Use of common blood parameters for the differential diagnosis of childhood infections.

BACKGROUND: Routine laboratory investigations are not rapidly available to assist clinicians in the diagnosis of pediatric acute infections. Our objective was to evaluate some common blood parameters and use them for the differential diagnosis of childhood infections.
METHODS: This retrospective study was conducted between October 2019 and September 2020 at Guangzhou Women and Children's Medical Center, China. We performed blood tests in patients infected with DNA viruses (n = 402), RNA viruses (n = 602), gram-positive organisms (G+; n = 421), gram-negative organisms (G-; n = 613), or Mycoplasma pneumoniae (n = 387), as well as in children without infection (n = 277). The diagnostic utility of blood parameters to diagnose various infections was evaluated by logistic regression analysis.
RESULTS: The most common G+ organism, G- organism, and virus were Streptococcus pneumoniae (39.7%), Salmonella typhimurium (18.9%), and influenza A virus (40.2%), respectively. The value of logit (P) = 0.003 × C-reactive protein (CRP) - 0.011 × hemoglobin (HGB) + 0.001 × platelets (PLT) was significantly different between the control, RNA virus, DNA virus, M. pneumoniae, G- organism, and G+ organism groups (2.46 [95% CI, 2.41-2.52], 2.60 [2.58-2.62], 2.70 [2.67-2.72], 2.78 [2.76-2.81], 2.88 [2.85-2.91], and 2.97 [2.93-3.00], respectively; p = 0.00 for all). The logistic regression-based model showed significantly greater accuracy than the best single discriminatory marker for each group (logit [Pinfection] vs. CRP, 0.90 vs. 0.84, respectively; logit [PRNA] vs. lymphocytes, 0.83 vs. 0.77, respectively; p = 0.00). The area under curve values were 0.72 (0.70-0.74) for HGB and 0.81 (0.79-0.82) for logit (Pvirus/bacteria) to diagnose bacterial infections, whereas they were 0.72 (0.68-0.74) for eosinophils and 0.80 (0.78-0.82) for logit (Pvirus/bacteria) to diagnose viral infections. Logit (Pvirus/bacteria) < -0.45 discriminated bacterial from viral infection with 78.9% specificity and 70.7% sensitivity.
CONCLUSIONS: The combination of CRP, HGB, PLT, eosinophil, monocyte, and lymphocyte counts can distinguish between the infectious pathogens in children.

Background

Bacterial infections are important causes of morbidity and mortality among children. It is crucial to diagnose bacterial infections and distinguish between bacterial and non-bacterial infections. Unfortunately, almost three-quarters of patients with a viral syndrome receive antibiotics [1]. Laboratory parameters, such as leukocytes or white blood cells (WBCs) and C-reactive protein (CRP), provide diagnostic information. The hepatic acute phase reactant CRP is the most commonly used biomarker of bacterial infections, which is also recommended by the febrile neutropenia guideline of the European Society of Medical Oncology (ESMO) [2]. Currently, bacterial and viral infections are mainly differentiated on the basis of WBC and CRP levels [3, 4], which typically has sensitivity of 60% and specificity of 70%, resulting in a high rate of misdiagnosis [5]. Notably, there is no standard cutoff level to diagnose bacterial infections, a low CRP level does not exclude bacterial infection, and a high CRP level can also occur in the absence of bacterial infection. This highlights the limitations of use of CRP level as an inflammatory biomarker.

In children, the neutrophil-to-lymphocyte ratio (NLR) can differentiate between viral and bacterial pneumonia [6], and is a diagnostic marker of acute appendicitis [7]. However, physical examination findings and routine laboratory investigations cannot accurately differentiate between benign viral and severe bacterial infections in children with fever [8]. New markers for bacterial infection have been discovered, including presepsin, procalcitonin, CD64, and pro-adrenomedullin (proADM). Presepsin, procalcitonin, and CD64 are diagnostic markers for severe sepsis and septic shock, whereas proADM is a prognostic marker of bacterial infections [9-12]; however, these markers cannot be used for the diagnosis of mild bacterial or viral infections [13]. Although microbiological culture is the gold standard for diagnosing bacterial infections, culturing of bacteria is time-consuming [14]. Early administration of antibiotics in bacterial infections improves the outcome and reduces the mortality among patients [15]. Therefore, the development of rapid and accurate methods of diagnosis is warranted. The aim of this study was to assess the usefulness of commonly available blood parameters and cut-off values thereof in differentiating infections due to RNA viruses, DNA viruses, Mycoplasma pneumoniae, gram-positive organisms (G+), and gram-negative organisms (G−) in febrile pediatric patients.

Patients and methods

Study population

Data were retrospectively collected from patients treated at the Guangzhou Women and Children’s Medical Center, China, which is a large, tertiary care children’s hospital, between October 2019 and September 2020. This study included 2,425 patients (aged ≤ 17 years) whose blood culture, polymerase chain reaction (PCR), or serological test (i.e., immunoglobulin test) suggested acute bacterial, viral, or M. pneumoniae infection. In addition, urine, stool, cerebrospinal fluid, or bronchoalveolar lavage fluid cultures were performed, where necessary. The PCR or immunoglobulin test tested for ten pathogens, namely Human Bocavirus (HBoV), influenza A virus (IAV), influenza B virus (IBV), parainfluenza virus (PIV), rhinovirus (RHV), respiratory syncytial virus (RSV), adenovirus (ADV), Epstein-Barr virus (EBV), enterovirus (EV), and herpes simplex virus (HSV). Two hundred and seventy-seven children without infection were included in the control group.

Inclusion criteria

Patients aged ≤ 17 years with suspected bacterial or viral infections were included in the study. Bacterial infections were identified by a positive bacterial blood, urine, stool, cerebrospinal fluid, or bronchoalveolar lavage fluid culture. Viral or M. pneumoniae infection was identified by a positive relevant PCR or serological test. In the case of multiple hospital admissions, only the first was analyzed.

Exclusion criteria

Based on a review of medical records, we excluded patients with a positive viral test, diagnosis of a potential bacterial infection, such as cellulitis, cholecystitis, erysipelas, pneumonia, pyelonephritis, or septicemia, that suggested multi-organism infection (n = 34), ≥ 1 pathogen type (n = 23), hematological cancer with variable blood cell counts due to the cancer or chemotherapy (n = 11), or bacterial contaminants on bacterial culture (i.e., negative cultures; n = 22) (Fig 1). Contamination was defined as the presence of multiple coagulase-negative Staphylococcus species, Bacillus species, Propionibacterium acnes, or Corynebacterium species in a single set of blood cultures; these bacteria are frequent contaminants [16].

