Abstract
Background
Intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) is associated with coronary artery lesions (CALs). Available studies suggest that inflammation and nutritional status play a key role in IVIG resistance. Given this, the neutrophil percentage to albumin ratio (NPAR), a new combined indicator of inflammation and nutritional status, may be an important predictor of IVIG resistance.
Methods
This was a retrospective cohort study involving 591 children diagnosed with KD. Participants were categorized based on their NPAR levels, and their clinical data were analyzed to assess the relationship between NPAR and IVIG resistance. Multivariable logistic regression model was conducted to evaluate the association between inflammatory biomarkers and IVIG resistance. A restricted cubic spline (RCS) model was employed to investigate the dose-response relationship between NPAR, other inflammatory biomarkers, and the risk of IVIG resistance. Furthermore, subgroup analyses were performed to assess the effects of age, sex, and other relevant factors on the association between NPAR and IVIG resistance. The receiver operating characteristic curve (ROC) was utilized to estimate the predictive power of NPAR and other inflammatory biomarkers.
Results
Among 591 participants with KD, 72 (12.2%) were IVIG resistance, and these patients showed a higher incidence of CALs (26.4% vs. 11.6%). Higher NPAR levels were significantly associated with an increased proportion of IVIG resistance. Specifically, the proportion of IVIG resistance was 2.5% in the lowest NPAR tertile, compared to 25.4% in the highest tertile (p < 0.001). The multivariable logistic regression model confirmed that NPAR was significantly associated with IVIG resistance. Each unit increase in NPAR was linked to 14.53 times increase in the odds of IVIG resistance (OR: 15.53, 95% CI: 7.83–30.84, p < 0.001). After adjusting for confounders such as age, gender, and laboratory parameters, the association remained strong (OR: 21.80, 95% CI: 8.84–53.74, p < 0.001). Additionally, the study demonstrated a dose-response relationship between NPAR levels and IVIG resistance, with higher NPAR values corresponding to greater risk. Subgroup analyses confirmed the stability of these findings. Furthermore, the area under the curve (AUC) for NPAR predicting IVIG resistance was 0.794, outperforming other inflammatory biomarkers.
Conclusion
This study demonstrated that NPAR, as a comprehensive indicator of inflammation and nutritional status, was significantly associated with IVIG resistance and can serve as a reliable predictor. Although the clinical application value of NPAR requires further validation, it shows promise as a novel biomarker for early identification of high-risk individuals and improving clinical management strategies.
Clinical trail number
Not applicable.
Keywords: Neutrophil percentage-to-albumin ratio (NPAR), Kawasaki disease (KD), Intravenous immunoglobulin resistance (IVIG), Coronary artery lesions (CALs)
Introduction
Kawasaki disease (KD) is a common pediatric vasculitis, and coronary artery lesions (CALs) are a major complication, leading to acquired heart disease in children [1]. Intravenous immunoglobulin (IVIG) combined with aspirin is the primary treatment, effectively reducing the risk of CALs. However, a significant proportion of patients (7.5–26.8%) exhibit resistance to initial IVIG treatment [2], which is associated with increased risk of coronary artery lesions (CALs) and other life-threatening complications [3]. Early identification of IVIG-resistant patients and timely intervention are crucial for preventing CALs and improving outcomes [3, 4]. Previous studies have shown that inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR) [5, 6], platelet-to-lymphocyte ratio (PLR) [5], and C-reactive protein-albumin ratio (CAR) [7] are associated with IVIG resistance and are used to predict IVIG resistance in KD patients. However, these indicators are not perfect because of their low sensitivity and/or specificity [8]. Thus, identifying novel inflammatory predictors of IVIG resistance is necessary.
Neutrophil percentage-to-albumin ratio (NPAR), as a comprehensive indicator of inflammation and nutritional status, has emerged as a novel marker with prognostic value across various diseases. Studies have demonstrated that elevated NPAR is associated with increased mortality risk in conditions such as acute ischemic stroke and cardiovascular diseases, highlighting its potential as a predictive biomarker [9, 10]. In addition, NPAR has been linked to systemic inflammation and disease progression in chronic conditions like liver cirrhosis, and chronic obstructive pulmonary disease [11, 12]. Preliminary studies suggest that NPAR may also serve as an independent risk factor for IVIG resistance in KD, exhibiting potential predictive utility [13].To our knowledge, evidence regarding the association between NPAR and IVIG resistance remains limited, and it is unclear whether NPAR is superior to other inflammatory biomarkers in predicting IVIG-resistance.
