The host response of COVID-19 and identification from other aetiologies of community-acquired pneumonia in children

doi:10.21203/rs.3.rs-3411953/v1

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Research Article

The host response of COVID-19 and identification from other aetiologies of community-acquired pneumonia in children

https://doi.org/10.21203/rs.3.rs-3411953/v1

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Background

Community-acquired pneumonia (CAP) was a common respiratory tract infection in children, which can be caused by various pathogens, including bacteria, mycoplasma (MP), respiratory syncytial virus (RSV), and recently SARS-CoV-2 are the most widespread. We attempt to distinguish common and pathogen-specific host response characteristics by comparing the CAP of different pathogens.

Methods

We included 200 CAP hospitalized cohort caused by SARS-CoV-2 (COVID-19, n = 50), mycoplasma (CAP-MP, n = 50), RSV (CAP-RSV, n = 50) and other bacteria (CAP-Bacteria, n = 50), of whom were balanced the potentially confounding factors (such as age and gender) based on Propensity Score Matching algorithm(PSM). We compared hematologic and biochemical indicators for different CAPs, samples were taken within 48 hours of admission.

Results

Main clinical features of COVID-19 were fever, faster heart rate and lower antibiotic use. Notably, markers of immuno-inflammatory, including white blood cell, lymphocyte and procalcitonin (PCT) were not different among the CAP groups. Biomarkers reflecting nutrient metabolism showed total protein (TP) and albumin (ALB) levels in the COVID-19 group were lower than those in the CAP-MP group, the creatinine and urea levels of the COVID-19 patients were higher than that of CAP-MP group. The serum sodium and calcium levels in the COVID-19 group were the lowest and significantly lower than that in the CAP-MP group, while serum phosphorus levels were opposite. Moreover, we observed that the creatine kinase (CK) and creatine kinase-MB (CK-MB) levels in the COVID-19 were higher than those in the CAP-MP groups.

Conclusions

Our study revealed common and unique pathophysiological features of different pathogens-associated CAP, which may facilitate the pathogen-specific precision diagnosis and treatment.

COVID-19

community-acquired pneumonia

host response

Community-acquired pneumonia (CAP) was a common complication of respiratory tract infection in pediatric infection, and it was a major cause of morbidity and mortality in children under five years of age, particularly in developing countries[1, 2]. CAP could be caused by some different pathogens, but about half of all inpatient pathogens remain unidentified. The Lancet PERCH study found that the viruses accounted for 61.4%, bacteria accounted for 27.3%, among which respiratory syncytial virus (RSV) had the largest proportion (31.1%) in the pathogenic analysis of severe pneumonia in non-HIV infected children under 5 years old[3]. The microbial etiology of CAP has changed remarkably since late 2019 with the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19).

COVID-19 pneumonia exhibited several similarities with CAP caused by other pathogens but also displays distinct features. The course of COVID-19 is notably unique, and individuals with metabolic comorbidities such as obesity and type 2 diabetes are more prone to experiencing severe disease[4]. Loss of taste and smell function is a unique feature of COVID-19 in adults and children, particularly in western countries[5]. In all kinds of CAP, dysregulation of host response is a key pathophysiological characteristic, and determine the progression of the disease.

Fever, cough, diarrhea and dyspnoea are common clinical symptoms of COVID-19 and CAP. In addition, numerous studies on COVID-19 host response have shown that these features of systemic inflammation and altered immune cell profiling are also well-recognized in CAP[6].

Despite these superficial similarities, a comprehensive overview of the host response to COVID-19 and other aetiologies of CAP in children is lacking. Children under 15 years old are more likely to infect SARS-CoV-2, although most of them are asymptomatic or show mild symptoms. Notably, children with COVID-19 could lead to multisystem inflammatory syndrome (MIS), which has an overall 2% mortality[7]. Therefore, a comprehensive overview may facilitate distinguishing pathogen-specific characteristics from common features of host dysregulation during the early stage of CAP. This may contributes to our better understanding of the pathophysiology process of CAP caused by different pathogens, and assist the pediatrician to make accurate diagnosis and treatment decisions.

In the present study, we compared clinical manifestation and a broad set of blood indexes indicating key pathophysiological domains in hospitalized pediatric CAP patients, and describe common and distinct host response features of different pathogens of pediatric CAP.

2.1 Patients

Trained research pediatricians screened CAP patients younger than 18 years hospitalized to Taizhou Hospital of Zhejiang Province between January 2019 and November 2022 for eligibility. All patients younger than 18 years hospitalized at Taizhou Hospital of Zhejiang Province with COVID-19 were included in December 2022 and February 2023 when the SARS-CoV-2 Omicron variant became dominant in China. CAP was defined by WHO criteria. The presence of any alveolar or interstitial opacity on a chest radiograph accompanied by one of the following symptoms or signs: 1) cough or difficulty breathing; 2) axillary fever ≥ 38.3°C; 3) age-specific tachypnea (≥ 60/min for Children < 2months; ≥50/min for children 2–11 months; ≥40/min for children 1–5years; >30/min for children 6–12years). Exclusion criteria: (1) children with clinically suspected aspiration or hospital-acquired pneumonia or mixed with other pathogens; (2) children with other respiratory diseases; (3) children with heart, liver and kidney dysfunction, blood and immune system and malignant tumors; (4) children with mental diseases. The four groups of patients were matched by Propensity Score Matching (PSM) for sex and age (Fig. 1).

