = 0.025) and OS (HR for SII = 0.16, 95% CI 0.05-0.51, P = 0.005; HR for PNI = 0.20, 95% CI 0.06-0.62, P = 0.002). SII and PNI are promising predictors for twofold expressor DLBCL. Combining these increase prediction accuracy.

Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?

Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.

Preoperative prognostic nutrition index can independently predict the 6-month prognosis of elderly patients undergoing neurosurgical clipping for aneurysmal subarachnoid hemorrhage

The number of elderly patients with aneurysmal subarachnoid hemorrhage (aSAH) is increasing annually. The prognostic nutritional index (PNI) is used as a novel and valuable prognostic marker for various neoplastic diseases and other critical illnesses. This study aimed to identify the short-term prognostic value of preoperative PNI in elderly patients who underwent neurosurgical clipping for aSAH. This retrospective study included elderly patients with aSAH who underwent neurosurgical clipping from January 2018 to December 2020. Clinical variables and 6-month outcomes were collected and compared. Epidemiological data and effect factors of prognosis were evaluated. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were used to evaluate the predictive value of preoperative PNI. Multiple logistic regression was performed to establish a nomogram. A total of 124 elderly patients were enrolled. Multivariate logistic regression analysis showed that preoperative PNI (odds ratio (OR), 0.779; 95% confidence interval (CI), 0.689-0.881; P < 0.001), Hunt-Hess grade (OR, 3.291; 95%CI, 1.816-5.966; P < 0.001), and hydrocephalus (OR, 9.423; 95%CI, 2.696-32.935; P < 0.001) were significant predictors. The area under the ROC curve of PNI was 0.829 (95% CI, 0.755-0.903; P < 0.001) with a sensitivity and specificity of 68.4% and 83.3%, respectively, and the cutoff value was 46.36. Patients with preoperative PNI of < 46.36 had a significantly unfavorable 6-months prognosis (F = 40.768, P < 0.001). Preoperative PNI is independently correlated with the 6-month prognosis in elderly patients who undergo neurosurgical clipping for aSAH.

Prognostic role of the pretreatment systemic immune-inflammation index in patients with glioma: A meta-analysis

Background

The systemic immune-inflammation index (SII) has been recognized as the indicator that reflects the status of immune responses. The SII is related to the prognostic outcome of many malignancies, whereas its role in gliomas is controversial. For patients with glioma, we, therefore, conducted a meta-analysis to determine if the SII has a prognostic value.

Methods

Studies relevant to this topic were searched from 16 October 2022 in several databases. In patients with glioma, the relation of the SII level with the patient prognosis was analyzed based on hazard ratios (HRs) as well as corresponding 95% confidence intervals (CIs). Moreover, subgroup analysis was conducted to examine a possible heterogeneity source.

Results

There were eight articles involving 1,426 cases enrolled in the present meta-analysis. The increased SII level predicted the dismal overall survival (OS) (HR = 1.81, 95% CI = 1.55–2.12, p &lt; 0.001) of glioma cases. Furthermore, an increased SII level also predicted the prognosis of progression-free survival (PFS) (HR = 1.87, 95% CI = 1.44–2.43, p &lt; 0.001) in gliomas. An increased SII was significantly associated with a Ki-67 index of ≥30% (OR = 1.72, 95% CI = 1.10–2.69, p = 0.017). However, a high SII was not correlated with gender (OR = 1.05, 95% CI = 0.78–1.41, p = 0.734), KPS score (OR = 0.64, 95% CI = 0.17–2.37, p = 0.505), or symptom duration (OR 1.22, 95% CI 0.37–4.06, p = 0.745).

Conclusion

There was a significant relation between an increased SII level with poor OS and the PFS of glioma cases. Moreover, patients with glioma with a high SII value have a positive relationship with a Ki-67 of ≥30%.

Development of a Machine Learning-Based Prediction Model for Chemotherapy-Induced Myelosuppression in Children with Wilms' Tumor