Therefore, we hypothesized that NPAR is associated with IVIG-resistance in KD patients, and aimed to confirm the value of NPAR as a potential biomarker to predict the incidence of IVIG resistance.
Materials and methods
Study population
A total of 591 children diagnosed with Kawasaki disease (KD) at the Children’s Hospital affiliated with Shandong University between February 2020 and February 2025 were included in this study based on predefined inclusion and exclusion criteria. Data extraction is performed leveraging the Hospital Health Medical Big Data Research and Innovation Platform. The inclusion and exclusion criteria were as follows:
Inclusion criteria: ① Patients with first-onset KD; ② Diagnosis confirmed according to the 2020 Chinese expert consensus diagnostic criteria [14]; ③ Aged 0 to 10 years; ④ Initial treatment with a standard dose of IVIG (2 g/kg); ⑤ Complete clinical data available.
Exclusion criteria: ①Comorbid conditions including congenital heart disease, other connective tissue diseases, malignancies, or severe infectious diseases; ②Use of corticosteroids, immunosuppressants, or other immunomodulatory drugs within 2 weeks prior to treatment; ③Patients who did not receive IVIG treatment during hospitalization or had incomplete clinical data.
Study methods
Patients were divided into two groups based on their therapeutic response to IVIG treatment. The response group was defined as those achieving normothermia (≤ 38 °C) within 36 h post-IVIG administration, without subsequent fever recurrence, accompanied by progressive resolution of characteristic Kawasaki disease (KD) manifestations, including rash, conjunctival injection, lip cracking, extremity changes, and cervical lymphadenopathy. Patients with IVIG resistance were identified as those demonstrating either persistent fever beyond 36 h after initial IVIG treatment or recurrent fever accompanied by sustained KD symptoms, which necessitated additional therapeutic interventions, such as a second IVIG dose or alternative treatments [14]. Coronary artery lesions (CALs) were defined according to the 2017 American Heart Association guidelines as a coronary artery Z-score ≥ 2.5, or absolute coronary diameter ≥ 3.0 mm in children < 5 years old or ≥ 4.0 mm in children ≥ 5 years old, assessed via echocardiography [1].
Comprehensive demographic and laboratory data were collected prior to initial IVIG administration. Demographic characteristics included fever duration before treatment initiation, age, and gender. Laboratory parameters encompassed white blood cell count, neutrophil count and percentage, lymphocyte count, hemoglobin level (anemia), platelet count (PLT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, total bilirubin, and serum sodium.
Several established inflammatory biomarkers were calculated to assess their association with IVIG resistance and to calculate their predictive value for IVIG resistance. These biomarkers included the following ratios:
Neutrophil percentage-to-albumin ratio (NPAR) = (neutrophil percentage / albumin) × 100;
Neutrophil-to-lymphocyte ratio (NLR) = neutrophil count / lymphocyte count;
Platelet-to-lymphocyte ratio (PLR) = platelet count / lymphocyte count;
C-reactive protein-to-albumin ratio (CAR) = C-reactive protein / albumin.
These hematological indices were selected based on their previously demonstrated association with IVIG resistance in Kawasaki disease (KD).
Statistical analysis
All statistical analyses were performed using R software (version 4.4.1). Continuous variables were expressed as mean ± standard deviation (x̄ ± s) when normally distributed, with between-group comparisons conducted using independent samples t-tests. Non-normally distributed data were presented as median [interquartile range] (M [Q1, Q3]) and analyzed using the Mann-Whitney U test. Categorical variables were described as frequencies (percentages), with between-group comparisons performed using χ² tests or Fisher’s exact tests, as appropriate. Multivariable logistic regression model was used to investigate the relationship between NPAR and IVIG-resistance. Model I was unadjusted. Adjustments for age and sex were added in Model II. Adjustments for age, sex, fever duration, hemoglobin, platelet count, alanine aminotransferase, aspartate aminotransferase, total bilirubin, and serum sodium (Na⁺) were added in Model III.