The baseline information was extracted from each patient: gender, age, time of pathogen confirmation, medical history, clinical manifestations, radiologic and laboratory findings on admission, treatment and outcome. This retrospective study was approved by the Ethics Committee of Taizhou Hospital of Zhejiang Province (No. K20230116) and informed consent was obtained from the parents of each enrolled patient.

2.2 (Primary) pathogen assessment

We compared patients according to the etiology of CAP: COVID-19 (primary pathogen SARS-CoV-2) and non-COVID-19 CAP (all patients without COVID-19), CAP-MP (primary pathogen mycoplasma pneumoniae), CAP-RSV (primary pathogen respiratory syncytial virus), CAP-bacteria (Haemophilus influenzae, Klebsiella pneumoniae, Escherichia coli, Streptococcus pneumoniae). A group of trained pediatricians evaluated clinical microbiology results on admission, combined with clinical notes from electronic health records. Mycoplasma pneumoniae infection was confirmed by positive detection of PCR in the acute stage, SARS-CoV-2 was confirmed by PCR or antigen dectection, RSV was confirmed by antigen detection or IgM in the acute stage, and bacteria were identified mainly by culture.

2.3 Statistical Analysis

All statistical tests were two-sided and p < 0.05 was considered statistically signifificant. Categorical variables were displayed as count (percentage) among different groups. For continuous variables, data were showed as mean ± standard deviation or median (interquartile range). To ensure conformity with a normal distribution, all biomarker and clinical data underwent the Shapiro-Wilk test before conducting any statistical analysis. Due to the non-normal distribution of the data, subsequent statistical analyses were conducted using non-parametric tests. In the case of volcanic maps, Wilcoxon tests were employed to compare biomarker levels. To correct for multiple tests, the Benjamin-Hochberg (BH) method was applied appropriately. To generate effect size heatmaps, we computed the Hedges' g between the CAP groups and the COVID-19 group[8]. To assess the overall differences between groups, a Kruskal-Wallis test was performed. If the results were found to be significant, a post-hoc pairwise Nemenyi test was conducted for further analysis. All statistical analyses were performed using R version 4.1.1(Vienna, Austria).

3.1 Patient characteristics and clinical outcomes

We analyzed 200 enrolled hospitalized patients with CAP paired by PSM, and 50 cases in each group. Basic information and clinical outcomes of all patients were displayed in Table 1. The four study groups had no statistically significant difference in age and sex distribution. Fever was not unique to COVID-19, but it occured most frequently in COVID-19 (p < 0.01). Cough is a common symptom in COVID-19 and other CAP, with the highest incidence in the CAP-RSV group. Compared with other groups, moist and wheezing rales are the least prominent in the COVID-19 group.

Table 1

Clinical characteristics and disease course of patients.
Variables	COVID-19 (n = 50)	CAP-Bacteria (n = 50)	CAP-MP (n = 50)	CAP-RSV (n = 50)	p-value
Age groups (years), n (%)
< 1	33	33	33	33	-
1–3	10	10	10	10	-
4–9	7	7	7	7	-
Gender, n (%)
Boy	25	24	24	24	-
Previous history
Wheeze	1 (2)	4 (8)	1 (2)	2 (4)	0.37
Rhinitis	0 (0)	2 (4)	0 (0)	1 (2)	0.29
Vital signs and disease severity
Fever^a	44 (88)	14 (28)	25 (50)	18 (36)	< 0.01
Temperature, °C	38.86 ± 0.80	38.86 ± 0.72	39.31 ± 0.54	39.06 ± 0.79	0.0815
Cough	40 (80)	45 (90)	47 (94)	50 (100)	< 0.01
Tachypnea^b	14 (28)	16 (32)	13 (26)	16 (32)	0.89
Vomiting	9 (18)	9 (18)	13 (26)	8 (16)	0.60
Diarrhea	0 (0)	3 (6)	3 (6)	4 (8)	0.29
Respiratory rate, bpm	45(34, 56)	46(35, 55)	37(32, 45)	45(35, 56)	0.01
Heart rate, bpm	145 ± 21	143 ± 19	132 ± 19	141 ± 19	< 0.01
Moist rales	15 (30)	26 (52)	31 (62)	28 (56)	< 0.01
Wheezing rales	3 (6)	18 (36)	17 (34)	19 (38)	< 0.01
Abnormal percutaneous oxygen saturation	4 (8)	8 (16)	8 (16)	5 (10)	0.51
Treatment
Antibiotic	40 (80)	49 (98)	50 (100)	47 (94)	< 0.01
Anti-allergic agents	5 (10)	4 (8)	3 (6)	5 (10)	0.87
Leukotriene receptor antagonist	7 (14)	9 (18)	20 (40)	17 (34)	< 0.01
Intravenous dexamethasone or methylprednisolone	4 (8)	8 (16)	25 (50)	17 (34)	< 0.01
inhalation of budesonide	25 (50)	38 (76)	46 (92)	49 (98)	< 0.01
Bronchodilators	7 (14)	21 (42)	34 (68)	27 (54)	< 0.01
Human immunoglobulin	0 (0)	0 (0)	2 (4)	1 (2)	0.29
Oxygen inhalation treatment	4 (8)	10 (20)	13 (26)	20 (40)	< 0.01
TCM treatment	14 (28)	15 (30)	16 (32)	13 (26)	0.92
Disease course
Symptoms to admission, days	2(1,5)	5(3,8)	7(4,8)	4(3,5)	< 0.01
Length of hospital stay, days	6(4,7)	7(6,9)	8(7,10)	8(7,10)	< 0.01
* The p values in bold font indicate statistical signifificance.
Abbreviations: COVID-19, coronavirus disease 2019; CAP, community-acquired pneumonia; MP, mycoplasma; RSV, respiratory syncytial virus; bpm, breaths/beats per minute; TCM, Traditional Chinese Medicine
a Fever was defined as a body temperature above 38°C (axilla).
b Tachypnea was defined as a respiration rate/minute greater than normal for age.

