Neonatal cell population data: reference intervals and relevance for detecting sepsis and necrotizing enterocolitis

Neonatal cell population data: reference intervals and relevance for detecting sepsis and necrotizing enterocolitis | 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 Article Neonatal cell population data: reference intervals and relevance for detecting sepsis and necrotizing enterocolitis Flavia Ferraro, Laura Fillistorf, Varvara Dimopoulou, Lorenzo Alberio, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5984143/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Pediatric Research → Version 1 posted You are reading this latest preprint version Abstract Timely diagnosis of neonatal sepsis is crucial but remains challenging with existing tools. Cell Population Data (CPD) provide high-resolution phenotypic analysis of leukocytes, offering potential for sepsis detection. We aimed to establish neonatal CPD reference intervals and explore their potential for early recognition of sepsis and necrotizing enterocolitis (NEC). Neutrophil, monocyte, and lymphocyte CPD from hospitalized newborns were analyzed. Reference intervals (5th to 95th percentiles), at birth and during the first 28 days, were derived from newborns without conditions potentially impacting CPD (controls). CPD obtained on the day of clinical suspicion from newborns with blood culture-proven sepsis and/or NEC were compared to controls, and their performance in detecting sepsis/NEC was compared with complete blood count (CBC) and C-reactive protein (CRP). Reference intervals from 905 controls showed that mean CPD values had distinct trajectories for each parameter, while distribution width generally decreased with increasing gestational and postnatal age. CPD in 39 sepsis/NEC cases differed from controls, particularly neutrophil fluorescence intensity (NE-SFL) (56.2 vs. 41.1 arbitrary units, P < 0.001). NE-SFL had superior accuracy over other CPD, CBC, and CRP, with 90% sensitivity and 76% specificity. This study establishes neonatal CPD reference intervals and identifies NE-SFL as a potential sepsis biomarker. Health sciences/Medical research/Biomarkers Health sciences/Medical research/Paediatric research Monocytes neutrophils lymphocytes normal values newborns infection Figures Figure 1 Figure 2 Figure 3 Introduction Sepsis is a leading cause of death and disability in newborns, with preterm infants being the most vulnerable population 1 . The rapid progression of neonatal sepsis necessitates early detection to initiate timely empirical antibiotic treatment and optimize outcomes 2 , 3 . However, current diagnostic tools have limited accuracy for early identification and differentiation of sepsis from non-infectious conditions 4 . As a result, some newborns receive delayed antibiotic treatments, while others undergo unnecessary treatment, increasing the risk of adverse outcomes and promoting the emergence and spread of antimicrobial resistance 5 . This highlights the need for novel biomarkers that can assist clinicians in the early and accurate detection of sepsis. State-of-the-art hematologic analyzers use a combination of impedance and fluorescence flow cytometry to deliver a high-resolution analysis of leukocyte cell volume, complexity, and fluorescence intensity 6 – 9 . This detailed information, known as Cell Population Data (CPD), is used in clinical practice to determine the complete blood count (CBC) and leukocyte differential 9 . Given the critical role of leukocytes in host response to infection, CPD could provide valuable insights for early detection of neonatal sepsis 10 . In addition, studying CPD could provide new insights on the developmental biology of blood leucocytes. However, the lack of neonatal reference intervals hinders CPD interpretation. Few studies have suggested the potential of specific neutrophil characteristics as biomarkers for neonatal sepsis 11 – 18 . However, no comprehensive evaluation of leukocyte CPD has been undertaken to date. Moreover, previous studies have not leveraged fluorescence flow cytometry technology, underscoring the need for further research in this area. To address these knowledge gaps, we established reference intervals for leukocyte CPD in a large cohort of newborns. In addition, we explored the potential of CPD as early biomarkers of sepsis and necrotizing enterocolitis (NEC). Methods This single-center, retrospective observational study was conducted in the neonatal unit of the University Hospital of Lausanne from January 2021 to June 2023. It was approved by the Cantonal Ethics Committee of Vaud (Lausanne, Switzerland, ID 2022 − 00528). All the research was conducted in accordance with the relevant guidelines and regulations. All patients hospitalized in the neonatal unit who had at least one CBC between birth and a corrected age (defined as the sum of gestational and postnatal age) of 44 weeks were eligible, regardless of the clinical indication for the CBC. We excluded newborns whose parents or legal guardians refused general consent for research and those with missing CPD parameters. Neonatal sepsis was diagnosed based on blood culture-proven infection 19 , 20 . Contaminated blood cultures were defined by the growth of bacteria typically considered as contaminants (e.g., Micrococcus species or diphtheroids), growth of coagulase-negative staphylococci (CoNS) in patients without a central or peripheral catheter, or cultures deemed contaminated by clinicians, leading to discontinuation of antimicrobial therapy within less than 5 days 20 . NEC was defined as Bell stage 2 or higher 20 , 21 . We identified a control group of newborns without any condition potentially impacting neutrophils, monocytes and/or lymphocytes. The following conditions were considered as potentially impacting CPD: congenital neutropenia, trisomy 13, 18, and 21, birth asphyxia (initial pH < 7.0, 5-minute Apgar score < 5), hypoxic-ischemic encephalopathy, grade 3 or 4 intraventricular hemorrhage, cystic periventricular leukomalacia, stage 3 or 4 retinopathy of prematurity, and any suspected or proven systemic or focal bacterial, fungal or viral infection leading to antimicrobial treatment for 5 days or more 22 – 25 . Data were extracted from the hospital’s clinical information systems, electronic health records, and laboratory information system. CPD parameters were acquired using the Sysmex XN-9000® hematology analyzer which uses a combination of impedance and fluorescence flow cytometry to provide leukocyte differential counts derived from CPD. For neutrophils, monocytes, and lymphocytes, we collected data on side scatter (X-axis), indicating cellular complexity, fluorescence intensity (Y-axis), reflecting nucleic acid content, and forward scatter (Z-axis), denoting cell volume. Additionally, we collected data on the distribution width of each parameter, representing signal heterogeneity. This resulted in a total of 18 CPD variables ( Supplementary Table S1 ). CPD of neutrophils, monocytes, and lymphocytes obtained from the control group were analyzed to determine reference intervals, defined as the interval between the 5th and 95th percentiles of observed values, in line with prior studies 22 . We presented CPD obtained on the day of birth according to gestational age. To provide data on the neonatal period, we presented CPD from the first 28 postnatal days stratified into three gestational age groups: 36 weeks 22 , 25 . To get insights into the potential of CPD as novel biomarkers, we conducted exploratory analyses of CPD parameters at the onset of sepsis/NEC. We analyzed CPD obtained at the time of blood culture and/or abdominal X ray for NEC. For patients who did not have CPD collected at this time, we analyzed the closest CBC ( Supplementary Table S2 ). We compared CPD from sepsis/NEC cases to controls and applied the same analytical approach to CBC and CRP. We defined leukopenia as a leucocyte count < 5 G/L, neutropenia as an absolute neutrophil count 0.2 26–28 . Patient characteristics were summarized by descriptive statistics. Continuous variables were presented as medians and interquartile ranges (IQR), while categorical variables were expressed as absolute numbers and percentages. Differences between the sepsis/NEC group and the control group were assessed using Wilcoxon–Mann Whitney tests for continuous variables and Pearson chi-square tests for categorical variables. To construct reference intervals, data were grouped by gestational age at birth. The median, 5th and 95th percentiles were calculated, and data were smoothed using the Locally Estimated Scatterplot Smoothing (LOESS) technique to provide a continuous and regular representation of trends. A mixed-effects model and Estimated Marginal Means (EMMs) post-hoc analysis was employed to quantify changes in CPD parameters over time and among different gestational age groups. The diagnostic accuracy of each variable was assessed using Receiver Operating Characteristic (ROC) curves and Precision-Recall Curve (PRC) analyses, the latter being better suited in contexts with imbalanced groups 29 . Area under the ROC curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and average precision (AP) were evaluated. A multivariate logistic regression model was developed to predict sepsis/NEC using CRP and the most accurate CPD parameters. The variance inflation factor (VIF) was calculated to assess multicollinearity, and a 10-fold cross-validation ensured robustness. Predicted probabilities were used to generate ROC and PRC curves, with performance metrics quantifying diagnostic effectiveness. The same statistical analyses (descriptive statistics, non-parametric tests, diagnostic accuracy metrics, and logistic regression) were applied to a population without exclusion of controls with comorbidities potentially affecting CPD to account for the potential impact of these conditions on the diagnostic performance of CPD parameters. Statistical analyses were carried out using R version 4.3.0. Results Participants Among 1693 eligible patients, 512 (30%) were excluded due to missing CPD parameters, and 90 (5%) due to refusal of general consent for research ( Supplementary Figure S1 ). A control group of 905 newborns was obtained after exclusion of 135 patients with comorbidities potentially impacting neutrophils, monocytes and/or lymphocytes. We identified 39 newborns with sepsis/NEC. Main demographic, clinical and microbiological characteristics of the study population are reported in Table 1 , while those of the population not excluding controls with comorbidities potentially affecting CPD are provided in Supplementary Table S3 . Table 1 Demographics, clinical and microbiological characteristics of patients Control group n = 905 Sepsis/NEC group n = 39 P value Postnatal age at sepsis or NEC onset, days — 9 (6–20) — Birth weight, kg 2.8 (2.0-3.4) 0.9 (0.7-2.0) < 0.001 Gestational age, weeks 37 (34–40) 28 (26–34) < 0.001 Female sex 380 (42) 23 (59) 0.05 Pathogens in blood culture proven sepsis — 34 (87) — Escherichia coli — 8 (24) — Coagulase-negative staphylococci — 6 (18) — Staphylococcus aureus — 4 (12) — Streptococcus agalactiae — 3 (9) — Klebsiella pneumoniae — 3 (9) — Klebsiella oxytoca — 2 (6) — Candida albicans — 2 (6) — Others pathogens 1 — 6 (17) — Early-onset sepsis — 8 (24) — Late-onset sepsis — 26 (76) — Necrotizing enterocolitis — 10 (26) — Continuous variables are reported as median (interquartile range). Categorical variables are reported as number (percent). Early-onset sepsis: onset before 72 hours of life; late-onset sepsis: onset after 72 hours of life. Other pathogens include Achromobacter ssp., Bifidobacterium