Peripheral Blood Cell Ratios: Promising Predictive Biomarkers for the Diagnosis of Pediatric Autoimmune Encephalitis

Abstract

Background: Autoimmune encephalitis (AE) is an increasingly well recognized disorder in the past decade both in adults and in children, yet pediatric data are still limited. A full peripheral blood cell count is a routine examination that provides valuable information regarding the immune system. Thus, there are peripheral blood cell count (PBCC)-derived ratios that reflect systemic inflammatory activity and they have been associated with disease severity in adults: the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune–inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). Methods: This study is a retrospective chart review of children under 18 years diagnosed with definite or probable AE and treated in our institution from 1 January 2018 until 1 December 2025. Only patients with available PBCC results at the time of the first hospital admission after neurological/psychiatric symptom onset were included. An age-matched control group was created by selecting the results of PBCC of patients presenting for routine pediatric follow-ups with normal inflammatory and hematologic parameters. The group means were compared using an independent-samples t-test or the Mann–Whitney U test for non-normally distributed data. Analysis of the receiver operating characteristics curve (ROC curve) was conducted, followed by the area under the curve ROC curve (AUC). Results: A total of 45 children with AE and 150 controls were included in the study. Of these, 22 patients (49%) had probable AE and 23 patients (51%) had definite AE. The NLR, PLR, SII, SIRI and AISI values were significantly higher in AE patients compared with the controls, but the AUC values (~0.58–0.66) indicate poor-to-fair discriminative ability. Youden’s index-based cut-off values were associated with high specificity and modest sensitivity. The likelihood ratios in the range of 2–3 (LR+) and 0.6–0.7 (LR−) suggest weak rule-in capacity and limited rule-out utility. Conclusions: Our results suggest that at the time of the initial hospitalization, children with AE already show altered peripheral immune cell profiles compared to their age-matched peers. The high specificity and the low sensitivity of the inflammatory indices make them more suitable for supporting the AE diagnosis in suggestive clinical circumstances, but not for screening. These results represent a foundation for further investigation of the roles that these indices have both as diagnostic and prognostic factors for these children.

1. Introduction

Autoimmune encephalitis (AE) is an increasingly well recognized disorder in the past decade. As clinical and pathophysiological studies mostly report on adult patients, pediatric autoimmune encephalitis (PAE) remains less well studied, presumably due to its lower incidence [1].

Patients with an initial PAE presentation require thorough clinical and laboratory examinations to establish diagnosis and treatment [2,3]. A full peripheral blood cell count is a routine investigation providing important information regarding the immune system.

The concept of calculating the ratios of various components of the peripheral blood cell count (PBCC) as a measure of the immune and inflammatory status of patients was initially described in a paper investigating the neutrophil-to-lymphocyte ratio (NLR) in critically ill cancer patients [4]. Since then, the scope of peripheral blood cell ratios has expanded. Considerable data exists from both adult and pediatric studies involving the NLR, platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR, sometimes calculated as the inverse of the MLR, the “lymphocyte-to-monocyte ratio” or LMR) in various conditions such as cancer, infection and a variety of autoimmune or inflammatory conditions [5,6,7,8,9,10]. Several indices have also been developed, based solely on readily available peripheral blood cell count: the systemic immune–inflammation index (SII) [11], the systemic inflammation response index (SIRI) [12], and the aggregate index of systemic inflammation (AISI) [13].

The platelet count is an important factor in many of the ratios and indices summarized above. There is evidence that platelet-specific parameters such as the mean platelet volume (MPV), platelet distribution width (PDW), and plateletcrit (PCT) are also correlated with immune or inflammatory disease states in adults [14,15] but data from children is more ambiguous [16,17,18].

There has been comparatively little evidence published regarding peripheral blood cell ratios and indices in patients with autoimmune encephalitis.