In terms of treatment, the usage rate of hormones and antibiotics is the lowest compared to other CAPs, as well as oxygen inhalation. In addition, COVID-19 had the shortest disease course, including the duration of symptoms to admission and hospital stay (p < 0.01).

3.2 Unique and overlapping laboratory indicators of host response in non-COVID-19 CAP and COVID-19 patients

We analyzed laboratory indicators for all patients within 48 hours of admission. First, we compared all common blood parameters between non-COVID-19 CAP and COVID-19 patients. 14 blood parameters showed significant differences between the two groups (BH-adjusted p < 0.05; 7 higher in non-COVID-19 CAP, 7 higher in COVID-19; Fig. 2A). The top differentially abundant biomarker in non-COVID-19 CAP was sodium, while procalcitonin (PCT), creatinine (Crea), and creatine kinase-MB (CK-MB) were the most significantly higher in the COVID-19 group.

3.3 Direct comparison of the host response among COVID-19, CAP-MP, CAP-RSV and CAP-bacteria

Next, we investigated whether different pathogens in CAP have distinctive host response. Comparing COVID-19 with CAP-MP directly, we found differences that were similar to the observed result when comparing with the all CAP groups (Fig. 2B). In contrast, when comparing COVID-19 with another etiology, CAP-MP, it was observed that the procalcitonin (PCT) levels were lower in the COVID-19 group (Fig. 2D). This could be due to the presence of two larger outlier values in the CAP-MP group, which causes the mean to be biased towards higher values, while the median is less affected by outliers. It is worth noting that among the indicators examined, only sodium exhibited significant differences between COVID-19 and CAP-RSV group (Fig. 2C). Additionally, Fig. 2E demonstrated the common response indicators and described the effect size (Hedges’g) of the difference from COVID-19. In summary, compared to non-COVID-19 CAP patients, COVID-19 patients exhibited unique host responses, although we also observed considerable overlap in host responses among different CAP etiologies.

3.4 Immuno-inflammatory responses

In the early stage of COVID-19 infection, patients may show normal or reduced total peripheral blood leukocyte counts, as well as decreased lymphocyte counts[9, 10].

In our results, we demonstrated that the white blood cell counts of COVID-19 and CAP-RSV patients were lower than that of CAP-Bacteria patients [8.8 (6.6–10.5) vs 11.1 (8.7–14.2)×10⁹/L, p < 0.001; 8.2 (6.4–10.9) vs 11.1 (8.7–14.2)×10⁹/L, p < 0.001]. Similarly, lymphocyte and eosinophil counts in COVID-19 patients were lower than those in CAP-Bacteria patients[3.9 (2.1–6.1) vs 5.7 (3.9-7.0)×10⁹/L, p < 0.05; 0.1 (0.0-0.2) vs 0.3 (0.1–0.5)×10⁹ /L]. In addition, we found that the platelet counts of the COVID-19 group were significantly lower than that of the CAP-Bacteria and CAP-MP groups [286.0 (208.0-428.0) vs 407.0 (321.8–494.0)×10⁹/L, p < 0.01; 286.0 (208.0-428.0) vs 368.0 (325.3-490.3)×10⁹/L, p < 0.01]. However, there was no significant difference in the counts of neutrophil and monocyte between the COVID-19 group and other groups. Regarding the immunoglobulin, there was no statistical difference in the levels of immunoglobulins IgG, IgA, and IgM among the four groups (Table 2, Fig. 3).