breve, Clostridium neonatale, Enterobacter cloacae, Enterococcus faecalis, Lacticaseibacillus rhamnosus. Reference intervals for CPD parameters Reference intervals for CPD parameters were established in the control group based on gestational and postnatal age. We computed reference intervals for CPD parameters on the day of birth according to gestational age (Fig. 1 ), and reference intervals over the first 28 postnatal days in three gestational age groups (Fig. 2 ). CPD parameters varied with gestational age at birth, showing distinct patterns between neutrophils, monocytes, and lymphocytes ( Supplementary Table S4 ). Across neutrophil, monocyte and lymphocyte CPD, we observed a general pattern of reduction in the distribution width of most CPD parameters with increasing gestational age. When analyzing the effect of postnatal age on CPD parameters across the three gestational age groups, we found substantial differences not only in initial values but also in the rate and direction of changes over time ( Supplementary Table S5 ). This analysis also indicated a general pattern of reduction in the distribution width of most analyzed CPD parameters, both with increasing gestational and postnatal age. CPD accuracy for the detection of sepsis and NEC In the sepsis/NEC group, 29 (74%) newborns had culture-proven sepsis, 5 (13%) had NEC, and 5 (13%) had both conditions. Neonates with sepsis/NEC had a lower gestational age and birth weight compared to controls ( Table 1 , Supplementary Table S3) . On the day of clinical suspicion, substantial differences emerged between sepsis/NEC cases and controls for most CPD parameters (Table 2 ). Similar findings were obtained in the population including controls with comorbidities potentially affecting CPD ( Supplementary Table S6) . Neutrophil fluorescence intensity (NE-SFL) and lymphocytes cell complexity (LY-X) showed superior diagnostic accuracy to identify sepsis/NEC compared to other CPD parameters, leukocyte and neutrophil counts, bands, I/T ratio and CRP (Table 3 , Fig. 3 ). In particular, NE-SFL had a sensitivity of 90%, a specificity of 76%, a positive predictive value (PPV) of 13%, a negative predictive value (NPV) of 99%, and an average precision (AP) of 0.20 (Table 3 , Fig. 3 ). Including controls with comorbidities potentially affecting CPD did not reduce diagnostic accuracy of CPD ( Supplementary Table S7 , Supplementary Figure S2). We built three logistic regression models using the most performant CPD parameters, NE-SFL and LY-X, combined with leukopenia, and/or CRP ( Supplementary Table S8 , S9, S10, and S11 ). All parameters showed a positive association with sepsis/NEC with a low multicollinearity among the predictors ( Supplementary Table S8 and S10 ). All models provided a slight improvement in diagnostic performance compared to NE-SFL alone, with no single model showing clear superiority over the others (Table 3 , Supplementary Figure S3 and S4, Table S9 and S11 ). Table 2 Cell Population Data in controls and patients with sepsis/necrotizing enterocolitis Parameter Control group Sepsis/NEC group P value NE-SSC 149.9 (147.0-152.8) 148.7 (144.4-152.6) 0.08 NE-SFL 41.1 (38.8–44.0) 56.2 (46.5–63.7) < 0.001 NE-FSC 86.7 (84.2–89.1) 84.3 (80.9–86.2) < 0.001 NE-WX 354.0 (338.0-381.0) 358.0 (334.0-400.2) 0.61 NE-WY 789.0 (697.0-900.0) 904.5 (788.2-1123.5) < 0.001 NE-WZ 739.0 (710.0-770.8) 747.5 (718.2–794.0) 0.12 MO-X 116.2 (114.4-118.4) 118.7 (115.6-123.2) < 0.001 MO-Y 85.6 (80.1–91.4) 94.4 (85.7-103.8) < 0.001 MO-Z 69.1 (67.2–71.1) 69.8 (67.3–73.0) 0.30 MO-WX 278.0 (261.0-299.0) 302.5 (273.0-367.2) < 0.001 MO-WY 815.0 (740.2–903) 869.0 (710.0-997.2) 0.45 MO-WZ 679.5 (639.2–718.0) 683.5 (621.2–731.0) 0.88 LY-X 78.3 (76.6–80.4) 81.3 (80.6–82.2) < 0.001 LY-Y 58.8 (54.1–63.3) 65.4 (58.2–70.6) < 0.05 LY-Z 58.7 (56.8–60.1) 59.7 (58.2–62.0) < 0.05 LY-WX 500.0 (470.0-536.0) 492.0 (458.0-523.0) 0.30 LY-WY 1000.0 (933.0-1143.8) 1225.0 (1078.0-1342.0) < 0.001 LY-WZ 701.0 (664.0-804.0) 790.0 (690.0-864.0) < 0.05 Data are expressed as median (interquartile range). Cell Population Data are reported in arbitrary units of light scattering (ch). LY-X, LY-Y, LY-Z, LY-WX, LY-WY, and LY-WZ were analyzed in 433 out of 905 patients (48%), as data was not stored for 472 patients. Table 3 Diagnostic accuracy of Cell Population Data, classical hematological parameters, C-reactive protein, and combined models. Parameter AUROC Cut off value Sp% Se% PPV% NPV% AP NE-SSC 0.57 147.2 73 46 7 97 0.01 NE-SFL 0.88 44.2 76 90 13 99 0.20 NE-FSC 0.70 85.7 63 73 8 98 0.01 NE-WX 0.52 422.5 93 21 10 97 0.03 NE-WY 0.68 888.5 73 58 8 98 0.05 NE-WZ 0.57 773.5 77 44 7 97 0.03 MO-X 0.67 117.2 64 71 8 98 0.16 MO-Y 0.71 93.6 81 56 11 98 0.09 MO-Z 0.54 72.8 90 27 11 97 0.06 MO-WX 0.69 322.5 89 46 14 98 0.05 MO-WY 0.53 962.5 86 35 9 97 0.03 MO-WZ 0.49 698.5 64 48 5 97 0.02 LY-X 0.83 79.8 69 95 11 100 0.06 LY-Y 0.66 64.8 80 57 11 98 0.04 LY-Z 0.63 61.7 92 38 16 97 0.05 LY-WX 0.57 525.5 31 86 5 98 0.04 LY-WY 0.75 1077.5 66 76 9 99 0.01 LY-WZ 0.65 753.5 66 62 7 98 0.04 Leukocytes (G/L) 0.60 6.3 92 46 19 98 0.04 Neutrophils (G/L) 0.62 3.6 72 51 7 97 0.06 Bands (G/L) 0.66 0.2 45 87 6 99 0.05 Leucopenia (G/L) 0.60 < 5 97 33 31 97 0.04 Neutropenia (G/L) 0.62 < 1.5 95 22 14 97 0.06 I/T ratio (%) 0.77 20 96 20 16 97 0.08 CRP (mg/L) 0.73 24.5 79 61 11 98 0.23 NE-SFL + LY-X + CRP (mg/L) 0.89 0.03 92 75 29 99 0.26 NE-SFL + LY-X + leucopenia (G/L) 0.89 0.02 90 77 25 99 0.27 NE-SFL + LY-X + leucopenia (G/L) + CRP (mg/L) 0.90 0.02 91 77 25 99 0.28 Cell Population Data are reported in arbitrary units of light scattering, ch; AUROC, area under the Receiver Operating Characteristic curve; Se, sensitivity, Sp, specificity; PPV, positive predictive value; NPV, negative predictive value; AP, Average Precision; CRP, C-reactive protein; I/T ratio, immature-to-total ratio. LY-X, LY-Y, LY-Z, LY-WX, LY-WY, and LY-WZ were analyzed in 433 out of 905 patients (48%), as data was not stored for 472 patients. Discussion In this retrospective observational study, we established reference intervals for neutrophil, monocyte, and lymphocyte CPD in newborns, defining ranges from birth to postnatal day 28 across different gestational age groups. In an exploratory analysis of sepsis and NEC cases, we identified early alterations of most CPD parameters, suggesting that CPD could provide attractive biomarker candidates for the diagnosis of sepsis and NEC. Previous studies have shown that hematological parameters vary with gestational and postnatal age, highlighting the importance of establishing reference intervals in newborns 22 – 25 , 30 . Our study contributes to this body of knowledge by establishing reference intervals for CPD in a large cohort of hospitalized newborns. While reference intervals for CPD have already been established in the adult population, to our knowledge, this is the first report on neonatal intervals 31 – 33 . Furthermore, our findings highlight the considerable impact of gestational and postnatal age on neutrophil, monocyte, and lymphocyte CPD, revealing variations in complexity, fluorescence, and size across gestational age and throughout the neonatal period, contributing to increase our understanding of the developmental biology of blood leukocytes. Reference intervals for CPD parameters, which capture the evolution of leukocyte subpopulation morphology and functionality, reveal trends that differ from those of absolute cellular counts 23 – 25 . Across the three CPD subpopulations assessed in our study, we observed a reduction in the distribution width of most CPD parameters with increasing gestational age, probably indicating enhanced cellular homogeneity in more mature infants. In line with those findings, CPD distribution width reference intervals in adults are lower than those of full-term neonates, suggesting a developmental process towards greater cellular homogeneity 31 , 32 , 34 . In contrast, each mean CPD parameter exhibited its own unique pattern of change with both gestational and postnatal age. NE-SSC, an indicator of neutrophil cell complexity and granule content, progressively increases with gestational but not postnatal age, indicating that the maturation process of neutrophils could depend mainly on gestational age at birth 35 . The reference intervals in full-term newborns are close to those observed in adults 31 , 32 , 34 . NE-SFL, which depends on the RNA/DNA neutrophil content ratio, may reflect cellular immaturity or activation 36 , 37 . In our results, NE-SFL shows complex changes across gestational and postnatal age, with reference intervals in full term newborns closely aligning with adult ranges, likely reflecting the dynamic interplay between neutrophil maturation and immune system activation in early life 31 , 32 , 34 , 35 , 38 . NE-FSC, proportional to neutrophil size, also shows a complex pattern of changes with increasing gestational and postnatal age. As NE-FSC values are higher in adults, it could indicate that the maturation process in terms of neutrophil size extends beyond the neonatal period 31 , 32 , 34 . MO-X, reflecting monocyte cellular complexity, exhibits complex variations according to gestational age at birth. Values in term newborns approach those observed in adults 31 , 32 , 34 . MO-Y, which depends on the RNA and DNA content of monocytes, exhibits a complex pattern in relation to gestational age at birth. Neonatal values remain lower than those observed in the adult population, likely reflecting a reduced transcriptional ability in neonates, particularly in preterm infants 31 , 32 , 34 , 39 . MO-Z, reflecting monocyte cellular size, remains relatively stable across different gestational and postnatal ages, aligning closely with the reference ranges established in adults 31 , 32 , 34 . LY-X, indicating lymphocytic complexity and granularity, decreases with increasing gestational age at birth, while it remains relatively stable with increasing postnatal age. Adults present even lower reference ranges 31 , 32 , 34 . LY-Y, which depends on RNA and DNA content, increases with gestational age at birth and subsequently exhibits a complex evolution during the neonatal period. Term newborns exhibit values comparable to adult ranges 31 , 32 , 34 . This variation could be related to phenotypical changes, such as increased expression of transcriptional factors in more mature infants at birth and the rise in cytotoxic T/NK lymphocytes and B lymphocytes during the first postnatal weeks 40 , 41 . LY-Z, reflecting lymphocytic size, remains stable across gestational ages at birth and throughout the neonatal period, with term neonates showing values lower than those observed in adults 31 , 32 , 34 . In our exploratory analysis on CPD diagnostic accuracy for sepsis/NEC, most CPD parameters show substantial differences between cases and controls at the onset of disease. NE-SFL and LY-X exhibit the highest diagnostic accuracy, which is superior to classical hematological parameters and CRP. NE-SFL shows the strongest diagnostic potential for sepsis/NEC. The higher NE-SFL might be related to increased transcriptional activity in immature or activated neutrophils mobilized into peripheral blood during sepsis, in line with previous literature 36 , 37 . In adults, studies have identified a potential of NE-SFL to detect sepsis at the onset of symptoms, and a correlation to bacterial load 36 , 42 – 45 . A pilot study showed higher NE-SFL in critically ill children with sepsis compared to those without infection 46 . Few studies have investigated LY-X in sepsis, with conflicting results in adults 42 , 44 , 47 . Our exploratory analysis identifies high LY-X, in newborns with sepsis/NEC, suggesting a