Restricted cubic spline (RCS) analysis was employed to assess the nonlinear association between independent risk factors and IVIG-resistance. Subsequently, subgroup analyses and interaction tests were performed to examine in depth the potential differences between sex groups, age groups, and other relevant factors. In the subgroup analysis, the covariates were the same as in Model III, except that the corresponding subgroup variables were not adjusted. Forest plots were then drawn to visualize the results. Finally, receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the NPAR and other inflammatory biomarkers. All statistical tests were two-tailed, with P < 0.05 considered statistically significant.
Results
Baseline characteristics
As shown in Table 1, among 591 children with KD, 72 (12.2%) were IVIG resistance. These patients showed a higher prevalence of CALs (26.4% vs. 11.6%, P < 0.001) and shorter pre-treatment fever duration (5 vs. 6 days, P < 0.001). In the IVIG resistant group (n = 72), the median age was 22.5 months and 44.4% were female, whereas the IVIG responsive group (n = 519) had a median age of 24.0 months and 34.9% were female. The age and gender were similar among the two groups (P>0.05). Laboratory findings in the IVIG resistant group demonstrated greater neutrophilia, higher NLR, PLR, CAR and NPAR (all P < 0.001), lower lymphocyte and platelet counts (P < 0.001), elevated CRP (73.2 vs. 45.3 mg/L), lower albumin (35.9 vs. 37.0 g/L), raised transaminases and bilirubin, and slightly reduced sodium (all P < 0.01). WBC, Hb, and ESR did not differ. Thus, IVIG resistance is associated with intensified inflammation, hepatic involvement, and increased coronary risk.
Table 1.
Comparison of clinical data among the IVIG-response and IVIG-resistance in KD patients
| Variables | Total (n = 591) |
IVIG-resistance (n = 72) |
IVIG-response (n = 519) |
P-value |
|---|---|---|---|---|
| CALs, n (%) | 79 (13.4) | 19 (26.4) | 60 (11.6) | < 0.001 |
| Fever duration (days) | 6.00 (5.00, 7.50) | 5.00 (4.00, 6.00) | 6.00 (5.00, 8.00) | < 0.001 |
| Age (months) | 24.00 (14.00, 47.00) | 22.50 (13.00, 47.50) | 24.00 (14.00, 47.00) | 0.774 |
| Gender, n (%) | 0.113 | |||
| Male | 378 (64.0) | 40 (55.6) | 338 (65.1) | |
| Female | 213 (36.0) | 32 (44.4) | 181 (34.9) | |
| WBC (×109/L) | 14.62 (11.68, 18.56) | 15.05 (12.14, 19.25) | 14.56 (11.56, 18.48) | 0.381 |
| Neu (×109/L) | 9.73 (6.82, 13.07) | 11.88 (9.18, 15.80) | 9.48 (6.55, 12.80) | < 0.001 |
| Neu% (%) | 67.20 (55.60, 77.70) | 78.95 (71.33, 87.62) | 65.20 (54.05, 75.90) | < 0.001 |
| Lym (×109/L) | 3.35 (2.21, 4.92) | 2.17 (1.33, 3.11) | 3.57 (2.32, 5.12) | < 0.001 |
| Hb (g/L) | 113.00 (106.00, 120.00) | 112.50 (104.50, 120.00) | 113.00 (106.00, 120.00) | 0.741 |
| PLT (×109/L) | 379.00 (308.00, 465.50) | 318.00 (244.50, 388.50) | 386.00 (314.00, 481.50) | < 0.001 |
| CRP (mg/L) | 48.70 (23.59, 80.12) | 73.22 (43.66, 114.66) | 45.30 (22.05, 74.30) | < 0.001 |
| ESR (mm/h) | 56.44 ± 24.24 | 56.39 ± 26.89 | 56.45 ± 23.87 | 0.985 |
| ALT (U/L) | 25.00 (13.00, 67.50) | 64.50 (24.00, 174.75) | 23.00 (13.00, 55.50) | < 0.001 |