Table 2

Laboratory index characteristics in the four CAP groups
Lab parameters	COVID-19 (n = 50)	Non-COVID-19				P-value
		CAP-Bacteria (n = 50)	CAP-MP (n = 50)	CAP-RSV (n = 50)	All (n = 150 )
WBC(10⁹/L)						< 0.01
Mean ± SD	8.8 ± 3.3	12.3 ± 4.9	10.6 ± 4.0	8.7 ± 3.2	10.5 ± 4.3
Median(IQR)	8.8 (6.6–10.5)	11.1 (8.7–14.2)	10.4 (8.2–11.9)	8.2 (6.4–10.9)	9.9 (7.7–12.0)
Range	2.3–16.6	5.6–28.2	4.5–22.9	2.4–17.0	2.4–28.2
NEU(10⁹/L)						0.346
Mean ± SD	3.4 ± 2.4	5.1 ± 4.8	4.3 ± 3.7	2.5 ± 1.6	4.0 ± 3.7
Median(IQR)	2.8 (1.6–4.6)	3.6 (2.3–5.9)	3.2 (2.5–4.7)	2.0 (1.5–3.2)	2.9 (2.0-4.3)
Range	0.2–10.4	1.0-36.3	1.0-23.9	0.7–8.2	0.7–26.3
LYM(10⁹/L)						< 0.01
Mean ± SD	4.1 ± 2.7	5.8 ± 2.8	5.1 ± 2.4	5.0 ± 2.7	5.3 ± 2.6
Median(IQR)	3.9 (2.1–6.1)	5.7 (3.9-7.0)	5.4 (3.3–7.2)	4.4 (3.1–6.7)	5.1 (3.4–6.9)
Range	0.3–12.5	1.1–13.2	0.8–9.7	0.9–12.3	0.8–13.2
MONO(10⁹/L)						0.501
Mean ± SD	1.0 ± 0.6	1.1 ± 0.4	0.8 ± 0.4	0.9 ± 0.5	1.0 ± 0.4
Median(IQR)	0.9(0.5–1.3)	1.1(0.7–1.4)	0.8(0.6–1.1)	0.8(0.5–1.3)	0.9(0.6–1.3)
Range	0.2–2.9	0.4–2.2	0.1–1.8	0.1–1.9	0.1–2.2
EO(10⁹/L)						< 0.01
Mean ± SD	0.2 ± 0.2	0.3 ± 0.3	0.2 ± 0.2	0.2 ± 0.2	0.3 ± 0.3
Median(IQR)	0.1 (0.0-0.2)	0.3 (0.1–0.5)	0.2 (0.0-0.3)	0.2 (0.0-0.3)	0.2 (0.0-0.4)
Range	0.0-1.2	0.0-1.7	0.0–1.0	0.0-0.7	0.0-1.7
PLT(10⁹/L)						< 0.01
Mean ± SD	321.3 ± 136.4	406.4 ± 122.6	406.6 ± 154.8	336.1 ± 113.2	383.0 ± 134.70
Median(IQR)	286.0 (208.0-428.0)	407.0 (321.8–494.0)	368.0 (325.3-490.3)	332.5 (256.3-405.3)	362.5 (299.0-461.3)
Range	138.0-631.0	57.0-656.0	89.0-1004.0	64.0-626.0	57.0-1004
IgG(g/L)	n = 28	n = 33	n = 43	n = 32	n = 108	0.806
Mean ± SD	6.5 ± 2.7	6.8 ± 3.4	6.7 ± 2.5	6.5 ± 2.4	6.6 ± 2.8
Median(IQR)	6.7 (4.6–8.1)	6.3(4.2–9.3)	6.4(5.0-8.1)	6.4(4.7–8.1)	6.4(4.7–8.4)
Range	1.0-11.3	1.5–13.6	1.8–13.4	2.6–11.7	1.5–13.6
IgM(g/L)	n = 28	n = 33	n = 43	n = 32	n = 108	0.186
Mean ± SD	0.8 ± 0.4	0.9 ± 0.5	1.0 ± 0.5	1.0 ± 0.5	1.0 ± 0.5
Median(IQR)	0.8 (0.4–1.1)	1.0(0.4–1.3)	1.0(0.6–1.3)	0.9(0.5–1.4)	1.0(0.6–1.3)
Range	0.2–1.9	0.2–2.3	0.2–2.25	0.2–2.2	0.2–2.3
IgA(g/L)	n = 28	n = 33	n = 43	n = 32	n = 108	0.302
Mean ± SD	0.6 ± 0.7	0.5 ± 0.7	0.5 ± 0.6	0.5 ± 0.4	0.5 ± 0.5
Median(IQR)	0.4(0.08–0.9)	0.3(0.1–0.7)	0.3(0.1–0.6)	0.4(0.1–0.8)	0.3(0.1–0.7)
Range	0.01–2.6	0.01–2.8	0.01–2.9	0.01–1.3	0.01–2.9
CRP(mg/L)						0.389
Mean ± SD	10.3 ± 22.8	9.4 ± 21.7	6.3 ± 10.5	6.1 ± 15.4	7.3 ± 16.5
Median(IQR)	1.7(1.3–5.9)	1.6(1.3–6.6)	3.9(1.3–7.5)	1.3(0.8–4.2)	1.8(1.3–6.2)
Range	0.3-105.6	0.1-136.9	0.2–70.4	0.1-101.9	0.1-136.9
SAA(mg/L)						0.285
Mean ± SD	113.1 ± 256.0	106.8 ± 256.1	58.2 ± 113.9	46.5 ± 188.5	70.5 ± 195.5
Median(IQR)	4.9(2.0-62.1)	4.5(1.7–94.0)	11.1(4.8–57.3)	5(2.0-13.3)	6.1(2-39.6)
Range	0.5–1148	0.5–1527	0.5-550.9	0.5–1321	0.5–1527
PCT(ng/ml)						0.925
Mean ± SD	0.2 ± 0.1	0.1 ± 0.1	0.2 ± 0.4	0.2 ± 0.3	0.2 ± 0.3
Median(IQR)	0.1(0.1–0.2)	0.08(0.07–0.12)	0.06(0.05–0.10)	0.09(0.07–0.15)	0.08(0.06–0.12)
Range	0.02–0.56	0.03–0.7	0.02–2.8	0.02–2.1	0.02–2.8
TP(g/L)						< 0.01
Mean ± SD	58.2 ± 8.4	62.6 ± 8.8	65.6 ± 6.4	62.5 ± 7.5	63.7 ± 7.7
Median(IQR)	56.0 (52.7–64.5)	62.5 (56.2–68.6)	67.3 (61.9–69.5)	62.2 (55.9–69.0)	63.7 (58.3–69.1)