Fig 1

Flowchart for patient selection.

The numbers of neonates are presented in parentheses. BALF, bronchoalveolar lavage fluid; M. pneumoniae, Mycoplasma pneumoniae; G+, Gram-positive organisms; G−, Gram-negative organisms.

Patients were categorized into those with bacterial (n = 1034), viral (n = 1004), and M. pneumoniae (n = 387) infections. Based on the diagnostic criteria for pediatric sepsis [17], patients in the bacterial infection group were further classified into G+ (n = 421) and G− (n = 613) organism groups. Similarly, based on the classification of viruses [18], patients in the viral infection group were further classified into DNA (n = 402) and RNA (n = 602) virus groups. The control group included 277 healthy children without infections or inflammatory diseases who underwent routine health check at the study center.

Laboratory data

The medical records were reviewed to record the medical history (sex and age) and results of the laboratory evaluation (including blood cell counts, CRP level, throat swab, bronchoalveolar lavage fluid reverse transcription (RT)-PCR, blood, urine, and stool cultures, and lumbar puncture to identify the infection source). CRP secretion is regulated by cytokines, and the CRP level reaches its peak at 48 hours [19, 20]. After the bacterial trigger for inflammation is eliminated, CRP levels decrease rapidly, with a half-life of almost 19 hours [20, 21]. Delayed normalization of CRP levels after the first 3–7 days of follow up is suggestive of inappropriate antibiotic selection [22]. Hence, we recorded the blood counts and CRP levels on days 3–7 after symptom onset. We did not evaluate the procalcitonin level because our aim was to study the commonly available diagnostic markers.

Our aim was to assess the diagnostic utility of commonly used laboratory parameters in distinguishing between certain pathogen types in pediatric patients, using the best single discriminatory marker for each infection group as a comparator. We further used logistic regression to develop models for distinguishing among the six groups.

Statistical analyses

We compared quantitative data using Student’s t-test or one-way ANOVA and compared frequencies using the chi-square test between the groups. Correlations were analyzed using Pearson’s R and Spearman’s R. After adjusting for the potential predictors, multivariate logistic regression was performed for selected data, and receiver operating characteristic (ROC) curve was constructed to calculate probabilities. To test the performance of a numerical parameter as a biomarker for classification, we used the ROC to calculate the area under curve (AUC), where the positive class was the pathogen type. The sensitivity and specificity were calculated to evaluate the diagnostic accuracy. The measurement data were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR). The 95% confidence intervals (CIs) were used to quantify uncertainty. P values < 0.05 were considered statistically significant. All statistical analyses were performed using SPSS statistical software (version 21.0; IBM Corp., Armonk, NY, USA).

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Guangzhou Women and Children’s Medical Center Ethics Committee (no.: 2020110819342581). Patient consent or additional permission from the hospital was not required because all study data were retrospectively collected from the medical records as part of the usual clinical process.

Results

Demographic characteristics of patients

The bacterial infection group consisted of 1034 patients (566 males; median age [IQR]: 2.1 [0.4–3.1] years). Patients with bacterial infections were younger than those with viral or M. pneumoniae infections; however, the proportion of male gender was similar among the three groups. The ICU occupancy rate was higher among patients with bacterial infection than those with other infections. These analyses are presented in Table 1. The specific pathogens are reported in Table 1 and Fig 2A. A wide array of bacterial species was isolated from the cohort, with Streptococcus spp. (n = 167), Enterococcus spp. (n = 127), Staphylococcus spp. (n = 127), Salmonella spp. (116), Klebsiella spp. (n = 112), Escherichia coli (n = 73), and Pseudomonas aeruginosa (n = 53) most often cultured. The most common G+ organism was Streptococcus spp. (39.7%). S. Typhimurium was the most common G− organism (18.9%), followed by Klebsiella pneumoniae (18.3%), and E. coli (11.9%), consistent with the results of a previous study [23]. In the viral infection group, 40.2% had IAV infection (579 males; median age: 4.2 years). The median age of patients with M. pneumoniae infection (221 males) and controls (157 males) were 3.9 (IQR 1.9–5.7) and 2.5 (0.0–1.0) years, respectively. There was no statistical difference between the groups in terms of gender. Because the disease prevalence differs by age, age is considered the most important demographic characteristic [24]. Importantly, the median age was higher in the viral infection group than bacterial infection group, and the morbidity of bacterial infections was higher in neonates than older children (78.60% vs. 42.39%, respectively). Fig 2B and 2C presents the etiological distribution of infections by months, and may assist physicians to predict the prevalent pathogen in each month. As an example, up to 48.3% of patients with RNA viral infections presented in December, whereas 25.4% with DNA viral infections presented in July. Among these patients, IAV and ADV was present in 68.9% and 60.2% of the total patients in the RNA and DNA virus groups, respectively.

Table 1

Demographic and clinical characteristics of patients infected with RNA viruses, DNA viruses, M. pneumoniae, G− organisms, and G+ organisms.