potential role as a biomarker. In line with previous studies, leukopenia, neutropenia, and elevated I/T ratio have low to moderate accuracy in detecting sepsis and NEC, with high specificity but low sensitivity and PPV 26 – 28 , 48 . Similarly, CRP has moderate sensitivity and specificity, consistent with published findings, underscoring its limited utility as a standalone diagnostic marker 49 , 50 . In our exploratory analysis, NE-SFL and LY-X provide a better trade-off between sensitivity and specificity, further supporting the potential of CPD parameters in the diagnosis of neonatal sepsis and NEC. Moreover, CPD offer several benefits compared to classical biomarkers: since they are automatically generated with the CBC, they are available 24/7 without additional costs or extra sampling, a crucial feature for preterm newborns. In addition, as numerical data, CPD provide an objective, accurate, and faster alternative to manual differential counts 17 . In our study, we evaluated three multivariate logistic regression models integrating the most accurate biomarkers from our univariate analysis, namely NE-SFL, LY-X, leukopenia, and CRP. While measuring multiple biomarkers concurrently has shown promise in diagnosing sepsis in adults, evidence supporting its use in neonatal sepsis remains limited 47 , 51 – 55 . In our exploratory analysis, these models slightly improve specificity and PPV compared to NE-SFL alone, but the overall performance remains mostly unchanged, with PPV still limited. This study benefits from its large population of hospitalized newborns, ensuring comprehensive and representative data. Reference intervals were rigorously defined following recommendations from neonatal literature 22 . The use of mixed-effects models allowed us to analyze the effects of gestational and postnatal age, while accounting for interindividual variability. This study has limitations. Although our large cohort provided an effective basis for determining reference intervals for CPD parameters, the small number of sepsis/NEC cases restricted our ability to evaluate their diagnostic performance, allowing only exploratory analyses in the early identification of sepsis/NEC. The monocentric and retrospective design limits the generalizability of the findings. As 30% of patients were excluded due to missing CPD parameters, this could have introduced some bias in our analyses. Conclusion This study defines reference intervals for CPD in a large cohort of hospitalized newborns, enabling the interpretation of these preclinical parameters. We uncover unique trends in CPD based on gestational and postnatal age, providing a comprehensive assessment of the morphological characteristics of neutrophils, lymphocytes, and monocytes during the neonatal period. Our exploratory analysis suggests that CPD parameters hold potential for identifying patients who might require antibiotic treatment. Among CPD, NE-SFL emerges as a promising tool for decision-making in cases of sepsis or NEC. Declarations Author contributions F.F., J.D., E.G., and S.M. contributed to conceptualization and design. F.F., J.D., and E.G. contributed to methodology. F.F. curated the data, performed the formal analysis, and wrote the manuscript. L.F., V.D., and R.M. contributed to data curation. L.A. and C.C. provided resources. S.M. and E.G. acquired funding. J.D. and E.G. ensured supervision. All authors contributed to manuscript review and editing, approved the final version, and agreed to be personally accountable for the work. Consent statement The need for informed consent was waived by the Cantonal Ethics Committee of Vaud, as the potential difficulties in obtaining consent were considered disproportionate to the low risk and observational nature of the study. Caregiver acceptance or non-refusal of the general consent for research was required for inclusion in this study. Only those who explicitly refused consent were excluded. Competing interests No, I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Data availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Lausanne University Hospital. References Strunk, T., Molloy, E. J., Mishra, A. & Bhutta, Z. A. Neonatal bacterial sepsis. The Lancet 404 , 277–293 (2024). Fleischmann-Struzek, C. et al. The global burden of paediatric and neonatal sepsis: a systematic review. Lancet Respir. Med. 6 , 223–230 (2018). van Herk, W., Stocker, M. & van Rossum, A. M. C. Recognising early onset neonatal sepsis: an essential step in appropriate antimicrobial use. J. Infect. 72 Suppl , S77-82 (2016). Pierrakos, C., Velissaris, D., Bisdorff, M., Marshall, J. C. & Vincent, J.-L. Biomarkers of sepsis: time for a reappraisal. Crit. Care Lond. Engl. 24 , 287 (2020). Giannoni, E. et al. Analysis of Antibiotic Exposure and Early-Onset Neonatal Sepsis in Europe, North America, and Australia. JAMA Netw. Open 5 , e2243691 (2022). Green, R. & Wachsmann-Hogiu, S. Development, History, and Future of Automated Cell Counters. Clin. Lab. Med. 35 , 1–10 (2015). Serrando Querol, M., Nieto-Moragas, J., Marull Arnall, A., Figueras, M. D. & Jiménez-Romero, O. Evaluation of the New Beckmann Coulter Analyzer DxH 900 Compared to Sysmex XN20: Analytical Performance and Flagging Efficiency. Diagn. Basel Switz. 11 , 1756 (2021). Briggs, C., Longair, I., Kumar, P., Singh, D. & Machin, S. J. Performance evaluation of the Sysmex haematology XN modular system. J. Clin. Pathol. 65 , 1024–1030 (2012). Linssen, J. et al. Automation and validation of a rapid method to assess neutrophil and monocyte activation by routine fluorescence flow cytometry in vitro. Cytometry B Clin. Cytom. 74B , 295–309 (2008). Urrechaga, E. Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis. Ann. Transl. Med. 8 , 953 (2020). Celik, I. H., Demirel, G., Erdeve, O. & Dilmen, U. Neutrophil Volume, Conductivity and Scatter in Neonatal Sepsis. Pediatr. Infect. Dis. J. 32 , 301 (2013). Celik, I. H. et al. Automated determination of neutrophil VCS parameters in diagnosis and treatment efficacy of neonatal sepsis. Pediatr. Res. 71 , 121–125 (2012). Raimondi, F. et al. Automated Determination of Neutrophil Volume as Screening Test for Late-Onset Sepsis in Very Low Birth Infants. Pediatr. Infect. Dis. J. 29 , 288 (2010). Abiramalatha, T. et al. Utility of neutrophil volume conductivity scatter (VCS) parameter changes as sepsis screen in neonates. J. Perinatol. Off. J. Calif. Perinat. Assoc. 36 , 733–738 (2016). Mardi, D., Fwity, B., Lobmann, R. & Ambrosch, A. Mean cell volume of neutrophils and monocytes compared with C-reactive protein, interleukin-6 and white blood cell count for prediction of sepsis and nonsystemic bacterial infections. Int. J. Lab. Hematol. 32 , 410–418 (2010). Bhargava, M. et al. Volume, Conductivity and Scatter Properties of Leukocytes (VCS Technology) Is a Highly Sensitive and Specific Predictor of Blood Culture Proven Neonatal Sepsis. Blood 116 , 4720 (2010). Morotti, F. et al. Automated Cell Counter-derived Neutrophil Cell Population Data by VCS Technology as a Marker of Early-onset Neonatal Sepsis. Newborn 1 , 209–214 (2022). Nesargi, P., Niranjan, H. S., Bandiya, P. & Benakappa, N. Neutrophil Volume, conductivity and scatter (VCS) as a screening tool in neonatal sepsis. Sci. Rep. 10 , 4457 (2020). Rio, L. et al. Monitoring of heart rate characteristics to detect neonatal sepsis. Pediatr. Res. 92 , 1070–1074 (2022). Giannoni, E. et al. Neonatal Sepsis of Early Onset, and Hospital-Acquired and Community-Acquired Late Onset: A Prospective Population-Based Cohort Study. J. Pediatr. 201 , 106-114.e4 (2018). Walsh, M. C. & Kliegman, R. M. Necrotizing enterocolitis: treatment based on staging criteria. Pediatr. Clin. North Am. 33 , 179–201 (1986). Henry, E. & Christensen, R. D. Reference Intervals in Neonatal Hematology. Clin. Perinatol. 42 , 483–497 (2015). Christensen, R. D. et al. Reference Ranges for Lymphocyte Counts of Neonates: Associations Between Abnormal Counts and Outcomes. Pediatrics 129 , e1165–e1172 (2012). Christensen, R. D., Jensen, J., Maheshwari, A. & Henry, E. Reference ranges for blood concentrations of eosinophils and monocytes during the neonatal period defined from over 63 000 records in a multihospital health-care system. J. Perinatol. 30 , 540–545 (2010). Schmutz, N., Henry, E., Jopling, J. & Christensen, R. D. Expected ranges for blood neutrophil concentrations of neonates: the Manroe and Mouzinho charts revisited. J. Perinatol. 28 , 275–281 (2008). Newman, T. B., Puopolo, K. M., Wi, S., Draper, D. & Escobar, G. J. Interpreting complete blood counts soon after birth in newborns at risk for sepsis. Pediatrics 126 , 903–909 (2010). Hornik, C. P. et al. Use of the complete blood cell count in late-onset neonatal sepsis. Pediatr. Infect. Dis. J. 31 , 803–807 (2012). Hornik, C. P. et al. Use of the complete blood cell count in early-onset neonatal sepsis. Pediatr. Infect. Dis. J. 31 , 799–802 (2012). Saito, T. & Rehmsmeier, M. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE 10 , e0118432 (2015). Christensen, R. D., Henry, E., Jopling, J. & Wiedmeier, S. E. The CBC: Reference Ranges for Neonates. Semin. Perinatol. 33 , 3–11 (2009). Park, S. H. et al. Establishment of Age- and Gender-Specific Reference Ranges for 36 Routine and 57 Cell Population Data Items in a New Automated Blood Cell Analyzer, Sysmex XN-2000. Ann. Lab. Med. 36 , 244–249 (2016). Pelt, J. L. van et al. Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort. Clin. Chem. Lab. Med. CCLM 60 , 907–920 (2022). Choi, Y. J. et al. Reference intervals of cell population data parameters in Sysmex XN-Series and its patterns of changes from early adulthood to geriatric ages in South Korea. Int. J. Lab. Hematol. 46 , 466–473 (2024). Choi, Y. J. et al. Reference intervals of cell population data parameters in Sysmex XN-Series and its patterns of changes from early adulthood to geriatric ages in South Korea. Int. J. Lab. Hematol. 46 , 466–473 (2024). Lawrence, S. M., Corriden, R. & Nizet, V. Age-Appropriate Functions and Dysfunctions of the Neonatal Neutrophil. Front. Pediatr. 5 , 23 (2017). Park, S. H. et al. Sepsis affects most routine and cell population data (CPD) obtained using the Sysmex XN-2000 blood cell analyzer: neutrophil-related CPD NE-SFL and NE-WY provide useful information for detecting sepsis. Int. J. Lab. Hematol. 37 , 190–198 (2015). Lemkus, L., Lawrie, D. & Vaughan, J. The utility of extended differential parameters as a biomarker of bacteremia at a tertiary academic hospital in persons with and without HIV infection in South Africa. PloS One 17 , e0262938 (2022). Olin, A. et al. Stereotypic Immune System Development in Newborn Children. Cell 174 , 1277-1292.e14 (2018). de Jong, E., Strunk, T., Burgner, D., Lavoie, P. M. & Currie, A. The phenotype and function of preterm infant monocytes: implications for susceptibility to infection. J. Leukoc. Biol. 102 , 645–656 (2017). Hibbert, J. et al. Composition of early life leukocyte populations in preterm infants with and without late-onset sepsis. PLoS ONE 17 , e0264768 (2022). Qazi, K. R. et al. Extremely Preterm Infants Have Significant Alterations in Their Conventional T Cell Compartment during the First Weeks of Life. J. Immunol. 204 , 68–77 (2020). Urrechaga, E., Bóveda, O. & Aguirre, U. Improvement in detecting sepsis using leukocyte cell population data (CPD). Clin. Chem. Lab. Med. CCLM 57 , 918–926 (2019). Urrechaga, E., Bóveda, O. & Aguirre, U. Role of leucocytes cell population data in the early detection of sepsis. J. Clin. Pathol. 71 , 259–266 (2018). Buoro, S. et al. Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with our without liver impairment. Ann. Transl. Med. 4 , 418 (2016). Miyajima, Y. et al. Predictive value of cell population data with Sysmex XN-series hematology analyzer for culture-proven bacteremia. Front. Med. 10 , 1156889 (2023). Biban, P. et al. Cell Population Data (CPD) for Early Recognition of Sepsis and Septic Shock in Children: A Pilot Study. Front. Pediatr. 9 , 642377 (2021). Urrechaga, E., Bóveda, O. & Aguirre, U. Role of leucocytes cell population data in the early detection of sepsis. J. Clin. Pathol. 71 , 259–266 (2018). Newman, T. B., Draper, D., Puopolo, K. M., Wi, S. & Escobar, G. J. Combining immature and total neutrophil counts to predict early onset sepsis in term and late preterm newborns: use of the I/T2. Pediatr. Infect. Dis. J. 33 , 798–802 (2014). Benitz, W. E., Han, M. Y., Madan, A. & Ramachandra, P. Serial Serum C-Reactive Protein Levels in the Diagnosis of Neonatal Infection. Pediatrics 102 , e41 (1998). Brown, J. V. E., Meader, N., Wright, K., Cleminson, J. & McGuire, W. Assessment of C-Reactive Protein Diagnostic Test Accuracy for Late-Onset Infection in Newborn Infants: A Systematic Review and Meta-analysis. JAMA Pediatr. 174 , 260–268 (2020). Stocker, M. et al. C-Reactive Protein, Procalcitonin, and White Blood Count to Rule Out Neonatal Early-onset Sepsis Within 36 Hours: A Secondary Analysis of the Neonatal Procalcitonin Intervention Study. Clin. Infect. Dis. 73 , e383–e390 (2021). Schlapbach, L. J. et al. Pancreatic stone protein as a novel marker for neonatal sepsis. Intensive Care Med. 39 , 754–63 (2013). Ruan, L. et al. The combination of procalcitonin and C-reactive protein or presepsin alone improves the accuracy of diagnosis of neonatal sepsis: a meta-analysis and systematic review. Crit. Care 22 , 316 (2018). Kim, H. et al. Multi-marker approach using procalcitonin, presepsin, galectin-3, and soluble suppression of tumorigenicity 2 for the prediction of mortality in sepsis. Ann. Intensive Care 7 , 27 (2017). Grover, V. et al. A Biomarker Panel (Bioscore) Incorporating Monocytic Surface and Soluble TREM-1 Has High Discriminative Value for Ventilator-Associated Pneumonia: A Prospective Observational Study. PLOS ONE 9 , e109686 (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.docx Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Pediatric Research → Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5984143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414539351,"identity":"7159a4c2-6765-4d6e-a5f9-8f2c26836fbc","order_by":0,"name":"Flavia Ferraro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBAC9nYgkcDAwA/l2zAYgCjGBtxaeA5DtEhC1aQRqYUBoeUwEVqYmZ9JPKhhkOCXPvvwc0XFeXlziRwDhp878GlhM5NIOMYgIdmXbix55sxtw50zcgwYe8/g1mLPzGBskMDGUGdwho1BsrHtdoLBjbQEZsY2fLawfzZI+McgYX+GjflnY9s5YrTwGD5IbGOQMOBhYwPacgCoJfkAIS2FDxL7JCQkzrCxWTacSTbccObxgYO9+LSwt284+OObjQR/DxvzzYYKO3mD44mND37i0QIFEqjcAwQ1jIJRMApGwSjACwCDmkj6swY8ewAAAABJRU5ErkJggg==","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":true,"prefix":"","firstName":"Flavia","middleName":"","lastName":"Ferraro","suffix":""},{"id":414539352,"identity":"b69081fc-b09d-4af3-9185-fbc00ba71542","order_by":1,"name":"Laura Fillistorf","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Fillistorf","suffix":""},{"id":414539353,"identity":"6bacedab-9ca6-4358-b708-0b436269af51","order_by":2,"name":"Varvara Dimopoulou","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Varvara","middleName":"","lastName":"Dimopoulou","suffix":""},{"id":414539354,"identity":"d1fc153d-55f5-4671-a105-84a7d3800fef","order_by":3,"name":"Lorenzo Alberio","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Alberio","suffix":""},{"id":414539355,"identity":"0497bf51-d41d-474a-9061-b26f3ed4ee8a","order_by":4,"name":"Christine Coutaz","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Coutaz","suffix":""},{"id":414539356,"identity":"76fd1eb3-8c72-4278-937b-e4968260fc70","order_by":5,"name":"Sylvain Meylan","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"","lastName":"Meylan","suffix":""},{"id":414539357,"identity":"e8f15f88-fff8-4631-abad-66583f904aa0","order_by":6,"name":"Raphael Matusiak","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Raphael","middleName":"","lastName":"Matusiak","suffix":""},{"id":414539358,"identity":"3c0cd043-699d-475f-8a2b-80354597f172","order_by":7,"name":"Jeremie Despraz","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Jeremie","middleName":"","lastName":"Despraz","suffix":""},{"id":414539359,"identity":"38c99055-9459-4390-b344-b6d165a5547e","order_by":8,"name":"Eric Giannoni","email":"","orcid":"","institution":"Lausanne University Hospital, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Giannoni","suffix":""}],"badges":[],"createdAt":"2025-02-07 23:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5984143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5984143/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41390-025-04159-x","type":"published","date":"2025-06-06T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76297320,"identity":"8182830d-d9fc-45bd-a6fe-148a6f811c3e","added_by":"auto","created_at":"2025-02-14 13:25:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116477,"visible":true,"origin":"","legend":"\u003cp\u003eReference intervals for Cell Population Data on the day of birth.\u003c/p\u003e\n\u003cp\u003eThe lines represent the smoothed median values; the shaded areas represent the 5th to 95th percentile range. For lymphocyte CPD, data on 433/905 (48%) patients is provided, as data was not stored in 472 patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5984143/v1/05c6d4ebfa853fae6ffa62d3.png"},{"id":76297321,"identity":"7305fce0-a112-4ea9-956a-3ce5a453ca16","added_by":"auto","created_at":"2025-02-14 13:25:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142998,"visible":true,"origin":"","legend":"\u003cp\u003eReference intervals for Cell Population Data during the first 28 days of life.\u003c/p\u003e\n\u003cp\u003eThe lines represent the smoothed median values; the shaded areas represent the 5th to 95th percentile range. \u003cstrong\u003ea\u003c/strong\u003e) Reference intervals for CPD parameters in newborns \u0026lt; 28 weeks gestation.\u003cstrong\u003e b)\u003c/strong\u003eReference intervals for CPD parameters in newborns 28 to 36 weeks gestation. \u003cstrong\u003ec)\u003c/strong\u003eReference intervals for CPD parameters in newborns \u0026gt; 36 weeks gestation. For lymphocyte CPD, data on 433/905 (48%) patients is provided, as data was not stored in 472 patients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5984143/v1/2853045f4fb0146d7cd1eb47.png"},{"id":76297323,"identity":"ae580255-09ac-4e5e-a28c-d76a26450c9e","added_by":"auto","created_at":"2025-02-14 13:25:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50787,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic curve and Precision-Recall Curve of NE-SFL, LY-X, classical hematological parameters and C-reactive protein. \u003cstrong\u003ea\u003c/strong\u003e) Receiver Operating Characteristic curve, \u003cstrong\u003eb\u003c/strong\u003e) Precision-Recall Curve. CRP, C-reactive protein; I/T ratio, immature-to-total ratio.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5984143/v1/27e1a0986fab031849ffead0.png"},{"id":84243312,"identity":"96fb7688-ab5a-437e-9347-5bb897f9e6c3","added_by":"auto","created_at":"2025-06-09 16:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5984143/v1/09a04753-59af-4078-8c1b-6525ee3ebce7.pdf"},{"id":76297322,"identity":"62e6e6cd-4cf1-43f9-a85e-31fd0bd200b4","added_by":"auto","created_at":"2025-02-14 13:25:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":898098,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-5984143/v1/d608125da7086306f28acd43.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neonatal cell population data: reference intervals and relevance for detecting sepsis and necrotizing enterocolitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a leading cause of death and disability in newborns, with preterm infants being the most vulnerable population \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The rapid progression of neonatal sepsis necessitates early detection to initiate timely empirical antibiotic treatment and optimize outcomes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, current diagnostic tools have limited accuracy for early identification and differentiation of sepsis from non-infectious conditions \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. As a result, some newborns receive delayed antibiotic treatments, while others undergo unnecessary treatment, increasing the risk of adverse outcomes and promoting the emergence and spread of antimicrobial resistance \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This highlights the need for novel biomarkers that can assist clinicians in the early and accurate detection of sepsis.\u003c/p\u003e \u003cp\u003eState-of-the-art hematologic analyzers use a combination of impedance and fluorescence flow cytometry to deliver a high-resolution analysis of leukocyte cell volume, complexity, and fluorescence intensity \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This detailed information, known as Cell Population Data (CPD), is used in clinical practice to determine the complete blood count (CBC) and leukocyte differential \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Given the critical role of leukocytes in host response to infection, CPD could provide valuable insights for early detection of neonatal sepsis \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, studying CPD could provide new insights on the developmental biology of blood leucocytes. However, the lack of neonatal reference intervals hinders CPD interpretation. Few studies have suggested the potential of specific neutrophil characteristics as biomarkers for neonatal sepsis \u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, no comprehensive evaluation of leukocyte CPD has been undertaken to date. Moreover, previous studies have not leveraged fluorescence flow cytometry technology, underscoring the need for further research in this area.\u003c/p\u003e \u003cp\u003eTo address these knowledge gaps, we established reference intervals for leukocyte CPD in a large cohort of newborns. In addition, we explored the potential of CPD as early biomarkers of sepsis and necrotizing enterocolitis (NEC).