Most data come from adult studies. Recent work shows that peripheral immune cell ratios are associated with a worse prognosis of AE in adults with higher ratios [19,20]. Other studies indicated that higher NLR is correlated with treatment failure of first-line treatment options such as intravenous corticosteroids, plasma exchange or intravenous immunoglobulins [21,22]. Very limited data exists regarding the additional peripheral blood cell indices in patients with AE. Data from a single study shows that higher values of SII correlate with worse outcomes 30 days after treatment initiation [23].

To date, there is a single study concerning pediatric AE indicating that in 36 children with AE associated with N-methyl-D-aspartate receptor antibodies (NMDA AE), a higher NLR predicted the need for intubation in the ICU of these patients.

The aim of this study was to evaluate the diagnostic performance of PBCC-derived ratios (NLR, PLR, MLR) and composite indices (SII, SIRI, AISI) in pediatric patients with suspected AE at initial presentation prior to immunomodulatory treatment. We sought to determine their potential role as rapid, accessible, and cost-effective adjunctive tools in the early diagnostic workup.

2. Methods

This is a retrospective study (1 January 2018 to 1 December 2025) of the medical records of a group of patients with AE and an age-matched control group, admitted to a single Reference Center for Rare Pediatric Neurology disorders. All data were anonymized prior to analysis to ensure patient confidentiality. This study was approved by the Ethics Committee of the “Prof. Dr. Alexandru Obregia” Clinical Hospital of Psychiatry, according to the Declaration of Helsinki (Ethics approval number: 142/17 May 2024).

2.1. The Study Group

We searched the electronic archive of patients admitted to the hospital using the diagnostic keywords “Autoimmune Encephalitis”.

The diagnosis of possible, probable or definite autoimmune encephalitis was established at the onset of this research, using de Bruijn et al. (2020) criteria [1]. Definite antibody-positive AE was defined by: 1. subacute onset (<3 months) of neurological and/or psychiatric symptoms; 2. at least two clinical features of neurological dysfunction (altered mental status or EEG abnormalities, focal deficits, cognitive dysfunction, developmental regression, movement disorder, psychiatric symptoms, or new-onset seizures); 3. at least one paraclinical marker of neuroinflammation (CSF pleocytosis and/or oligoclonal bands, MRI features of encephalitis, or inflammatory brain biopsy); 4. presence of well-characterized neuronal autoantibodies; and 5. reasonable exclusion of alternative etiologies. Probable AE lacked detectable neuronal autoantibodies, while possible AE lacked both neural autoantibodies and paraclinical evidence of neuroinflammation.

The inclusion criteria for the study group were: 1. diagnosis of definite or probable AE [1]; 2. <18 years old at the time of disease onset; 3. PBCC available at the time of the first hospital admission after neurological/psychiatric symptom onset; 4. immunomodulatory treatment not used before PBCC.

Exclusion criteria were: 1. lack of PBCC at first admission; 2. immunomodulatory treatment prior to first available PBCC; 3. clinical or paraclinical evidence (MRI, CSF analysis, infectious workup) suggesting a structural, metabolic or infectious etiology as a more reasonable explanation for seizures or encephalopathy.

2.2. The Control Group

A group of hospital-based control participants was created by selecting anonymized normal results of PBCCs of patients presenting for routine pediatric follow-ups and children presenting for physical therapy and rehabilitation procedures in our institution. Exclusion criteria were: 1. any elevated inflammatory biomarkers at the time of PBCC; and 2. abnormal absolute values of white blood count, neutrophils, lymphocytes, monocytes, red blood cells, hemoglobin or platelets.

Extracted data included: sex, age at onset, type of AE (probable or definite AE, specific antibody-defined categories when available), PBCC at diagnosis (absolute values of white blood count, neutrophils, lymphocytes, monocytes, red blood cells, hemoglobin and platelets, MPV, PDW, PCT). The normal reference ranges for these hematological parameters are provided in the Supplementary Material. We calculated the peripheral blood cell ratios (NLR, MLR, PLR, SII, SIRI, AISI).