	Total	Control	RNA virus	DNA virus	M. pneumoniae	G− organism	G+ organism	R	P
Male, n (%)	1523 (58.8)	157 (56.8)	330 (54.9)	249 (61.9)	221 (57.1)	331 (61.5)	235 (60.6)	−0.04	0.14
Female, n (%)	1179 (41.2)	120 (43.2)	272 (45.1)	153 (37.1)	166 (42.9)	282 (38.5)	186 (39.4)
Median age, years (IQR)	3.2 (0.5–5.0)	2.5 (0.0–1.0)	5.4 (2.7–8.0)	3.1 (0.7–4.6)	3.9 (1.9–5.7)	1.9 (0.1–2.1)	2.3 (0.3–3.6)	0.02	0.27
Neonates	356	99	17	19	19	137	65
Setting, n (%)								0.45	0
ICU	315	0	9 (1.5)	71 (17.8)	2 (0.5)	136 (22.6)	97 (23.9)
Outpatients	1786	260 (100)	558 (92.7)	254 (63.9)	336 (86.4)	238 (39.6)	172 (41.2)
Month with the highest morbidity (n,%)	12 (519, 20.5)	_	12 (291, 48.3)	7 (92, 25.4)	7 (77, 19.7)	10 (100, 16.4)	11 (66, 15.8)	−0.11	0
Most frequent pathogen (n,%)	IAV (415, 16.3%)	_	IAV (415, 68.9%)	ADV (262, 60.2%)	_	Salmonella Typhimurium (116, 18.9%)	Streptococcus (167, 39.7%)	0.04	0.03
Laboratory findings
CRP	19.94 (18.64–21.22)	0.86 (0.62–1.11)	10.07 (8.64–11.52)	19.14 (16.51–21.76)	9.86 (8.20–11.52)	28.21 (24.86–31.57)	40.11 (35.76–44.56)	0.11	0
Eosinophils	0.27 (0.37–0.43)	0.40 (0.37–0.43)	0.09 (0.07–0.11)	0.27 (0.23–0.32)	0.24 (0.22–0.27)	0.36 (0.32–0.40)	0.32 (0.29–0.36)	0.02	0.34
Hemoglobin	120.23 (119.46–121.00)	136.22 (132.97–139.48)	125.79 (124.81–126.78)	117.25 (115.75–118.76)	124.75 (123.56–125.94)	112.98 (111.12–114.84)	111.08 (109.13–113.03)	−0.18	0
Lymphocytes	3.97 (3.72–4.23)	5.08 (4.87–5.28)	2.29 (2.14–2.44)	5.31 (3.72–6.90)	3.73 (3.54–3.92)	4.21 (4.00–4.46)	4.27 (4.02–4.52)	−0.04	0.06
Monocytes	0.88 (0.81–0.94)	0.81 (0.76–0.87)	0.75 (0.72–0.78)	0.89 (0.84–0.94)	0.64 (0.61–0.68)	1.11 (0.79–1.43)	0.99 (0.93–1.05)	0.01	0.62
Neutrophils	5.57 (5.41–5.73)	3.63 (3.28–3.97)	5.44 (5.17–5.72)	5.44 (5.07–5.82)	5.02 (4.74–5.30)	5.93 (5.53–6.32)	7.14 (6.61–7.67)	0.09	0
Platelets	328.39 (323.22–333.56)	356.64 (344.04–369.24)	266.14 (258.21–274.07)	330.73 (317.52–343.93)	351.75 (339.38–364.12)	339.06 (326.85–351.26)	359.29 (343.92–374.66)	0.06	0
RBCs	4.46 (4.40–4.53)	4.56 (4.50–4.64)	4.67 (4.63–4.71)	4.45 (4.39–4.51)	4.69 (4.64–4.75)	4.29 (4.03–4.54)	4.15 (4.08–4.23)	−0.02	0.32
NLR	2.39 (2.27–2.51)	0.90 (0.75–1.05)	3.73 (3.44–4.01)	1.92 (1.71–2.13)	1.79 (1.64–1.95)	2.33 (1.99–2.66)	2.56 (2.27–2.86)	0.08	0
WBCs	10.68 (10.42–10.94)	9.94 (9.53–10.34)	8.74 (8.28–9.20)	11.18 (10.70–11.67)	9.67 (9.33–10.01)	11.80 (11.00–12.60)	12.80 (12.14–13.46)	0.05	0.02

Abbreviations: M. pneumoniae, Mycoplasma pneumoniae; G−, gram-negative organisms; G+, gram-positive organisms; IQR, interquartile range; ICU, intensive care unit; CRP, C-reactive protein; RBCs, red blood cells; NLR, neutrophil-to-lymphocyte ratio; WBCs, white blood cells.

Fig 2

A, Number of patients infected with specific pathogens. B, Distribution of patients infected with DNA viruses, RNA viruses, G+ bacteria, G− bacteria, and M. pneumoniae in 12 months. C, Distribution of specific pathogens in the five groups by the 12 months. The X axis represents the months, and the Y axis represents the number of patients infected with specific pathogens. M. pneumoniae, Mycoplasma pneumoniae group; G+, gram-positive organisms group; G−, gram-negative organism group; HBoV, human Bocavirus; IAV/FAV, influenza A virus; IBV/FBV, influenza B virus; PIV, parainfluenza virus; RHV, rhinovirus; RSV, respiratory syncytial virus; ADV, adenovirus; EBV, Epstein-Barr virus; EV, enterovirus; HSV, herpes simplex virus; Efa, Enterococcus faecium; Hin, Haemophilus influenzae; Kpn, Klebsiella pneumoniae; Sau, Staphylococcus aureus; E. coli, Escherichia coli; Spn, Streptococcus Peroris; Sty, Salmonella typhimurium; Aba, Acinetobacter baumannii; Mca, Moraxella catarrhalis; Cje, Campylobacter Jejuni; Pae, Pseudomonas aeruginosa; Sca, Shigella.

Patients with bacterial infections had higher levels of CRP, leukocytes, neutrophils, eosinophils, and monocytes compared with patients with viral infections. Both groups with infection showed higher CRP levels compared with controls, but the mean CRP level was higher in the bacterial group compared with the viral group (32.94 ± 43.88 vs. 13.58 ± 21.86 mg/l, respectively; p  = 0.00) and in the G+ organism group compared with the G− organism group (40.11 ± 42.56 vs. 28.21 ± 21.57 mg/l, respectively; p = 0.00). The CRP level was significantly higher in patients with DNA virus infections compared with RNA virus infections, in line with previous studies [8]. However, the NLR was lower in patients with DNA virus infections compared with those with RNA virus and M. pneumoniae infections (p  = 0.00). Patients with RNA virus infections had significantly higher NLR compared with patients with DNA virus, M. pneumoniae, and bacterial infections (3.73 ± 3.18, 1.92 ± 2.34, 1.80 ± 1.53, and 2.41 ± 3.80, respectively; p  = 0.00) Furthermore, the bacterial group, as compared with the viral group, had lower hemoglobin (HGB) level (112.25 ± 22.24 vs. 122.38 ± 14.23 g/L, respectively; p  =  0.00), but higher levels of WBCs, neutrophils, and platelets (PLTs) (12.19 × 109/L ± 8.92 × 109/L and 9.71 × 109/L ± 5.55× 109/L; 6.41 ± 5.24 × 109/L and 5.44 × 109/L ± 3.61× 109/L; 347.44 × 109 /L ± 156.79 × 109/L and 292.00 × 109/L ± 118.87 × 109/L, respectively; p  =  0.00); however, lymphocyte, eosinophil, and monocyte counts were not significantly different between the groups. The WBC and PLT counts were significantly lower in pediatric patients with RNA virus infections than other patients (p = 0.00). In addition, in the general cohort of patients with acute infections, HGB level was significantly reduced (119.49 ± 17.82 vs. 136.22 ± 27.38 g/L, respectively), while NLR and neutrophil counts increased, compared with controls without infections (2.52 ± 3.29 vs. 0.90 ± 1.27; 5.60 ± 4.17 vs. 3.63 ± 2.91 × 109/L, respectively; p  =  0.00). Since the reference blood counts for neonates differ from those for older children, the results for neonates are presented separately; the results indicated higher morbidity with bacterial infections than viral infections (Table 1).