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis single-center, retrospective observational study was conducted in the neonatal unit of the University Hospital of Lausanne from January 2021 to June 2023. It was approved by the Cantonal Ethics Committee of Vaud (Lausanne, Switzerland, ID 2022\u0026thinsp;\u0026minus;\u0026thinsp;00528). All the research was conducted in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eAll patients hospitalized in the neonatal unit who had at least one CBC between birth and a corrected age (defined as the sum of gestational and postnatal age) of 44 weeks were eligible, regardless of the clinical indication for the CBC. We excluded newborns whose parents or legal guardians refused general consent for research and those with missing CPD parameters. Neonatal sepsis was diagnosed based on blood culture-proven infection \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Contaminated blood cultures were defined by the growth of bacteria typically considered as contaminants (e.g., Micrococcus species or diphtheroids), growth of coagulase-negative staphylococci (CoNS) in patients without a central or peripheral catheter, or cultures deemed contaminated by clinicians, leading to discontinuation of antimicrobial therapy within less than 5 days \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. NEC was defined as Bell stage 2 or higher \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We identified a control group of newborns without any condition potentially impacting neutrophils, monocytes and/or lymphocytes. The following conditions were considered as potentially impacting CPD: congenital neutropenia, trisomy 13, 18, and 21, birth asphyxia (initial pH\u0026thinsp;\u0026lt;\u0026thinsp;7.0, 5-minute Apgar score\u0026thinsp;\u0026lt;\u0026thinsp;5), hypoxic-ischemic encephalopathy, grade 3 or 4 intraventricular hemorrhage, cystic periventricular leukomalacia, stage 3 or 4 retinopathy of prematurity, and any suspected or proven systemic or focal bacterial, fungal or viral infection leading to antimicrobial treatment for 5 days or more \u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Data were extracted from the hospital\u0026rsquo;s clinical information systems, electronic health records, and laboratory information system. CPD parameters were acquired using the Sysmex XN-9000\u0026reg; hematology analyzer which uses a combination of impedance and fluorescence flow cytometry to provide leukocyte differential counts derived from CPD. For neutrophils, monocytes, and lymphocytes, we collected data on side scatter (X-axis), indicating cellular complexity, fluorescence intensity (Y-axis), reflecting nucleic acid content, and forward scatter (Z-axis), denoting cell volume. Additionally, we collected data on the distribution width of each parameter, representing signal heterogeneity. This resulted in a total of 18 CPD variables (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eCPD of neutrophils, monocytes, and lymphocytes obtained from the control group were analyzed to determine reference intervals, defined as the interval between the 5th and 95th percentiles of observed values, in line with prior studies \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We presented CPD obtained on the day of birth according to gestational age. To provide data on the neonatal period, we presented CPD from the first 28 postnatal days stratified into three gestational age groups: \u0026lt; 28 weeks, 28 to 36 weeks, and \u0026gt;\u0026thinsp;36 weeks \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To get insights into the potential of CPD as novel biomarkers, we conducted exploratory analyses of CPD parameters at the onset of sepsis/NEC. We analyzed CPD obtained at the time of blood culture and/or abdominal X ray for NEC. For patients who did not have CPD collected at this time, we analyzed the closest CBC (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). We compared CPD from sepsis/NEC cases to controls and applied the same analytical approach to CBC and CRP. We defined leukopenia as a leucocyte count\u0026thinsp;\u0026lt;\u0026thinsp;5 G/L, neutropenia as an absolute neutrophil count\u0026thinsp;\u0026lt;\u0026thinsp;1.5 G/L, and an elevated immature-to-total (I/T) ratio as a ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.2 \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatient characteristics were summarized by descriptive statistics. Continuous variables were presented as medians and interquartile ranges (IQR), while categorical variables were expressed as absolute numbers and percentages. Differences between the sepsis/NEC group and the control group were assessed using Wilcoxon\u0026ndash;Mann Whitney tests for continuous variables and Pearson chi-square tests for categorical variables. To construct reference intervals, data were grouped by gestational age at birth. The median, 5th and 95th percentiles were calculated, and data were smoothed using the Locally Estimated Scatterplot Smoothing (LOESS) technique to provide a continuous and regular representation of trends. A mixed-effects model and Estimated Marginal Means (EMMs) post-hoc analysis was employed to quantify changes in CPD parameters over time and among different gestational age groups. The diagnostic accuracy of each variable was assessed using Receiver Operating Characteristic (ROC) curves and Precision-Recall Curve (PRC) analyses, the latter being better suited in contexts with imbalanced groups \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Area under the ROC curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and average precision (AP) were evaluated. A multivariate logistic regression model was developed to predict sepsis/NEC using CRP and the most accurate CPD parameters. The variance inflation factor (VIF) was calculated to assess multicollinearity, and a 10-fold cross-validation ensured robustness. Predicted probabilities were used to generate ROC and PRC curves, with performance metrics quantifying diagnostic effectiveness.\u003c/p\u003e \u003cp\u003eThe same statistical analyses (descriptive statistics, non-parametric tests, diagnostic accuracy metrics, and logistic regression) were applied to a population without exclusion of controls with comorbidities potentially affecting CPD to account for the potential impact of these conditions on the diagnostic performance of CPD parameters. Statistical analyses were carried out using R version 4.3.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eAmong 1693 eligible patients, 512 (30%) were excluded due to missing CPD parameters, and 90 (5%) due to refusal of general consent for research (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). A control group of 905 newborns was obtained after exclusion of 135 patients with comorbidities potentially impacting neutrophils, monocytes and/or lymphocytes. We identified 39 newborns with sepsis/NEC. Main demographic, clinical and microbiological characteristics of the study population are reported in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, while those of the population not excluding controls with comorbidities potentially affecting CPD are provided in \u003cstrong\u003eSupplementary Table S3\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographics, clinical and microbiological characteristics of patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;905\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSepsis/NEC group\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;39\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostnatal age\u003c/p\u003e\n \u003cp\u003eat sepsis or NEC onset, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6\u0026ndash;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBirth weight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8 (2.0-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9 (0.7-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational age, weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (34\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (26\u0026ndash;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogens in blood culture proven sepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoagulase-negative staphylococci\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus agalactiae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers pathogens\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly-onset sepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLate-onset sepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNecrotizing enterocolitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous variables are reported as median (interquartile range). Categorical variables are reported as number (percent). Early-onset sepsis: onset before 72 hours of life; late-onset sepsis: onset after 72 hours of life. Other pathogens include \u003cem\u003eAchromobacter ssp., Bifidobacterium breve, Clostridium neonatale, Enterobacter cloacae, Enterococcus faecalis, Lacticaseibacillus rhamnosus.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReference intervals for CPD parameters\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eReference intervals for CPD parameters were established in the control group based on gestational and postnatal age. We computed reference intervals for CPD parameters on the day of birth according to gestational age (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and reference intervals over the first 28 postnatal days in three gestational age groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). CPD parameters varied with gestational age at birth, showing distinct patterns between neutrophils, monocytes, and lymphocytes (\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). Across neutrophil, monocyte and lymphocyte CPD, we observed a general pattern of reduction in the distribution width of most CPD parameters with increasing gestational age. When analyzing the effect of postnatal age on CPD parameters across the three gestational age groups, we found substantial differences not only in initial values but also in the rate and direction of changes over time (\u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e). This analysis also indicated a general pattern of reduction in the distribution width of most analyzed CPD parameters, both with increasing gestational and postnatal age.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCPD accuracy for the detection of sepsis and NEC\u003c/h3\u003e\n\u003cp\u003eIn the sepsis/NEC group, 29 (74%) newborns had culture-proven sepsis, 5 (13%) had NEC, and 5 (13%) had both conditions. Neonates with sepsis/NEC had a lower gestational age and birth weight compared to controls \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cstrong\u003eSupplementary Table S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eOn the day of clinical suspicion, substantial differences emerged between sepsis/NEC cases and controls for most CPD parameters (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar findings were obtained in the population including controls with comorbidities potentially affecting CPD (\u003cstrong\u003eSupplementary Table S6)\u003c/strong\u003e. Neutrophil fluorescence intensity (NE-SFL) and lymphocytes cell complexity (LY-X) showed superior diagnostic accuracy to identify sepsis/NEC compared to other CPD parameters, leukocyte and neutrophil counts, bands, I/T ratio and CRP (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In particular, NE-SFL had a sensitivity of 90%, a specificity of 76%, a positive predictive value (PPV) of 13%, a negative predictive value (NPV) of 99%, and an average precision (AP) of 0.20 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Including controls with comorbidities potentially affecting CPD did not reduce diagnostic accuracy of CPD (\u003cstrong\u003eSupplementary Table S7\u003c/strong\u003e, \u003cstrong\u003eSupplementary Figure S2).\u003c/strong\u003e We built three logistic regression models using the most performant CPD parameters, NE-SFL and LY-X, combined with leukopenia, and/or CRP (\u003cstrong\u003eSupplementary Table S8\u003c/strong\u003e, \u003cstrong\u003eS9, S10, and S11\u003c/strong\u003e). All parameters showed a positive association with sepsis/NEC with a low multicollinearity among the predictors (\u003cstrong\u003eSupplementary Table S8 and S10\u003c/strong\u003e). All models provided a slight improvement in diagnostic performance compared to NE-SFL alone, with no single model showing clear superiority over the others (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cstrong\u003eSupplementary Figure S3\u003c/strong\u003e and \u003cstrong\u003eS4, Table S9 and S11\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\u003cbr\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCell Population Data in controls and patients with sepsis/necrotizing enterocolitis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSepsis/NEC group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.9 (147.0-152.