2.3. Statistical Analysis

All data were analyzed using JASP software, version 0.95.4, which is based on the R language for statistical programming [24,25]. Normality was assessed using the Shapiro–Wilk test and homogeneity of variance was evaluated using the Brown–Forsythe test. The groups were compared using an independent-samples t-test or the Mann–Whitney U test for non-normally distributed variables. Effect sizes were reported using Cohen’s d.

The receiver operating characteristics ROC (analysis) and the area under the ROC curve (AUROC) were computed to evaluate the discriminative ability of each PBCC-derived inflammatory ratio for distinguishing AE patients from controls. For calculation of the ROC, AUROC and Youden’s index we used Jamovi software, version 2.7.16.

Within the study group, subgroups based on etiology (NMDA, GAD, VGCK, Rasmussen, seronegative) and type of AE (probable/definite) were created to compare the PBCC ratios.

3. Results

A total of 55 children and adolescents diagnosed with AE during the study period (January 2018–December 2025) were initially screened. After applying the predefined inclusion and exclusion criteria, 45 patients were included in the final analysis. The primary reasons for exclusion were absence of a PBCC at initial presentation or before the initiation of immunomodulatory therapy and the diagnosis of possible AE. Among them, 27 (60%) were female and 18 (40%) male (female-to-male ratio 1.5:1). Based on previously published pediatric AE criteria [1,2], 22 patients (49%) had probable AE, and 23 patients (51%) had definite AE.

The control group included 150 individuals that fulfilled the inclusion and exclusion criteria.

Among etiological subtypes, anti-NMDA receptor encephalitis was the most frequent form, occurring in 12 of 45 cases (26.7%), consistent with current epidemiologic data describing its predominance among pediatric AE presentations [26]. Four patients (8.9%) were diagnosed with AE associated with antibodies against voltage-gated potassium channel complex (VGCK). One patient had dual anti-NMDA and anti-GAD antibodies; one patient had isolated anti-GAD antibody-associated encephalitis. Five patients (11.1%) were diagnosed with Rasmussen encephalitis.

Due to the small number of cases within each etiological subgroup, no subgroup-specific comparisons of inflammatory indices were performed. Consequently, analyses were limited to comparisons between the control group and study group (

Table 1. Study and control group PBCC indices analysis.

			Mann–Whitney U (t-Test *)
	C/P	N	Median (Mean *)	IQR (SD *)	p	Effect Size
Age (months) #	C	150	94.500	72.750	0.920	−0.010
	P	45	92.000	91.000
WBC	C	150	7.365	2.708	0.017	0.210
	P	45	8.450	4.250
Ne	C	150	3.155	1.545	0.002	0.292
	P	45	4.209	5.060
Ly ⱡ	C	150	2.810	1.367	0.003	−0.276
	P	45	2.370	1.160
Mon	C	150	0.565	0.280	0.204	0.081
	P	45	0.590	0.380
Hgb	C	150	12.800	1.500	0.005	−0.124
	P	45	12.700	1.300
PLT *	C	150	291.200	68.370	0.120	−0.129
	P	45	309.900	98.520
MPV	C	150	9.900	1.100	0.045	0.172
	P	45	10.350	1.975
PDW	C	150	12.550	5.650	0.827	−0.096
	P	45	12.200	3.200
PCT	C	150	0.285	0.090	0.007	0.251
	P	45	0.320	0.122
NLR	C	150	1.105	0.735	<0.001	0.310
	P	45	1.609	2.983
MLR	C	150	0.183	0.106	0.050	0.162
	P	45	0.220	0.199
PLR	C	150	104.613	45.198	0.008	0.238
	P	45	110.550	108.244
SII	C	150	330.735	253.917	<0.001	0.308
	P	45	403.930	794.003
SIRI	C	150	0.580	0.545	0.004	0.259
	P	45	0.968	1.829
AISI	C	150	170.017	205.956	0.004	0.262
	P	45	248.698	448.443

