The differential diagnostic role of complete blood cell count parameters in patients with bacterial pneumonia from non-bacterial pneumonia

The differential diagnostic role of complete blood cell count parameters in patients with bacterial pneumonia from non-bacterial pneumonia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The differential diagnostic role of complete blood cell count parameters in patients with bacterial pneumonia from non-bacterial pneumonia Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Sintayehu Admas, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7971696/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background currently, the differential diagnosis of pneumonia is challenging. As a result, the uses of less challenging and easily affordable methods, such as complete blood cell (CBC) parameters, are important. However, the differential diagnostic role of CBC parameters in pneumonia has not been well studied. Therefore, the current study aimed to assess the differential diagnostic role of CBC parameters in patients with bacterial pneumonia from non-bacterial pneumonia. Methods A cross-sectional study with Systematic random sampling was conducted in 234 patients. Socio-demographic and clinical data were collected by trained nurses via a semi-structured questionnaire. In addition, sputum and 5mL of the venous blood sample were collected. X - ray and sputum examinations were performed for initial screening of study participants. The blood was s ubsequently analyzed with mindray-5150 hematology analyzer. The data were entered into Epi-data (3.0.4) and analyzed via SPSS (25.0 ). The summary statistics were used. A Mann-Whitney U test was used to compare median differences between groups. The area under the curve (AUC) determined via the receiver-operating characteristic (ROC) curve. P-value < 0.05 was considered to indicate statistically significance. Results A total of 234 study participants were included in the present study. The total WBC, ANC, PLT, NLR, AMC, and MLR values were higher and the AEC levels were lower in the bacterial group than in the non-bacterial group. The ROC curve results revealed that WBC and ANC had higher AUC (0.75 (95% CI: 0.68, 0. 82) and0.74 (95% CI: 0.67, 0.81), respectively) values than the other variables did. Moreover, the WBC and ANC count for vomiting, dyspnea, and both, had good discriminative ability for the diagnosis of pneumonia with AUCs of 0.78, 0.79 and 0.77 vs 0.77, 0.78, and 0.76, respectively. Conclusion and Recommendation: Blood parameters notably, WBC and ANC had greater differential diagnostic value for the differentiation of pneumonia. Moreover, the combination of WBC and ANC count with clinical parameters had good discriminative ability for the differential diagnosis of pneumonia. Therefore, regular screening of blood parameters and/or with clinical symptoms in patients with bacterial pneumonia is important for early diagnosis. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Microbiology Bacterial pneumonia Specificity Sensitivity Complete blood cell count Northwest Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pneumonia is defined as an acute inflammation of the parenchymal tissue of the lungs [ 1 , 2 ]. Pneumonia may be caused by microorganisms such as viruses, fungi, parasites and bacteria. Compared with other cases of pneumonia, bacterial pneumonia has a significant impact on the overall morbidity and mortality rates [ 2 , 3 ]. Bacterial pneumonia is characterized by inflammation in the alveoli and lung parenchyma. The predominant etiologies of bacterial pneumonia are Streptococcus pneumoniae, Staphylococcus aureus, Haemophilusinfluenzae, and Gram-negative bacilli [ 4 , 5 ]. On the other hand, the etiology of viral pneumonia are influenza virus, respiratory syncytial virus, adenovirus , and herpesvirus [ 6 ]. Ascarislumbricoides, strongyloidesstercoralis, paragonimus species and toxoplasma gondii are the major causative agent for parasitic pneumonia [ 7 ], while, cryptococcusneoformans, candida and aspergillus species, histoplasmacapsulatum , and pneumocystis jirovecii are the major causative agent for fungal pneumonia [ 8 ]. Pneumonia is mainly associated with change in blood parameters. However, alteration in blood parameters may vary based on etiology, severity, and others conditions. Destruction of lung tissue and inflammatory responses in pneumonia patients cause the release of cytokines or signaling molecules, leading to alteration in leucocytes. Cytokines such as chemokine-like interleukin-1 (IL-1), interleukin-8 (IL-8), tumor necrosis factor alpha (TNF- α), and granulocyte colony-stimulating factor promote chemotaxis and maturation of neutrophils, leading to leukocytosis [ 9 , 10 ]. The mechanism involved in altering of blood parameters in bacterial, parasitic and fungal pneumonia patients is an increase in the number of immune cells such as neutrophils, eosinophils, macrophages, and other inflammatory cells, which are involved in immune response the body to infection. As a host defense mechanism to prevent pathogen proliferation and survival, infiltration of immune cells, with a subsequent increase in their number, is triggered in patients with pneumonia [ 11 – 13 ]. On the other hand, bacterial pneumonia, particularly Streptococcus pneumoniae , induces lymphopenia in the circulation due to the disappearance of activated T lymphocytes with a type 1 cytokine profile [ 14 , 15 ]. In case of viral pneumonia, change in total leucocytes is mainly related with lymphocytes. Cytokine induced proliferation of B and T cells in viral pneumonia patients involved in increment in lymphocytes [ 16 , 17 ]. The generation of adenosine diphosphate (ADP) from the immune cells as a result of inflammatory responses and destruction of lung tissue is an additional mechanism that alters platelets (PLTs). The generation of ADP activates PLTs and promotes the degranulation of both dense granules and α-granules. Moreover, as a response to vascular injury caused by pathogen, PLT activation, and proliferation with a subsequent increase in count can occur [ 18 , 19 ]. Furthermore, evidence shows that red blood cell (RBC) parameters, including hemoglobin (Hb), RBC count, and red cell distribution width (RDW), can also be affected by pneumonia through a number of mechanisms, such as direct pathogen-RBC interactions, oxidative stress, and immune-mediated damage. These results, including increased reactive oxygen species, inflammation and activated immune cells may inadvertently target RBCs, leading to changes in their parameters [ 20 – 22 ]. According to evidences, in pneumonia patients change in blood cell count and their derived parameters is varied based on causative agent. As a result, those parameters may have clinical value in the differential diagnosis of pneumonia [ 23 – 25 ]. The differential diagnosis of bacterial pneumonia from non-bacterial pneumonia is often challenging, and includes assessments of clinical [ 26 ], chest radiography [ 27 ], and laboratory tests (complete blood cell count (CBC), C-reactive protein (CRP) measurement, erythrocyte sedimentation rate (ESR) determination, Gram staining, sputum culture, blood culture, serology technique, and the polymerase chain reaction (PCR) technique) [ 28 ]. A comprehensive assessment that takes into account clinical, radio-graphic, molecular techniques, and microbiological aspects is necessary for pneumonia diagnosis, because it has a good capacity to predict outcomes [ 29 – 31 ]. However, the high cost, limited availability and applicability, required professional expertise, long TAT, the need for invasive procedures, and the absence of commercial assays for many organisms make the diagnostic process difficult [ 32 – 34 ]. Therefore, easily available, applicable, and affordable methods for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia, such as the CBC, can be utilized in developing nations where bacterial pneumonia is most prevalent [ 35 ]. Although bacterial pneumonia is involved in the alteration of CBC parameters, the use of parameters as diagnostic tools specifically in resources limited counties plays a crucial role in the early diagnosis and management of the patients [ 23 – 25 ]. Accordingly, studies are needed to select the best differential diagnostic value of CBC parameters. Determining the value of CBC parameters as a differential diagnostic tool for the detection of bacterial pneumonia among suspected patients was the primary objective of this study. Materials and Methods Study design, period and setting An institutionally based cross-sectional study was conducted in patients with pneumonia at the University of Gondar Comprehensive Specialized Hospital, from June to October, 2024. The hospital is one the biggest teaching and referral hospitals located in northwest Ethiopia. It provides both elective and follow-up health care services through its various units including internal medicine, surgery, emergency, pediatrics and obstetrics and gynecology. Most of the pneumonia patients are admitted and diagnosed in pediatric, internal medicine, emergency, and gynecology wards [ 36 ]. Population All adult patients admitted with pneumonia (bacterial and non-bacterial pneumonia) and who were available during study period were our source populations and study populations, respectively. Inclusion and Exclusion criteria All patients who were initially diagnosed with pneumonia using sign and symptom plus chest x-ray and later isolated pathogenic bacteria using sputum culture were included as bacterial pneumonia. Meanwhile, negative for pathogenic bacteria in sputum culture were negative for pathogenic bacteria in sputum culture were included as non-bacterial pneumonia. Participants who were pregnant or had hematological disorders, chronic infections, malignancy, rheumatoid arthritis, hepatic diseases, human immunodeficiency virus (HIV), hypertension, asthma, kidney disease, heart failure, smokers, TB and malaria patients, and patients who received steroids, anticoagulants, anti-inflammatory drugs were excluded from the study. Participants who were unable to provide sputum sample were exclude from the study. Study variables In this study, CBC parameters ( WBC, RBC, PLT, ANC, ALC, AMC, ABC, AEC, RDW, MCV, MCH, MCHC, MPV, PDW, NLR, MLR, and PLR) were taken as the dependent variable, whereas socio-demographic (sex, age, and residence) and clinical characteristics (type of pneumonia (CAP or HAP), symptoms (presence of symptoms such as, fever, cough, vomiting, dyspnea, productive cough, and chest pain), length of hospitalization, and recurrent pulmonary infection) of adult patients were included as independent variables. Sample size determination and sampling techniques The sample size was determined using G*Power software (version 3.1) by taking the following assumptions: effect size: 0.5 (medium effect size), α error probability: 0.05, study power (1-β probability): 0.95, sample allocation ratios (bacterial pneumonia to non-bacterial pneumonia ratio): 1 to 2, and two-tailed level of significance. Accordingly, when these values were input into the G*Power software, the sample size was 236 (79 bacterial pneumonia and 157 non-bacterial pneumonia patients). In the present study, only 234 samples (81 bacterial pneumonia and 153 non-bacterial pneumonia patients) were enrolled with a study power of 0.96. There were no significant differences in power assumptions between the actual and theoretical assumptions. Moreover, all study participants were fulfilled the eligibility criteria and using a systematic random sampling technique. Operational definitions Pneumonia a condition characterized by clinical features (fever, fast breathing, fast pulse, cough, dyspnea, sputum production, and pleuritic chest pain) and which can also have positive results according to chest x-rays [ 37 ]. Bacterial pneumonia is type of pneumonia that initially diagnosed with sign and symptom plus chest x-ray and later pathogenic bacteria identified using sputum culture [ 38 ]. Non-bacterial pneumonia is a type of pneumonia that may involve be fungal, viral, or parasitic, and atypical pneumonia and anaerobic bacteria; with a positive result on a chest X-ray and a negative result on bacterial sputum culture [ 39 ]. The diagnostic performance of each parameter was defined as perfect, excellent, good, fair, Poor (weak) and failed if AUC = 1, 0.9 ≤ AUC < 1, 0.8 ≤ AUC < 0.9, 0.7 ≤ AUC < 0.8, 0.6 ≤ AUC < 0.7 and 0.5 ≤ AUC < 0.6 respectively [ 40 , 41 ]. Data collection and laboratory methods A semi-structured questionnaire and a data collection sheet were used to obtain socio-demographic, clinical, and laboratory data. The data was collected by professionals such as nurses and laboratory technologists. Purulent sputum and 5mL of the venous blood sample were taken under aseptic conditions into in dry, sterile, leak-proof, translucent, and screw-capped plastic containers and di-potassium ethylene diaminetetraacetic acid test tube from each patient for bacterial identifications and complete blood counts. Smears of sputum samples were prepared and subjected to Gram staining. It allowed for the rapid differentiation of Gram-positive and Gram-negative bacteria, providing valuable information for the diagnosis and treatment of respiratory infections. After gram staining, all sputum sample at least 25 polymorphonuclear leukocytes and fewer than 10 epithelial cells observed microscopically were culture for identification of pathogenic bacteria. The purulent part of the accepted sputum sample was inoculated onto blood agar plates (BAP), MacConkey agar (MAC), mannitol salt agar (MSA), and chocolate agar plates (CHO) (all sourced from Oxoid, Hampshire Company, UK) with a sterile wire loop. Pure colonies were sub-cultured on nutrient agar plates (NAP) (Oxoid, Hampshire, UK). The bacterial species were subsequently identified by colony morphology, Gram stain, and hemolytic reactions to BAP. The identification of bacteria at the species level was performed via biochemical tests such as catalase, coagulase, optochin (5 µg), and bacitracin (0.04 U or 10 U) test for Gram-positive identification and satellite test for BAP and NAP (indole production, urease, citrate utilization, lysine decarboxylation, carbohydrate fermentation, gas production, hydrogen peroxide production, oxidase and motility tests for Gram-negative bacteria). The automated Mindray BC-5150 hematology analyzer system was used for complete blood count. This advanced analyzer employs techniques such as electrical impedance, laser scatter technology, and colorimetric analysis to detect and quantify various blood components. During the analysis, a diluted blood sample is subjected to an electrical current, which classifies cells based on their size and conductivity, allowing for the differentiation of RBCs, WBCs, and PLTs. Laser scatter technology further identifies and classifies different types of WBCs according to their size, granularity, and structural