Systemic Immune–Inflammation Index and Prognostic Nutritional Index as Predictors of Clinical Outcomes in Chronic Granulomatous Disease: A Retrospective Cohort Study

Systemic Immune–Inflammation Index and Prognostic Nutritional Index as Predictors of Clinical Outcomes in Chronic Granulomatous Disease: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systemic Immune–Inflammation Index and Prognostic Nutritional Index as Predictors of Clinical Outcomes in Chronic Granulomatous Disease: A Retrospective Cohort Study Serdar GÖKTAŞ, Saliha ESENBOĞA, Zehra GENÇ, Ceren ÜSTÜN, Elif Soyak AYTEKİN, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8757304/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Chronic granulomatous disease (CGD) is associated with recurrent infections and inflammatory manifestations, which are major contributors to morbidity, hospitalization, and early mortality. Routinely available laboratory parameters obtained during regular outpatient follow-up may facilitate early risk stratification, yet the clinical relevance of systemic inflammatory and nutritional indices in CGD remains incompletely defined. This study examined the associations of the systemic immune–inflammation index (SII) and the prognostic nutritional index (PNI) with selected clinical outcomes in CGD. In this retrospective, single-center cohort study, patients with CGD followed at a tertiary referral center between 1984 and 2025 were included. Outpatient SII (/100) and PNI values were calculated from laboratory data obtained at the first outpatient visit following diagnosis, whereas inpatient values were derived from samples collected during index hospitalization. Associations between these indices and hospitalization status, infectious and non-infectious manifestations, and survival outcomes were evaluated using regression-based and time-to-event analyses. The cohort comprised 74 patients (median age, 19 years); 61 (82.4%) experienced at least one hospitalization and 14 (18.9%) died during follow-up. Higher outpatient SII (/100) values and lower outpatient PNI values were independently associated with hospitalization status, whereas neither index was associated with hospitalization frequency. Inpatient SII (/100) values were higher among non-survivors and demonstrated good discriminative ability for survival status (AUC, 0.806). Higher inpatient SII (/100) and lower inpatient PNI values were associated with shorter overall survival. These findings indicate context-dependent associations, with inpatient indices reflecting survival, while outpatient indices primarily indicate hospitalization susceptibility rather than cumulative disease burden. Chronic granulomatous disease hospitalization prognostic nutritional index survival systemic immune–inflammation index Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic granulomatous disease (CGD) is a rare inborn errors of immunity (IEI) resulting from defective nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity, leading to impaired phagocyte-mediated microbial killing. Individuals with CGD are highly susceptible to recurrent and severe infections, chronic systemic inflammation, and granuloma formation, all of which substantially contribute to disease-related morbidity and adverse long-term clinical outcomes 1 . Although CGD is predominantly diagnosed during childhood, disease-related complications frequently persist into adolescence and adulthood, imposing a sustained clinical burden across the lifespan. Beyond primary immune dysfunction, accumulating evidence underscores the pivotal roles of systemic inflammation and nutritional status in modulating host defense mechanisms and susceptibility to infectious and inflammatory complications. In chronic inflammatory disorders, composite biomarkers derived from routinely available laboratory parameters have garnered increasing attention as pragmatic tools for prognostic stratification, as they capture the complex interplay between immune competence and nutritional reserve 2 , 3 . The systemic immune-inflammation index (SII) is a composite biomarker integrating key inflammatory and immune cell components, thereby providing a comprehensive measure of systemic inflammatory burden and immune equilibrium 4 . Likewise, the prognostic nutritional index (PNI) reflects immune–nutritional status by incorporating indicators of nutritional reserve and lymphocyte-mediated immune function 5 . Both indices have demonstrated prognostic relevance across a broad range of clinical contexts, including malignant and infectious diseases 6 , 7 . Nevertheless, evidence regarding their clinical applicability in IEI, and CGD in particular, remains limited. Despite the recognized contributions of immune dysregulation and nutritional status to CGD pathophysiology, the prognostic utility of simple, readily accessible indices such as SII and PNI has not been systematically examined across the full age spectrum of patients with CGD. Current approaches to prognostic assessment in CGD predominantly rely on clinical characteristics, genetic background, and infection history, with minimal incorporation of routinely available inflammatory and nutritional biomarkers. Accordingly, the present study aimed to evaluate the prognostic relevance of SII and PNI in patients with CGD across pediatric and adult populations and to examine their associations with clinically meaningful outcomes, with a particular emphasis on survival status and hospitalization burden. Methods Study Design, Setting, and Population This retrospective, single-center cohort study was conducted at a tertiary referral center and included patients diagnosed with CGD who were followed at Hacettepe University between 1984 and 2025. Patients of all ages with a confirmed diagnosis of CGD were eligible for inclusion if laboratory data obtained during a clinically stable outpatient period and/or during hospitalization were available together with complete longitudinal follow-up data. The diagnosis of CGD was established based on genetic confirmation and/or abnormal neutrophil oxidative burst testing, including the dihydrorhodamine (DHR) assay or nitroblue tetrazolium (NBT) test, in conjunction with compatible clinical findings. Patients were followed longitudinally from the time of diagnosis until death or last documented follow-up. Patients with missing key laboratory parameters required for index calculation or incomplete follow-up data for primary outcomes were excluded from the analysis (Figure 1). Given the retrospective design, the study may be subject to selection bias related to the availability of complete laboratory and follow-up data. Clinical Data Collection and Outcome Definitions Demographic and clinical variables extracted from medical records included age, sex, age at diagnosis, parental consanguinity, family history of CGD, hospitalization history, total number of hospitalizations, genetic data, advanced treatment exposure, and survival status. Clinical manifestations were categorized as infectious or non-infectious. Hospitalization was defined as an inpatient admission lasting at least 24 hours. Hospitalization status (yes/no) and the total number of hospitalizations during follow-up were recorded. Infectious outcomes included clinically significant infections requiring systemic antimicrobial therapy and/or hospitalization and were categorized as pneumonia, sepsis, deep tissue abscess, tuberculosis, and Aspergillus infection. Deep tissue abscess was included as a predefined infectious manifestation, with its operational definition provided in the corresponding table footnote. Non-infectious clinical manifestations included chronic diarrhea, inflammatory bowel disease–like colitis, bronchiectasis, atelectasis, and growth retardation. Growth retardation was defined using age- and sex-adjusted growth charts documented in the medical records. Advanced treatment exposure was defined as receipt of interferon-gamma, intravenous immunoglobulin replacement, granulocyte transfusion, or hematopoietic stem cell transplantation. Overall survival was defined as the time from diagnosis to death from any cause or last follow-up, with surviving patients censored at the time of last clinical contact. Genetic classification was based on the affected gene identified through molecular analysis (NCF1, NCF2, CYBA, or CYBB). Patients without available genetic data were categorized as genetic not available. Laboratory Parameters and Index Calculation SII was calculated as the product of absolute platelet and neutrophil counts divided by the absolute lymphocyte count and expressed as SII (/100) for analysis. PNI was calculated using serum albumin and lymphocyte counts, defined as albumin ×10 plus lymphocyte count ×0.005. Outpatient SII and PNI values were derived from laboratory parameters obtained at the first outpatient visit following diagnosis during a clinically stable period. Inpatient SII and PNI values were calculated using laboratory parameters from the first blood sample collected at the time of initial hospitalization, prior to the initiation of therapeutic interventions. Statistical Analysis Continuous variables were assessed for normality using visual inspection and analytical methods and are presented as median (range) or mean ± standard deviation, as appropriate. Categorical variables are presented as counts and percentages. Outpatient and inpatient SII (/100) and PNI values demonstrated non-normal distributions. Accordingly, comparisons between two independent groups were performed using non-parametric tests. Associations between outpatient inflammatory and nutritional indices and subsequent infectious outcomes or advanced treatment requirements were evaluated using the Mann–Whitney U test. Relationships between dichotomized index groups and non-infectious clinical manifestations were assessed using the chi-square test. Hospitalization frequency was treated as a count outcome. Among patients with at least one prior hospitalization, factors associated with the total number of hospitalizations during follow-up were evaluated using negative binomial regression due to overdispersion. Outpatient SII (/100) and outpatient PNI were included as covariates, and results are reported as regression coefficients, incidence rate ratios, and 95% confidence intervals. Correlations between outpatient index values and hospitalization count were