AISI, aggregate index of systemic inflammation; C, control group; effect size reflects the magnitude of group differences; Hgb, hemoglobin; IQR, interquartile range; Ly, lymphocytes; median, median value for the whole group; MLR, monocyte-to-lymphocyte ratio; Mon, monocytes; MPV, mean platelet volume; N, number; Ne, neutrophils; NLR, neutrophil-to-lymphocyte ratio; p, measure of statistical significance (significant < 0.05); PBCC, peripheral blood cell count; PCT, plateletcrit; PDW, platelet distribution width; PLR, platelet-to-lymphocyte ratio; PLT, platelet count; P, study group (AE patients); SII, systemic immune–inflammation index; SIRI, systemic inflammation response index; WBC, white blood cell count. * Normally distributed variables analyzed using the t-test. # Two-sided hypothesis (P ≠ C). ^ⱡ One-sided hypothesis (P < C). All the other values are compared using a one-sided hypothesis (P > C).

), and between definite and probable AE (

Table 2. Comparison between probable and definite AE patients (Mann–Whitney U test).

	U	p	Rank Biserial Correlation	SE Rank Biserial Correlation
NLR	230.0	0.613	0.091	0.172
MLR	234.0	0.677	0.075	0.172
PLR	248.0	0.919	0.020	0.172
SII	245.0	0.866	0.032	0.172
SIRI	252.0	0.991	0.004	0.172
AISI	265.0	0.796	−0.047	0.172

AISI, aggregate index of systemic inflammation; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; p, p-value; PLR, platelet-to-lymphocyte ratio; rank biserial correlation, effect size measure for the Mann–Whitney U test; SE, standard error; SII, systemic immune–inflammation index; SIRI, systemic inflammation response index; U, Mann–Whitney U statistic.

).

Before performing the group comparisons, the data distribution and homogeneity of variance were assessed. The Shapiro–Wilk test showed that most parameters displayed non-normal distributions in both groups (p < 0.05). The Brown–Forsythe test further indicated unequal variances between the patients and controls for most variables, supporting the use of non-parametric testing for the majority of comparisons.

Table 1 provides descriptive statistics of the study and control groups. There is no statistically significant difference in age between the two groups (p = 0.959). Patients with AE displayed significantly higher leukocyte counts than controls and elevated neutrophil counts. In contrast, lymphocyte levels are lower in the AE group. Platelet characteristics showed a heterogeneous pattern. Platelet count and PDW did not differ significantly. MPV was modestly but significantly higher in AE patients, while PCT demonstrated a more robust elevation. Inflammatory composite indices derived from PBCC demonstrated the most discriminative power. Compared with controls, AE patients exhibited significantly increased NLR, PLR, MLR, SII, AISI, SIRI. NLR and SII had the highest effect size.

There are no significant differences regarding PBCC indices in patients with definite versus probable AE (Table 2).

Table 3. ROC curve summary.

		95% Confidence Interval
	AUC	Lower	Upper	p
NLR	0.655	0.550	0.760	0.004
MLR	0.581	0.473	0.689	0.141
PLR	0.619	0.514	0.724	0.026
SII	0.654	0.551	0.757	0.003
SIRI	0.629	0.522	0.737	0.018
AISI	0.631	0.525	0.737	0.015

AISI, aggregate index of systemic inflammation; AUC, area under the curve; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; p, measure of statistical significance (significant < 0.05); PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; SII, systemic immune–inflammation index; SIRI, systemic inflammation response index.

presents ROC results and AUC values; the ROC curve analysis confirmed modest but statistically significant discriminatory performance across all markers. The NLR, PLR, SII, SIRI and AISI have AUC values between 0.6 and 0.7, while MLR has a lower AUC value.

The optimal cut-off values identified by Youden’s index are listed in

Table 4. Diagnostic performance of inflammatory indices (based on ROC Youden’s index).