complexity. Additionally, the analyzer utilizes a cyanide-free colorimetric method to accurately measure Hb concentration in the blood [ 42 ]. The blood collection and laboratory analysis were carried out by qualified personnel through strict following of study protocols and procedures to assure the quality. Additionally, on the spot, the investigators closely followed the data collection process to ensure data quality. Screening tests for HIV, hepatitis B virus, hepatitis C virus and pregnancy were conducted using the STAT-PAK, HBsAg, HCV antibody and HCG test kits, respectively. The reliability of the test results has been ensured with known positive and negative samples for each test category. The presence of a control line adjacent to the sample line was duly verified. Malaria infection in participants was assessed by examining 3% Giemsa-stained blood films. Giemsa quality was checked with 1 + and 2 + plasmodium-positive samples. Participants who were negative for all these tests were included in the study. Data management and quality control All relevant Socio-demographic and clinical data were collected by a well-trained clinical nurse, while blood samples were collected by an experienced laboratory technologist, sample collection, processing, and laboratory testing were performed in accordance with the prepared standard operating procedures. Quality control tests were implemented following standard operating procedures. Adherence to specified conditions, such as temperature, time, guidelines for sample-reagent mixing was strictly followed. Sputum quality The quality of the collected sputum sample was assessed via Bartlett’s scores method by considering the score of pus cells and squamous epithelial cells, and performing macroscopic observation. Gram stain quality Gram stained control slides were verified before patient smears were examined and reported. Manufacturer or user prepared QC slides were used. If your own QC slides are prepared, 18- to 24-hour cultures of known gram-positive and gram-negative organisms are used. Sputum culture quality The culture media were prepared according to the manufacturer’s instructions. Before fresh culture media, 5% of the prepared batches were incubated at 35–37°C for 24 hours to confirm their sterility. Standard reference bacterial strains of S. aureus (American Type Culture Collection (ATCC)-13812), P. aeruginosa (ATCC (12934), E. coli (ATCC 25922), H. influenzae (ATCC-49766), S. pyogenes (ATCC 12696), and S. pneumoniae (ATCC 12977) were used as control strains to assess the ability of the prepared media to support the growth of bacteria. Data analysis and interpretations To enter and analyze data, Epidata version 3.1 and SPSS version 25.0 software were utilized. The categorical variables were reported using precise values and percentages, while continuous variables were reported using mean ± standard deviation (SD) and median (25th-75th percentile). Mean ± SD was used for normally distributed data, while median (25th-75th percentile) was used for skewed data. The Kolmogorov-Smirnov test was conducted to check the normality of data. The Mann-Whitney U test was used when the numerical variables did not have shown a normal distribution to the means of bacterial pneumonia and non- bacterial pneumonia. The logistic regression was used to assess the diagnostic ability of CBC parameters with clinical variables. The chi-square test was used to assess any associations between categorical variables. To ascertain the discrimination ability (specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV)) of blood parameters for bacterial pneumonia from non-bacterial pneumonia, receiver operator characteristic (ROC) curve analysis was performed. Additionally, the Youden Index was used to calculate a cut-off value for optimizing the sensitivity and specificity of variables. After ROC curve analysis, blood parameters score area under the curve (AUC) greater than 0.7 was selected as the best diagnostic marker. The accepted threshold for statistical significance was set at P < 0.05 for all tests. To present data, text, tables, and figures were employed. Ethical consideration The ethical committee of the School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences University of Gondar approved the study protocol, issuing it the number ( Ref. No: SBMLS/766/20 24). Subsequently, permission for data collection was obtained from the hospital medical director. The objectives, possible benefits, and risks were explained to the parents or legal guardians of each neonate and informed consent was obtained. Any personal identifiers were not used to keep the confidentiality of the collected data. Results Socio-demographic and clinical characteristics of the study participants Among the pneumonia patients included in this study, 81 had bacterial pneumonia, and 153 had non-bacterial pneumonia. More than half (51.7%) of the study participants were females. Most of the study participants (23.1%) were in the age group of 28–37 years, followed by 18–27 years (20.5%). Moreover, most (59%) of the pneumonia patients were rural residents. The mean hospitalization length was 2.95 ± 2.89 days, and most (77.8%) bacterial pneumonia patients acquired the disease from hospital. Recurrent pulmonary infections and cough were present in 100% of the study participants. Dyspnea and vomiting were significantly different between the bacterial pneumonia and non-bacterial pneumonia patients. The most common findings observed in bacterial pneumonia patients' initial physical examinations were tachycardia (25.9%), chest retraction (16%), and chest pain (32.1%). About 33.3% of bacterial pneumonia patients had fever (Table 1 ). Table 1 Socio-demographic and clinical characteristics of the study participants at UoGCSH, Northwest Ethiopia, 2024 (N = 234). Variable Categories Pneumonia cases Total P-value Bacterial pneumonia Non-bacterial pneumonia N (%) N (%) N (%) Sex Male 37(45.7%) 76(49.7%) 113(48.3%) 0.585 Female 44(54.3%) 77(50.3%) 121(51.7%) Age 18–27 17(21%) 31(20.3%) 48(20.5%) 0.911 28–37 17(21%) 37(24.2%) 54(23.1%) 38–47 9(11.1%) 24(15.7%) 33(14.1%) 48–57 15(18.5%) 25(16.3%) 40(17.15) 58–67 9(11.1%) 17(11.1%) 26(9.8%) 68–77 10(12.3%) 14(9.1%) 24(10.4%) 78–87 4(5%) 5(3.3%) 9(3.8%) Residence Rural 50(61.7%) 88(57.5%) 138(59%) 0.578 Urban 31(38.3%) 65(42.5%) 96(41%) Length of hospitalization < 2 days (CAP) 18(22.2%) 52(34%) 70(29.9%) 0.072 ≥ 2 days (HAP) 63(77.8%) 101(66%) 164(70.1%) Presence of fever yes 27(33.3%) 50(32.7%) 77(32.9%) 1 No 54(66.7%) 103(67.3%) 157(67.1%) Presence of dyspnea Yes 45(55.6%) 47(30.75) 92(38.3%) < 0.01 No 36(44.4%) 106(69.3%) 142(60.7%) Presence of vomiting yes 17(21%) 15(9.8%) 32(13.7%) 0.027 no 64(79%) 138(90.2%) 202(86.3) Presence of headache yes 30(37%) 71(46.4%) 101(43.2%) 0.212 No 51(63%) 82(53.4%) 133(56.8%) Showing Sore throat Yes 18(22.2%) 23(15%) 41(17.5%) 0.206 No 63(77.8%) 130(85%) 193(82.5%) Presence of Chest pain Yes 26(32.1%) 40(26.1%) 66(28.2%) 0.362 No 55(67.9%) 113(73.9%) 168(71.8%) Chest retraction Yes 13(16%) 22(14.4%) 35(15%) 0.847 No 68(84%) 131(85.6%) 199(85%) Presence of Tachycardia Yes 21(25.9%) 37(24.2%) 58(24.8%) 0.847 No 60(74.1%) 116(75.8%) 176(75.2%) Abbreviations: CAP: community acquired pneumonia, HAP: hospital acquired pneumonia N: frequency Comparison of complete blood count parameters among bacterial pneumonia and non-bacterial pneumonia patients In the present study, CBC parameters such as WBC, RBC, Hb, HCT, ANC, PLT, and NLR were significantly different