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.7 (144.4-152.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.1 (38.8\u0026ndash;44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.2 (46.5\u0026ndash;63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-FSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7 (84.2\u0026ndash;89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.3 (80.9\u0026ndash;86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354.0 (338.0-381.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358.0 (334.0-400.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e789.0 (697.0-900.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904.5 (788.2-1123.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e739.0 (710.0-770.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e747.5 (718.2\u0026ndash;794.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.2 (114.4-118.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.7 (115.6-123.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.6 (80.1\u0026ndash;91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.4 (85.7-103.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.1 (67.2\u0026ndash;71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.8 (67.3\u0026ndash;73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278.0 (261.0-299.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302.5 (273.0-367.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e815.0 (740.2\u0026ndash;903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869.0 (710.0-997.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e679.5 (639.2\u0026ndash;718.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e683.5 (621.2\u0026ndash;731.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.3 (76.6\u0026ndash;80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.3 (80.6\u0026ndash;82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.8 (54.1\u0026ndash;63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.4 (58.2\u0026ndash;70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7 (56.8\u0026ndash;60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.7 (58.2\u0026ndash;62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500.0 (470.0-536.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492.0 (458.0-523.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000.0 (933.0-1143.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1225.0 (1078.0-1342.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e701.0 (664.0-804.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e790.0 (690.0-864.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eData are expressed as median (interquartile range). Cell Population Data are reported in arbitrary units of light scattering (ch). LY-X, LY-Y, LY-Z, LY-WX, LY-WY, and LY-WZ were analyzed in 433 out of 905 patients (48%), as data was not stored for 472 patients.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic accuracy of Cell Population Data, classical hematological parameters, C-reactive protein, and combined models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUROC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut off value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSp%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSe%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-FSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e888.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e773.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e962.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMO-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e698.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1077.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY-WZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeukocytes (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophils (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBands (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucopenia (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutropenia (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI/T ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SFL\u0026thinsp;+\u0026thinsp;LY-X\u0026thinsp;+\u0026thinsp;CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SFL\u0026thinsp;+\u0026thinsp;LY-X\u0026thinsp;+\u0026thinsp;leucopenia (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE-SFL\u0026thinsp;+\u0026thinsp;LY-X\u0026thinsp;+\u0026thinsp;leucopenia (G/L)\u0026thinsp;+\u0026thinsp;CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eCell Population Data are reported in arbitrary units of light scattering, ch; AUROC, area under the Receiver Operating Characteristic curve; Se, sensitivity, Sp, specificity; PPV, positive predictive value; NPV, negative predictive value; AP, Average Precision; CRP, C-reactive protein; I/T ratio, immature-to-total ratio. LY-X, LY-Y, LY-Z, LY-WX, LY-WY, and LY-WZ were analyzed in 433 out of 905 patients (48%), as data was not stored for 472 patients.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective observational study, we established reference intervals for neutrophil, monocyte, and lymphocyte CPD in newborns, defining ranges from birth to postnatal day 28 across different gestational age groups. In an exploratory analysis of sepsis and NEC cases, we identified early alterations of most CPD parameters, suggesting that CPD could provide attractive biomarker candidates for the diagnosis of sepsis and NEC.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that hematological parameters vary with gestational and postnatal age, highlighting the importance of establishing reference intervals in newborns \u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our study contributes to this body of knowledge by establishing reference intervals for CPD in a large cohort of hospitalized newborns. While reference intervals for CPD have already been established in the adult population, to our knowledge, this is the first report on neonatal intervals \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, our findings highlight the considerable impact of gestational and postnatal age on neutrophil, monocyte, and lymphocyte CPD, revealing variations in complexity, fluorescence, and size across gestational age and throughout the neonatal period, contributing to increase our understanding of the developmental biology of blood leukocytes.\u003c/p\u003e \u003cp\u003eReference intervals for CPD parameters, which capture the evolution of leukocyte subpopulation morphology and functionality, reveal trends that differ from those of absolute cellular counts \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Across the three CPD subpopulations assessed in our study, we observed a reduction in the distribution width of most CPD parameters with increasing gestational age, probably indicating enhanced cellular homogeneity in more mature infants. In line with those findings, CPD distribution width reference intervals in adults are lower than those of full-term neonates, suggesting a developmental process towards greater cellular homogeneity \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In contrast, each mean CPD parameter exhibited its own unique pattern of change with both gestational and postnatal age.\u003c/p\u003e \u003cp\u003eNE-SSC, an indicator of neutrophil cell complexity and granule content, progressively increases with gestational but not postnatal age, indicating that the maturation process of neutrophils could depend mainly on gestational age at birth \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The reference intervals in full-term newborns are close to those observed in adults \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. NE-SFL, which depends on the RNA/DNA neutrophil content ratio, may reflect cellular immaturity or activation \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In our results, NE-SFL shows complex changes across gestational and postnatal age, with reference intervals in full term newborns closely aligning with adult ranges, likely reflecting the dynamic interplay between neutrophil maturation and immune system activation in early life \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. NE-FSC, proportional to neutrophil size, also shows a complex pattern of changes with increasing gestational and postnatal age. As NE-FSC values are higher in adults, it could indicate that the maturation process in terms of neutrophil size extends beyond the neonatal period \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMO-X, reflecting monocyte cellular complexity, exhibits complex variations according to gestational age at birth. Values in term newborns approach those observed in adults \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. MO-Y, which depends on the RNA and DNA content of monocytes, exhibits a complex pattern in relation to gestational age at birth. Neonatal values remain lower than those observed in the adult population, likely reflecting a reduced transcriptional ability in neonates, particularly in preterm infants \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. MO-Z, reflecting monocyte cellular size, remains relatively stable across different gestational and postnatal ages, aligning closely with the reference ranges established in adults \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLY-X, indicating lymphocytic complexity and granularity, decreases with increasing gestational age at birth, while it remains relatively stable with increasing postnatal age. Adults present even lower reference ranges \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. LY-Y, which depends on RNA and DNA content, increases with gestational age at birth and subsequently exhibits a complex evolution during the neonatal period. Term newborns exhibit values comparable to adult ranges \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This variation could be related to phenotypical changes, such as increased expression of transcriptional factors in more mature infants at birth and the rise in cytotoxic T/NK lymphocytes and B lymphocytes during the first postnatal weeks \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. LY-Z, reflecting lymphocytic size, remains stable across gestational ages at birth and throughout the neonatal period, with term neonates showing values lower than those observed in adults \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our exploratory analysis on CPD diagnostic accuracy for sepsis/NEC, most CPD parameters show substantial differences between cases and controls at the onset of disease. NE-SFL and LY-X exhibit the highest diagnostic accuracy, which is superior to classical hematological parameters and CRP. NE-SFL shows the strongest diagnostic potential for sepsis/NEC. The higher NE-SFL might be related to increased transcriptional activity in immature or activated neutrophils mobilized into peripheral blood during sepsis, in line with previous literature \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In adults, studies have identified a potential of NE-SFL to detect sepsis at the onset of symptoms, and a correlation to bacterial load \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. A pilot study showed higher NE-SFL in critically ill children with sepsis compared to those without infection \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Few studies have investigated LY-X in sepsis, with conflicting results in adults \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Our exploratory analysis identifies high LY-X, in newborns with sepsis/NEC, suggesting a potential role as a biomarker.