Marker	Optimal Cut-Off	Sensitivity (%)	Specificity (%)	LR+	LR−	Accuracy (%)
NLR	≥1.95	44.44	87.33	3.51	0.64	77.44
MLR	≥0.299	37.78	86.67	2.83	0.72	75.38
PLR	≥147.38	40.00	89.33	3.75	0.67	77.95
SII	≥669.79	42.22	91.33	4.87	0.63	80.00
SIRI	≥0.959	51.11	78.00	2.32	0.63	71.79
AISI	≥325.54	48.89	80.67	2.53	0.63	73.33

AISI, aggregate index of systemic inflammation; LR+, positive likelihood ratio; LR−, negative likelihood ratio; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; SII, systemic immune–inflammation index; SIRI, systemic inflammation response index.

. Across the analyzed inflammatory indices, sensitivities ranged from 37.8% to 51.1%, while specificities varied between 78.0% and 91.3%, resulting in overall accuracies between 71.8% and 89.0%. Among the evaluated markers, SII demonstrated the highest diagnostic performance, with the greatest specificity, positive likelihood ratio and overall accuracy. In contrast, negative likelihood ratios remained relatively high across all indices (0.63–0.73), indicating limited ability to rule out AE.

Pairwise AUC comparisons using DeLong’s test for NLR, SII, SIRI, and AISI revealed no statistically significant differences between inflammatory markers (

Table 5. Pairwise AUC comparisons (DeLong’s test).

		95% Confidence Interval
	AUC Difference	Lower	Upper	z	p
NLR vs. MLR	0.07407	−0.0294	0.1776	1.4026	0.161
NLR vs. PLR	0.03593	−0.0621	0.1340	0.7181	0.473
NLR vs. SII	0.00096	−0.0351	0.0370	0.0524	0.958
NLR vs. SIRI	0.02556	−0.0404	0.0915	0.7599	0.447
NLR vs. AISI	0.02393	−0.0476	0.0955	0.6552	0.512
MLR vs. PLR	−0.03815	−0.1533	0.0770	−0.6494	0.516
MLR vs. SII	−0.07311	−0.1790	0.0328	−1.3528	0.176
MLR vs. SIRI	−0.04852	−0.1124	0.0154	−1.4881	0.137
MLR vs. AISI	−0.05015	−0.1205	0.0202	−1.3967	0.162
PLR vs. SII	−0.03496	−0.1156	0.0457	−0.8493	0.396
PLR vs. SIRI	−0.01037	−0.1276	0.1069	−0.1733	0.862
PLR vs. AISI	−0.01200	−0.1205	0.0965	−0.2167	0.828
SII vs. SIRI	0.02459	−0.0465	0.0957	0.6778	0.498
SII vs. AISI	0.02296	−0.0408	0.0868	0.7054	0.481
SIRI vs. AISI	−0.00163	−0.0290	0.0257	−0.1169	0.907

AISI, aggregate index of systemic inflammation; AUC, area under the curve; DeLong’s test, non-parametric test for comparison of correlated ROC curves; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; p, measure of the statistical significance (significant < 0.05); PLR, platelet-to-lymphocyte ratio; SII, systemic immune–inflammation index; SIRI, systemic inflammation response index; vs., versus; z, z-statistic.

).

4. Discussion

AE is a group of conditions that have been better categorized and scientifically described in the past decade [26]. Both innate and adaptative immunity play a role in the pathogenesis of AE. Different subtypes of AE are influenced by different effectors both in the innate and the adaptative immune system [27]. The innate immune response plays an important role in the pathogenesis of AE. Current evidence suggests that innate immune cells are involved in the disruption of the blood–brain barrier (BBB) [28,29]. After disruption of the BBB, microglial activation occurs, leading to recruitment of the adaptative side of the immune system, explaining the pathogenesis of AE.