between bacterial pneumonia patients and non-bacterial pneumonia patients. Hematocrit, Hb, WBC, and NLR scores had a significantly low median rank difference (MRD) between the groups. On the other hand, the PLT, AEC, and MLR had high MRD between the groups (Table 2 ). Table 2 Comparison of CBC parameters between bacterial pneumonia patients and non-bacterial pneumonia patients at UoGCSH, Northwest Ethiopia, 2024 (N = 234). CBC parameters Bacterial pneumonia Non-bacterial pneumonia MRD Mann-Whitney U test P –value Median (IQR) Median (IQR) RBC (*10 12 /L) 4.3(3.5,5.3) 4.0(2.7,5.0) -54.31 -2.429 0.015 Hb (g/dl) 12.8(10.5,15.6) 11.9(7.0,14.7) -46.80 -2.429 0.031 HCT (%) 38.6(29.6,45.5) 35.3(21.5,43.8) -22.70 -2.023 0.043 MCV (fl) 87.9(79.9,93.4) 87.6(82.5,93.4) 29.60 -0.153 0.878 MCH (pg) 30.5(28.6,32.2) 30.5(28.9,32.3) -28.20 -0.257 0.797 MCHC (g/dl) 33.9(32.8,36.6) 34.2(32.2,35.6) -117.30 -1.100 0.272 RDWcv (%) 45.5(42.2,50.5) 45.0(42.0,52.5) -9.9 -0.043 0.966 WBC (*10 9 /L) 10.43(5.4,14.2) 5.0(3.3,7.9) -49.0 -6.412 < 0.01 ANC (*10 9 /L) 7.5(4.0,12.1) 3.2(1.9,6.3) -51.57 -6.143 < 0.01 ALC (*10 9 /L) 1.0(0.7,1.3) 0.9(0.6,1.3) -58.59 -1.247 0.213 AMC (*10 9 /L) 0.5(0.3,0.9) 0.4(0.2,0.3) -58.20 -3.594 < 0.01 AEC (*10 9 /L) 0.04(0.01,0.06) 0.05(0.02,0.12) -62.43 -2.069 0.039 ABC (*10 9 /L) 0.01(0.0,0.05) 0.02(0.0,0.05) -59.49 -0.240 0.81 PLT (*10 9 /L) 213(141.5,285) 155(99,236) 117.00 -2.720 0.007 MPV (fl) 9.6(8.7,10.4) 9(8.4,10.3) -50.60 -1.174 0.240 PDW (%) 16(15.5,16.6) 16(15.6,16.6) -43.00 -0.282 0.778 NLR 6.9(3.1,13.1) 3.4(1.9,6.2) -48.15 -4.428 < 0.01 MLR 0.5(0.2,0.8) 0.4(0.25,0.55) -58.74 -2.415 .016 PLR 170.3(114.6,304) 170.3(95.4,259.5) 116.16 -0.884 0.377 Abbreviations : ABC; absolute basophil count, AEC: absolute eosinophil count, ALC: absolute lymphocyte count, AMC: absolute monocyte count, ANC: absolute neutrophil count, HCT: hematocrit, Hb: hemoglobin, IQR : interquartile range, MCH: mean corpuscular hemoglobin, MCHC: mean corpuscular hemoglobin concentration, MCV: mean corpuscular volume, MRD: median rank difference, MLR: monocyte to lymphocyte ratio, MPV: mean platelet volume, NLR: neutrophil to lymphocyte ratio, PDW: platelet distribution width, PLR: platelet to lymphocyte ratio, PLT: platelet, RBC: red blood cell, RDW: red cell distribution width, and WBC: white blood cell. The discriminative values of complete blood count parameters for differentiating pneumonia A ROC analysis was performed to assess the discriminative ability of CBC parameters in differentiating pneumonia. Accordingly, total WBC (at cut-of point ≥ 10.14 x 10 9 /L) and ANC (at cut-off points ≥ 5.36 x 10 9 /L) with AUC of 0.75 and 0.74, respectively were found to be acceptable differential diagnostic parameters. In addition, poor discriminative ability was observed for RBC (at cut-off points ≥ 4.69 x 10 9 /L), AMC (at cut-off points ≥ 0.79 x 10 9 /L), PLT (at cut-off points ≥ 129 x 10 9 /L), NLR (at cut-off points ≥ 4.83), and MLR (at cut-off points ≥ 0.47 ) with the AUC values of 0 .60, 0.64, 0.61, 0.67, and 0.60, respectively. On the other hand, remaining CBC parameters were found to have poor discriminative ability for differentiating bacterial pneumonia from non-bacterial pneumonia. The total WBC and ANC counts had a sensitivity and specificity of 56.7% and 69.1% vs 88.0% and 69.9%, respectively. The positive predictive value (PPV) and negative predictive value (NPV) of the total WBC and ANC were 71.9% and 79.4% vs 46.7% and 81%, respectively. Additionally, the sensitivities of RBC, AMC, PLT, NLR, and MLR to differentiate pneumonia was 56.7%, 35.8%, 48.1%, 65.4%, and 53.1%, respectively. The specificities of RBC, AMC, PLT, NLR, and MLR for differentiating pneumonia were 70.6, 94.1, 35.9, 67.5, and 68.6, respectively (Table 3 and Fig. 1 ). Table 3 Discriminative values of CBC parameters for differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N = 234). Parameters AUC (95% CI) Cut off Se (%) Sp (%) PPV (%) NPV (%) YI WBC (*10 9 /L) .75(.68, 82) ≥ 10.14 56.7 88.0 71.9 79.4 0.45 RBC (*10 12 /L) .60(.52, .72) ≥ 4.69 46.9 70.6 45.8 71.5 0.175 Hb (g/dl) .59(.51, .66) ≥ 9.60 82.7 37.9 41.3 80.6 0.206 HCT (%) .58(.50, .65) ≥ 27.75 83.9 35.3 40.7 80.6 0.192 ANC (*10 9 /L) .74(.67, .81) ≥ 5.36 69.1 69.9 46.7 81 0.391 AMC (*10 9 /L) .64(.56, .72) ≥ 0.795 35.8 94.1 76.3 73.5 0.297 AEC (*10 9 /L) .58(.34, .49) ≤ 0.045 48.1 35.9 28.5 56.7 0.159 PLT (*10 9 /L) .61(.53, .68) ≥ 129 48.1 35.9 28.5 56.7 0.195 NLR .67(.60, .74) ≥ 4.83 65.4 67.5 51.5 78.6 0.328 MLR .60(.51, .67) ≥ 0.47 53.1 68.6 47.2 73.4 0,235 Abbreviations : AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, AUC: area under the curve, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, Se: sensitivity, Sp: specificity, and WBC: white blood cell. Logistic regression analysis to determine the combined effect of complete blood count parameters with other clinical factors in differentiating pneumonia In the current study, the discriminative ability of CBC parameters was assessed in addition to clinical parameters, and significant discriminative value was observed. A statistically significant difference in RBC, Hb, HCT, WBC, ANC, AMC, NLR, and MLR was detected when these parameters combined with clinical factors such as dyspnea and vomiting to differentiate bacterial pneumonia from non-bacterial pneumonia. According to logistic regression analysis, combining of WBC and ANC counts with dyspnea, vomiting, and both dyspnea and vomiting increased the differentiation ability of patients by approximately 3-fold. Similarly, the ability of vomiting, dyspnea and both (vomiting plus dyspnea) combined with RBC, Hb, HCT, AEC, AMC, PLT, NLR, and MLR to differentiate bacterial pneumonia from non-bacterial pneumonia increases the discriminative power by more than twofold (Table 4 ). Table 4 Logistic regression analysis to determine the discriminative effect of CBC parameters combined with other clinical factors for differentiating pneumonia at UoGCSH, Northwest Ethiopia, in 2024 (N = 234). Parameters Odds ratio (95%CI) Plus-vomiting Plus-dyspnea Plus-vomiting and dyspnea WBC (*10 9 /L) 2.95(1.27–6.85) 2.87(1.55–5.3) 3.16(1.9–8.4) RBC (*10 12 /L) 2.54(1.16–5.5) 2.71(1.5–4.8) 2.5(0.9–6.2) Hb (g/dl) 2.7(1.3–5.8) 2.7(1.6–4.8) 2.6(1.1–6.4) HCT (%) 2.6(1.2–5.6) 2.8(1.7–4.9) 2.6(1.05–6.3) ANC (*10 9 /L) 3.2(1.4–7.6) 2.8(1.5–5.2) 3.3(1.2–8.9) AMC (*10 9 /L) 2.4(1.06–5.2) 2.7(1.6–4.8) 2.5(0.9–6.4) AEC (*10 9 /L) 2.5(1.2–5.3) 2.8(1.6–4.9) 2.5(1.04–6.2) PLT (*10 9 /L) 2.5(1.2–5.4) 3.0(1.7–5.2) 2.6(1.04-63) NLR 2.7(1.3–5.9) 2.9(1.6–5.2) 2.7(1.07–6.7) MLR 2.5(1.2–5.5) 2.8(1.6–4.9) 2.5(1.02–6.1) Abbreviations : AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, AUC: area under the curve, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, Se: sensitivity, Sp: Specificity and WBC: white blood cell. The combined discriminative values of complete blood count parameters with other clinical factors in differentiating pneumonia. A clinical model including vomiting, dyspnea and both (vomiting and dyspnea) with WBC counts increased the AUC values from 0.75 to 0.78, 0.79, and 0.77, respectively. The sensitivity and specificity of WBC plus vomiting, plus dyspnea and plus (both vomiting and dyspnea) for differentiating bacterial pneumonia from non-bacterial pneumonia were 85.0%, 78.4%, and 85.6% vs 61.7%, 71.4%, and 62.7%, respectively. The inclusion of vomiting, dyspnea and (both vomiting and dyspnea) in the clinical model increased the AUC (sensitivity, specificity) value to 0.77 (93.5%, 51.9%), 