Multivariate logistic regression analysis

The multivariate logistic regression analysis revealed significant associations of CRP, HGB, neutrophil, PLT, and WBC levels, and NLR with infections (p < 0.05 for all). The associations with CRP, HGB, and PLT levels remained statistically significant (p = 0.00) after the application of the forward regression model, whereas WBC and neutrophil counts, and NLR were excluded from the model. Based on the variables selected for multivariate logistic regression analysis, we developed a logistic regression‐based model for distinguishing among the six groups:

Logit (P) = 0.003 × (CRP − 0.011) × (HGB + 0.001) × PLT

The mean logit (P) values were 2.46 (95% CI, 2.41–2.52), 2.60 (2.58–2.62), 2.70 (2.67–2.72), 2.78 (2.76–2.81), 2.88 (2.85–2.91), and 2.97 (2.93–3.00) for children in the control, RNA virus, DNA virus, M. pneumoniae, G− organism, and G+ organism groups, respectively (p = 0.00 for comparison between any two means). Using a combination of HGB, PLT, and CRP levels, the AUCs for predicting acute infections and infections due to RNA viruses, DNA viruses, M. pneumoniae, G− organisms, and G+ organisms were 0.75 (95% CI, 0.72–0.78), 0.76 (0.73–0.78), 0.52 (0.47–0.54), 0.60 (0.57–0.62), 0.65 (0.63–0.67), and 0.72 (0.69–0.74), respectively. The classification quality of the parameter for identifying DNA viruses, M. pneumoniae, and G− organisms (AUCs < 0.70) was unacceptable according to the criteria developed by Hosmer and Lemeshow, who suggested that AUCs of 0.70–0.80, 0.80–0.90, and ≥ 0.9 respectively offer acceptable, excellent, and outstanding discrimination abilities [25]. Furthermore, there were no significant differences in these AUCs compared with the largest AUC for a single biomarker in these three groups (eosinophils in DNA virus group: 0.60, 95% CI 0.57–0.63; monocytes in M. pneumoniae group: 0.65, 0.62–0.68; HGB in the G− organism group: 0.65, 0.62–0.68). We addressed this limitation by developing an additional logistic regression‐based model comprised of statistically significant components in each group as a supplement:

Logit (Pinfection) = 0.542 * (CRP − 0.05) * (HGB + 24.345 − 0.035) * (LYMPH + 7.946)

Logit (Pvirus/bacteria) = −0.988–0.013 * (CRP − 3.457) * (EO + 0.018) * (HGB − 0.003) * PLT

Logit (PRNA) = 0.941 − 0.031 * (CRP − 2.343) * (EO − 0.41) * LYMPH

Logit (PDNA) = −1.809 − 2.615 * (EO + 0.089) * LYMPH

Logit (P) = −3.117 + 0.023 * (HGB − 1.214) * (MONO − 0.192) * NLR

Logit (PG−) = 0.861 + 1.104 * (EO − 0.02) * HGB

Logit (PG+) = 0.012 * (CRP − 0.018) * (HGB + 0.001) * PLT + EO * 0.555

Based on the overall study population, logit (Pinfection) showed a concentration-response relationship between children with and without infections. Using data from all patients with infections, logit (Pvirus/bacteria) was developed for differentiating patients with and without bacterial infections, and showed a concentration-response relationship among children with and without viral infection. Lower logit (Pvirus/bacteria) is associated with greater likelihood of bacterial infection, whereas higher logit (Pvirus/bacteria) is associated with viral infections. Similarly, based on all patients with infections, logit (PRNA), logit (PDNA), logit (P), logit (PG−), and logit (PG+) were developed for differentiating children with and without RNA virus, DNA virus, M. pneumoniae, G− organism, and G+ organism infections (logit [PRNA] AUC: 0.83 [95% CI, 0.81–0.85], logit [PDNA] AUC: 0.67 [95% CI, 0.64–0.69], logit [P] AUC: 0.75 [95% CI, 0.70–0.77], logit [PG−] AUC: 0.68 [95% CI, 0.65–0.70], and logit [PG+]: AUC 0.73 [95% CI, 0.70–0.75]). The combination of CRP, eosinophil, and either lymphocyte (AUC: 0.83) or HGB (AUC: 0.80 and 0.81) levels offer excellent ability to identify RNA virus infection and distinguish between bacterial and viral infections. The ROC curve showed that logit (Pinfection) (AUC: 0.90, 0.88–0.92) had outstanding discrimination ability for the assessment of acute infection.

Using CRP level alone, the AUC value for predicting acute infections was 0.84 (95% CI, 0.79–0.86), which was greater than that for logit (P) (AUC: 0.75, 0.72–0.78) but lower than that of logit (Pinfection) (AUC: 0.90, 0.88–0.92; p = 0.00). Using a combination of eosinophil, lymphocyte, and CRP levels, logit (PRNA) showed significantly greater diagnostic accuracy (AUC: 0.83, 0.81–0.85) compared with the best single discriminatory marker (i.e., lymphocyte count), which had the greatest accuracy for predicting RNA virus infection (AUC: 0.77, 0.74–0.79). The combination of eosinophil and lymphocyte counts showed the best diagnostic accuracy, with AUC, sensitivity, and specificity of 0.67, 62.6%, and 67.4%, respectively, for predicting DNA virus infection. Although the AUC of the combination was < 0.70, it was statistically greater than that of eosinophil and lymphocyte counts alone (0.67 [95% CI, 0.64–0.69] vs. 0.60 [95% CI, 0.57–0.65] and 0.52 [95% CI, 0.49–0.55], respectively; p = 0.00 for both). Logit (PG+) had significantly higher diagnostic accuracy than HGB and CRP levels (AUC: 0.77 [95% CI, 0.68–0.87] vs. 0.65 [95% CI, 0.60–0.67] and 0.63 [95% CI, 0.60–0.65], respectively). Compared with the combination of HGB, monocyte level, and NLR, logit (P) had significantly better diagnostic accuracy than logit (P) for M. pneumoniae infection. The area under the ROC curve (AUROC) value was significantly higher for logit (P) than monocyte count alone, which had the greatest accuracy for predicting M. pneumoniae infection (AUC: 0.75 [95% CI, 0.70–0.77] vs. 0.65 [95% CI, 0.62–0.68], respectively; p = 0.00). The AUC value for logit (PG−) was < 0.70 and we failed to construct better models to diagnose DNA viral and G− organism infections (Table 2).