Purpose: Develop and validate an accessible prediction model using machine learning (ML) to predict the risk of chemotherapy-induced myelosuppression (CIM) in children with Wilms' tumor (WT) before chemotherapy is administered, enabling early preventive management. Methods: A total of 1433 chemotherapy cycles in 437 children with WT who received chemotherapy in our hospital from January 2009 to March 2022 were retrospectively analyzed. Demographic data, clinicopathological characteristics, hematology and blood biochemistry baseline results, and medication information were collected. Six ML algorithms were used to construct prediction models, and the predictive efficacy of these models was evaluated to select the best model to predict the risk of grade ≥ 2 CIM in children with WT. A series of methods, such as the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA) were used to test the model's accuracy, discrimination, and clinical practicability. Results: Grade ≥ 2 CIM occurred in 58.5% (839/1433) of chemotherapy cycles. Based on the results of the training and validation cohorts, we finally identified that the extreme gradient boosting (XGB) model has the best predictive efficiency and stability, with an AUROC of up to 0.981 in the training set and up to 0.896 in the test set. In addition, the calibration curve and the DCA showed that the XGB model had the best discrimination and clinical practicability. The variables were ranked according to the feature importance, and the five variables contributing the most to the model were hemoglobin (Hgb), white blood cell count (WBC), alkaline phosphatase, coadministration of highly toxic chemotherapy drugs, and albumin. Conclusions: The incidence of grade ≥ 2 CIM was not low in children with WT, which needs attention. The XGB model was developed to predict the risk of grade ≥ 2 CIM in children with WT for the first time. The model has good predictive performance and stability and has the potential to be translated into clinical applications. Based on this modeling and application approach, the extension of CIM prediction models to other pediatric malignancies could be expected.

A nomogram model based on the combination of the systemic immune-inflammation index and prognostic nutritional index predicts weight regain after laparoscopic sleeve gastrectomy

BACKGROUND

The high rate of weight regain after laparoscopic sleeve gastrectomy is a great challenge. The systemic immune-inflammation index (SII; calculated by neutrophils, lymphocytes, and platelets) and prognostic nutritional index (PNI; calculated by albumin and lymphocytes) are widely used as prognostic factors in various diseases.

OBJECTIVES

The objective of this study was to investigate independent the independent risk factors associated with weight regain in patients after laparoscopic sleeve gastrectomy.

SETTING

A single-center retrospective study.

METHODS

Weight regain was defined as the percentage of increase in body weight ≥10% in comparison with the nadir weight postoperatively. Eligible patients admitted to the bariatric center of our hospital were consecutively enrolled and grouped according to the occurrence of weight regain within 5 postoperative years. Univariate and multivariate logistic regression analyses were performed to assess potential risk factors. A nomogram model containing the risk factors was then constructed and evaluated by R.

RESULTS

A total of 217 patients were enrolled, and 87 (40.1%) patients experienced weight regain. Univariate and logistic regression analyses indicated that depression (odds ratio [OR]: 2.51, 95% confidence interval [CI]: 1.20-5.22, P = .015), psychological counseling (OR: 2.27, 95% CI: 1.20-4.33, P = .017), preoperative C-reactive protein (OR: 2.20, 95% CI: 1.18-4.13, P = .012), and combination of SII-PNI scores (OR: .45, 95% CI: .31-.67, P < .001) were 4 independent risk factors for postoperative weight regain in laparoscopic sleeve gastrectomy patients. The area under the curve of the constructed nomogram model for predicting weight regain was .706.

CONCLUSIONS

This study concluded that the combination of the SII-PNI was an independent risk factor for weight regain and that the nomogram model based on the combination of the SII-PNI had a good predictive value.

The Association of Albumin-Bilirubin (ALBI) Grade with Mortality Risk in Trauma Patients with Liver Injuries

Introduction

The albumin-bilirubin (ALBI) grade objectively assesses liver function with better performance than the Child-Pugh and end-stage liver disease scores. However, the evidence is lacking on the ALBI grade in trauma cases. This study aimed to identify the association between the ALBI grade and mortality outcomes in trauma patients with liver injury.

Methods

Data from 259 patients with traumatic liver injury at a level I trauma center between January 1, 2009, and December 31, 2021 were retrospectively analyzed. Independent risk factors for predicting mortality were identified using multiple logistic regression analysis. Participants were characterized by ALBI score into grade 1 (≤ -2.60, n = 50), grade 2 (-2.60 < and ≤ -1.39, n = 180), and grade 3 (> -1.39, n = 29).

Results

Compared to survival (n = 239), death (n = 20) was associated with a significantly lower ALBI score (2.8±0.4 vs 3.4±0.7, p < 0.001). The ALBI score was a significant independent risk factor for mortality (OR, 2.79; 95% CI, 1.27-8.05; p = 0.038). Compared with grade 1 patients, grade 3 patients had a significantly higher mortality rate (24.1% vs 0.0%, p < 0.001) and a longer hospital stay (37.5 days vs 13.5 days, p < 0.001).

Discussion

This study showed that ALBI grade is a significant independent risk factor and an useful clinical tool to discover liver injury patients who are more susceptible to death.