| AST (U/L) | 30.00 (23.00, 41.00) | 36.50 (25.00, 65.00) | 30.00 (23.00, 40.00) | < 0.001 |
| ALB (g/L) | 36.70 (34.35, 38.75) | 35.85 (32.95, 37.60) | 37.00 (34.40, 38.95) | 0.002 |
| TBIL (µmmol/L) | 6.20 (4.40, 9.20) | 9.00 (6.57, 13.77) | 5.90 (4.30, 9.00) | < 0.001 |
| Na+ (mmol/L) | 137.00 (135.00, 138.00) | 136.00 (134.00, 137.00) | 137.00 (135.00, 138.00) | 0.003 |
| NLR | 2.81 (1.60, 4.99) | 5.25 (3.37, 9.72) | 2.53 (1.51, 4.47) | < 0.001 |
| PLR | 114.26 (80.44, 164.59) | 148.15 (98.81, 238.45) | 110.13 (79.09, 159.63) | < 0.001 |
| CAR | 1.30 (0.63, 2.21) | 2.22 (1.22, 3.26) | 1.19 (0.57, 2.06) | < 0.001 |
| NPAR | 1.82 ± 0.47 | 2.29 ± 0.46 | 1.75 ± 0.44 | < 0.001 |
Note: Continuous variables are summarized as mean (SD) or medians (quartile interval); categorical variables are displayed as a percentage (%)
Abbreviations: IVIG: intravenous immunoglobulin; CALs: coronary artery lesions; WBC: white blood cell count; Neu: neutrophil count; Neu%: neutrophil percentage; Lym: lymphocyte count; Hb: hemoglobin; PLT: platelet count; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; TBIL: total bilirubin; Na+: serum sodium; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; CAR: C-reactive protein-to-albumin ratio; NPAR: neutrophil percentage -to-albumin ratio
Baseline characteristics across NPAR tertiles were shown in Table 2. Patients in the highest tertile (T3, NPAR 2.02–3.42) were significantly older (median 41.0 vs. 17.0 months in T1, P < 0.001) and exhibited a shorter pre-treatment fever duration (median 5.0 vs. 7.0 days, P < 0.001). Markers of systemic inflammation intensified progressively: white blood cell count, neutrophil count and percentage, C-reactive protein, erythrocyte sedimentation rate, alanine aminotransferase, total bilirubin, and the composite ratios NLR, PLR and CAR all rise with increasing NPAR (all P < 0.001), whereas lymphocyte count, albumin level and platelet count declined (all P < 0.001). Importantly, IVIG resistance escalated from 2.5% in T1 to 25.4% in T3 (P < 0.001), whereas the prevalence of coronary artery lesions remained comparable across tertiles (≈ 13%, P = 0.903). These data indicate that a higher NPAR value is associated with a markedly increased risk of IVIG resistance.
Table 2.
Baseline characteristics according to the NPAR tertiles
| Variables | Total (n = 591) |
T1(0.643–1.61) (n = 197) |
T2(1.61–2.02) (n = 197) |
T3(2.02–3.42) (n = 197) |
p-value |
|---|---|---|---|---|---|
| IVIG-resistance | 72 (12.2) | 5 (2.5) | 17 (8.6) | 50 (25.4) | < 0.001 |
| CALs, n (%) | 79 (13.4) | 28 (14.2) | 26 (13.2) | 25 (12.7) | 0.903 |
| Fever duration (days) | 6.00 (5.00, 7.50) | 7.00 (5.00, 8.00) | 6.00 (5.00, 8.00) | 5.00 (5.00, 7.00) | < 0.001 |
| Age (months) | 24.00 (14.00, 47.00) | 17.00 (8.00, 26.00) | 24.00 (14.00, 48.00) | 41.00 (21.00, 56.00) | < 0.001 |
| Gender, n (%) | 0.916 | ||||
| Male | 378 (64.0) | 128 (65.0) | 124 (62.9) | 126 (64.0) | |
| Female | 213 (36.0) | 69 (35.0) | 73 (37.1) | 71 (36.0) | |
| WBC (×109/L) | 14.62 (11.68, 18.56) | 13.18 (10.18, 16.28) | 14.94 (12.42, 18.69) | 15.76 (12.64, 19.97) | < 0.001 |