additionally assessed using Spearman’s rank correlation coefficient. For categorical analyses, inpatient SII (/100) values were dichotomized using an optimal cut-off determined by receiver operating characteristic (ROC) curve analysis to reflect acute inflammatory burden. Inpatient PNI values were dichotomized using a predefined cut-off value of 40, consistent with thresholds commonly applied in prior prognostic studies 8,9 . Outpatient SII (/100) and PNI values were categorized using predefined cut-off values reflecting clinically stable disease status, as specified in the relevant analyses. The prognostic performance of inpatient inflammatory burden for mortality was evaluated by calculating the area under the ROC curve with corresponding confidence intervals. Overall survival analyses were conducted using time-to-event methods. All statistical tests were two-sided, and a p value <0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics version 2025 (IBM Corp., Armonk, NY, USA). Patients with missing key laboratory or outcome data were excluded from the analysis; therefore, no imputation methods were applied. Ethics Approval and Consent to Participate The study was approved by the Ethics Committee of Hacettepe University (approval number: SBA 24/809).Due to the retrospective design and the use of anonymized data, the requirement for informed consent was waived. Results The study cohort comprised 74 patients with chronic granulomatous disease. The median age was 19 years (range, 1–68), and 45 patients (60.8%) were male. The median age at diagnosis was 10 years (range, 1–57). Parental consanguinity was observed in 45 patients (60.8%), and 17 patients (23.0%) had a family history of CGD. At least one hospitalization was recorded in 61 patients (82.4%), with a median of four hospitalizations per patient (range, 1–18) (Table 1). At the time of analysis, 14 patients (18.9%) had died. Median outpatient SII (/100) and PNI values were 8.03 (range, 1.17–39.88) and 57.09 (range, 21.00–86.10), respectively. Corresponding inpatient values were 8.71 (range, 1.30–30.95) for SII (/100) and 51.70 (range, 21.50–98.40) for PNI (Table 1). Infectious complications were common. Pneumonia occurred in 54 patients (73.0%), tuberculosis in 29 (39.2%), deep tissue abscess in 18 (24.3%), sepsis in 7 (9.5%), and Aspergillus infection in 17 patients (23.0%). The median number of Aspergillus infection episodes per patient was two (range, 0–24). Non-infectious manifestations included growth retardation in 38 patients (51.4%), chronic diarrhea in 17 (23.0%), atelectasis in 15 (20.3%), bronchiectasis in 6 (8.1%), and inflammatory bowel disease–like colitis in 7 patients (9.5%) (Supplementary Table S1). Genetic analysis identified CYBB variants in 16 patients (21.6%), CYBA in 15 (20.3%), NCF1 in 13 (17.6%), and NCF2 in 6 patients (8.1%), while genetic data were unavailable for 24 patients (32.4%). Interferon-γ therapy was administered to 42 patients (56.8%), intravenous immunoglobulin to 7 (9.5%), granulocyte transfusion to 6 (8.1%), and hematopoietic stem cell transplantation to 9 patients (12.2%) (Supplementary Table S1). Outpatient inflammatory and nutritional indices differed significantly between hospitalized and non-hospitalized patients. Outpatient SII (/100) was higher among hospitalized patients than among non-hospitalized patients (mean rank, 41.52 vs. 18.65; p = 0.001), whereas outpatient PNI was lower (mean rank, 33.56 vs. 56.00; p = 0.001) (Table 2). Inpatient inflammatory and nutritional indices differed according to survival status. Inpatient SII (/100) values were higher among non-survivors compared with survivors (mean rank, 46.00 vs. 27.33; p = 0.001), whereas inpatient PNI values were lower among non-survivors (mean rank, 21.38 vs. 33.36; p = 0.036) (Table 2). In a multivariable logistic regression analysis including outpatient SII (/100) and PNI simultaneously, both indices were independently associated with hospitalization status [SII (/100): OR = 1.465; 95% CI, 1.030–2.083; p = 0.034; PNI: OR = 0.926; 95% CI, 0.862–0.994; p = 0.034] (Table 3). In a multivariable logistic regression analysis including inpatient SII (/100) and PNI simultaneously, higher inpatient SII (/100) was independently associated with survival status (OR = 0.885; 95% CI, 0.785–0.998; p = 0.046), whereas inpatient PNI was not independently associated with survival status (Table 3). Negative binomial regression analysis showed that outpatient SII (/100) and outpatient PNI were not independently associated with hospitalization frequency during follow-up in patients with a history of hospitalization (Supplementary Table S2). Consistently, Spearman correlation analysis demonstrated no significant monotonic association between hospitalization count and outpatient SII (/100) or outpatient PNI (Supplementary Table S3). ROC analysis demonstrated good discriminative ability of inpatient SII (/100) for survival status, with an area under the curve of 0.806 (95% CI, 0.693–0.919; p = 0.001) (Figure 2). An optimal cut-off value of 8.4 was identified and subsequently used for risk stratification. Kaplan–Meier analysis demonstrated significantly shorter overall survival in patients with high inpatient SII (/100) compared with those with low values (log-rank p = 0.001). Mean survival time estimates were lower in the high SII (/100) group, whereas median survival could not be reliably estimated in the low SII (/100) group because of the high proportion of censored observations (Figure 3A). Univariable Cox proportional hazards regression analysis demonstrated that increasing inpatient SII (/100), analyzed as a continuous variable, was associated with a higher risk of death during follow-up (HR = 1.092; 95% CI, 1.025–1.162; p = 0.006) (Figure 3B). Model-based survival estimates indicated a progressive decline in overall survival with increasing inpatient SII (/100) values. ROC analysis assessing the discriminative ability of inpatient PNI for hospitalization yielded an AUC of 0.304 (95% CI, 0.117–0.491; p = 0.036), indicating inverse discrimination, with lower PNI values corresponding to a higher likelihood of hospitalization; therefore, no cut-off value was derived. Consistently, inpatient PNI analyzed as a continuous variable was independently associated with improved survival in univariable Cox regression analysis (HR = 0.919; 95% CI, 0.862–0.980; p = 0.010) (Figure 3C). Overall survival was further evaluated using a predefined inpatient PNI cut-off of 40. Kaplan–Meier analysis showed significantly shorter survival in the low PNI group compared with the high PNI group (log-rank p < 0.001) (Figure 4). Median survival was 12.2 months (95% CI, 7.4–16.9) in the low PNI group, whereas it was not reached in the high PNI group due to a high proportion of censored observations (Supplementary Table S4). Outpatient inflammatory and nutritional indices were evaluated for associations with clinical outcomes (Supplementary Tables S5 and S6). Outpatient SII (/100) values were significantly higher in patients who subsequently developed pneumonia (p = 0.011), whereas outpatient PNI showed no association. No significant associations were observed for other infectious outcomes. Among non-infectious manifestations, lower outpatient PNI was associated with bronchiectasis (p = 0.042), while outpatient SII (/100) was not. CRP levels, genetic subgroups, infection status, IVIG administration, and HSCT status were not associated with differences in outpatient SII (/100) or PNI values (all p > 0.05). Discussion This study provides an evaluation of inflammatory and nutritional indices in a real-world cohort of patients with CGD spanning childhood and adulthood. By integrating outpatient and inpatient assessments of the SII and the PNI with hospitalization and survival outcomes, the present analysis offers insight into the context-dependent clinical relevance of routinely available laboratory-based markers in CGD. Composite indices derived from standard hematological parameters have been increasingly applied in chronic inflammatory and infectious conditions, providing a relevant conceptual framework for their evaluation in CGD 10,11 . The infectious spectrum observed in this cohort, particularly pneumonia, tuberculosis, and Aspergillus infections, is consistent with prior CGD reports and provides clinical context for interpreting index-based associations 12,13 . The frequency of gastrointestinal and growth-related complications further aligns with the notion that chronic immune dysregulation and recurrent inflammatory insults contribute to a sustained disease burden over prolonged follow- up 14 . Inn this clinical setting, the associations observed between outpatient indices and hospitalization status suggest that inter-individual differences in inflammatory burden and nutritional reserve during periods of clinical stability may be clinically meaningful. However,in patients with a prior history of hospitalization, outpatient indices were not independently associated with frequency of subsequent hospitalization when hospitalization burden was analyzed as a count outcome. This finding highlights an important methodological distinction between prognostic stratification and prediction of event counts 15,16 . While composite indices such as SII and PNI are commonly used for prognostic stratification, they appear to be more effective in reflecting survival-related risk than inpredicting cumulative event burden or event frequency over time. The consistency of findings across regression-based and correlation-based analyses supports the robustness of the observed lack of association between outpatient indices and hospitalization frequency. Together, these findings emphasize that outpatient inflammatory and nutritional markers may capture susceptibility to hospitalization rather than cumulative hospitalization burden over time. The consistency of findings across regression- and correlation-based analyses supports the robustness of the observed lack of association between outpatient indices and hospitalization frequency. Taken together, these findings suggest that outpatient inflammatory and nutritional markers may reflect the likelihoodof hospitalization rather than cumulative hospitalization burden over time. For infectious outcomes, outpatient inflammatory indices demonstrated context-dependent associations. Elevated outpatient SII (/100) values were associated with subsequent pneumonia development, whereas outpatient PNI was not, indicating that baseline systemic immune–inflammatory activity may be informative with respect to pulmonary infection susceptibility in this cohort. Consistent with these observations, elevated SII and reduced PNI have been associated with an increased risk of postoperative pneumonia in various surgical populations; however, this evidence is cited here to provide contextual support rather than direct extrapolation to CGD 17,18 . In contrast, outpatient SII (/100) and PNI were not significantly associated with other infectious complications, including sepsis, tuberculosis, deep tissue abscess, and Aspergillus infection. These outcomes may be influenced by multifactorial determinants—such as pathogen-specific characteristics, underlying immune profiles, exposure patterns, and treatment-related variables—that are unlikely to be fully captured by single outpatient measurements. Accordingly, non-significant associations should be interpreted cautiously. Outpatient indices were not associated with non-infectious clinical manifestations related to chronic inflammation or long-term disease burden, including chronic diarrhea, inflammatory bowel disease–like colitis, bronchiectasis, atelectasis, and growth retardation. These conditions typically evolve over extended periods and reflect cumulative inflammatory exposure and disease-specific mechanisms that are unlikely to be fully captured by single time-point biomarker measurements. Admission C-reactive protein levels did not differ across inpatient SII (/100) or PNI stratification groups, suggesting limited concordance between CRP and composite inflammatory or nutritional indices. Whereas CRP primarily reflects acute-phase inflammatory activity, SII (/100) and PNI integrate hematological and nutritional components and may capture broader immune–metabolic states, indicating that these measures provide complementary rather than interchangeable information. In contrast to outpatient indices, hospitalization-phase indices demonstrated clearer associations with survival outcomes. Inpatient SII (/100) values were higher in non-survivors and remained independently associated with survival status, consistent with prior observations in infectious and inflammatory conditions 19,20 . Inpatient PNI differed between survivors and non-survivors in unadjusted analyses but did not retain an independent association with survival after multivariable adjustment, suggesting that inflammatory burden may exert a stronger influence on survival status than nutritional reserve within the analytical framework of this cohort. The prognostic relevance of PNI in acute and critical illness appears to vary across non-CGD populations. Time-to-event analyses further supported the prognostic stratification value of inpatient indices, while underscoring that ROC-based discrimination and survival analyses assess distinct outcome structures and should be interpreted within their respective methodological contexts. Although no external validation cohort was available, the consistency of associations across multiple analytical approaches supports the internal validity of the observed findings. From a clinical perspective, these results suggest that inflammatory indices derived during hospitalization may be more informative for short- to mid-term prognostic assessment than indices measured during clinically stable follow-up. Outpatient SII (/100) and PNI values did not differ according to exposure to advanced treatment modalities, including interferon-γ therapy, IVIG, or HSCT, nor across genetic subgroups. This observation suggests that inflammatory indices derived during clinically stable follow-up may be relatively insensitive to treatment-specific or genotype-specific differences, potentially reflecting shared downstream immune responses rather than intervention- or gene-specific effects. Several limitations warrant consideration. The retrospective design may introduce selection bias, residual confounding cannot be excluded, and genetic characterization was unavailable for a subset of patients. In addition, both outpatient and inpatient indices were derived from single time-point measurements, which may not capture temporal variability in inflammatory and nutritional status. Despite these limitations, the inclusion of pediatric and adult patients, the extended follow-up duration, and the application of complementary analytical approaches enhance the interpretability of the findings and their relevance to real-world CGD populations. In conclusion, inflammatory and nutritional indices derived from routine laboratory parameters provide context-dependent clinical information in CGD. Outpatient indices were associated with hospitalization status but not with hospitalization frequency when modeled as a count outcome. Inpatient indices—particularly inflammatory burden captured by SII (/100) were associated with survival outcomes, supporting their potential role in prognostic assessment during hospitalization. Future prospective studies incorporating repeated measurements and external validation are warranted to further clarify their utility in risk stratification and longitudinal monitoring in CGD. Declarations Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements: None. Authorship Contributions: SE: Conceptualization, study design, supervision, and overall scientific leadership. ZG, SG: Data collection, statistical analysis, and drafting of the initial manuscript. CÜ, ZG, ESA, SG: Data collection and data interpretation. DÇ: Study design refinement and data interpretation. All authors: Critical revision of the manuscript and approval of the final version. Disclosure of Conflicts of Interest: The authors declare no conflict of interest. References Yu HH, Yang YH, Chiang BL. Chronic granulomatous disease: a comprehensive review. Clin Rev Allergy Immunol. 2021;61(2):101–113. doi:10.1007/s12016-020-08800-x. Alwarawrah Y, Kiernan K, MacIver NJ. Changes in nutritional status impact immune cell metabolism and function. Front Immunol. 2018;9:1055. doi:10.3389/fimmu.2018.01055. Méndez López LF, González Llerena JL, Vázquez Rodríguez JA, Medellín Guerrero AB, González Martínez BE, Solís Pérez E, et al. Dietary modulation of the immune system. Nutrients. 2024;16(24):4363. doi:10.3390/nu16244363. Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. 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Baseline demographic, clinical, and inflammatory index characteristics of patients with chronic granulomatous disease Category Variable Value Patient Information Number of patients, n 74 Age, years, median (range) 19 (1-68) Sex (male/female), n (%) 45/29 (60.8/39.2) Age at diagnosis, years, median (range) 10 (1-57) Consanguinity, n (%) 45 (60.8) Family history of CGD, n (%) 17 (23.0) Disease Status At least one hospitalization, n (%) 61(82.4) Number of hospitalizations, median (range) 4 (1-18) Laboratory Indices Outpatient SII (/100), median (range) 8.03 (1.17-39.88) Outpatient PNI, median (range) 57.09 (21.00-86.10) Inpatient SII (/100), median (range) 8.71 (1.30-30.95) Inpatient PNI, median (range) 51.70 (21.50-98.40) Data are presented as median (range) unless otherwise specified. SII values were divided by 100 for readability. CGD, chronic granulomatous disease; PNI, prognostic nutritional index; SII, systemic immune–inflammation index. Table 2. Group comparisons of the SII (/100) and PNI by clinical outcomes Variable Group N Mean Rank U Z p value Panel A. Hospitalization status (outpatient indices) Outpatient SII (/100) Hospitalized 61 41.52 151.50 -3.480 0.001 Not hospitalized 13 18.65 Outpatient PNI Hospitalized 61 33.56 156.00 -3.416 0.001 Not hospitalized 13 56.00 Panel B. Survival status (inpatient indices) Inpatient SII (/100) Non-survivor 12 46.00 114.0 −3.266 0.001 Survivor 49 27.33 Inpatient PNI Non-survivor 12 21.38 178.5 −2.096 0.036 Survivor 49 33.36 Comparisons were performed using the Mann–Whitney U test for hospitalization status (Panel A) and survival status (Panel B). U and Z values represent overall group comparisons. SII values were divided by 100 for readability. CI, confidence interval; OR, odds ratio; PNI, prognostic nutritional index; SII, systemic immune–inflammation index. Table 3. Associations of SII (/100) and PNI with clinical outcomes Variable B Standard error Wald χ² p value OR 95% CI for OR Panel A. Hospitalization status (outpatient indices) Outpatient SII (/100) 0.382 0.180 4.518 0.034 1.465 1.030–2.083 Outpatient PNI −0.077 0.037 4.484 0.034 0.926 0.862–0.994 Panel B. Survival status (inpatient indices) Inpatient SII (/100) −0.122 0.061 3.979 0.046 0.885 0.785 – 0.998 Inpatient PNI 0.074 0.041 3.323 0.068 1.077 0.994 – 1.167 Associations were assessed using multivariable logistic regression analyses, with SII (/100) and PNI entered simultaneously into the models. In Panel A, hospitalization status was coded as 0 = no and 1 = yes. In Panel B, survival status was defined as survival at last follow-up and coded as 1 = survivor and 0 = non-survivor. All models included an intercept term. CI, confidence interval; OR, odds ratio; PNI, prognostic nutritional index; SII, systemic immune–inflammation index. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 01 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8757304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589908123,"identity":"2fd395ef-5993-408f-bbcc-5e1354e06c4e","order_by":0,"name":"Serdar GÖKTAŞ","email":"","orcid":"","institution":"Mersin City Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Serdar","middleName":"","lastName":"GÖKTAŞ","suffix":""},{"id":589908124,"identity":"dc2b7b26-14a9-4ec7-9b10-7a1b3635aae2","order_by":1,"name":"Saliha ESENBOĞA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFAC5obDUMYBBsYGorQwwrSwJRCvhRnC4DEgTot8+8HGwwU1Nvn80j3fJH7usJFjYD98dAM+LQZnEhsOzziWZjlzztltkr1n0owZeNLSbuDVwgDUwsN22MDgRu42Cd62w4kNEjxmeLXI9z8Eavn338D+Rs4zyb/EaGG4AbSFt+2AgYFEDps0UbYY3ADawtuXbCBxI83YWrYtzZiNkF/k+5MPf+b5ZmfAPyP54c23bTZy/OyHj+F3GBJgkQCRbMQqBwHmD6SoHgWjYBSMgpEDAK7vTWOa73LCAAAAAElFTkSuQmCC","orcid":"","institution":"Hacettepe University","correspondingAuthor":true,"prefix":"","firstName":"Saliha","middleName":"","lastName":"ESENBOĞA","suffix":""},{"id":589908125,"identity":"014f9c17-a305-4b12-b457-a475f1b7f388","order_by":2,"name":"Zehra GENÇ","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Zehra","middleName":"","lastName":"GENÇ","suffix":""},{"id":589908126,"identity":"c9d9e18b-4fbf-418e-b8ef-3b8b4f62ac0f","order_by":3,"name":"Ceren ÜSTÜN","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Ceren","middleName":"","lastName":"ÜSTÜN","suffix":""},{"id":589908127,"identity":"b306b04f-5e30-4750-901a-618b2f632a89","order_by":4,"name":"Elif Soyak AYTEKİN","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Elif","middleName":"Soyak","lastName":"AYTEKİN","suffix":""},{"id":589908128,"identity":"df8ab85e-3b9e-4074-8125-b1f96db743dd","order_by":5,"name":"Deniz ÇAĞDAŞ","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Deniz","middleName":"","lastName":"ÇAĞDAŞ","suffix":""}],"badges":[],"createdAt":"2026-02-01 15:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8757304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8757304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102851990,"identity":"8fde02a7-d222-4b23-8659-a827c818508c","added_by":"auto","created_at":"2026-02-17 14:34:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of patient selection and inclusion in analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow diagram illustrating patient selection, exclusions, and inclusion in the final analyses. Of 81 patients with genetically confirmed and/or functionally proven chronic granulomatous disease, 74 patients with available longitudinal follow-up were included in the study. Thirteen patients were excluded from survival and inpatient analyses due to unknown vital status, insufficient follow-up duration, or absence of inpatient SII/PNI data. A total of 61 patients were included in survival and longitudinal clinical analyses, including survival, hospitalization frequency, and correlation analyses. CGD, chronic granulomatous disease; PNI, prognostic nutritional index; SII, systemic immune–inflammation index.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/bdbcad88185f1329d7c1877e.png"},{"id":102851991,"identity":"6f6c1987-769a-4846-b417-9246e093fefe","added_by":"auto","created_at":"2026-02-17 14:34:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminatory performance of inpatient SII (/100) for survival status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve illustrating the discriminative ability of inpatient systemic immune–inflammation index divided by 100 (SII/100) for survival status. The AUC was 0.806 (95% CI, 0.693–0.919; p = 0.001), indicating good discriminatory performance. The optimal cut-off value for SII (/100) was 8.4, determined using the Youden index. The diagonal line represents the reference line corresponding to an AUC of 0.5. AUC, area under the curve; CI, confidence interval; SII, systemic immune–inflammation index.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/88f5776bb9747b6fd4278209.png"},{"id":102851993,"identity":"e7369b69-1541-478d-bdd7-e8660f7f369e","added_by":"auto","created_at":"2026-02-17 14:34:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInpatient inflammatory and nutritional indices and overall survival.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overall survival according to inpatient SII (/100) groups, estimated using the Kaplan–Meier method and compared with the log-rank test.\u003c/p\u003e\n\u003cp\u003e(B) Forest plot showing the association between inpatient SII (/100) and overall survival.\u003c/p\u003e\n\u003cp\u003e(C) Forest plot showing the association between inpatient PNI and overall survival.\u003c/p\u003e\n\u003cp\u003eFor panels B and C, hazard ratios (HRs) and 95% confidence intervals (CIs) were derived from univariable Cox proportional hazards regression analyses with inpatient SII and PNI analyzed as continuous variables, respectively. Squares represent HRs, horizontal lines indicate 95% CIs, and dashed vertical lines denote HR = 1. CI, confidence interval; HR, hazard ratio; PNI, prognostic nutritional index; SII, systemic immune–inflammation index.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/1f94ccbdb69ceab7691da740.jpeg"},{"id":102963094,"identity":"22216ca0-0d2a-4bc2-917d-1f140ef415fd","added_by":"auto","created_at":"2026-02-19 04:13:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival according to inpatient PNI groups\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival curves comparing overall survival between patients with low and high inpatient PNI. Patients were stratified into groups using a predefined PNI cut-off value of 40 for exploratory purposes. Survival distributions between groups were compared using the log-rank test. Tick marks indicate censored observations. PNI, prognostic nutritional index.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/60989fa1680765966f1c39c0.jpeg"},{"id":103056401,"identity":"6a21e908-5f4e-4374-a1e1-232d24c74395","added_by":"auto","created_at":"2026-02-20 09:09:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1124160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/6d6e62df-42f0-4d61-b61b-7d46c1ce28cb.pdf"},{"id":102851994,"identity":"c7b0e942-43ae-40c7-a13c-2f8facf38144","added_by":"auto","created_at":"2026-02-17 14:34:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":225533,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8757304/v1/6d34f7c421614f08aa581c32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systemic Immune–Inflammation Index and Prognostic Nutritional Index as Predictors of Clinical Outcomes in Chronic Granulomatous Disease: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic granulomatous disease (CGD) is a rare inborn errors of immunity (IEI) resulting from defective nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity, leading to impaired phagocyte-mediated microbial killing. Individuals with CGD are highly susceptible to recurrent and severe infections, chronic systemic inflammation, and granuloma formation, all of which substantially contribute to disease-related morbidity and adverse long-term clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although CGD is predominantly diagnosed during childhood, disease-related complications frequently persist into adolescence and adulthood, imposing a sustained clinical burden across the lifespan.\u003c/p\u003e \u003cp\u003eBeyond primary immune dysfunction, accumulating evidence underscores the pivotal roles of systemic inflammation and nutritional status in modulating host defense mechanisms and susceptibility to infectious and inflammatory complications. In chronic inflammatory disorders, composite biomarkers derived from routinely available laboratory parameters have garnered increasing attention as pragmatic tools for prognostic stratification, as they capture the complex interplay between immune competence and nutritional reserve\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe systemic immune-inflammation index (SII) is a composite biomarker integrating key inflammatory and immune cell components, thereby providing a comprehensive measure of systemic inflammatory burden and immune equilibrium\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Likewise, the prognostic nutritional index (PNI) reflects immune\u0026ndash;nutritional status by incorporating indicators of nutritional reserve and lymphocyte-mediated immune function\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Both indices have demonstrated prognostic relevance across a broad range of clinical contexts, including malignant and infectious diseases\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, evidence regarding their clinical applicability in IEI, and CGD in particular, remains limited.\u003c/p\u003e \u003cp\u003eDespite the recognized contributions of immune dysregulation and nutritional status to CGD pathophysiology, the prognostic utility of simple, readily accessible indices such as SII and PNI has not been systematically examined across the full age spectrum of patients with CGD. Current approaches to prognostic assessment in CGD predominantly rely on clinical characteristics, genetic background, and infection history, with minimal incorporation of routinely available inflammatory and nutritional biomarkers.\u003c/p\u003e \u003cp\u003eAccordingly, the present study aimed to evaluate the prognostic relevance of SII and PNI in patients with CGD across pediatric and adult populations and to examine their associations with clinically meaningful outcomes, with a particular emphasis on survival status and hospitalization burden.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Design, Setting, and Population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective, single-center cohort study was conducted at a tertiary referral center and included patients diagnosed with CGD who were followed at Hacettepe University between 1984 and 2025. Patients of all ages with a confirmed diagnosis of CGD were eligible for inclusion if laboratory data obtained during a clinically stable outpatient period and/or during hospitalization were available together with complete longitudinal follow-up data.\u003c/p\u003e\n\u003cp\u003eThe diagnosis of CGD was established based on genetic confirmation and/or abnormal neutrophil oxidative burst testing, including the dihydrorhodamine (DHR) assay or nitroblue tetrazolium (NBT) test, in conjunction with compatible clinical findings. Patients were followed longitudinally from the time of diagnosis until death or last documented follow-up.\u003c/p\u003e\n\u003cp\u003ePatients with missing key laboratory parameters required for index calculation or incomplete follow-up data for primary outcomes were excluded from the analysis (Figure 1). Given the retrospective design, the study may be subject to selection bias related to the availability of complete laboratory and follow-up data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Data Collection and Outcome Definitions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and clinical variables extracted from medical records included age, sex, age at diagnosis, parental consanguinity, family history of CGD, hospitalization history, total number of hospitalizations, genetic data, advanced treatment exposure, and survival status. Clinical manifestations were categorized as infectious or non-infectious.\u003c/p\u003e\n\u003cp\u003eHospitalization was defined as an inpatient admission lasting at least 24 hours. Hospitalization status (yes/no) and the total number of hospitalizations during follow-up were recorded.