0.78 (90.2%, 56.8%) 0.76 (94.8%, 47.9%), respectively in the case of ANC. Moreover, the diagnostic efficacy, sensitivity and specificity of RBC, Hb, HCT, AMC, AEC, PLT, NLR and MLR were significantly improved by including the clinical factors vomiting, dyspnea and both (vomiting and dyspnea) (Table 5 , Fig. 2 , Fig. 3 and Fig. 4 ). Table 5 Discriminative values of CBC parameters with other clinical factors in differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N = 234). Parameters Category AUC (95% CI) Se (%) Sp (%) YI WBC (*10 9 /L) Plus-vomiting 0.777 85.0 61.7 0.467 Plus-dyspnea 0.788 78.4 71.4 0.488 Plus-dyspnea and vomiting 0.771 85.6 62.7 0.473 RBC (*10 12 / L) Plus-vomiting 0.630 65.4 68.0 0.234 Plus-dyspnea 0.664 68.0 68.0 0.234 Plus-dyspnea and vomiting 0.612 66.8 53.1 0.234 Hb (g/dl) Plus-vomiting 0.626 33.3 99.1 0.235 Plus-dyspnea 0.661 77.8 47.9 0.247 Plus-dyspnea and vomiting 0.608 30.07 99.1 0.208 HCT (%) Plus-vomiting 0.621 31.4 99.2 0.227 Plus-dyspnea 0.656 71.2 54.3 0.256 Plus-dyspnea and vomiting 0.599 31.1 89.9 0.203 ANC (*10 9 /L) Plus-vomiting 0.768 93.5 51.9 0.416 Plus-dyspnea 0.777 90.2 56.8 0.470 Plus-dyspnea and vomiting 0.760 94.8 47.9 0.417 AMC (*10 9 /L) Plus-vomiting 0.673 89.5 43.2 0.328 Plus-dyspnea 0.705 68.0 70.4 0.383 Plus-dyspnea and vomiting 0.663 88.2 44.4 0.327 AEC (*10 9 /L) Plus-vomiting 0.609 66.0 54.3 0.203 Plus-dyspnea 0.660 56.9 71.6 0.285 Plus-dyspnea and vomiting 0.609 68.6 51.9 0.205 PLT (*10 9 /L) Plus-vomiting 0.646 77.1 45.7 0.228 Plus-dyspnea 0.678 77.9 56.3 0.341 Plus-dyspnea and vomiting 0.626 38.6 81.5 0.200 NLR Plus-vomiting 0.692 58.8 74.1 0.329 Plus-dyspnea 0.711 71.2 55.4 0.367 Plus-dyspnea and vomiting 0.690 61.4 69.1 0.337 MLR Plus-vomiting 0.620 72.5 51.9 0.244 Plus-dyspnea 0.654 62.7 62.9 0.306 Plus-dyspnea and vomiting 0.612 62.1 61.7 0.238 Abbreviations : AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, and WBC: white blood cell. Discussion The pathophysiological state of pneumonia can alter the normal hematopoietic activity of blood cells as documented in the literature [ 9 , 10 , 18 , 19 ]. It is associated with either the induction or dampening of the production, differentiation, and function of blood cells [ 20 – 22 ]. Despite advancements in diagnostic methods, pneumonia remains a significant cause of complications and fatalities, emphasizing the need for simple, quick, and affordable differential diagnostic method. The use of CBC parameters for the differential diagnosis of pneumonia is important. This is because, testing those parameters is simple and quick, and they are easily affordable in all healthcare facilities [ 43 ].The main objective of the current study was to assess the differential diagnostic value of blood parameters in patients with bacterial pneumonia from non-bacterial pneumonia. According to the current study, WBC, HCT, RBC, Hb, ANC, AMC, PLT, MLR, and NLR values were significantly greater in patients with bacterial pneumonia than in non-bacterial pneumonia. However, AEC levels were lower in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by studies conducted in China [ 44 ], Turkey [ 23 ], and Greece [ 45 ]. The possible reason for this phenomenon is that these blood parameters play important roles in systemic inflammation and infection [ 23 , 44 ]. This study revealed an increased WBC count among patients with bacterial pneumonia compared with non-bacterial pneumonia. This finding was supported by studies conducted in Egypt [ 46 ], Turkey [ 23 ], India [ 47 ], Romania [ 48 ], China [ 44 , 49 ], and USA [ 50 ]. A possible explanation for the high WBC count in bacterial pneumonia could be related to the destruction of lung tissue by bacteria and the subsequent induction of inflammatory responses that induce the release of cytokines or signaling molecules. The release of inflammatory cytokines promotes the production, chemotaxis and maturation of neutrophils, leading to leukocytosis in bacterial pneumonia patients than non-bacterial pneumonia [ 9 , 10 ]. Additionally, presence of high physiological stress of tissue in bacterial further contribute to elevated in WBC counts with compared to others [ 51 , 52 ]. Whereas in non-bacterial pneumonia had a lower WBC count due to the immune response's nature, where viral infections activate lymphocytes (T and B cells) rather than neutrophils. Additionally, bone marrow (BM) may produce fewer neutrophils during viral infections, focusing instead on lymphocyte production, leading to a lower overall WBC count, ANC, AMC, MLR, and NLR. Consequently, while lymphocytes may increase, this does not significantly elevate the total WBC count if offset by a decrease in other WBC types [ 16 , 17 ]. According to the current study, the NLR and ANC were also significantly increased in bacterial pneumonia patients. This finding was supported by studies conducted in China [ 44 ], Greece [ 45 ] and Mexico [ 53 ]. A possible explanation for these changes is that cytokines, such as IL-1α, IL-6, IL-7, IL-8, IL-12, and PLT-derived growth factor, initiate a strong immune response to infection, resulting in increased production and release of neutrophils and suppression of lymphocytes in the BM in response to inflammatory cytokines. This may increase the ANC and NLR [ 54 , 55 ]. An elevated NLR often indicates the regulation of neutrophils and the enhancement of the intensity of action in patients [ 56 ]. Furthermore, the finding of ANC in this study was supported by studies in China [ 49 ] and India [ 47 ]. The possible mechanism for this phenomenon is that increased neutrophil counts occur due to the rapid mobilization of neutrophils from the BM into the bloodstream, triggered by inflammatory cytokines such as IL-1 and TNF-α in response to bacterial components. These cytokines enhance hematopoiesis and release mature neutrophils to infection sites for phagocytosis, degranulation, and the release reactive oxygen species. While this response is vital for fighting bacteria and promoting healing, excessive neutrophil activity can cause tissue damage and inflammation [ 57 ]. According to the current study, AMC was significantly greater in bacterial pneumonia patients than in non-bacterial pneumonia patients. This finding is supported by studies conducted in China [ 44 ] and Turkey [ 58 ]. The possible mechanism for this is that, during a bacterial infection, monocytes become activated, playing a key role in the immune response. They perform bactericidal and phagocytic functions, acting as the first line of defense by engulfing and eliminating pathogens. Additionally, monocytes produce cytokines and present microbial antigens to T cells, initiating and regulating both cellular and humeral immunity [ 49 ]. According to this study, the value of the MLR was significantly greater in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by a study conducted in China [ 44 ]. This is driven by increased monocyte recruitment due to inflammatory cytokines (e.g., IL-6 and TNF-α). This increase in monocytes aids in pathogen clearance and tissue repair, whereas lymphocyte levels may decrease due to stress or infection effects [ 59 ]. Moreover, monocytes can transform into various specialized cells that carry out diverse functions during infections. They can increase their ability to kill microbes by generating TNF-α and nitric oxide synthases [ 60 ]. Further differentiation into either macrophages or dendritic cells aids effective microbial clearance at infected sites. Mobilization of monocytes into