There is also evidence of the role that platelets play in neuroinflammation [30]. Platelets activate and modulate macrophages and subgroups of T cells leading to activation and progression of the neuroinflammatory effects [30,31].

To our knowledge, our results are the first report of peripheral immune cell ratios comparing children with AE and hospital-based controls. Our study is also the first one to publish data regarding the AISI ratio in patients with AE and to compare it with those of a control group of children attending the pediatric neurology service for non-acute evaluations and treatments. Our results suggest that at the time of the initial hospitalization, children with AE already show altered peripheral immune cell profiles compared to their age-matched peers. This may be helpful when first considering differential diagnosis at presentation. Neurologic examination is time-consuming and often difficult in children, more so in children with prominent behavioral symptoms. Furthermore, establishing a positive diagnosis of AE requires additional neurophysiological testing, imaging and specialized blood and cerebrospinal fluid (CSF) testing for antibodies and other advanced parameters that require a long time for processing [2]. By comparison, a PBCC is readily available, often within minutes to hours of the initial presentation of a patient. Knowing that patients with AE present with high immune cell ratios could be of significant help to the clinician considering various differential diagnoses at the time of initial presentation, with the presence of these measures favoring AE diagnosis which may lead to early specific treatment.

The NLR is the most studied ratio both in organic (including neurologic) and psychiatric illnesses [32,33,34]. Studies of NLR in adult populations with AE have indicated that larger NLR values at admission or at the time of treatment initiation are correlated with worse long-term outcomes and less satisfactory response to treatment, respectively. The recent literature showed that values of 4 or higher for NLR are associated with higher morbidity and mortality in AE patients [20,22]. The only study that estimated NLR values in children with anti-NMDAR antibody AE also found that an NLR above 6 predicted the need for intubation and mechanical ventilation [35].

In our study, patients with AE have higher NLRs than their hospital-based age-matched control peers. Although NLR and SII yielded the highest effect sizes among the analyzed indices, the magnitude of these effects was small. In our group, we could not differentiate among patients with better rather poorer prognoses using the NLR value. This is due to the relatively low number of patients with definite or probable AE with sufficiently complete records that we were able to recover.

Other PBCC-derived ratios also showed statistically significant differences between patients with AE and controls, with higher values observed in the AE group; however, their effect sizes were even smaller. This finding is not unexpected, as SII, SIRI, and AISI all incorporate the neutrophile count, which is also the numerator of the NLR.

Youden’s index-based cut-off values were associated with high specificity and modest sensitivity. The corresponding AUC values (~0.58–0.66) indicate poor-to-fair discriminative ability. In practical terms, likelihood ratios in the range of 2–3 (LR+) and 0.6–0.7 (LR−) suggest weak rule-in capacity and limited rule-out utility. Accordingly, these indices appear to have limited screening value but may provide supportive information when interpreted alongside clinical and paraclinical findings.

A study conducted in Australia on adults with antibody-positive AE proved that SII has a good predictive value concerning acute treatment outcomes, i.e., 30 days after immune modulation was initiated [21]. The same study found that the NLR and PLR also share this predictive capacity, albeit with the smaller AUC values.

Our study also provides the first description of the aggregate index of systemic inflammation (AISI) in patients with AE. Similarly to the other ratios, AISI is higher in children with AE but with a small effect size.

Platelets are another class of cellular elements in peripheral blood. Production of platelets from megakaryocytes is driven by thrombopoietin, a liver-produced protein that acts like an acute-phase reactant [36,37]. Platelets play an important role in the immune system. They interact both with the innate and adaptative branches of the immune system. Platelets activate neutrophils and monocytes; they have complex feedback interactions with the complement system and they interact with and influence the differentiation of T lymphocytes [38]. Platelets are known to contribute significantly to neuroinflammation, interacting with different T lymphocyte subtypes and altering the blood–brain barrier (BBB) [30,31].