\u003c/p\u003e\n\u003cp\u003eInfectious outcomes included clinically significant infections requiring systemic antimicrobial therapy and/or hospitalization and were categorized as pneumonia, sepsis, deep tissue abscess, tuberculosis, and Aspergillus infection. Deep tissue abscess was included as a predefined infectious manifestation, with its operational definition provided in the corresponding table footnote.\u003c/p\u003e\n\u003cp\u003eNon-infectious clinical manifestations included chronic diarrhea, inflammatory bowel disease\u0026ndash;like colitis, bronchiectasis, atelectasis, and growth retardation. Growth retardation was defined using age- and sex-adjusted growth charts documented in the medical records.\u003c/p\u003e\n\u003cp\u003eAdvanced treatment exposure was defined as receipt of interferon-gamma, intravenous immunoglobulin replacement, granulocyte transfusion, or hematopoietic stem cell transplantation. Overall survival was defined as the time from diagnosis to death from any cause or last follow-up, with surviving patients censored at the time of last clinical contact.\u003c/p\u003e\n\u003cp\u003eGenetic classification was based on the affected gene identified through molecular analysis (NCF1, NCF2, CYBA, or CYBB). Patients without available genetic data were categorized as genetic not available.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory Parameters and Index Calculation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSII was calculated as the product of absolute platelet and neutrophil counts divided by the absolute lymphocyte count and expressed as SII (/100) for analysis. PNI was calculated using serum albumin and lymphocyte counts, defined as albumin \u0026times;10 plus lymphocyte count \u0026times;0.005. Outpatient SII and PNI values were derived from laboratory parameters obtained at the first outpatient visit following diagnosis during a clinically stable period. Inpatient SII and PNI values were calculated using laboratory parameters from the first blood sample collected at the time of initial hospitalization, prior to the initiation of therapeutic interventions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were assessed for normality using visual inspection and analytical methods and are presented as median (range) or mean \u0026plusmn; standard deviation, as appropriate. Categorical variables are presented as counts and percentages.\u003c/p\u003e\n\u003cp\u003eOutpatient and inpatient SII (/100) and PNI values demonstrated non-normal distributions. Accordingly, comparisons between two independent groups were performed using non-parametric tests. Associations between outpatient inflammatory and nutritional indices and subsequent infectious outcomes or advanced treatment requirements were evaluated using the Mann\u0026ndash;Whitney U test. Relationships between dichotomized index groups and non-infectious clinical manifestations were assessed using the chi-square test.\u003c/p\u003e\n\u003cp\u003eHospitalization frequency was treated as a count outcome. Among patients with at least one prior hospitalization, factors associated with the total number of hospitalizations during follow-up were evaluated using negative binomial regression due to overdispersion. Outpatient SII (/100) and outpatient PNI were included as covariates, and results are reported as regression coefficients, incidence rate ratios, and 95% confidence intervals. Correlations between outpatient index values and hospitalization count were additionally assessed using Spearman\u0026rsquo;s rank correlation coefficient.\u003c/p\u003e\n\u003cp\u003eFor categorical analyses, inpatient SII (/100) values were dichotomized using an optimal cut-off determined by receiver operating characteristic (ROC) curve analysis to reflect acute inflammatory burden. Inpatient PNI values were dichotomized using a predefined cut-off value of 40, consistent with thresholds commonly applied in prior prognostic studies\u003csup\u003e8,9\u003c/sup\u003e. Outpatient SII (/100) and PNI values were categorized using predefined cut-off values reflecting clinically stable disease status, as specified in the relevant analyses.\u003c/p\u003e\n\u003cp\u003eThe prognostic performance of inpatient inflammatory burden for mortality was evaluated by calculating the area under the ROC curve with corresponding confidence intervals. Overall survival analyses were conducted using time-to-event methods.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, and a p value \u0026lt;0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics version 2025 (IBM Corp., Armonk, NY, USA). Patients with missing key laboratory or outcome data were excluded from the analysis; therefore, no imputation methods were applied.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Hacettepe University (approval number: SBA 24/809).Due to the retrospective design and the use of anonymized data, the requirement for informed consent was waived.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study cohort comprised 74 patients with chronic granulomatous disease. The median age was 19 years (range, 1\u0026ndash;68), and 45 patients (60.8%) were male. The median age at diagnosis was 10 years (range, 1\u0026ndash;57). Parental consanguinity was observed in 45 patients (60.8%), and 17 patients (23.0%) had a family history of CGD. At least one hospitalization was recorded in 61 patients (82.4%), with a median of four hospitalizations per patient (range, 1\u0026ndash;18) (Table 1). At the time of analysis, 14 patients (18.9%) had died.\u003c/p\u003e\n\u003cp\u003eMedian outpatient SII (/100) and PNI values were 8.03 (range, 1.17\u0026ndash;39.88) and 57.09 (range, 21.00\u0026ndash;86.10), respectively. Corresponding inpatient values were 8.71 (range, 1.30\u0026ndash;30.95) for SII (/100) and 51.70 (range, 21.50\u0026ndash;98.40) for PNI (Table 1).\u003c/p\u003e\n\u003cp\u003eInfectious complications were common. Pneumonia occurred in 54 patients (73.0%), tuberculosis in 29 (39.2%), deep tissue abscess in 18 (24.3%), sepsis in 7 (9.5%), and Aspergillus infection in 17 patients (23.0%). The median number of Aspergillus infection episodes per patient was two (range, 0\u0026ndash;24). Non-infectious manifestations included growth retardation in 38 patients (51.4%), chronic diarrhea in 17 (23.0%), atelectasis in 15 (20.3%), bronchiectasis in 6 (8.1%), and inflammatory bowel disease\u0026ndash;like colitis in 7 patients (9.5%) (Supplementary Table S1).\u003c/p\u003e\n\u003cp\u003eGenetic analysis identified CYBB variants in 16 patients (21.6%), CYBA in 15 (20.3%), NCF1 in 13 (17.6%), and NCF2 in 6 patients (8.1%), while genetic data were unavailable for 24 patients (32.4%). Interferon-\u0026gamma; therapy was administered to 42 patients (56.8%), intravenous immunoglobulin to 7 (9.5%), granulocyte transfusion to 6 (8.1%), and hematopoietic stem cell transplantation to 9 patients (12.2%) (Supplementary Table S1).\u003c/p\u003e\n\u003cp\u003eOutpatient inflammatory and nutritional indices differed significantly between hospitalized and non-hospitalized patients. Outpatient SII (/100) was higher among hospitalized patients than among non-hospitalized patients (mean rank, 41.52 vs. 18.65; p = 0.001), whereas outpatient PNI was lower (mean rank, 33.56 vs. 56.00; p = 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003eInpatient inflammatory and nutritional indices differed according to survival status. Inpatient SII (/100) values were higher among non-survivors compared with survivors (mean rank, 46.00 vs. 27.33; p = 0.001), whereas inpatient PNI values were lower among non-survivors (mean rank, 21.38 vs. 33.36; p = 0.036) (Table 2).\u003c/p\u003e\n\u003cp\u003eIn a multivariable logistic regression analysis including outpatient SII (/100) and PNI simultaneously, both indices were independently associated with hospitalization status [SII (/100): OR = 1.465; 95% CI, 1.030\u0026ndash;2.083; p = 0.034; PNI: OR = 0.926; 95% CI, 0.862\u0026ndash;0.994; p = 0.034] (Table 3).\u003c/p\u003e\n\u003cp\u003eIn a multivariable logistic regression analysis including inpatient SII (/100) and PNI simultaneously, higher inpatient SII (/100) was independently associated with survival status (OR = 0.885; 95% CI, 0.785\u0026ndash;0.998; p = 0.046), whereas inpatient PNI was not independently associated with survival status (Table 3).\u003c/p\u003e\n\u003cp\u003eNegative binomial regression analysis showed that outpatient SII (/100) and outpatient PNI were not independently associated with hospitalization frequency during follow-up in patients with a history of hospitalization (Supplementary Table S2). Consistently, Spearman correlation analysis demonstrated no significant monotonic association between hospitalization count and outpatient SII (/100) or outpatient PNI (Supplementary Table S3).\u003c/p\u003e\n\u003cp\u003eROC analysis demonstrated good discriminative ability of inpatient SII (/100) for survival status, with an area under the curve of 0.806 (95% CI, 0.693\u0026ndash;0.919; p = 0.001) (Figure 2). An optimal cut-off value of 8.4 was identified and subsequently used for risk stratification.\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier analysis demonstrated significantly shorter overall survival in patients with high inpatient SII (/100) compared with those with low values (log-rank p = 0.001). Mean survival time estimates were lower in the high SII (/100) group, whereas median survival could not be reliably estimated in the low SII (/100) group because of the high proportion of censored observations (Figure 3A).\u003c/p\u003e\n\u003cp\u003eUnivariable Cox proportional hazards regression analysis demonstrated that increasing inpatient SII (/100), analyzed as a continuous variable, was associated with a higher risk of death during follow-up (HR = 1.092; 95% CI, 1.025\u0026ndash;1.162; p = 0.006) (Figure 3B). Model-based survival estimates indicated a progressive decline in overall survival with increasing inpatient SII (/100) values.\u003c/p\u003e\n\u003cp\u003eROC analysis assessing the discriminative ability of inpatient PNI for hospitalization yielded an AUC of 0.304 (95% CI, 0.117\u0026ndash;0.491; p = 0.036), indicating inverse discrimination, with lower PNI values corresponding to a higher likelihood of hospitalization; therefore, no cut-off value was derived. Consistently, inpatient PNI analyzed as a continuous variable was independently associated with improved survival in univariable Cox regression analysis (HR = 0.919; 95% CI, 0.862\u0026ndash;0.980; p = 0.010) (Figure 3C).