the peripheral circulation results in an elevated MLR [ 61 ]. Conversely, a study conducted in Turkey [ 23 ], reported that the MLR was lower in pneumonia patients than in controls. This change may be due to changes in the sample size and age of the participants. Moreover, it can be affected by various factors, unrelated to pneumonia, such as other chronic infections, inflammatory conditions, or even medication [ 62 ]. In this study, eosinophil counts were found to be significantly lower in bacterial pneumonia patients. This finding was supported by a study conducted in Turkey [ 23 ]. The underlying mechanism is that in bacterial pneumonia, during acute stress or infection, eosinophil levels decrease due to a shift in the immune response favoring neutrophils, which are the primary WBCs that fight infection. Pro-inflammatory cytokines such as IL-6 and TNF-α increase neutrophil production while suppressing eosinophils. Additionally, the BM prioritizes neutrophil production, leading to increased apoptosis in eosinophils and consequently reduced eosinophil counts [ 63 ]. Non-bacterial pneumonia (viral or allergic pneumonia), involves distinct immune responses compared to bacterial pneumonia. In allergic pneumonia, eosinophils are often elevated due to cytokines like IL-4, IL-5, and IL-13 [ 7 ], while in viral pneumonia, the immune system activates both innate (IFN-γ) and adaptive responses, including T-lymphocytes, particularly CD8 + cytotoxic T cells, to target infected cells. They drive inflammation, recruit immune cells, and may cause lung tissue damage [ 64 , 65 ]. According to the present study, PLT counts were significantly greater in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by studies conducted in China [ 66 – 68 ], Turkey [ 23 , 69 ], and India [ 70 ]. A possible explanation is that PLTs are essential inflammatory cells that generate a significant portion of cytokines and can also behave as acute phase reactants. It generates ADP from immune cells resulting from the inflammatory response and tissue injury, which promotes degranulation, PLT activation, and proliferation with subsequent increase in the PLT count [ 18 , 19 ]. Moreover, PLTs are vital in adaptive immunity and in eliciting an inflammatory response in addition to their primary role in haemeostasis [ 61 ]. Lower PLT counts in non-bacterial pneumonia during viral infections results from several mechanisms: immune-mediated PLT activation occurs via cytokines like IL-6 and interferon, leading to PLT aggregation and consumption at inflammation sites; BM suppression from cytokines such as IFN-α and IFN-γ reduces megakaryocyte activity, decreasing PLT production; PLT sequestration happens in inflamed lung tissue or the spleen due to increased vascular permeability and immune interactions; and immune complex formation from viral or allergic responses activates and promotes the clearance of PLT. Together, these factors contribute to thrombocytopenia in non-bacterial pneumonia [ 71 – 73 ]. According to current study, the RBC and Hb levels were lower in bacterial pneumonia patients than in non-bacterial pneumonia patients. This finding was supported by studies in China [ 44 ] and Turkey[ 23 ]. The possible reason for this finding may be that RBC parameters are affected by different mechanisms such as direct bacteria-RBC interactions, oxidative stress due to bacterial release, and finally, immune-mediated damage. These results, indicated that increased reactive oxygen species, inflammation and activated immune cells may inadvertently target RBCs, leading to changes in their parameters [ 20 – 22 ]. Pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α have been shown to shorten RBC survival. Erythropoietin production and erythroid precursor cell differentiation are suppressed [ 61 ]. In non-bacterial pneumonia, RBC and Hb levels may rise due to hypoxia from impaired oxygen exchange in inflamed lungs, prompting the kidneys to release erythropoietin that stimulates increased RBC production in the BM; this compensatory response is more pronounced in non-bacterial cases due to the diffuse or prolonged lung tissue damage caused by viral or allergic inflammation, unlike the localized damage typically seen in bacterial pneumonia [ 74 , 75 ]. Furthermore, in the present study, the combination of blood parameters (WBC, RBC, Hb, ANC, AMC, NLR, and MLR) with clinical symptoms (dyspnea and vomiting) had statistically significant value for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia. This finding supported by studies conducted in Switzerland [ 76 , 77 ] and China [ 44 ]. The reason for this finding was that there is a significant systemic inflammatory response and potential respiratory distress due to infection. Elevated blood parameters suggest that the body is mounting an immune response to combat bacterial pathogens, whereas dyspnea reflects impaired gas exchange and reduced oxygenation resulting from pulmonary inflammation and fluid accumulation in the alveoli [ 57 , 76 , 77 ]. Vomiting may occur due to the systemic effects of infection, including the release of toxins and inflammatory mediators by pathogens that can stimulate the vomiting center in the brain [ 57 , 78 ]. In this study the ROC curve revealed that the WBC count (at cut-off point ≥ 10.14) and ANC (at cut-off points ≥ 5.36) with AUC of 0.75 and 0.74, had a sensitivity and specificity of 56.7%, 69.1% vs 88.0%, 69.9%, respectively. These findings indicating that the WBC and ANC count were acceptable differential diagnostic parameters of bacterial pneumonia from non-bacterial pneumonia. This findings was comparable to studies conducted in Norway [ 79 ], Hong Kong [ 61 ], Romania [ 48 ] and Turkey [ 80 ]. According to the findings of the current study, it is possible to use WBC and ANC as differential diagnostic markers along with other markers to differentially diagnose bacterial pneumonia from non-bacterial pneumonia. However, the value AUC in ANC is lower than reported other study performed in China [ 44 ]. This could be due to the small sample size and methodological variation. Besides, ROC curve analysis in the current study revealed that the NLR, AMC, PLT, MLR, RBC, HCT, and AEC had AUCs between 0.6 and 0.67. These parameters have a poor ability to discriminate bacterial pneumonia from non-bacterial pneumonia. This finding was comparable to those studies conducted in Turkey [ 58 , 80 ], Norway [ 79 ], Romania [ 23 ], Hong Kong [ 61 ], and China [ 66 – 68 ]. However, the findings of this study regarding the NLR and MLR were lower than those from earlier research in Turkey [ 58 , 80 ] and China [ 44 ]. This difference in results could be attributed to the small sample size and variations in methodology. It also involves patients with liver injury and is affected by pro-inflammatory cytokines released from damaged liver cells [ 80 ], which skews immune cell ratios. The body may also initiate compensatory mechanisms to adjust immune cell production in response to liver dysfunction [ 80 ]. Moreover, the discriminative ability of CBC parameters (WBC and ANC) for diagnosing pneumonia, was assessed with the combination of other clinical parameters (vomiting, dyspnea and both (vomiting and dyspnea)), which had the highest AUCs (0.78, 0.79, and 0.77 vs 0.77, 0.78, and 0.76), respectively. This finding is supported by study conducted in Switzerland [ 76 ]. These findings indicate that the combination of blood parameters with clinical symptoms has good diagnostic accuracy