| Neu (×109/L) | 9.73 (6.82, 13.07) | 6.47 (4.71, 8.82) | 10.13 (8.19, 12.83) | 12.65 (9.71, 16.53) | < 0.001 |
| Neu% (%) | 67.20 (55.60, 77.70) | 50.80 (44.70, 56.40) | 67.20 (62.40, 72.00) | 79.90 (76.10, 85.40) | < 0.001 |
| Lym (×109/L) | 3.35 (2.21, 4.92) | 4.98 (3.80, 6.30) | 3.57 (2.66, 4.52) | 2.05 (1.53, 2.68) | < 0.001 |
| Hb (g/L) | 113.00 (106.00, 120.00) | 112.00 (104.00, 118.00) | 113.00 (107.00, 120.00) | 115.00 (106.00, 121.00) | 0.060 |
| PLT (×109/L) | 379.00 (308.00, 465.50) | 411.00 (320.00, 502.00) | 385.00 (325.00, 486.00) | 335.00 (279.00, 408.00) | < 0.001 |
| CRP (mg/L) | 48.70 (23.59, 80.12) | 29.20 (12.70, 54.59) | 48.74 (28.01, 71.98) | 67.93 (39.40, 111.59) | < 0.001 |
| ESR (mm/h) | 56.44 ± 24.24 | 52.97 ± 22.87 | 56.14 ± 23.63 | 60.22 ± 25.70 | 0.012 |
| ALT (U/L) | 25.00 (13.00, 67.50) | 20.00 (13.00, 37.00) | 24.00 (12.00, 54.00) | 41.00 (18.00, 118.00) | < 0.001 |
| AST (U/L) | 30.00 (23.00, 41.00) | 30.00 (23.00, 41.00) | 29.00 (22.00, 37.00) | 33.00 (24.00, 44.00) | 0.041 |
| ALB (g/L) | 36.70 (34.35, 38.75) | 38.10 (35.70, 40.10) | 37.00 (34.80, 39.00) | 35.00 (32.60, 37.10) | < 0.001 |
| TBIL (µmmol /L) | 6.20 (4.40, 9.20) | 5.70 (4.00, 8.00) | 5.70 (4.20, 8.50) | 7.80 (5.30, 11.00) | < 0.001 |
| Na+ (mmol/L) | 137.00 (135.00, 138.00) | 137.00 (136.00, 139.00) | 137.00 (135.00, 138.00) | 136.00 (134.00, 137.00) | < 0.001 |
| NLR | 2.81 (1.60, 4.99) | 1.36 (1.03, 1.68) | 2.79 (2.29, 3.60) | 5.70 (4.22, 8.60) | < 0.001 |
| PLR | 114.26 (80.44, 164.59) | 80.26 (60.00, 112.95) | 108.18 (85.96, 147.16) | 165.86 (119.19, 220.56) | < 0.001 |
| CAR | 1.30 (0.63, 2.21) | 0.76 (0.34, 1.45) | 1.32 (0.74, 2.03) | 1.97 (1.11, 3.28) | < 0.001 |
| NPAR | 1.82 ± 0.47 | 1.30 ± 0.23 | 1.82 ± 0.12 | 2.33 ± 0.27 | < 0.001 |
Note: Continuous variables are summarized as mean (SD) or medians (quartile interval); categorical variables are displayed as a percentage (%)
Abbreviations: IVIG: intravenous immunoglobulin; CALs: coronary artery lesions; WBC: white blood cell count; Neu: neutrophil count; Neu%: neutrophil percentage; Lym: lymphocyte count; Hb: hemoglobin; PLT: platelet count; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; TBIL: total bilirubin; Na+: serum sodium; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; CAR: C-reactive protein-to-albumin ratio; NPAR: neutrophil percentage -to-albumin ratio
Multivariable logistic regression model of the relationship between inflammatory biomarkers and the IVIG-resistance in KD patients
As shown in Fig. 1, the multivariable logistic regression model revealed significant associations between inflammatory biomarkers and IVIG resistance in KD patients, with a particular focus on the NPAR. In Model I, which did not adjust for covariates, NPAR as a continuous variable maintained a striking odds ratio (OR) of 15.53 (95% CI: 7.83–30.84, p < 0.001), surpassing the other markers. This strong association persisted in Model II with an OR of 23.55 (95% CI: 10.99–50.47, p < 0.001) and in Model III, yielding an OR of 21.80 (95% CI: 8.84–53.74, p < 0.001).