\u003c/p\u003e\n\u003cp\u003eOverall survival was further evaluated using a predefined inpatient PNI cut-off of 40. Kaplan\u0026ndash;Meier analysis showed significantly shorter survival in the low PNI group compared with the high PNI group (log-rank p \u0026lt; 0.001) (Figure 4). Median survival was 12.2 months (95% CI, 7.4\u0026ndash;16.9) in the low PNI group, whereas it was not reached in the high PNI group due to a high proportion of censored observations (Supplementary Table S4).\u003c/p\u003e\n\u003cp\u003eOutpatient inflammatory and nutritional indices were evaluated for associations with clinical outcomes (Supplementary Tables S5 and S6). Outpatient SII (/100) values were significantly higher in patients who subsequently developed pneumonia (p = 0.011), whereas outpatient PNI showed no association. No significant associations were observed for other infectious outcomes. Among non-infectious manifestations, lower outpatient PNI was associated with bronchiectasis (p = 0.042), while outpatient SII (/100) was not. CRP levels, genetic subgroups, infection status, IVIG administration, and HSCT status were not associated with differences in outpatient SII (/100) or PNI values (all p \u0026gt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides an evaluation of inflammatory and nutritional indices in a real-world cohort of patients with CGD spanning childhood and adulthood. By integrating outpatient and inpatient assessments of the SII and the PNI with hospitalization and survival outcomes, the present analysis offers insight into the context-dependent clinical relevance of routinely available laboratory-based markers in CGD. Composite indices derived from standard hematological parameters have been increasingly applied in chronic inflammatory and infectious conditions, providing a relevant conceptual framework for their evaluation in CGD\u003csup\u003e10,11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe infectious spectrum observed in this cohort, particularly pneumonia, tuberculosis, and Aspergillus infections, is consistent with prior CGD reports and provides clinical context for interpreting index-based associations\u003csup\u003e12,13\u003c/sup\u003e. The frequency of gastrointestinal and growth-related complications further aligns with the notion that chronic immune dysregulation and recurrent inflammatory insults contribute to a sustained disease burden over prolonged follow- up\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInn this clinical setting, the associations observed between outpatient indices and hospitalization status suggest that inter-individual differences in inflammatory burden and nutritional reserve during periods of clinical stability \u0026nbsp;may be clinically meaningful. However,in patients with a prior history of hospitalization, outpatient indices were not independently associated with frequency of subsequent hospitalization when hospitalization burden was analyzed as a count outcome. This finding highlights an important methodological distinction between prognostic stratification and prediction of event counts\u003csup\u003e15,16\u003c/sup\u003e. While composite indices such as SII and PNI are commonly used for prognostic stratification, they appear to be more effective in reflecting \u0026nbsp;survival-related risk than inpredicting cumulative event burden or event frequency over time.\u003c/p\u003e\n\u003cp\u003eThe consistency of findings across regression-based and correlation-based analyses supports the robustness of the observed lack of association between outpatient indices and hospitalization frequency. Together, these findings emphasize that outpatient inflammatory and nutritional markers may capture susceptibility to hospitalization rather than cumulative hospitalization burden over time.\u003c/p\u003e\n\u003cp\u003eThe consistency of findings across regression- and correlation-based analyses supports the robustness of the observed lack of association between outpatient indices and hospitalization frequency. Taken together, these findings suggest that outpatient inflammatory and nutritional markers may reflect the likelihoodof hospitalization rather than cumulative hospitalization burden over time.\u003c/p\u003e\n\u003cp\u003eFor infectious outcomes, outpatient inflammatory indices demonstrated context-dependent associations. Elevated outpatient SII (/100) values were associated with subsequent pneumonia development, whereas outpatient PNI was not, indicating that baseline systemic immune–inflammatory activity may be informative with respect to pulmonary infection susceptibility in this cohort. Consistent with these observations, elevated SII and reduced PNI have been associated with an increased risk of postoperative pneumonia in various surgical populations; however, this evidence is cited here to provide contextual support rather than direct extrapolation to CGD\u003csup\u003e17,18\u003c/sup\u003e. In contrast, outpatient SII (/100) and PNI were not significantly associated with other infectious complications, including sepsis, tuberculosis, deep tissue abscess, and Aspergillus infection. These outcomes may be influenced by multifactorial determinants—such as pathogen-specific characteristics, underlying immune profiles, exposure patterns, and treatment-related variables—that are unlikely to be fully captured by single outpatient measurements. Accordingly, non-significant associations should be interpreted cautiously.\u003c/p\u003e\n\u003cp\u003eOutpatient indices were not associated with non-infectious clinical manifestations related to chronic inflammation or long-term disease burden, including chronic diarrhea, inflammatory bowel disease–like colitis, bronchiectasis, atelectasis, and growth retardation. These conditions typically evolve over extended periods and reflect cumulative inflammatory exposure and disease-specific mechanisms that are unlikely to be fully captured by single time-point biomarker measurements.\u003c/p\u003e\n\u003cp\u003eAdmission C-reactive protein levels did not differ across inpatient SII (/100) or PNI stratification groups, suggesting limited concordance between CRP and composite inflammatory or nutritional indices. Whereas CRP primarily reflects acute-phase inflammatory activity, SII (/100) and PNI integrate hematological and nutritional components and may capture broader immune–metabolic states, indicating that these measures provide complementary rather than interchangeable information.\u003c/p\u003e\n\u003cp\u003eIn contrast to outpatient indices, hospitalization-phase indices demonstrated clearer associations with survival outcomes. Inpatient SII (/100) values were higher in non-survivors and remained independently associated with survival status, consistent with prior observations in infectious and inflammatory conditions\u003csup\u003e19,20\u003c/sup\u003e. Inpatient PNI differed between survivors and non-survivors in unadjusted analyses but did not retain an independent association with survival after multivariable adjustment, suggesting that inflammatory burden may exert a stronger influence on survival status than nutritional reserve within the analytical framework of this cohort. The prognostic relevance of PNI in acute and critical illness appears to vary across non-CGD populations.\u003c/p\u003e\n\u003cp\u003eTime-to-event analyses further supported the prognostic stratification value of inpatient indices, while underscoring that ROC-based discrimination and survival analyses assess distinct outcome structures and should be interpreted within their respective methodological contexts. Although no external validation cohort was available, the consistency of associations across multiple analytical approaches supports the internal validity of the observed findings. From a clinical perspective, these results suggest that inflammatory indices derived during hospitalization may be more informative for short- to mid-term prognostic assessment than indices measured during clinically stable follow-up.\u003c/p\u003e\n\u003cp\u003eOutpatient SII (/100) and PNI values did not differ according to exposure to advanced treatment modalities, including interferon-γ therapy, IVIG, or HSCT, nor across genetic subgroups. This observation suggests that inflammatory indices derived during clinically stable follow-up may be relatively insensitive to treatment-specific or genotype-specific differences, potentially reflecting shared downstream immune responses rather than intervention- or gene-specific effects.\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. The retrospective design may introduce selection bias, residual confounding cannot be excluded, and genetic characterization was unavailable for a subset of patients. In addition, both outpatient and inpatient indices were derived from single time-point measurements, which may not capture temporal variability in inflammatory and nutritional status. Despite these limitations, the inclusion of pediatric and adult patients, the extended follow-up duration, and the application of complementary analytical approaches enhance the interpretability of the findings and their relevance to real-world CGD populations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, inflammatory and nutritional indices derived from routine laboratory parameters provide context-dependent clinical information in CGD. Outpatient indices were associated with hospitalization status but not with hospitalization frequency when modeled as a count outcome. Inpatient indices—particularly inflammatory burden captured by SII (/100) were associated with survival outcomes, supporting their potential role in prognostic assessment during hospitalization. Future prospective studies incorporating repeated measurements and external validation are warranted to further clarify their utility in risk stratification and longitudinal monitoring in CGD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSE: Conceptualization, study design, supervision, and overall scientific leadership.\u003c/p\u003e\n\u003cp\u003eZG, SG: Data collection, statistical analysis, and drafting of the initial manuscript.