A predictive nomogram model for postoperative delirium in elderly patients following laparoscopic surgery for gynecologic cancers

BACKGROUND

This study aimed to investigate potential risk factors associated with postoperative delirium (POD) in elderly patients following laparoscopic surgery for gynecologic cancers and construct a nomogram predictive model based on these factors.

METHODS

Eligible elderly patients who underwent laparoscopic surgery for gynecologic cancers were enrolled and grouped according to the development of POD within postoperative 7 days. Potential risk factors were assessed by the univariate and multivariate logistic regression analyses. A nomogram model was constructed based on these factors and evaluated by R.

RESULTS

A total of 226 elderly patients were enrolled in the final data analysis and 39 patients had suffered POD with an incidence of 17.3%. Older age, modified frailty index (mFI) ≥ 0.225, C-reactive protein (CRP) ≥ 8.0, systemic immune-inflammation index (SII), and albumin/fibrinogen ratio (AFR) were five independent risk factors for POD by univariate and multivariate analyses. The area under the curve (AUC) of the constructed nomogram model based on these five factors was 0.833.

CONCLUSIONS

The constructed nomogram model based on age, CRP, SII, mFI, and AFR could effectively predict POD in elderly patients with gynecologic cancers.

The ALBI score: From liver function in patients with HCC to a general measure of liver function

The (albumin-bilirubin) ‘ALBI’ score is an index of ‘liver function’ that was recently developed to assess prognosis in patients with hepatocellular carcinoma, irrespective of the degree of underlying liver fibrosis. Other measures of liver function, such as model for end-stage liver disease (MELD) and Child-Pugh score, which were introduced for specific clinical scenarios, have seen their use extended to other areas of hepatology. In the case of ALBI, its application has been increasingly extended to chronic liver disease in general and in some instances to non-liver diseases where it has proven remarkably accurate in terms of prognosis. With respect to chronic liver disease, numerous publications have shown that ALBI is highly prognostic in patients with all types and stages of chronic liver disease. Outside of liver disease, ALBI has been reported as being of prognostic value in conditions ranging from chronic heart failure to brain tumours. Whilst in several of these reports, explanations for the relationship of liver function to a clinical condition have been proposed, it has to be acknowledged that the specificity of ALBI for liver function has not been clearly demonstrated. Nonetheless, and similar to the MELD and Child-Pugh scores, the lack of any mechanistic basis for ALBI’s clinical utility does not preclude it from being clinically useful in certain situations. Why albumin and bilirubin levels, or a combination thereof, are prognostic in so many different diseases should be studied in the future.

Predictive role of sampling-time specific prognostic nutritional index cut-off values for intravenous immunoglobulin resistance and cardiovascular complications in Kawasaki disease

BACKGROUND

Intravenous immunoglobulin (IVIG) resistance and cardiovascular complications prediction are pivotal topic of interests in Kawasaki disease (KD). The prognostic nutritional index (PNI) has been proposed to be valuable in predicting the severity of inflammatory status and prognosis in clinical circumstances, with limited data in KD. Therefore, we prospectively investigated the role of sampling-time specific PNI cut-off values in predicting initial IVIG resistance as well as cardiovascular complications in patients with KD for the first time.

METHODS

A total of 755 patients with KD were prospectively recruited between January 2015 and December 2019. Patients with KD were subgrouped based on the presence of IVIG resistance or cardiovascular complications. The clinical and laboratory parameters were compared. Multivariate logistic regression analysis was performed to identify the independent risk factors for IVIG resistance and cardiovascular complications. The receiver operating characteristic (ROC) curve was further applied to assess the predictive values of PNI in IVIG resistance and cardiovascular complications.

RESULTS

The lower level of PNI was identified as independent risk factors for initial IVIG resistance and cardiovascular complications. The discriminating cut-off values of the PNI for IVIG resistance, all cardiovascular complications, CALs, KDSS and myocarditis were 47.8, 52.2, 38.6, 48.2 and 52.0, with the corresponding sensitivities of 0.573, 0.679, 0.174, 0.750, 0.851, and specificities of 0.753, 0.549, 0.957, 0.679 and 0.576, respectively. After sampling time stratification, the sensitivities and specificities of the PNI obtained at the sixth day from fever onset for prediction of both IVIG resistance (0.778, 0.787) and all cardiovascular complications (0.667, 0.753) remarkably improved.

CONCLUSION

PNI may serve as a promising predictor for KDSS in patients with KD. PNI obtained at sixth day from fever onset possess good predictive power for both IVIG resistance and all cardiovascular complications in KD.