\u003c/p\u003e \u003cp\u003eIn line with previous studies, leukopenia, neutropenia, and elevated I/T ratio have low to moderate accuracy in detecting sepsis and NEC, with high specificity but low sensitivity and PPV\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Similarly, CRP has moderate sensitivity and specificity, consistent with published findings, underscoring its limited utility as a standalone diagnostic marker \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In our exploratory analysis, NE-SFL and LY-X provide a better trade-off between sensitivity and specificity, further supporting the potential of CPD parameters in the diagnosis of neonatal sepsis and NEC. Moreover, CPD offer several benefits compared to classical biomarkers: since they are automatically generated with the CBC, they are available 24/7 without additional costs or extra sampling, a crucial feature for preterm newborns. In addition, as numerical data, CPD provide an objective, accurate, and faster alternative to manual differential counts \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, we evaluated three multivariate logistic regression models integrating the most accurate biomarkers from our univariate analysis, namely NE-SFL, LY-X, leukopenia, and CRP. While measuring multiple biomarkers concurrently has shown promise in diagnosing sepsis in adults, evidence supporting its use in neonatal sepsis remains limited \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52 CR53 CR54\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In our exploratory analysis, these models slightly improve specificity and PPV compared to NE-SFL alone, but the overall performance remains mostly unchanged, with PPV still limited.\u003c/p\u003e \u003cp\u003eThis study benefits from its large population of hospitalized newborns, ensuring comprehensive and representative data. Reference intervals were rigorously defined following recommendations from neonatal literature \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The use of mixed-effects models allowed us to analyze the effects of gestational and postnatal age, while accounting for interindividual variability. This study has limitations. Although our large cohort provided an effective basis for determining reference intervals for CPD parameters, the small number of sepsis/NEC cases restricted our ability to evaluate their diagnostic performance, allowing only exploratory analyses in the early identification of sepsis/NEC. The monocentric and retrospective design limits the generalizability of the findings. As 30% of patients were excluded due to missing CPD parameters, this could have introduced some bias in our analyses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study defines reference intervals for CPD in a large cohort of hospitalized newborns, enabling the interpretation of these preclinical parameters. We uncover unique trends in CPD based on gestational and postnatal age, providing a comprehensive assessment of the morphological characteristics of neutrophils, lymphocytes, and monocytes during the neonatal period. Our exploratory analysis suggests that CPD parameters hold potential for identifying patients who might require antibiotic treatment. Among CPD, NE-SFL emerges as a promising tool for decision-making in cases of sepsis or NEC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.F., J.D., E.G., and S.M. contributed to conceptualization and design. F.F., J.D., and E.G. contributed to methodology. F.F. curated the data, performed the formal analysis, and wrote the manuscript. L.F., V.D., and R.M. contributed to data curation. L.A. and C.C. provided resources. S.M. and E.G. acquired funding. J.D. and E.G. ensured supervision. All authors contributed to manuscript review and editing, approved the final version, and agreed to be personally accountable for the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe need for informed consent was waived by the Cantonal Ethics Committee of Vaud, as the potential difficulties in obtaining consent were considered disproportionate to the low risk and observational nature of the study.\u0026nbsp;Caregiver acceptance or non-refusal of the general consent for research was required for inclusion in this study. Only those who explicitly refused consent were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo, I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Lausanne University Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eStrunk, T., Molloy, E. J., Mishra, A. \u0026amp; Bhutta, Z. A. Neonatal bacterial sepsis. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e404\u003c/strong\u003e, 277\u0026ndash;293 (2024).\u003c/li\u003e\n \u003cli\u003eFleischmann-Struzek, C. \u003cem\u003eet al.\u003c/em\u003e The global burden of paediatric and neonatal sepsis: a systematic review. \u003cem\u003eLancet Respir. Med.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 223\u0026ndash;230 (2018).\u003c/li\u003e\n \u003cli\u003evan Herk, W., Stocker, M. \u0026amp; van Rossum, A. M. C. Recognising early onset neonatal sepsis: an essential step in appropriate antimicrobial use. \u003cem\u003eJ. Infect.\u003c/em\u003e \u003cstrong\u003e72 Suppl\u003c/strong\u003e, S77-82 (2016).\u003c/li\u003e\n \u003cli\u003ePierrakos, C., Velissaris, D., Bisdorff, M., Marshall, J. C. \u0026amp; Vincent, J.-L. Biomarkers of sepsis: time for a reappraisal. \u003cem\u003eCrit. Care Lond. Engl.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 287 (2020).\u003c/li\u003e\n \u003cli\u003eGiannoni, E. \u003cem\u003eet al.\u003c/em\u003e Analysis of Antibiotic Exposure and Early-Onset Neonatal Sepsis in Europe, North America, and Australia. \u003cem\u003eJAMA Netw. Open\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e2243691 (2022).\u003c/li\u003e\n \u003cli\u003eGreen, R. \u0026amp; Wachsmann-Hogiu, S. Development, History, and Future of Automated Cell Counters. \u003cem\u003eClin. Lab. Med.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1\u0026ndash;10 (2015).\u003c/li\u003e\n \u003cli\u003eSerrando Querol, M., Nieto-Moragas, J., Marull Arnall, A., Figueras, M. D. \u0026amp; Jim\u0026eacute;nez-Romero, O. Evaluation of the New Beckmann Coulter Analyzer DxH 900 Compared to Sysmex XN20: Analytical Performance and Flagging Efficiency. \u003cem\u003eDiagn. Basel Switz.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1756 (2021).\u003c/li\u003e\n \u003cli\u003eBriggs, C., Longair, I., Kumar, P., Singh, D. \u0026amp; Machin, S. J. Performance evaluation of the Sysmex haematology XN modular system. \u003cem\u003eJ. Clin. Pathol.\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, 1024\u0026ndash;1030 (2012).\u003c/li\u003e\n \u003cli\u003eLinssen, J. \u003cem\u003eet al.\u003c/em\u003e Automation and validation of a rapid method to assess neutrophil and monocyte activation by routine fluorescence flow cytometry in vitro. \u003cem\u003eCytometry B Clin. Cytom.\u003c/em\u003e \u003cstrong\u003e74B\u003c/strong\u003e, 295\u0026ndash;309 (2008).\u003c/li\u003e\n \u003cli\u003eUrrechaga, E. Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis. \u003cem\u003eAnn. Transl. Med.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 953 (2020).\u003c/li\u003e\n \u003cli\u003eCelik, I. H., Demirel, G., Erdeve, O. \u0026amp; Dilmen, U. Neutrophil Volume, Conductivity and Scatter in Neonatal Sepsis. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 301 (2013).\u003c/li\u003e\n \u003cli\u003eCelik, I. H. \u003cem\u003eet al.\u003c/em\u003e Automated determination of neutrophil VCS parameters in diagnosis and treatment efficacy of neonatal sepsis. \u003cem\u003ePediatr. Res.\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 121\u0026ndash;125 (2012).\u003c/li\u003e\n \u003cli\u003eRaimondi, F. \u003cem\u003eet al.\u003c/em\u003e Automated Determination of Neutrophil Volume as Screening Test for Late-Onset Sepsis in Very Low Birth Infants. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 288 (2010).\u003c/li\u003e\n \u003cli\u003eAbiramalatha, T. \u003cem\u003eet al.\u003c/em\u003e Utility of neutrophil volume conductivity scatter (VCS) parameter changes as sepsis screen in neonates. \u003cem\u003eJ. Perinatol. Off. J. Calif. Perinat. Assoc.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 733\u0026ndash;738 (2016).\u003c/li\u003e\n \u003cli\u003eMardi, D., Fwity, B., Lobmann, R. \u0026amp; Ambrosch, A. Mean cell volume of neutrophils and monocytes compared with C-reactive protein, interleukin-6 and white blood cell count for prediction of sepsis and nonsystemic bacterial infections. \u003cem\u003eInt. J. Lab. Hematol.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 410\u0026ndash;418 (2010).\u003c/li\u003e\n \u003cli\u003eBhargava, M. \u003cem\u003eet al.\u003c/em\u003e Volume, Conductivity and Scatter Properties of Leukocytes (VCS Technology) Is a Highly Sensitive and Specific Predictor of Blood Culture Proven Neonatal Sepsis. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 4720 (2010).\u003c/li\u003e\n \u003cli\u003eMorotti, F. \u003cem\u003eet al.\u003c/em\u003e Automated Cell Counter-derived Neutrophil Cell Population Data by VCS Technology as a Marker of Early-onset Neonatal Sepsis. \u003cem\u003eNewborn\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 209\u0026ndash;214 (2022).\u003c/li\u003e\n \u003cli\u003eNesargi, P., Niranjan, H. S., Bandiya, P. \u0026amp; Benakappa, N. Neutrophil Volume, conductivity and scatter (VCS) as a screening tool in neonatal sepsis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 4457 (2020).\u003c/li\u003e\n \u003cli\u003eRio, L. \u003cem\u003eet al.\u003c/em\u003e Monitoring of heart rate characteristics to detect neonatal sepsis. \u003cem\u003ePediatr. Res.\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 1070\u0026ndash;1074 (2022).\u003c/li\u003e\n \u003cli\u003eGiannoni, E. \u003cem\u003eet al.\u003c/em\u003e Neonatal Sepsis of Early Onset, and Hospital-Acquired and Community-Acquired Late Onset: A Prospective Population-Based Cohort Study. \u003cem\u003eJ. Pediatr.\u003c/em\u003e \u003cstrong\u003e201\u003c/strong\u003e, 106-114.e4 (2018).\u003c/li\u003e\n \u003cli\u003eWalsh, M. C. \u0026amp; Kliegman, R. M. Necrotizing enterocolitis: treatment based on staging criteria. \u003cem\u003ePediatr. Clin. North Am.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 179\u0026ndash;201 (1986).\u003c/li\u003e\n \u003cli\u003eHenry, E. \u0026amp; Christensen, R. D. Reference Intervals in Neonatal Hematology. \u003cem\u003eClin. Perinatol.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 483\u0026ndash;497 (2015).\u003c/li\u003e\n \u003cli\u003eChristensen, R. D. \u003cem\u003eet al.\u003c/em\u003e Reference Ranges for Lymphocyte Counts of Neonates: Associations Between Abnormal Counts and Outcomes. \u003cem\u003ePediatrics\u003c/em\u003e \u003cstrong\u003e129\u003c/strong\u003e, e1165\u0026ndash;e1172 (2012).\u003c/li\u003e\n \u003cli\u003eChristensen, R. D., Jensen, J., Maheshwari, A. \u0026amp; Henry, E. Reference ranges for blood concentrations of eosinophils and monocytes during the neonatal period defined from over 63 000 records in a multihospital health-care system. \u003cem\u003eJ. Perinatol.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 540\u0026ndash;545 (2010).\u003c/li\u003e\n \u003cli\u003eSchmutz, N., Henry, E., Jopling, J. \u0026amp; Christensen, R. D. Expected ranges for blood neutrophil concentrations of neonates: the Manroe and Mouzinho charts revisited. \u003cem\u003eJ. Perinatol.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 275\u0026ndash;281 (2008).