Range	40.2–75.5	46.6–83.7	39.4–75.1	46.4–79.1	39.4–83.7
ALB(g/L)						0.045
Mean ± SD	40.5 ± 5.0	40.8 ± 4.1	42.9 ± 3.7	42.0 ± 4.4	41.9 ± 4.2
Median(IQR)	40.2 (38.7–43.5)	40.7 (38.6–43.2)	42.6 (40.9–45.1)	41.9 (39.1–44.9)	41.8 (39.4–44.7)
Range	16.7–47.6	32.9–52.1	27.2–50.8	34.0-52.8	27.2–52.8
Crea(mmol/L)						< 0.01
Mean ± SD	27.7 ± 7.8	23.6 ± 8.9	22.2 ± 7.6	23.3 ± 6.6	23.0 ± 7.8
Median(IQR)	25.5 (22.0-31.3)	22.0 (16.0–30.0)	21(18-25.2)	23.5 (19.0–27.0)	22.0 (17.7–27.0)
Range	16.0–48.0	10.0–49.0	9.0–46.0	12.0–44.0	9.0–49.0
Urea(mmol/L)						< 0.01
Mean ± SD	3.3 ± 1.2	2.8 ± 1.2	2.7 ± 1.1	3.0 ± 1.1	2.8 ± 1.2
Median(IQR)	3.3 (2.5–3.9)	2.6 (1.7–3.9)	2.6(1.7–3.5)	3.1 (1.9–3.7)	2.7 (1.9–3.7)
Range	1.2–6.2	0.8–4.2	0.9–5.9	0.8–6.3	0.8–6.3
K(mmol/L)						0.507
Mean ± SD	4.5 ± 0.6	4.6 ± 0.7	4.5 ± 0.5	4.5 ± 0.6	4.5 ± 0.6
Median(IQR)	4.4 (4.1–4.9)	4.5 (4.1–4.9)	4.6(4.1–4.9)	4.5 (4.0-4.9)	4.5 (4.1–4.9)
Range	3.3–5.8	3.4–7.6	3.0-5.7	3.2–6.6	3.0-7.6
Na(mmol/L)						< 0.01
Mean ± SD	136.1 ± 2.8	138.2 ± 2.5	138.6 ± 2.0	138.8 ± 2.0	138.5 ± 2.5
Median(IQR)	135.7 (134.1-138.4)	138.4 (136.7-139.9)	138.7 (137.3-139.9)	139.1 (137.4-140.5)	138.7 (137.1-139.9)
Range	126.8-141.9	132.9–144.0	133.5-142.6	132.6–142.0	132.6–144.0
Cl(mmol/L)						0.892
Mean ± SD	104.4 ± 2.7	104.4 ± 2.7	104.7 ± 3.2	103.9 ± 2.2	104.3 ± 2.7
Median(IQR)	104.0 (102.2-106.1)	104.3 (102.7-106.1)	104.3 (102.4-106.5)	103.8 (102.4-105.6)	104.0 (102.6-105.9)
Range	95.0-110.1	97.4-110.2	100-118.7	99.7-109.5	97.4-118.7
P(mmol/L)						< 0.01
Mean ± SD	1.9 ± 0.4	1.9 ± 0.3	1.7 ± 0.3	1.8 ± 0.4	1.8 ± 0.3
Median(IQR)	2.0 (1.7–2.2)	1.9 (1.5–2.1)	1.6 (1.4–1.9)	1.7 (1.4–2.1)	1.7 (1.5–2.1)
Range	1.1–2.7	1.1–2.6	0.9–2.2	1.1–2.9	0.9–2.9
Ca(mmol/L)						< 0.01
Mean ± SD	1.8 ± 0.6	2.1 ± 0.5	2.3 ± 0.3	2.1 ± 0.4	2.1 ± 0.4
Median(IQR)	1.30(1.2–2.4)	2.0 (1.8–2.5)	2.3 (1.9–2.5)	2.0 (1.9–2.4)	2.2 (1.9–2.5)
Range	1.1–2.6	1.2–2.8	1.3–2.8	1.210–2.790	1.2–2.8
CK(U/L)						0.034
Mean ± SD	148.8 ± 99.4	124.8 ± 82.5	111.7 ± 81.4	120.4 ± 79.6	118.9 ± 80.8
Median(IQR)	126.0 (93.7-162.5)	106.0 (79.5–134.0)	98.5 (64.9-126.3)	92.5 (72.2-133.3)	100.0 (73.0-130.8)
Range	46.0-649.0	35.00-479.0	23.5–516.0	43.00-483.0	23.5–516.0
CK-MB(ng/ml)						< 0.01
Mean ± SD	5.7 ± 3.2	5.1 ± 3.2	3.118 ± 1.492	4.4 ± 2.2	4.2 ± 2.5
Median(IQR)	5.4 (3.8–6.9)	4.5 (3.2–6.2)	2.6 (2.1–4.2)	4.0 (3.0-5.3)	3.8 (2.4–5.2)
Range	0.8–15.5	0.2–18.0	0.7–6.6	1.1–12.1	0.2–18.0
LDH(U/L)						0.026
Mean ± SD	385.2 ± 118.2	357.5 ± 129.2	344.7 ± 80.2	336.5 ± 89.3	346.3 ± 101.5
Median(IQR)	354 (312.8-448.3)	317.0(275.0-385.5)	331.0 (297.3-384.8)	335.5 (265.8–380.0)	330.0 (277.0-379.3)
Range	228.0-891.0	226.0-844.0	221.5–622.0	173.0-684.0	173.0-844.0
ALT(U/L)						0.435
Mean ± SD	22.8 ± 12.9	31.1 ± 73.2	24.7 ± 25.3	28.9 ± 32.4	28.2 ± 48.2
Median(IQR)	19.50(13.7–28.2)	18.0 (12.0-24.7)	17 (12.7–25.5)	22(12.0-34.2)	18.0 (12.0-27.5)
Range	7.0–70.0	2.0-530.0	4.0-152	9.0-227.0	2.0-530.0
AST(U/L)						0.807
Mean ± SD	51.4 ± 19.5	50.2 ± 39.6	49.2 ± 21.1	51.7 ± 22.5	50.4 ± 28.8
Median(IQR)	48.5 (35.7–57.0)	40.0 (30.7–56.2)	46 (36.7–57)	47.7(36.5–60.5)	46.0 (34.0-57.2)
Range	25.0-106.0	17.0-277.0	29.0-172.0	21.0-141.0	17.0-277.0