Table 2

AUROC values of significant parameters, logit (P), and logit (Pcontrol/virus/bacteria/RNA/DNA/M. pneumoniae/G−/G+).

	CRP	WBC	HGB	Eosinophil	Monocyte	Lymphocyte	Logit (P)	Logit (Pinfection/virus/bacteria/RNA/DNA/M. pneumoniae/G−/G+)	P 1 ** /P 2 ***
Acute infections	0.84* (0.82–0.86)	0.51(0.48–0.53)	0.73 (0.68–0.77)	0.76 (0.73–0.79)	0.53 (0.47–0.58)	0.75 (0.71–0.78)	0.75 (0.72–0.78)	0.90 (0.88–0.92)	0.00/0.00
Viruses	0.53 (0.50–0.56)	0.60(0.59–0.62)	0.65 (0.63–0.67)	0.72* (0.68–0.74)	0.51 (0.49–0.54)	0.70 (0.69–0.73)	0.71 (0.69–0.73)	0.80 (0.78–0.82)	0.00/0.00
RNA viruses	0.59 (0.57–0.62)	0.73(0.63–0.86)	0.67 (0.65–0.69)	0.72 (0.69–0.74)	0.55 (0.52–0.57)	0.77* (0.75–0.80)	0.76 (0.73–0.78)	0.83 (0.81–0.85)	0.00/0.01
DNA viruses	0.59 (0.56–0.62)	0.63(0.61–0.65)	0.53 (0.50–0.56)	0.60* (0.57–0.63)	0.55 (0.52–0.59)	0.52 (0.49–0.55)	0.52 (0.47–0.54)	0.67 (0.64–0.69)	0.00/0.00
Bacteria	0.60 (0.58–0.63)	0.62(0.60–0.65)	0.72* (0.70–0.74)	0.65 (0.63–0.68)	0.60 (0.58–0.62)	0.66 (0.64–0.68)	0.75 (0.73–0.77)	0.81 (0.79–0.82)	0.01/0.01
G−	0.54 (0.51–0.57)	0.60(0.58–0.62)	0.65* (0.62–0.68)	0.61 (0.58–0.63)	0.58 (0.55–0.60)	0.59 (0.58–0.64)	0.65 (0.63–0.67)	0.68 (0.65–0.70)	0.28/0.16
G+	0.63 (0.59–0.66)	0.73(0.71–0.75)	0.67* (0.64–0.69)	0.61 (0.58–0.64)	0.57 (0.53–0.60)	0.62 (0.59–0.65)	0.72 (0.69–0.74)	0.73 (0.70–0.75)	0.00/0.27
M. pneumoniae	0.63 (0.60–0.66)	0.74(0.72–0.76)	0.63 (0.60–0.65)	0.61 (0.58–0.64)	0.65* (0.62–0.68)	0.57 (0.54–0.60)	0.60 (0.57–0.62)	0.75 (0.70–0.77)	0.00/0.00

*The best single discriminatory marker for each group

**P value for logit (Pcontrol/virus/bacteria/RNA/DNA/) compared with the best single discriminatory marker

*** P value for logit (Pcontrol/virus/bacteria/RNA/DNA/) compared with logit (P). Abbreviations: AUROC: area under the receiver operating characteristic curve; CRP, C-reactive protein; HGB, hemoglobin; M. pneumoniae, Mycoplasma pneumoniae; G−, gram-negative organisms; G+, gram-positive organisms.

As shown in Table 3, using the cutoff value, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of logit (Pinfection) for the diagnosis of acute infection were 26.50, 80.9%, 85.7%, 98.3%, and 31.4%, respectively. Patients with a logit (Pinfection) value of ≥ 26.50 mostly had infections, irrespective of the pathogen (PPV was 98.3% and specificity was 85.7%). The best cutoff values of logit (Pvirus/bacteria) to diagnose viral and bacterial infections were −0.30 and −0.45, respectively, with a sensitivity of 70.2% and 70.7%, specificity of 78.7% and 78.9%, PPV of 64.4% and 70.8%, and NPV of 74.9% and 71.6%, respectively. When logit (Pvirus/bacteria) was ≤ −0.30, the NPV to exclude a viral infection was 74.9%, while the PPV for the diagnosis of viral infection was 62.3% with a logit (Pvirus/bacteria) > −0.3. When the score was ≤ −0.45, the PPV for the diagnosis of bacterial infection was 70.8% (suggesting that antibiotic therapy would be required); and the NPV to exclude a bacterial infection was 76.0% with a logit (Pvirus/bacteria) > −0.45.

Table 3

Diagnostic accuracy, sensitivity, and specificity of logit (Pcontrol/virus/bacteria/RNA/DNA/M. pneumoniae/G−/G+) cutoffs for the overall study population and neonates.