\u003c/li\u003e\n \u003cli\u003eNewman, T. B., Puopolo, K. M., Wi, S., Draper, D. \u0026amp; Escobar, G. J. Interpreting complete blood counts soon after birth in newborns at risk for sepsis. \u003cem\u003ePediatrics\u003c/em\u003e \u003cstrong\u003e126\u003c/strong\u003e, 903\u0026ndash;909 (2010).\u003c/li\u003e\n \u003cli\u003eHornik, C. P. \u003cem\u003eet al.\u003c/em\u003e Use of the complete blood cell count in late-onset neonatal sepsis. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 803\u0026ndash;807 (2012).\u003c/li\u003e\n \u003cli\u003eHornik, C. P. \u003cem\u003eet al.\u003c/em\u003e Use of the complete blood cell count in early-onset neonatal sepsis. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 799\u0026ndash;802 (2012).\u003c/li\u003e\n \u003cli\u003eSaito, T. \u0026amp; Rehmsmeier, M. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0118432 (2015).\u003c/li\u003e\n \u003cli\u003eChristensen, R. D., Henry, E., Jopling, J. \u0026amp; Wiedmeier, S. E. The CBC: Reference Ranges for Neonates. \u003cem\u003eSemin. Perinatol.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 3\u0026ndash;11 (2009).\u003c/li\u003e\n \u003cli\u003ePark, S. H. \u003cem\u003eet al.\u003c/em\u003e Establishment of Age- and Gender-Specific Reference Ranges for 36 Routine and 57 Cell Population Data Items in a New Automated Blood Cell Analyzer, Sysmex XN-2000. \u003cem\u003eAnn. Lab. Med.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 244\u0026ndash;249 (2016).\u003c/li\u003e\n \u003cli\u003ePelt, J. L. van \u003cem\u003eet al.\u003c/em\u003e Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort. \u003cem\u003eClin. Chem. Lab. Med. CCLM\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 907\u0026ndash;920 (2022).\u003c/li\u003e\n \u003cli\u003eChoi, Y. J. \u003cem\u003eet al.\u003c/em\u003e Reference intervals of cell population data parameters in Sysmex XN-Series and its patterns of changes from early adulthood to geriatric ages in South Korea. \u003cem\u003eInt. J. Lab. Hematol.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 466\u0026ndash;473 (2024).\u003c/li\u003e\n \u003cli\u003eChoi, Y. J. \u003cem\u003eet al.\u003c/em\u003e Reference intervals of cell population data parameters in Sysmex XN-Series and its patterns of changes from early adulthood to geriatric ages in South Korea. \u003cem\u003eInt. J. Lab. Hematol.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 466\u0026ndash;473 (2024).\u003c/li\u003e\n \u003cli\u003eLawrence, S. M., Corriden, R. \u0026amp; Nizet, V. Age-Appropriate Functions and Dysfunctions of the Neonatal Neutrophil. \u003cem\u003eFront. Pediatr.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 23 (2017).\u003c/li\u003e\n \u003cli\u003ePark, S. H. \u003cem\u003eet al.\u003c/em\u003e Sepsis affects most routine and cell population data (CPD) obtained using the Sysmex XN-2000 blood cell analyzer: neutrophil-related CPD NE-SFL and NE-WY provide useful information for detecting sepsis. \u003cem\u003eInt. J. Lab. Hematol.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 190\u0026ndash;198 (2015).\u003c/li\u003e\n \u003cli\u003eLemkus, L., Lawrie, D. \u0026amp; Vaughan, J. The utility of extended differential parameters as a biomarker of bacteremia at a tertiary academic hospital in persons with and without HIV infection in South Africa. \u003cem\u003ePloS One\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e0262938 (2022).\u003c/li\u003e\n \u003cli\u003eOlin, A. \u003cem\u003eet al.\u003c/em\u003e Stereotypic Immune System Development in Newborn Children. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 1277-1292.e14 (2018).\u003c/li\u003e\n \u003cli\u003ede Jong, E., Strunk, T., Burgner, D., Lavoie, P. M. \u0026amp; Currie, A. The phenotype and function of preterm infant monocytes: implications for susceptibility to infection. \u003cem\u003eJ. Leukoc. Biol.\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 645\u0026ndash;656 (2017).\u003c/li\u003e\n \u003cli\u003eHibbert, J. \u003cem\u003eet al.\u003c/em\u003e Composition of early life leukocyte populations in preterm infants with and without late-onset sepsis. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e0264768 (2022).\u003c/li\u003e\n \u003cli\u003eQazi, K. R. \u003cem\u003eet al.\u003c/em\u003e Extremely Preterm Infants Have Significant Alterations in Their Conventional T Cell Compartment during the First Weeks of Life. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cstrong\u003e204\u003c/strong\u003e, 68\u0026ndash;77 (2020).\u003c/li\u003e\n \u003cli\u003eUrrechaga, E., B\u0026oacute;veda, O. \u0026amp; Aguirre, U. Improvement in detecting sepsis using leukocyte cell population data (CPD). \u003cem\u003eClin. Chem. Lab. Med. CCLM\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 918\u0026ndash;926 (2019).\u003c/li\u003e\n \u003cli\u003eUrrechaga, E., B\u0026oacute;veda, O. \u0026amp; Aguirre, U. Role of leucocytes cell population data in the early detection of sepsis. \u003cem\u003eJ. Clin. Pathol.\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 259\u0026ndash;266 (2018).\u003c/li\u003e\n \u003cli\u003eBuoro, S. \u003cem\u003eet al.\u003c/em\u003e Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with our without liver impairment. \u003cem\u003eAnn. Transl. Med.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 418 (2016).\u003c/li\u003e\n \u003cli\u003eMiyajima, Y. \u003cem\u003eet al.\u003c/em\u003e Predictive value of cell population data with Sysmex XN-series hematology analyzer for culture-proven bacteremia. \u003cem\u003eFront. Med.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1156889 (2023).\u003c/li\u003e\n \u003cli\u003eBiban, P. \u003cem\u003eet al.\u003c/em\u003e Cell Population Data (CPD) for Early Recognition of Sepsis and Septic Shock in Children: A Pilot Study. \u003cem\u003eFront. Pediatr.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 642377 (2021).\u003c/li\u003e\n \u003cli\u003eUrrechaga, E., B\u0026oacute;veda, O. \u0026amp; Aguirre, U. Role of leucocytes cell population data in the early detection of sepsis. \u003cem\u003eJ. Clin. Pathol.\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 259\u0026ndash;266 (2018).\u003c/li\u003e\n \u003cli\u003eNewman, T. B., Draper, D., Puopolo, K. M., Wi, S. \u0026amp; Escobar, G. J. Combining immature and total neutrophil counts to predict early onset sepsis in term and late preterm newborns: use of the I/T2. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 798\u0026ndash;802 (2014).\u003c/li\u003e\n \u003cli\u003eBenitz, W. E., Han, M. Y., Madan, A. \u0026amp; Ramachandra, P. Serial Serum C-Reactive Protein Levels in the Diagnosis of Neonatal Infection. \u003cem\u003ePediatrics\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, e41 (1998).\u003c/li\u003e\n \u003cli\u003eBrown, J. V. E., Meader, N., Wright, K., Cleminson, J. \u0026amp; McGuire, W. Assessment of C-Reactive Protein Diagnostic Test Accuracy for Late-Onset Infection in Newborn Infants: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Pediatr.\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 260\u0026ndash;268 (2020).\u003c/li\u003e\n \u003cli\u003eStocker, M. \u003cem\u003eet al.\u003c/em\u003e C-Reactive Protein, Procalcitonin, and White Blood Count to Rule Out Neonatal Early-onset Sepsis Within 36 Hours: A Secondary Analysis of the Neonatal Procalcitonin Intervention Study. \u003cem\u003eClin. Infect. Dis.\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, e383\u0026ndash;e390 (2021).\u003c/li\u003e\n \u003cli\u003eSchlapbach, L. J. \u003cem\u003eet al.\u003c/em\u003e Pancreatic stone protein as a novel marker for neonatal sepsis. \u003cem\u003eIntensive Care Med.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 754\u0026ndash;63 (2013).\u003c/li\u003e\n \u003cli\u003eRuan, L. \u003cem\u003eet al.\u003c/em\u003e The combination of procalcitonin and C-reactive protein or presepsin alone improves the accuracy of diagnosis of neonatal sepsis: a meta-analysis and systematic review. \u003cem\u003eCrit. Care\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 316 (2018).\u003c/li\u003e\n \u003cli\u003eKim, H. \u003cem\u003eet al.\u003c/em\u003e Multi-marker approach using procalcitonin, presepsin, galectin-3, and soluble suppression of tumorigenicity 2 for the prediction of mortality in sepsis. \u003cem\u003eAnn. Intensive Care\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 27 (2017).\u003c/li\u003e\n \u003cli\u003eGrover, V. \u003cem\u003eet al.\u003c/em\u003e A Biomarker Panel (Bioscore) Incorporating Monocytic Surface and Soluble TREM-1 Has High Discriminative Value for Ventilator-Associated Pneumonia: A Prospective Observational Study. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e109686 (2014).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Monocytes, neutrophils, lymphocytes, normal values, newborns, infection","lastPublishedDoi":"10.21203/rs.3.rs-5984143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5984143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTimely diagnosis of neonatal sepsis is crucial but remains challenging with existing tools. Cell Population Data (CPD) provide high-resolution phenotypic analysis of leukocytes, offering potential for sepsis detection. We aimed to establish neonatal CPD reference intervals and explore their potential for early recognition of sepsis and necrotizing enterocolitis (NEC). Neutrophil, monocyte, and lymphocyte CPD from hospitalized newborns were analyzed. Reference intervals (5th to 95th percentiles), at birth and during the first 28 days, were derived from newborns without conditions potentially impacting CPD (controls). CPD obtained on the day of clinical suspicion from newborns with blood culture-proven sepsis and/or NEC were compared to controls, and their performance in detecting sepsis/NEC was compared with complete blood count (CBC) and C-reactive protein (CRP). Reference intervals from 905 controls showed that mean CPD values had distinct trajectories for each parameter, while distribution width generally decreased with increasing gestational and postnatal age. CPD in 39 sepsis/NEC cases differed from controls, particularly neutrophil fluorescence intensity (NE-SFL) (56.2 vs. 41.1 arbitrary units, P \u0026lt; 0.001). NE-SFL had superior accuracy over other CPD, CBC, and CRP, with 90% sensitivity and 76% specificity. This study establishes neonatal CPD reference intervals and identifies NE-SFL as a potential sepsis biomarker.\u003c/p\u003e","manuscriptTitle":"Neonatal cell population data: reference intervals and relevance for detecting sepsis and necrotizing enterocolitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 13:25:06","doi":"10.21203/rs.3.rs-5984143/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c2227e3-0988-4d97-8a7b-76974ee7e451","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44204768,"name":"Health sciences/Medical research/Biomarkers"},{"id":44204769,"name":"Health sciences/Medical research/Paediatric research"}],"tags":[],"updatedAt":"2025-06-09T16:12:15+00:00","versionOfRecord":{"articleIdentity":"rs-5984143","link":"https://doi.org/10.1038/s41390-025-04159-x","journal":{"identity":"pediatric-research","isVorOnly":true,"title":"Pediatric Research"},"publishedOn":"2025-06-06 00:00:00","publishedOnDateReadable":"June 6th, 2025"},"versionCreatedAt":"2025-02-14 13:25:06","video":"","vorDoi":"10.1038/s41390-025-04159-x","vorDoiUrl":"https://doi.org/10.1038/s41390-025-04159-x","workflowStages":[]},"version":"v1","identity":"rs-5984143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5984143","identity":"rs-5984143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}