Increasing evidence have indicated that the inflammatory response perform a crucial role in the development of COVID-19[11]. The PCT levels in the COVID-19 group were considerably higher than those in the CAP-MP and CAP-Bacteria groups [0.1(0.1–0.2) vs 0.06(0.05–0.10)ng/ml, p < 0.0001; 0.1(0.1–0.2) vs 0.08(0.07–0.12)ng/ml, p < 0.05]. However, the differences in C-reactive protein (CRP) and serum amyloid A (SAA) levels among the groups are not significant, except for a slight increase in SAA in the CAP-MP group (Table 2, Fig. 3).

3.5 Nutrient metabolism and electrolyte balance

COVID-19 may also have some impact on nutrient intake and metabolism. Our study presented that the total protein (TP) and albumin (ALB) levels in the COVID-19 group are significantly lower than those in the CAP-MP group [56.0 (52.7–64.5) vs 67.3 (61.9–69.5) g/L, p < 0.001; 40.2 (38.7–43.5) vs 42.6 (40.9–45.1) g/L, p < 0.05]. Poor nutrition and insufficient protein intake may lead to changes in Crea metabolism. COVID-19 group showed the highest serum Crea level in the whole CAP groups, with the Crea level in COVID-19 patients was higher than that in CAP-Bacteria and CAP-MP groups [25.5 (22.0-31.3) vs 22.0 (16.0–30.0) mmol/L, p < 0.05; 25.5 (22.0-31.3) vs 21(18-25.2) mmol/L, p < 0.01]. However, urea displayed only minor difference between disease groups, with the COVID-19 group significantly higher than the CAP-MP group [3.3 (2.5–3.9) vs 2.6(1.7–3.5) mmol/L, p < 0.05] (Table 2, Fig. 4).

Previous studies had suggested the universal phenomenon of electrolyte and acid-base imbalances in many serious diseases[12]. We observed significant differences in the levels of sodium, phosphate and calcium among the four groups. Serum sodium levels in the COVID-19 group were the lowest and significantly lower than that in the CAP-MP and CAP-RSV groups [135.7 (134.1-138.4) vs 138.7 (137.3-139.9) mmol/L, p < 0.001; 135.7 (134.1-138.4) vs 139.1 (137.4-140.5) mmol/L, p < 0.001]. Serum phosphate levels were significantly higher in the COVID-19 group compared to the CAP-MP group, which was the lowest in the four groups [2.0 (1.7–2.2) vs 1.6 (1.4–1.9) mmol/L, p < 0.001]. It was worth noting that the serum calcium levels in the COVID-19 group are sharply decreased and significantly lower than the CAP-MP group [1.30(1.2–2.4) vs 2.3 (1.9–2.5) mmol/L, p < 0.001]. However, There were no significant differences in serum potassium and chlorine levels among all groups (Table 2, Fig. 4).