	Parameter with largest AUCs	Cutoff value		AUC			95% CI		Sensitivity (%)		Specificity (%)		PPV (%)		NPV (%)
		All children	Neonates	All children	Neonates	P *	All children	Neonates	All children	Neonates	All children	Neonates	All children	Neonates	All children	Neonates
Acute infections	Logit (Pinfection)	26.50	26.76	0.90	0.93	1.99	0.88–0.92	0.89–0.95	80.90	83.90	85.70	98.50	98.30	80.50	31.40	95.90
Viruses	Logit (Pvirus/bacteria)	−0.30	−1.80	0.80	0.62	0.15	0.78–0.82	0.43–0.68	70.20	64.70	78.70	58.90	64.40	20.80	74.90	98.30
RNA viruses	Logit (PRNA)	−0.60	−4.10	0.83	0.59	0.17	0.81–0.85	0.37–0.78	70.30	66.70	83.70	32.10	81.30	13.30	75.60	98.60
DNA viruses	Logit (PDNA)	−1.75	−1.80	0.67	0.64	0.63	0.64–0.69	0.46–0.72	62.60	72.20	67.40	46.40	30.50	29.30	87.80	89.20
Bacteria	Logit (Pvirus/bacteria)	−0.45	−0.35	0.81	0.63	0.00	0.79–0.82	0.56–0.75	70.70	69.90	78.90	72.60	70.80	17.10	71.60	91.10
G-	Logit (PG-)	−1.40	−0.35	0.68	0.55	0.00	0.65–0.70	0.48–0.63	67.90	42.50	70.20	66.70	45.20	67.80	83.20	39.30
G+	Logit (PG+)	−1.00	−0.78	0.73	0.76	1.18	0.70–0.75	0.65–0.80	68.20	71.90	74.10	67.30	38.60	48.20	90.20	84.10
M. pneumoniae	Logit (PM. pneumoniae)	−1.65	−1.60	0.75	0.86	2.00	0.70–0.77	0.81–0.92	78.20	83.30	65.90	81.80	33.40	13.30	92.20	93.30

*P value for AUCs of logit (Pinfection/virus/bacteria/RNA/DNA/) for the entire study population compared with neonates. Abbreviations: AUC, area under curve; PPV, positive predictive value; NPV, negative predictive value; M. pneumoniae, Mycoplasma pneumoniae; G−, gram-negative organisms; G+, gram-positive organisms.

When logit (PRNA) was ≥ −0.60, the PPV for the diagnosis of RNA virus infection was 81.3%; importantly, 68.9% of the total patients in the RNA virus group had IAV infection, suggesting that oseltamivir may be administered to these patients. Logit (PG+) > −1.00 discriminated patients with G+ organism infection from other patients with sensitivity and specificity of 68.2% and 74.1%, respectively. This would allow the detection of more than half of patients with G+ organism infection with < 26% of false positives. Patients with logit (P) value < −1.00 and logit (P) value < −1.65 were not likely to have G+ or M. pneumoniae infection (NPVs: 90.2% and 92.2%, respectively) (Table 3). Because the reference ranges of full blood counts vary between neonates and older children, we also validated the models separately in neonatal patients. The sample sizes of neonates with viral and M. pneumoniae infections were inadequate to determine the diagnostic accuracy of these subsets in neonates; however, the AUC of logit (P) (AUC: 0.86 [95% CI, 0.81–0.92]) was excellent (Tables 1–3). Similar to the overall study population, the AUC values for the DNA virus and G− organism infections in neonates were low (AUC: 0.64 [95% CI, 0.46–0.72], 0.55 [95% CI, 0.48–0.63]), indicating an unacceptable classification quality. The diagnostic utility of logit (Pvirus/bacteria) for viral and bacterial infections and logit (PG−) in neonates was low (AUC: 0.62 [95% CI, 0.43–0.68], 0.63 [95% CI, 0.56–0.75], and 0.55 (95% CI, 0.4–0.63), respectively]. Nonetheless, it had a good performance in distinguishing neonates with and without infections, including G+ bacterial infection (AUCs: 0.93 [95% CI, 0.89–0.95], 0.76 [95% CI, 0.65–0.80], respectively). Logit (Pinfection) and logit (PG+) had no discriminatory utility for neonates with p > 0.05 and a similar cutoff value was used for the overall study populations (Table 3).

For patients with logit (Pinfection) value ≥ 26.50, logit (Pvirus/bacteria) should be calculated; value < −0.45 indicates bacterial infection and value > −0.30 indicates viral infection. Then, logit (P) should be calculated; value > −1.65 indicates M. pneumoniae infection. The results from the various models are not invariably mutually exclusive, such as the values of logit (Pvirus/bacteria) between −0.45 and −0.30 indicated neither bacterial nor viral infection, since these formulae were not absolutely exact. However, the calculation of logit (P) would assist in the differential diagnosis of childhood infections. The logistic regression models for the different pathogens are presented in Fig 3A, whereas the algorithm of the suggested use of the model in routine clinical practice is presented in Fig 3B.

Fig 3

A, Logistic regression‐based model for distinguishing among the six groups. B, Flow chart of the recommended used of the formulae in routine clinical practice.

Discussion

This was the first study to demonstrate that RBC count, monocyte count, lymphocyte count, and eosinophil count did not perform well in distinguishing between the subsets of pathogens in infected individuals. The revised parameters developed in this study showed that these formulas had better accuracy than individual parameters with the largest AUROCs for the diagnosis of different pathogen types. The variables used in these models are widely used in clinical practice and easily available. The usefulness of marker combinations for distinguishing between M. pneumoniae, RNA virus, DNA virus, G− organism, and G+ organism infections has not been studied previously. These marker combinations may be better at guiding medication selection than dividing patients into viral and bacterial infection groups. Appropriate antiviral treatment and antibiotic selection requires timely determination of whether the infection is caused by DNA or RNA viruses, or G− or G+ bacteria. These marker combinations may be useful for the early diagnosis and improved outcomes of infections in pediatric patients.

Neonatal infections are particularly difficult to diagnose and no reliable predictors exist. Failure to identify acute infection may lead to delayed initiation of therapy and severe illness. Thus, the identification of predictors of neonatal infection is important. History and physical examination do not reliably exclude acute infections in neonates. Logit (Pinfection) had a PPV of 98.3% to predict acute infection, which allows the appropriate diagnosis and empirical antibiotic therapy.

The WBC count is increased in bacterial infections [9]. However, the total and differential WBC counts are also affected by clonal myeloid disorders as well as immune and inflammatory conditions. In line with previous prospective studies in children with infections [8], the WBC count had an AUC of < 0.70 and was a weak predictor of bacterial and viral infections in children (Table 2). The bacterial infection group had lower HGB and higher CRP levels, even after adjusting for patient’s age, consistent with the results of previous studies [8, 9]. Ballin et al. demonstrated that bacteremia is accompanied by a significant decrease in HGB level in children without evidence of hemolytic anemia [26]. In addition, the serum iron level is a strong predictor of disease outcome in intensive care unit patients [27].

It is difficult to differentiate between DNA virus and bacterial infections because the laboratory parameters were similar between them. For example, elevated CRP concentration was also noted in pediatric adenovirus patients in the absence of secondary bacterial infection as well as in patients with bacterial infection, indicating that adenoviruses trigger an immediate inflammatory host response resembling that triggered by invasive bacterial infection [28]. In this study, the eosinophil count in patients with infections was significantly lower than that in controls.