\u003c/p\u003e\n\u003cp\u003eCÜ, ZG, ESA, SG: Data collection and data interpretation.\u003c/p\u003e\n\u003cp\u003eDÇ: Study design refinement and data interpretation.\u003c/p\u003e\n\u003cp\u003eAll authors: Critical revision of the manuscript and approval of the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Conflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eYu HH, Yang YH, Chiang BL. Chronic granulomatous disease: a comprehensive review. Clin Rev Allergy Immunol. 2021;61(2):101\u0026ndash;113. doi:10.1007/s12016-020-08800-x.\u003c/li\u003e\n \u003cli\u003eAlwarawrah Y, Kiernan K, MacIver NJ. Changes in nutritional status impact immune cell metabolism and function. \u003cem\u003eFront Immunol.\u003c/em\u003e 2018;9:1055. doi:10.3389/fimmu.2018.01055.\u003c/li\u003e\n \u003cli\u003eM\u0026eacute;ndez L\u0026oacute;pez LF, Gonz\u0026aacute;lez Llerena JL, V\u0026aacute;zquez Rodr\u0026iacute;guez JA, Medell\u0026iacute;n Guerrero AB, Gonz\u0026aacute;lez Mart\u0026iacute;nez BE, Sol\u0026iacute;s P\u0026eacute;rez E, et al. Dietary modulation of the immune system. \u003cem\u003eNutrients.\u003c/em\u003e 2024;16(24):4363. doi:10.3390/nu16244363.\u003c/li\u003e\n \u003cli\u003eHu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune\u0026ndash;inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. \u003cem\u003eClin Cancer Res.\u003c/em\u003e 2014;20(23):6212\u0026ndash;6222. doi:10.1158/1078-0432.CCR-14-0442.\u003c/li\u003e\n \u003cli\u003eDe Rose L, Sorge J, Blackwell B, Benjamin M, Mohamed A, Rovers T, et al. Determining if the prognostic nutritional index can predict outcomes in community-acquired bacterial pneumonia. \u003cem\u003eRespir Med.\u003c/em\u003e 2024;226:107626. doi:10.1016/j.rmed.2024.107626.\u003c/li\u003e\n \u003cli\u003eJi Y, Wang H. Prognostic prediction of systemic immune\u0026ndash;inflammation index for patients with gynecological and breast cancers: a meta-analysis.\u003cem\u003e\u0026nbsp;\u003cem\u003eWorld J Surg Oncol. 2020;18(1):197\u003c/em\u003e.\u0026nbsp;\u003c/em\u003edoi:10.1186/s12957-020-01974-w.\u003c/li\u003e\n \u003cli\u003eOu S, Lu H, Qu R, Cui X, Xiong Z, Fan F, et al. The clinical value of systemic immune inflammatory index in predicting the prognosis of patients with bloodstream infection. \u003cem\u003eJ Inflamm Res.\u003c/em\u003e 2025;18:10181\u0026ndash;10192. doi:10.2147/JIR.S531272.\u003c/li\u003e\n \u003cli\u003eDoi S, Ishibashi Y, Suzuki N, Miyahara D, Sato Y, Kuwata S, et al. Prognostic nutritional index in risk of mortality following fulminant myocarditis. \u003cem\u003eSci Rep.\u003c/em\u003e 2025;15(1):41379. doi:10.1038/s41598-025-25385-7.\u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eZhang L, Ma W, Qiu Z, Kuang T, Wang K, Hu B, et al. Prognostic nutritional index as a prognostic biomarker for gastrointestinal cancer patients treated with immune checkpoint inhibitors. \u003cem\u003eFront Immunol.\u003c/em\u003e 2023;14:1219929. doi:10.3389/fimmu.2023.1219929.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eWu Y, Huang Y, Wu Y, Sun J, Xie Q, Yin G. Systemic immune\u0026ndash;inflammation index as a versatile biomarker in autoimmune disorders: insights from rheumatoid arthritis, lupus, and spondyloarthritis. \u003cem\u003eFront Immunol.\u003c/em\u003e 2025;16:1621209. doi:10.3389/fimmu.2025.1621209.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003eZhu S, Cheng Z, Hu Y, Chen Z, Zhang J, Ke C, et al. Prognostic value of the systemic immune\u0026ndash;inflammation index and prognostic nutritional index in patients with medulloblastoma undergoing surgical resection. \u003cem\u003eFront Nutr.\u003c/em\u003e 2021;8:754958. doi:10.3389/fnut.2021.754958.\u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eOikonomopoulou Z, Shulman ST, Mets M, Katz B. Chronic granulomatous disease: an updated experience, with emphasis on newly recognized features. \u003cem\u003eJ Clin Immunol.\u003c/em\u003e 2022;42(7):1411\u0026ndash;1419. doi:10.1007/s10875-022-01294-6.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eLee PPW, Chan KW, Jiang L, Chen T, Li C, Lee TL, \u003cem\u003eet al.\u003c/em\u003e Susceptibility to mycobacterial infections in children with X-linked chronic granulomatous disease: a review of 17 patients living in a region endemic for tuberculosis. \u003cem\u003ePediatr Infect Dis J.\u003c/em\u003e 2008;27(3):224\u0026ndash;230. doi:10.1097/INF.0b013e31815b494c.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eGrammatikos A, Gennery AR. Inflammatory complications in chronic granulomatous disease. \u003cem\u003eJ Clin Med.\u003c/em\u003e 2024;13(4):1092. doi:10.3390/jcm13041092.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003evan Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KGM. Clinical prediction models: diagnosis versus prognosis. \u003cem\u003eJ Clin Epidemiol.\u003c/em\u003e 2021;132:142\u0026ndash;145. doi:10.1016/j.jclinepi.2021.01.009.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eKent P, Cancelliere C, Boyle E, Cassidy JD, Kongsted A. A conceptual framework for prognostic research. BMC Med Res Methodol. 2020;20(1):172. doi:10.1186/s12874-020-01050-7.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003e\u0026nbsp;Li S, Li H, Qiu W, Wu B, Wang J, Li Y, et al. Relationship between novel inflammatory indices and the incidence of postoperative pneumonia after endovascular embolization for aneurysmal subarachnoid hemorrhage. J Inflamm Res. 2025;18:667\u0026ndash;679. doi:10.2147/JIR.S505797.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eWen Q, Kang Z, Shen Z. Association between systemic immune\u0026ndash;inflammation index and postoperative pulmonary infection in elderly patients undergoing laparoscopic abdominal surgery. Front Med (Lausanne). 2025;12:1532040. doi:10.3389/fmed.2025.1532040.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eLiang L, Su Q. Systemic immune\u0026ndash;inflammation index and the short-term mortality of patients with sepsis: a meta-analysis. Biomol Biomed. 2025;25(4):798\u0026ndash;809. doi:10.17305/bb.2024.11494.\u003c/address\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003caddress\u003eAgus HZ, Kahraman S, Arslan C, Yildirim C, Erturk M, Kalkan AK, et al. Systemic immune\u0026ndash;inflammation index predicts mortality in infective endocarditis. J Saudi Heart Assoc. 2020;32(1):58\u0026ndash;64. doi:10.37616/2212-5043.1010.\u003c/address\u003e\n \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline demographic, clinical, and inflammatory index characteristics of patients with chronic granulomatous disease\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003ePatient Information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eNumber of patients, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eAge, years, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e19 (1-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eSex (male/female), n (%) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; 45/29 (60.8/39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eAge at diagnosis, years, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10 (1-57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eConsanguinity, n (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e45 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eFamily history of CGD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e17 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eDisease Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eAt least one hospitalization, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e61(82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eNumber of hospitalizations, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4 (1-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eLaboratory Indices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eOutpatient SII (/100), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.03 (1.17-39.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eOutpatient PNI, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e57.09 (21.00-86.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eInpatient SII (/100), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.71 (1.30-30.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eInpatient PNI, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e51.70 (21.50-98.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003caddress\u003eData are presented as median (range) unless otherwise specified. SII values were divided by 100 for readability. CGD, chronic granulomatous disease; PNI, prognostic nutritional index; SII, systemic immune\u0026ndash;inflammation index.\u003c/address\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Group comparisons of the SII (/100) and PNI by clinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003caddress\u003eVariable\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003eGroup\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003caddress\u003eN\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003caddress\u003eMean Rank\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003caddress\u003eU\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003caddress\u003eZ\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003caddress\u003ep value\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003caddress\u003ePanel A. Hospitalization status\u003c/address\u003e\n \u003caddress\u003e(outpatient indices)\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003caddress\u003eOutpatient SII (/100)\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003caddress\u003eHospitalized\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003caddress\u003e61\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003caddress\u003e41.52\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003caddress\u003e151.50\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003caddress\u003e-3.480\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003caddress\u003e0.001\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003caddress\u003eNot hospitalized\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003caddress\u003e13\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003caddress\u003e18.65\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003caddress\u003eOutpatient PNI\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003caddress\u003eHospitalized\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003caddress\u003e61\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003caddress\u003e33.56\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003caddress\u003e156.00\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003caddress\u003e-3.416\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003caddress\u003e0.001\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003eNot hospitalized\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003caddress\u003e13\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003caddress\u003e56.00\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cem\u003ePanel B. Survival status\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;(inpatient indices)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eInpatient SII (/100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003eNon-survivor\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003e12\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003e46.00\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e114.