3.6 Myocardial and liver function response

COVID-19 could also have detrimental influences on the cardiovascular system, causing acute myocardial injury and chronic impairment of the cardiovascular system[13]. There were dramatically differences in the Creatine Kinase (CK) and CK-MB levels among the four groups. The CK levels in the COVID-19 were significantly higher than those in the CAP-MP and CAP-RSV groups [126.0 (93.7-162.5) vs 98.5 (64.9-126.3)U/L, p < 0.01; 126.0 (93.7-162.5) vs 92.5 (72.2-133.3)U/L, p < 0.05]. Similarly, the CK-MB levels in the COVID-19 group were significantly higher than those in the CAP-MP group [5.4 (3.8–6.9) vs 2.6 (2.1–4.2)ng/ml, p < 0.001]. However, no significant differences were found in lactate dehydrogenase (LDH) between the groups (Table 2, Fig. 5).

Liver injury in COVID-19 patients has received much attention[14], while investigations on its presence in other CAP were not so extensive. However, we observed no significant changes in the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the four CAP groups (Table 2, Fig. 5).

3.7 Lung imaging features

The typical lung CT findings of the COVID-19 and non-COVID-19 CAP groups were shown in Fig. 6. The most common CT feature of COVID-19 was ground-glass opacity (GGO), chest CT scan of a 1-month-old female COVID-19 patient showed subpleural patchy GGO with blurred edges in the lower lobes of both lungs (Fig. 6A). In the CAP-Bacteria group, chest CT of a 3-year-old male patient illustrated a large consolidation with blurred margins in the right upper lobe of the lung, with bronchial inflation observed within (Fig. 6B). In the CAP-MP group, chest CT of a 5-year-old female displayed multiple patchy opacities distributed along the lung parenchyma bilaterally, with blurred margins (Fig. 6C). Besides, chest CT of a 2-year-old boy in the CAP-RSV group revealed miliary and patchy opacities distributed along the bronchovascular bundles in both lungs, with blurred margins (Fig. 6D).

We analyzed host responses in CAP hospitalized patients with different microbial etiologies through clinical manifestations, a wide range of routine blood parameters and plasma biomarkers, revealing several common and unique characteristics in the pathophysiological areas. These findings provide deep understanding of the pathophysiology of CAP and may instruct the confirmation of identifiable host response dysregulation in CAP caused by particular pathogens.

Previous research noticed that half of CAP patients presented with lymphocytopenia on admission even though no immunosuppression history, which is also associated with poorer prognosis in CAP patients[15]. It has been reported that lymphopenia in 80% of critically ill adults with COVID-19, while another study indicated a lymphocyte reduction rate of only 25% in mild COVID-19 patients[16, 17]. Our results exhibited that the COVID-19 group had lower counts in lymphocyte, eosinophil and blood platelets compared to other CAP groups. There may be many reasons for the decrease of lymphocytes in COVID-19 patients. SARS-CoV-2 enters lymphocytes and disrupts their function, resulting in lymphocytopenia. Besides, humoral and cell-mediated immunity, impair lymphocyte production and increase lymphocyte apoptosis[18]. Additionally, viral inflammatory responses, leading to the accumulation of immune cells at the site of infection and the release of pro-inflammatory cytokines, which may result in a decrease in lymphocyte count.

According to the published studies, PCT values were more distinctive than WBC count and CRP in differentiating bacterial infections from other inflammatory processes[19]. In most severe COVID-19 patients, serum pro-inflammatory cytokines were abnormally elevated, and the increase in PCT was more pronounced in severe cases compared to non-severe cases[20, 21]. Another independent study also displayed increased PCT levels in pediatric COVID-19 patients, revealing bacterial coinfection[22]. Differing from the above findings, our results indicated that the PCT level in the COVID-19 group is significantly higher than that in other CAP groups, including the bacterial group, although we have ruled out coinfection. While the initial PCT value may help distinguish viral and bacterial infections, it may not always be a reliable predictor and may be influenced by other comorbidities. Therefore, in conjunction with the clinical context, PCT may provide valuable information.

Low serum ALB levels may be associated with malnutrition, infection disease, or other underlying disorder[23]. In a recent study, ALB was also considered as a reliable marker for predicting the prognosis of critically ill COVID-19 patients[24]. The results showed that the level of ALB and TB in the COVID-19 group were the lowest among the four groups, and significantly lower than that in the CAP-MP group. The inflammatory response caused by the virus and the body stress response may result in loss of appetite and reduced nutrient intake in COVID-19 patients, leading to low TP and ALB. Another study indicated that the nature of the specific binding site between the SARS-CoV-2 virus and ALB is unclear, but the possibility of some permanent binding cannot be ruled out, which may be one of the reasons for the lower level of ALB in COVID-19[25].