The blood WBC count varies with age, with higher counts in infants and toddlers compared with adolescents and adults [29]. Thus, the identification of predictors of neonatal sepsis is important. Nonetheless, logit (Pvirus/bacteria) and logit (PG−) were not useful for neonates. However, logit (Pinfection) is an excellent predictor of acute infection and logit (PG+) can improve the diagnostic efficiency in neonates.

Several limitations of our study should be acknowledged. First, the various models constructed in this study are not invariably mutually exclusive. Second, although we adjusted for several potential confounding factors, the possibility of the effect of residual confounding factors on risk factor analysis cannot be excluded. Third, the cohorts were categorized based on the laboratory results only. The performance of these tests suggests that the clinicians suspected a viral or bacterial infection so the sample population may be biased. Fourth, the results are not applicable to patients with multi-organism infections, cancer patients, or those with underlying inflammatory conditions. Fifth, this was a retrospective study; therefore, prospective validation of the models is required. Finally, the pathological course of the disease is unknown, i.e., the time from disease onset, which precedes hospitalization. Understanding the changes in blood parameters measured at disease onset and subsequently thereafter can help to make the accurate diagnosis. Presumably, days 3–7 after symptom onset may be ideal for testing blood parameters. However, this information requires further confirmation in prospective studies.

Conclusions

The combination of frequently tested peripheral blood parameters (such as CRP, HGB, eosinophil, monocyte, and lymphocyte levels) can differentiate between children with and without acute infection, and provide a relatively sensitive and specific indication of the infection type.

17 Jan 2022

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Additional Editor Comments:

The reviewers both felt that the manuscript had merit but both felt that there were a number of grammatical and typographical errors which required correction. This included a number of abbreviations which have not been defined. In addition, reviewer 1 raised some more substantive concerns. These include:

1. The clarification of the groups which were included in the analysis - for example those children with "contaminated" cultures. In addition, there is reference in the discussion to the utility of the models in neonates but neonatal blood counts are different from older population groups and should be independently assessed

2. All 3 tables were cut-off and table 2 and 3 should be reordered.

3. Some information included in the discussion is not adequately shown in the results section

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Reviewer #2: Yes

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Reviewer #1: This study has assessed the value of including multiple laboratory parameters in a model in order to differentiate children with and without infection, as well as to determine the likely aetiopathogen. Although cumbersome, the models have shown fair diagnostic performance in this retrospective cohort. However, the manuscript requires substantial editing/clarification.

1) The Title does not clearly convey the intent or findings of the study and should be edited.

2) The abstract requires extensive editing:

a. The introduction is too brief, and doesn’t clearly relay the rationale for the study.

b. The methods are too vague (particularly regarding the computational analysis performed).

c. The results are confusing and need to be re-written.

d. The conclusion should be edited to state that it is the combination of these blood parameters that are beneficial (as it stands it suggests that they are individually useful).

e. The abbreviation “MP” has not been defined.

f. The abbreviations for Gram Positive (G+) and negative (G-) have not been introduced, but have been used throughout the rest of the manuscript.

g. The country the study has been performed in should be stated.

3) In the introduction, the following statement: “lower concentrations of CRP are indicative of both bacterial and viral infections.” is not correct. Do you perhaps mean that lower concentrations of CRP can occur in both bacterial and viral infections?

4) In the introduction, the following statement: “This highlights the patients presenting with low CRP levels as a group with the most uncertainty.” is disputable, since a high CRP can also occur in the absence of infection (for instance in inflammatory or malignant conditions).

5) What criteria were used for including children as controls?

6) Please clarify if patients with contaminated cultures were excluded or included as controls? It states under the exclusion criteria that these were counted as having negative cultures.

7) Under Laboratory data, the meaning of the following sentence “Hence, patient had blood counts combined with CRP testing available that were drawn between 3 to 7 days after symptom onset.” is unclear. Do you mean that you retrieved/recorded full blood count and CRP results collected between 3 and 7 days from symptom onset?

8) All 3 of the Tables have been cut off. Please adjust so that they are visible in their entirety.

9) On the last page of the results, the following is stated: “When the score was equal to or less than -0.45, the PPV in the diagnosis of bacterial infection was 70.8%, suggesting that anti-infective therapy was required.” Should this not read: “When the score was equal to or greater than -0.45, the PPV in the diagnosis of bacterial infection was 70.8%, suggesting that anti-infective therapy was required”?

10) Were the results from the various models invariably mutually exclusive? In other words, did results from the different analyses ever indicate both bacterial and viral infection? If so, this would considerably ameliorate the clinical utility of these formulae.

11) It would be helpful to include a flow diagram of how the authors suggest these formulae would be used in routine clinical practice. Eg Begin with the Logit (Pcontrol) formula, If value >x proceed to Logit (Pbacterial/viral) formula, etc.

12) In the Discussion, the utility of this approach is discussed among neonatal patients. However, the number of neonates included in this study has not been specified in the results, and since neonatal full blood count results can be very different from those seen in older children, the models should be validated in neonatal patients separately before any conclusions about the utility of these findings can be made in this patient subgroup.

13) In the discussion, the following statement: “The relation of WBC levels and infection remains controversial as well as neutrophils” is unsubstantiated. The authors have already stated that the white cell count is well known to be elevated in bacterial infection.

14) There are several parts of the discussion which seem irrelevant. These include the following:

“Hepcidin is a key regulator of iron homeostasis.24 Inflammation-induced hepcidin interacts with ferroportin, which becomes internalized and degraded and ultimately leads to intracellular iron sequestration and decreased iron absorption in the duodeum.2522 Furthermore, hepcidin-induced low iron levels were related to both the long-term and short-term survival rates of critically ill individuals.23” and

“Lymphocytes play important roles in defense against and recovery from multiple virus infections especially retroviruses infections, demonstrated by studies using adoptive transfer or host immunosuppression.27-28 MP pneumonia is considered to be in part attributed to immune-mediated responses in which monocytes and its subsets appear to be important element 29 . Blood platelets were presented as active players in antimicrobial host defense and the induction of inflammation and tissue repair in addition to their participation in hemostasis via releasing the content of their alpha-granules, which include an arsenal of bioactive peptides, such as CC-chemokines and CXC-chemokines and growth factors for endothelial cells, smooth muscle cells and fibroblasts.30-31”

Please clarify or remove these sections.