Fig. 1.
Multivariable logistic regression model of the relationship between inflammatory biomarkers and the IVIG-resistance in KD patients. Data were presented as OR [95% CI]. Model I: no covariates were adjusted. Model II: age and gender were adjusted. Model III: age, gender, fever duration, hemoglobin, platelet count, alanine aminotransferase, aspartate aminotransferase, total bilirubin and Na+ were adjusted. NPAR: neutrophil percentage -to-albumin ratio; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; CAR: C-reactive protein-to-albumin ratio; OR, odds ratio; CI, confidence interval
In contrast, the NLR and CAR exhibited statistically significant but weaker associations. For example, NLR’s continuous measure showed an OR of 1.31 (95% CI: 1.22–1.41, p < 0.001) in Model I, while CAR demonstrated an OR of 1.47 (95% CI: 1.27–1.71, p < 0.001). Although the PLR had some significance, especially for T3, its confidence intervals reflected more variability. Overall, NPAR stands out as a critical biomarker for predicting IVIG resistance, highlighting its potential clinical relevance in KD management.
The dose-response relationship between inflammatory biomarkers and the IVIG-resistance in KD patients
As shown in Fig. 2, a restricted cubic spline (RCS) model was constructed to investigate the dose-response relationship between inflammatory biomarkers and the risk of IVIG-resistance in KD patients. The results indicated a linear correlation between all the investigated inflammatory biomarkers (NPAR, NLR, CAR, PLR) and the risk of IVIG-resistance (P for non-linearity>0.05). NPAR emerged as a particularly significant predictor as shown in Fig. 2A. The odds ratio for NPAR increases substantially after a threshold of 2.5, reaching an odds ratio of approximately 25, combined with an overall p-value of < 0.001, underscoring its strong association with IVIG resistance. In contrast, while NLR demonstrates a similar trend of increasing odds ratios, its nonlinearity p-value of 0.129 indicates that the relationship is less robust (Fig. 2B). CAR, which also exhibits a significant overall p-value (= 0.001), presents variability in predictive power indicated by a higher nonlinearity p-value of 0.177(Fig. 2C). Additionally, PLR displays the weakest correlation among the analyzed biomarkers, with an odds ratio that does not rise significantly and a nonlinearity p-value of 0.641(Fig. 2D). Collectively, these findings highlight NPAR as a critical inflammatory marker for assessing IVIG resistance in KD patients, demonstrating a more consistent relationship compared to NLR, CAR, and PLR.
Fig. 2.
The dose-response relationship between inflammatory biomarkers and the IVIG-resistance in KD patients
Subgroup analysis for the association between NPAR and the IVIG-resistance in KD patients
In this subgroup analysis, as shown in Fig. 3, we explored the association between NPAR and IVIG resistance in KD patients across various demographic and clinical characteristics. Younger patients (< 24 months) had an OR of 22.45 (95% CI, 6.32–79.80), while older patients (≥ 24 months) had an OR of 36.38 (95% CI, 9.26-142.85), with a non-significant interaction p-value of 0.590. Males presented an OR of 26.60 (95% CI, 8.31–85.14), and females showed an OR of 30.76 (95% CI, 6.89-137.35), reflecting gender consistency (p = 0.620). Patients with anemia had an OR of 15.93 (95% CI, 4.52–56.06), while those without anemia had an OR of 42.63 (95% CI, 11.51-157.96, p = 0.706). Platelet counts (PLT) < 400 had an OR of 41.00 (95% CI, 12.97-129.59) compared to 22.24 (95% CI, 3.89-127.25) for counts ≥ 400 (p = 0.775). With these data, we conclude that NPAR is consistently associated with IVIG resistance in KD patients, emphasizing its relevance in assessing IVIG resistance risk.
Fig. 3.
Subgroup analysis of the association between NPAR and IVIG-resistance
The predictive power of NPAR and other inflammatory biomarkers
The area under the curve (AUC) value for NPAR to predict IVIG-resistance was 0.794, yielding a sensitivity of 63.9% and a specificity of 79.4%. The AUC of NPAR was superior to NLR, PLR and CAR to predict IVIG-resistance (Fig. 4).
Fig. 4.