Malnutrition and inadequate protein intake may lead to changes in Crea metabolism, subsequently affecting renal filtration function. We found higher levels of Crea and Urea in the COVID-19 group compared to other CAP groups. Urea levels were affected by dietary protein intake and catabolism of endogenous proteins[26]. Meanwhile, we observed the number of patients with fever was higher in the COVID-19 group than that in other CAP groups. This may be due to poor appetite in patients with fever and insufficient energy intake, on the other hand, patients with fever consume a lot of energy, and the body could only consume storage protein to supply energy, which ultimately causes or aggravates negative nitrogen balance.

Hyponatremia is regarded as an ordinary laboratory finding in children with CAP, which can be clinically defined as previously healthy children who present with signs and symptoms of pneumonia due to an infection acquired outside of the hospital[27].

A case-control study showed that hyponatremia and hypokalemia are independently associated with COVID-19 infection in adults, and may serve as surrogate biomarkers in emergency patients with suspected COVID-19[28]. Basically consistent with our findings in children, the COVID-19 group displayed a significantly lower level of serum sodium compared to other CAP groups. It is interesting that laboratory indicators of COVID-19 and CAP-RSV groups are more similar compared to the CAP-Bacteria and CAP-MP groups. The only differing indicators are sodium and CK levels. This could be attributed to the fact that both SARS-CoV-2 and RSV are respiratory viruses, and the body immune response to these infections may have similarities.

Considering calcium as an inflammatory mediator, some authors support the theory that hypocalcemia can play as a modulator of inflammation[29]. Inflammation caused by infection may lead to calcium being stored in the bones, resulting in a decrease in blood calcium levels. Our results observed lower calcium levels in conjunction with higher levels of inflammatory markers such as PCT in the COVID-19 group, which could explain this mechanism. Unfortunately, since we did not analyze interleukin levels in our study, we could not comprehensively evaluate the correlation between low calcium levels and the degree of inflammation. Combining the above results, It could be inferred that COVID-19 patients may experience inflammatory response, inadequate food intake, increased energy expenditure and subsequent malnutrition, electrolyte imbalance.

Human coronaviruses had been reported to be associated with myocarditis in patients of all age groups[30]. Although children tend to exhibit milder symptoms of SARS-CoV-2, we noticed higher levels of CK and CK-MB in the COVID-19 group, which were dramatically higher than the CAP-MP group. Elevated transaminases are present in COVID-19, but also in influenza and other CAP etiologies[31, 32]. Our results showed no significant difference in the level of ALT and AST in the four groups. Taken together, these results indicated myocardial function is more likely to be affected than the liver in children with COVID-19.

Our research had some limitations. Due to strict matching requirements, the limited sample size may affect the reliability and validity of the results. As the time from onset of symptoms to hospitalization varied greatly among the majority of patients, we did not explore the influence of time between sampling and hospitalization any further, albeit this may illustrate a small portion of the differences in the result. In addition, we cannot completely exclude the possibility of unrecognized pathogens in the cohort, which could potentially affect pathogen-specific comparisons. However, these unrecognized pathogens are more likely to attenuate rather than overstate the differences between CAP subgroups, and they do not restrict the generalizability of our findings.

Overall, our study provides a detailed assessment of the systemic host response to CAP of varying etiologies and sheds light on common and unique pathophysiological mechanisms that may direct treatment for COVID-19 and other CAP population.

CAP

Community-acquired pneumonia

Mycoplasma

RSV

respiratory syncytial virus

SARS-CoV-2

respiratory syndrome coronavirus 2

COVID-19

coronavirus disease 2019

PCT

lymphocyte and procalcitonin

total protein

ALB

albumin

creatine kinase

CK-MB

creatine kinase-MB

MIS

multisystem inflammatory syndrome

PSM

Propensity Score Matching

Acknowledgements

Not applicable.

Author Contributions

All authors made a significant contribution to the work reported. LHY conceived and designed the study. LHY, TTB, YYP and ZF collected the data. LHY and TTB conducted all analyses. FYC provided CT imaging data. LHY, YYP and SB interpreted the results, drafted and revised the manuscript. All authors critically reviewed, revised and approved the final manuscript.

Funding

This work was supported by Zhejiang Provincial Health Department Project (2022KY1409, 2023RC302).

Availability of data and materials

The data underlying this article cannot be shared publicly due to the protection of privacy in individuals that participated in the study. The data will be shared on reasonable request to the corresponding author.

Ethics approval and consent to participate

Data from the patients were analyzed: Medical Ethics Review Board Taizhou Hospital of Zhejiang Province, K20230116.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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The host response of COVID-19 and identification from other aetiologies of community-acquired pneumonia in children

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Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Material and Methods

2.1 Patients

2.2 (Primary) pathogen assessment

2.3 Statistical Analysis

3. Results

3.1 Patient characteristics and clinical outcomes

3.2 Unique and overlapping laboratory indicators of host response in non-COVID-19 CAP and COVID-19 patients

3.3 Direct comparison of the host response among COVID-19, CAP-MP, CAP-RSV and CAP-bacteria

3.4 Immuno-inflammatory responses

3.5 Nutrient metabolism and electrolyte balance

3.6 Myocardial and liver function response

3.7 Lung imaging features

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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