for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia. Furthermore, the current study revealed that RDW, MPV, MCV, PDW, and PLR have no significant role in the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia, unlike other studies conducted in Romania [ 23 ], USA [ 34 , 81 ], China [ 44 , 82 ], and Turkey [ 58 ]. The possible reason for this difference in relation to the above studies may arise from differences in uses lower sample sizes, differences in study population demographics and age that influence disease susceptibility, and variations in study design methodology and diagnostic criteria that affect how outcomes are measured and reported. Strengths and limitations The current study could provide the value of CBC parameters in combination with other clinical factors, which may help to identify other possible differential diagnostic markers. Besides, screening sputum via Gram staining prior to culture may minimize the inclusion of non-pathogenic cases and helps in the selection of bacterial cases. Nevertheless, this study had some limitations. Being single center study is one of its limitations, because it may minimize its representativeness. Another limitation of this study is not inclusion of healthy control group and unable to differentially diagnosis of non-bacterial pneumonia may also consider as limitation of this study. Conclusion and recommendation According to the findings of this study, the WBC, HCT, ANC, AMC, PLT, and NLR values were significantly higher in patients with bacterial pneumonia than in those with non-bacterial pneumonia. The ROC curve analysis revealed that the WBC and ANC values had acceptable discriminative ability to differentiate bacterial pneumonia from non-bacterial pneumonia. However, other parameters had a poor discriminating ability to differentiate bacterial pneumonia from non-bacterial pneumonia. Moreover, the combination of CBC parameters (WBC and ANC) with clinical parameters (vomiting, dyspnea and both (vomiting plus dyspnea)) was increases the discriminative ability of bacterial pneumonia form non-bacterial pneumonia. In general, CBC parameters and combination of those parameters with clinical factors have the potential to differentially diagnosis of bacterial pneumonia form non-bacterial pneumonia. Therefore, regular screening and assessing of CBC parameters in patients with bacterial pneumonia with respect to clinical variables are important for early management and diagnosis. As a result, it is better healthcare provider and policy makers consider the differential diagnostic role of CBC parameters. Besides, it is better the current results be verified by conducting multicenter and longitudinal studies in large number of patients through rigorously analyzing. Finally, the use of a control group, and specific diagnosis of non-bacterial pneumonia could provide valuable insight. Abbreviations ABC Absolute Basophil Count ADP Adenosine Diphosphate AEC Absolute Eosinophil Count ALC Absolute lymphocyte count AMC Absolute:Monocyte Count ANC Absolute Neutrophil Count AUC Area Under the Curve BAP -plate Blood Agar plate BM Bone Marrow CAP Community-Acquired Pneumonia CBC Complete Blood Count CHO Chocolate agar CRP C-Reactive Protein HAP Hospital-Acquired Pneumonia Hb Hemoglobin IL Interleukin IQR Interquartile Range MAC MacConkey agar MCH Mean Corpuscular Hemoglobin MCHC Mean Corpuscular Hemoglobin Concentration MCV Mean Corpuscular Volume MLR Monocyte-to-Lymphocyte Ratio MPV Mean Platelet Volume MRD Median rank Difference NLR Neutrophil -to-lymphocyte Ratio NPV Negative Predictive Value PCR Polymerase Chain Reaction PDW Platelet Distribution Width PLR Platelet-to-Lymphocyte Ratio PLT Platelet PPV Positive Predictive Value QC Quality Control RBC Red Blood Cell RDW Red cell Distribution Width ROC Receiver-Operating Characteristic SOP Standard Operating Procedure TAT Turn Around Time TNF-α Tumor Necrosis Factor- alpha UoGCSH University of Gondar Comprehensive Specialized Hospital WBC White Blood Cell Declarations Consent to participate and ethical approval All the procedures were performed in accordance with the relevant guidelines and regulations. Ethical approval was obtained from the Ethical Review Committee of the School of Biomedical and Laboratory Sciences, College of Medicine and Health Science, the University of Gondar (Ref number SBMLS 667/2024). The objective and purpose of the study were explained to the medical director, and a permission letter was obtained to collect the data. After providing an explanation of the possible benefits and risks, informed written consent was obtained from the parents or legal guardians. The collected data were kept confidential. Any abnormal findings obtained were linked to physicians for appropriate patient management. Supporting information Supporting information is attached separate page. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding The authors declare that no founding for this work. Competing interests The authors have declared no competing interests exist. Acknowledgment We would like to express our great gratitude to the study participants for their willingness to participate in this study. We would also like to thank the University of Gondar Comprehensive Specialized Hospital nurse and laboratory staffs for their involvement during the data collection. Author Contributions Conceptualization: Yalweayker Eyayu, Aregawi Yalew, Yemataw Gelaw, Dereje Mengesha Berta Data curation : Yalweayker Eyayu, Aregawi Yalew, Yemataw Gelaw, Sintayehu Admas, Nigusie Alemu , Abebe Birhanu, Mitkie Tigabie, lyusera marilgn, Dereje Mengesha Berta Formal analysis : Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta Investigation : Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Abebe Birhanu, Mitkie Tigabie, Dereje Mengesha Berta Methodology : Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Sintayehu Admas, Nigusie Alemu , Abebe Birhanu, Mitkie Tigabie, lyusera marilgn, Dereje Mengesha Berta Software: Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta Supervision : Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta Validation: Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta Visualization: Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Abebe Birhanu, Mitkie Tigabie , Dereje Mengesha Berta Writing - original draft: Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta Writing – review & editing: Yalweayker Eyayu,Yemataw Gelaw, Aregawi Yalew, Sintayehu Admas, Nigusie Alemu , Abebe Birhanu, Mitkie Tigabie, lyusera marilgn, Dereje Mengesha Berta References Mackenzie, G. 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1","display":"","copyAsset":false,"role":"figure","size":50483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis to explore the discriminative ability of CBC parameters for the differentiation of pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N= 234).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/7ca8b99c7693dc89eeabc1b3.png"},{"id":98430754,"identity":"b6bf5e10-34b0-4aeb-93e9-a0573e1dc225","added_by":"auto","created_at":"2025-12-17 16:46:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminative values of WBC parameters with other clinical factors in differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N= 234).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/79f0f968f579b4e2e17e6c7b.png"},{"id":98432049,"identity":"3a09c7f3-e763-41f8-b041-464e1e3b0231","added_by":"auto","created_at":"2025-12-17 16:48:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminative values of RBC parameters with other clinical factors in differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N= 234).