ROC curve between inflammatory biomarkers (NPAR, NLR, CAR, PLR) and the risk of IVIG-resistance
Discussion
This retrospective cohort study demonstrates that elevated neutrophil percentage-to-albumin ratio (NPAR) is a potent independent predictor of intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD). Among 591 patients, those in the highest NPAR tertile exhibited substantially higher IVIG resistance rates (25.4% vs. 2.5%). Furthermore, multivariable regression model showed a strong linear relationship, with each unit increase in NPAR corresponding to a markedly increased risk of IVIG resistance (adjusted OR: 21.80, 95% CI: 8.84–53.74). Additionally, NPAR demonstrated superior predictive performance (AUC = 0.794) compared to established inflammatory biomarkers, such as neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-albumin ratio (CAR) [5, 6]. The association between elevated NPAR and IVIG resistance persisted consistently across demographic and clinical subgroups, indicating its reliability. These findings are consistent with previous research that identified NPAR as a reliable predictor of adverse outcomes in various inflammatory conditions [15, 16]. To our knowledge, evidence regarding the association between NPAR and IVIG resistance in KD patients remains limited. Therefore, the primary innovation of this study is the systematic evaluation of NPAR and IVIG resistance in KD, addressing a significant knowledge gap.
Previous research indicates that 10–20% of KD patients develop IVIG resistance after initial IVIG treatment [7], aligning with the observations in our study (12.2%). The incidence of CALs in the IVIG resistant group was significantly higher than that in the IVIG responsive group (26.4% vs. 11.6%). Early identification of IVIG resistance is crucial for preventing CALs and improving outcomes in KD patients [2]. Previous studies have explored various risk factors for IVIG resistance, including coagulation indicators, inflammatory markers, and scoring systems [17, 18]. Some studies have found that higher neutrophils levels and lower albumin levels are associated with IVIG resistance and CALs [19]. This is consistent with our research results. We found that compared with the IVIG responsive group, the neutrophil count was higher (11.88 vs. 9.48) and the albumin level (35.85 vs.37.00) was lower. However, these associations remain inconclusive due to methodological limitations and inconsistent results [19]. Recent studies have focused on novel predictors, such as the systemic immune-inflammation index (SII), NLR, platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI), to predict IVIG resistance [18, 20, 21]. These studies suggest that inflammatory cells and factors play a crucial role in the pathogenesis of KD and IVIG resistance [22]. However, further research is needed to validate these findings and find reliable markers to predict IVIG resistance in KD patients.
Our study, a retrospective cohort analysis of 591 KD patients, demonstrates that an elevated NPAR is a strong, independent predictor of IVIG resistance, with patients in the highest NPAR tertile exhibiting significantly higher resistance rates (25.4% vs. 2.5%). This finding aligns with Deng et al. [13], who also identified NPAR as a reliable predictor of IVIG resistance in KD. However, our study reveals a stronger association (adjusted OR: 21.80, 95% CI: 8.84–53.74) and a higher predictive performance (AUC = 0.794) compared to the previous study (AUC = 0.666) [13]. While Deng et al. [13]found NPAR to be superior to the Beijing model but not inferior to the Chongqing model, our study further demonstrates NPAR’s superiority over established inflammatory biomarkers like NLR, PLR and CAR. Both studies support the utility of NPAR as an easily accessible and reliable biomarker for IVIG resistance, consistent with its role as a predictor of adverse outcomes in other inflammatory conditions [23–25].
As a composite marker, NPAR incorporates neutrophil proportion, indicative of inflammatory activity, and albumin concentration, reflecting both nutritional status and the extent of systemic inflammation. Its prognostic ability has been demonstrated in acute kidney injury [24], liver cirrhosis [11], cardiogenic shock [23, 26], and myocardial infarction [27]. Mechanistically, elevated neutrophil counts, a component of increased NPAR values, correlate with heightened production of inflammatory mediators, a phenomenon previously implicated in IVIG non-response [28, 29]. Conversely, diminished albumin levels, also contributing to elevated NPAR, may signify vascular permeability, a recognized feature of KD pathophysiology, and are inversely related to the intensity of acute inflammation [30]. Reduced albumin concentrations can compromise immune competence and elevate susceptibility to infection [31, 32]. Prior studies have indicated that increased levels of vascular endothelial growth factor (VEGF) are associated with decreased serum albumin levels in KD, potentially explaining the vascular leakage [33]. Importantly, the current study revealed that NPAR outperformed the NLR, PLR and CAR in predicting IVIG resistance. Given its straightforward calculation utilizing routine laboratory assessments, NPAR emerges as a potentially convenient and dependable biomarker for IVIG resistance in clinical practice.