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;3.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003eSurvivor\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003caddress\u003e49\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003caddress\u003e27.33\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eInpatient PNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003eNon-survivor\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003e12\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003caddress\u003e21.38\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e178.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;2.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003eSurvivor\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003caddress\u003e49\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003caddress\u003e33.36\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eComparisons were performed using the Mann\u0026ndash;Whitney U test for hospitalization status (Panel A) and survival status (Panel B). U and Z values represent overall group comparisons. SII values were divided by 100 for readability. CI, confidence interval; OR, odds ratio; PNI, prognostic nutritional index; SII, systemic immune\u0026ndash;inflammation index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Associations of SII (/100) and PNI with clinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003caddress\u003eVariable\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003caddress\u003eB\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003caddress\u003eStandard error\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003caddress\u003eWald \u0026chi;\u0026sup2;\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003caddress\u003ep value\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003caddress\u003eOR\u0026nbsp;\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003e95% CI for OR\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003caddress\u003ePanel A. Hospitalization status\u003c/address\u003e\n \u003caddress\u003e(outpatient indices)\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003caddress\u003eOutpatient SII (/100)\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003caddress\u003e0.382\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003caddress\u003e0.180\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003caddress\u003e4.518\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003caddress\u003e0.034\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003caddress\u003e1.465\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003e1.030\u0026ndash;2.083\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003caddress\u003eOutpatient PNI\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003caddress\u003e\u0026minus;0.077\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003caddress\u003e0.037\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003caddress\u003e4.484\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003caddress\u003e0.034\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003caddress\u003e0.926\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003caddress\u003e0.862\u0026ndash;0.994\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cem\u003ePanel B. Survival status\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(inpatient indices)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003caddress\u003eInpatient SII (/100)\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003caddress\u003e\u0026minus;0.122\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003caddress\u003e0.061\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003caddress\u003e3.979\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003caddress\u003e0.046\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003caddress\u003e0.885\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003caddress\u003e0.785 \u0026ndash; 0.998\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003caddress\u003eInpatient PNI\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003caddress\u003e0.074\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003caddress\u003e0.041\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003caddress\u003e3.323\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003caddress\u003e0.068\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003caddress\u003e1.077\u003c/address\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003caddress\u003e0.994 \u0026ndash; 1.167\u003c/address\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAssociations were assessed using multivariable logistic regression analyses, with SII (/100) and PNI entered simultaneously into the models. In Panel A, hospitalization status was coded as 0 = no and 1 = yes. In Panel B, survival status was defined as survival at last follow-up and coded as 1 = survivor and 0 = non-survivor. All models included an intercept term. CI, confidence interval; OR, odds ratio; PNI, prognostic nutritional index; SII, systemic immune\u0026ndash;inflammation index.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-immunology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joci","sideBox":"Learn more about [Journal of Clinical Immunology](https://www.springer.com/journal/10875)","snPcode":"10875","submissionUrl":"https://submission.nature.com/new-submission/10875/3","title":"Journal of Clinical Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chronic granulomatous disease, hospitalization, prognostic nutritional index, survival, systemic immune–inflammation index","lastPublishedDoi":"10.21203/rs.3.rs-8757304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8757304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic granulomatous disease (CGD) is associated with recurrent infections and inflammatory manifestations, which are major contributors to morbidity, hospitalization, and early mortality. Routinely available laboratory parameters obtained during regular outpatient follow-up may facilitate early risk stratification, yet the clinical relevance of systemic inflammatory and nutritional indices in CGD remains incompletely defined. This study examined the associations of the systemic immune\u0026ndash;inflammation index (SII) and the prognostic nutritional index (PNI) with selected clinical outcomes in CGD. In this retrospective, single-center cohort study, patients with CGD followed at a tertiary referral center between 1984 and 2025 were included. Outpatient SII (/100) and PNI values were calculated from laboratory data obtained at the first outpatient visit following diagnosis, whereas inpatient values were derived from samples collected during index hospitalization. Associations between these indices and hospitalization status, infectious and non-infectious manifestations, and survival outcomes were evaluated using regression-based and time-to-event analyses. The cohort comprised 74 patients (median age, 19 years); 61 (82.4%) experienced at least one hospitalization and 14 (18.9%) died during follow-up. Higher outpatient SII (/100) values and lower outpatient PNI values were independently associated with hospitalization status, whereas neither index was associated with hospitalization frequency. Inpatient SII (/100) values were higher among non-survivors and demonstrated good discriminative ability for survival status (AUC, 0.806). Higher inpatient SII (/100) and lower inpatient PNI values were associated with shorter overall survival. These findings indicate context-dependent associations, with inpatient indices reflecting survival, while outpatient indices primarily indicate hospitalization susceptibility rather than cumulative disease burden.\u003c/p\u003e","manuscriptTitle":"Systemic Immune–Inflammation Index and Prognostic Nutritional Index as Predictors of Clinical Outcomes in Chronic Granulomatous Disease: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 14:34:50","doi":"10.21203/rs.3.rs-8757304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-29T17:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235756605358064830203931585804555478966","date":"2026-03-18T13:40:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114321106754213265312935854788522985374","date":"2026-02-17T17:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T00:01:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T00:15:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T00:15:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Immunology","date":"2026-02-01T15:15:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-immunology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joci","sideBox":"Learn more about [Journal of Clinical Immunology](https://www.springer.com/journal/10875)","snPcode":"10875","submissionUrl":"https://submission.nature.com/new-submission/10875/3","title":"Journal of Clinical Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"54e0fbb3-f7ce-420a-af98-215a4131a6fc","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T14:34:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 14:34:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8757304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8757304","identity":"rs-8757304","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}