Thus, it stands to reason that platelets might also be important contributors to the pathogenesis of inflammatory diseases. In adults, a single study has evaluated PLR (together with NLR and SII) in association with disease severity after treatment. Patients with higher values of these ratios had worse outcomes after treatment [23]. In our study, PLR is higher in patients with AE compared to their age-matched controls. The PCT is also significantly higher, but due to the high variability it was considered not useful in identifying AE patients. Exploring this finding in larger patient cohorts and in other immune–inflammatory diseases might lead to relevant results associated with disease pathogenesis and platelet physiology and their role in pathogenic processes.

Our study provides the first formal evaluation of PBCC ratios in children with probable and definite autoimmune encephalitis. The results represent a foundation for further research of the diagnostic and prognostic roles of the studied indices in children with AE. A complete PBCC is easily available in many clinical settings. Thus, during the acute presentation of a patient with suspected AE, higher values of these indices may provide supportive information and increase clinical suspicion, but they cannot replace standard diagnostic investigations for AE confirmation.

One limitation is the retrospective nature of the study. This led to the exclusion of patients with incomplete records. Another limitation was the considerable practical and financial challenge of testing for specific antibodies in the past (unconfirmed seropositivity placed these patients in the “probable AE” group and excluded from this study).

Autoimmune encephalitis represents a heterogeneous group of disorders with distinct underlying immunopathogenic mechanisms. This heterogeneity may contribute to variability in PBCC-derived indices across patients. Also, we could not compare the indices between AE subtypes due to low number of patients within each subgroup (e.g., anti NMDAR antibody AE compared to other seropositive patients).

The timing of PBCC assessment is another important consideration. In our study, blood samples were obtained at initial presentation, prior to immunomodulatory therapy; however, the interval between symptom onset and sampling may have varied between patients. Given the dynamic nature of systemic inflammatory responses, PBCC-derived ratios may fluctuate during the disease course, potentially influencing their diagnostic performance.

In our research there were no significant differences in the PBCC indices in patients with definite versus probable AE.

Another limitation of our study is that PBCC-derived ratios reflect systemic inflammation and are not disease-specific. These indices may be elevated in a variety of conditions frequently encountered in pediatric patients, including intercurrent infections, seizures, or other acute or chronic inflammatory states. Such confounding factors may have influenced our results.

Patient stratification regarding treatment outcomes was not possible, due to inhomogeneous evaluation of the functional results (no annual modified Rankin scale score was performed in all cases). This retrospective study highlighted the importance of a standardized approach for assessing and monitoring these patients, both for research and clinical utility purposes.

Great opportunity for future research in this area exists. The parameters we investigated are affordable and easily available in any medical setting. Analyzing larger cohorts of patients with clearly defined subgroups should be a priority. Whether or not those patients with AE with positive antibody testing differ significantly from seronegative AE patients is a topic that should receive more scrutiny. Additionally, there would be immense practical value in demonstrating whether immune cell ratios can function as monitors or predictors of therapy success or, respectively, of the chances of relapse.

5. Conclusions

Children with AE have significantly higher PBCC immune cell ratios at the time of first admission compared to age-matched hospital-based controls. The high specificity and the low sensitivity of the inflammatory indices make them more suitable for supporting the AE diagnosis in suggestive clinical circumstances, but not for screening. These inflammatory indices provide quick, easily available information to guide the differential diagnosis process and additional testing. These indices have a high practical value and do not incur supplementary costs as they involve only simple ratio calculation. While they do not replace standard diagnostic evaluation for AE, they may help direct the diagnostic process by reducing the need for some of the ancillary investigations within the differential diagnosis process, with great benefit to patients, care-givers, medical staff and healthcare systems as a whole. Additional research to prove that these parameters are useful for establishing the appropriate treatment and for predicting prognosis is necessary. Future research should focus on larger cohorts of patients with clearly defined serological diagnoses and standardized clinical approaches for monitoring outcomes.