This study’s innovation lies in its demonstration of the NPAR as a robust and readily accessible predictor of IVIG resistance in KD. First, our study leverages a larger cohort of KD patients (n = 591), enhancing the statistical power and reliability of our findings. Second, we identified a clear dose-response relationship between NPAR levels and IVIG resistance, providing further evidence for its role in the pathogenesis of IVIG resistance. Third, our results indicate that NPAR outperforms other inflammatory biomarkers in predicting IVIG resistance, highlighting its potential as a superior predictive marker. These findings suggest that NPAR, as a composite indicator of inflammation and nutritional status, holds promise as a novel biomarker for the early identification of high-risk individuals and the optimization of clinical management strategies.
Nevertheless, there were several limitations in our study. First, the retrospective design of our study restricts our ability to establish causality between NPAR and IVIG resistance. Second, as a single-center study, our findings may be susceptible to selection bias and may not be generalizable to other populations. Therefore, future prospective, multi-center studies are needed to validate our results in independent cohorts. Third, although we employed multivariable regression to adjust for potential confounders, residual confounding due to unmeasured or unknown factors cannot be entirely excluded. Finally, our study does not elucidate the underlying biological mechanisms linking NPAR to IVIG resistance, highlighting the need for further investigations to explore the pathophysiological basis of this association.
Conclusion
The present study showed that NPAR, as a comprehensive indicator of inflammation and nutritional status, was significantly associated with IVIG resistance and can serve as a reliable predictor. NPAR demonstrated superior predictive performance compared to established inflammatory biomarkers, such as NLR, PLR and CAR. Although the clinical application value of NPAR requires further validation, it shows promise as a novel biomarker for early identification of high-risk individuals and improving clinical management strategies.
Acknowledgements
We sincerely thank all clinical staff contributing to the data recording and all patients participating in this study.
Abbreviations
- IVIG
Intravenous immunoglobulin
- KD
Kawasaki disease
- CALs
Coronary artery lesions
- NPAR
The neutrophil percentage to albumin ratio
- RCS
A restricted cubic spline
- ROC
Receiver operating characteristic curve
- AUC
Area under the curve
- NLR
Neutrophil-to-lymphocyte ratio
- PLR
Platelet-to-lymphocyte ratio
- CAR
C-reactive protein-albumin ratio
- WBC
White blood cell count
- Neu
Neutrophil count
- Neu%
Neutrophil percentage
- Lym
Lymphocyte count
- Hb
Hemoglobin
- PLT
Platelet count
- CRP
C-reactive protein
- ESR
Erythrocyte sedimentation rate
- ALT
Alanine aminotransferase
- AST
Aspartate aminotransferase
- ALB
Albumin
- TBIL
Total bilirubin
- Na +
Serum sodium
- OR
Odds ratio
- CI
Confidence interval
Author contributions
All authors contributed to the study conception and design. Data collection and analysis were performed by DQX, ZX and BZZ. WXL, SHL and LXX performed statistical analysis. The first draft of the manuscript was written by DQX. LL and ZWR provided guidance in the study design. LL contributed to its design and made critical review of the manuscript. All authors commented on previous versions of the manuscript, read and approved the final manuscript.
Funding
This study was funded by Science and Technology Development Program of Jinan Municipal Health Commission (Award Number:2022-2-150; 2023-2-144).
Data availability
The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Research Ethics Committee of Jinan Children’s Hospital (Children’s Hospital Affiliated to Shandong University), and the requirement for informed consent was waived owing to the observation nature of this study. This study was performed in compliance with the Declaration of Helsinki andother relevant regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lei Li and Weiran Zhou contributed equally to this work and share corresponding authorship.
Qingxia Du and Xin Zhang contributed equally to this work and share first authorship.
Contributor Information
Lei Li, Email: talilei1981@163.com.
Weiran Zhou, Email: weiranxue2014@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.