15) In the discussion, it is stated that “CRP levels were independent of the duration of illness, indicating that adenoviruses trigger an immediate inflammatory host response resembling invasive bacterial infection.25” This has not been shown in the Results. Please include it.

16) In the Discussion, it is stated that “In this study, the number of eosinophils in the infected group was significantly lower than that of controls, while the percentage of eosinophils was similar in the two groups,”. Please clarify which 2 groups you are referring to.

17) In the same sentence, it is stated “suggesting that eosinophil depletion may also be the cause of infection”. This is incorrect. Do you mean that eosinophil depletion may be caused by infection?

18) Additional limitations to this study include the fact that the results are not applicable to patients with mulit-organism pathology, in cancer patients or in those with underlying inflammatory conditions.

19) The article contains numerous minor grammatical errors. Some, but not all, have been corrected below. The manuscript would benefit from English language editing.

20) The data for the study has not been provided.

Minor Corrections:

1) The Introduction has no heading

2) In the introduction, the abbreviation “FN” should be defined.

3) In the introduction, the abbreviation proADM should be spelled out/defined when first used.

4) In the introduction, the following sentence: “It is unfortunate that almost three-quarters of all patients thought to have a purely viral syndrome received treatment with antibiotics.” should be referenced.

5) In the introduction, the following statement: “However, reliable physical examination findings and routinely individual laboratory investigations are not currently available to help clinicians differentiate benign viral infections or a case of over-swaddling from serious bacterial infections in children.” suggests that over-swaddling is caused by bacterial infection. A suggested rephrasing of this sentence is as follows: “However, physical examination findings and routine laboratory investigations are not able to help clinicians accurately differentiate benign viral infections from over-swaddling or serious bacterial infections in febrile children.”

6) In the Introduction, the following sentence: “The aim of this study was to identified optimal，commonly available parameters in the differential diagnosis of RNA, DNA viral, MP, G- and G+ infection, which has not been previously studied in these groups and to determine cut-off values that could aid clinicians in the evaluation of febrile pediatric patients.” is unclear. A suggested rephrasing is as follows: “The aim of this study was to assess commonly available blood parameters in differentiating RNA viral, DNA viral, MP, G- and G+ infection, and to determine cut-off values that could aid clinicians in the evaluation of febrile pediatric patients.”.

7) Under the exclusion criteria and in Figure 1, change “cross-contamination” to “multi-organism infection” or “dual pathology”.

8) In the methods, 2 sentences have been started with a number in numerical format. These numbers should be written out.

9) Under the Study Population, the abbreviation ED should be spelled out.

10) All of the organisms abbreviated in Figure 2 should be listed in the Legend.

11) In the results, the meaning of the following sentence: “Because the diseases have different age prevalence, age is considered the most important demographic characteristic” is not clear.

12) In Table 1, please include the interquartile range for the age (not the 95% CI).

13) Table 2 should be placed before Table 3 or they should be renamed accordingly.

14) In the Discussion, the following sentence: “However, total and differential WBC are also associated with medullar, immune and inflammatory disorders.” should be rephrased as follows: “However, total and differential WBC counts are also affected by clonal myeloid disorders as well as immune and inflammatory conditions”.

15) The Conclusion is poorly phrased. A suggested rephrasing is as follows: “The proposed approach for using the computational method, combining frequently tested peripheral blood parameters such as CRP, HGB, eosinophil, monocyte and lymphocyte counts, can assist in differentiating between children with and without acute infection and provide a relatively sensitive and specific indication of the type of infection present.”

Reviewer #2: The manuscript is scientifically sound, however requires grammatical review and update by an English scientific writer.

I have made a number of comments and edits, however these are not comprehensive.

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Reviewer #1: No

Reviewer #2: Yes: Jessica June Sancroft Opie

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1 Apr 2022

Details are responded in the letter to reviewers.

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15 Jun 2022

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13 Jul 2022

Dear Dr. Mayne and Reviewers,

Thank you for giving us the opportunity to correct the manuscript. We have reviewed the comments provided by the journal editors and reviewers. We have addressed the comments in the revised manuscript and the changes are highlighted.

Reviewer #1. Thank you for your constructive comments. Please see my responses to your comments below:

1) Abstract

We have added a sentence regarding the background.

2) Figure 2B–C can assist physicians to predict the prevalent pathogen in each month.

3–4) We have made additional revisions to the revised manuscript.

5) Although there were significant differences in the WBCs, neutrophils, and NLR between the groups (Table 1), their AUCs were < 0.70. We have added the AUC of WBCs in Table 2.

6) Our statement refers to the absolute neutrophil and lymphocyte counts, rather than the percentages of neutrophils and lymphocytes. We have clarified this in the manuscript.

7) The AUC of WBCs was < 0.70 (Table 2). The total and differential WBC counts are affected by stringent state as well as immune and inflammatory conditions.

8) The data are correct, but we have not presented the percentage of eosinophils. We have deleted the sentence.

9–10) The changes made to the revised manuscript are highlighted.

11) An R-value is presented as a correlation coefficient between the parameters and the groups using bivariate analysis.

12) In Figure 3b, the Logit (PRNA) and Logit (PDNA) were not only performed in cases with a Logit (Pvirus/bacteria) > −0.3; and Logit (PM. pneumonia), Logit (PG+), and Logit (PG−) were not only performed in cases with Logit (Pvirus/bacteria) ≤ −0.45. The model was developed to rapidly determine the pathogens using the formula after obtaining the blood routine examination results. The Logit (Pinfection), Logit (PRNA), Logit (PDNA), Logit (PG+), and Logit (PM. pneumoniae) should be used to classify all children (p > 0.05 for the comparison between neonates and other children; Table 3). Logit (Pvirus/bacteria) should not be used to classify neonates because of a p < 0.05 for the comparison between neonates and other children.

Reviewer #2. We appreciate your positive comments. We have revised the manuscript for language and scientific content; the changes are highlighted in the revised manuscript. Figure 2B–C may assist physicians to predict the prevalent pathogen in each month.

Yours sincerely,

Wei-ying Wang

Submitted filename: Response to Reviewers.docx

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5 Aug 2022

Use of common blood parameters for the differential diagnosis of childhood infections

PONE-D-21-32536R2

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2 Sep 2022

PONE-D-21-32536R2

Use of common blood parameters for the differential diagnosis of childhood infections

Dear Dr. Wang:

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