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/a370b8aae738ecd87d9b8a21.png"},{"id":98181442,"identity":"41cc74c8-0331-4505-932e-ec2f5b394f00","added_by":"auto","created_at":"2025-12-15 01:10:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminative values of PLT, NLR and MLR with other clinical factors in differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N= 234).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/e269fc2f49fe1f593b3bbf78.png"},{"id":98444818,"identity":"c66bb5f0-3f34-4bcd-b575-0d7b812912bc","added_by":"auto","created_at":"2025-12-17 17:17:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2409590,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/dee26ca7-eace-4f1d-a2ff-6bdf9905bdbc.pdf"},{"id":98431780,"identity":"a610332d-b2a6-4cd4-a205-628c8964d3cd","added_by":"auto","created_at":"2025-12-17 16:48:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12753,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarypapper.docx","url":"https://assets-eu.researchsquare.com/files/rs-7971696/v1/85f26a407dcd4705b65ef5e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The differential diagnostic role of complete blood cell count parameters in patients with bacterial pneumonia from non-bacterial pneumonia","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePneumonia is defined as an acute inflammation of the parenchymal tissue of the lungs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pneumonia may be caused by microorganisms such as viruses, fungi, parasites and bacteria. Compared with other cases of pneumonia, bacterial pneumonia has a significant impact on the overall morbidity and mortality rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBacterial pneumonia is characterized by inflammation in the alveoli and lung parenchyma. The predominant etiologies of bacterial pneumonia are \u003cem\u003eStreptococcus pneumoniae, Staphylococcus aureus, Haemophilusinfluenzae, and Gram-negative bacilli\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. On the other hand, the etiology of viral pneumonia are \u003cem\u003einfluenza virus, respiratory syncytial virus, adenovirus\u003c/em\u003e, and\u003cem\u003eherpesvirus\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eAscarislumbricoides, strongyloidesstercoralis, paragonimus\u003c/em\u003e species and \u003cem\u003etoxoplasma gondii\u003c/em\u003e are the major causative agent for parasitic pneumonia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while, \u003cem\u003ecryptococcusneoformans, candida\u003c/em\u003e and \u003cem\u003easpergillus\u003c/em\u003e species, \u003cem\u003ehistoplasmacapsulatum\u003c/em\u003e, and \u003cem\u003epneumocystis jirovecii\u003c/em\u003e are the major causative agent for fungal pneumonia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePneumonia is mainly associated with change in blood parameters. However, alteration in blood parameters may vary based on etiology, severity, and others conditions. Destruction of lung tissue and inflammatory responses in pneumonia patients cause the release of cytokines or signaling molecules, leading to alteration in leucocytes. Cytokines such as chemokine-like interleukin-1 (IL-1), interleukin-8 (IL-8), tumor necrosis factor alpha (TNF- α), and granulocyte colony-stimulating factor promote chemotaxis and maturation of neutrophils, leading to leukocytosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mechanism involved in altering of blood parameters in bacterial, parasitic and fungal pneumonia patients is an increase in the number of immune cells such as neutrophils, eosinophils, macrophages, and other inflammatory cells, which are involved in immune response the body to infection. As a host defense mechanism to prevent pathogen proliferation and survival, infiltration of immune cells, with a subsequent increase in their number, is triggered in patients with pneumonia [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, bacterial pneumonia, particularly \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, induces lymphopenia in the circulation due to the disappearance of activated T lymphocytes with a type 1 cytokine profile [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In case of viral pneumonia, change in total leucocytes is mainly related with lymphocytes. Cytokine induced proliferation of B and T cells in viral pneumonia patients involved in increment in lymphocytes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe generation of adenosine diphosphate (ADP) from the immune cells as a result of inflammatory responses and destruction of lung tissue is an additional mechanism that alters platelets (PLTs). The generation of ADP activates PLTs and promotes the degranulation of both dense granules and α-granules. Moreover, as a response to vascular injury caused by pathogen, PLT activation, and proliferation with a subsequent increase in count can occur [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, evidence shows that red blood cell (RBC) parameters, including hemoglobin (Hb), RBC count, and red cell distribution width (RDW), can also be affected by pneumonia through a number of mechanisms, such as direct pathogen-RBC interactions, oxidative stress, and immune-mediated damage. These results, including increased reactive oxygen species, inflammation and activated immune cells may inadvertently target RBCs, leading to changes in their parameters [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to evidences, in pneumonia patients change in blood cell count and their derived parameters is varied based on causative agent. As a result, those parameters may have clinical value in the differential diagnosis of pneumonia [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The differential diagnosis of bacterial pneumonia from non-bacterial pneumonia is often challenging, and includes assessments of clinical [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], chest radiography [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and laboratory tests (complete blood cell count (CBC), C-reactive protein (CRP) measurement, erythrocyte sedimentation rate (ESR) determination, Gram staining, sputum culture, blood culture, serology technique, and the polymerase chain reaction (PCR) technique) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comprehensive assessment that takes into account clinical, radio-graphic, molecular techniques, and microbiological aspects is necessary for pneumonia diagnosis, because it has a good capacity to predict outcomes [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the high cost, limited availability and applicability, required professional expertise, long TAT, the need for invasive procedures, and the absence of commercial assays for many organisms make the diagnostic process difficult [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, easily available, applicable, and affordable methods for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia, such as the CBC, can be utilized in developing nations where bacterial pneumonia is most prevalent [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough bacterial pneumonia is involved in the alteration of CBC parameters, the use of parameters as diagnostic tools specifically in resources limited counties plays a crucial role in the early diagnosis and management of the patients [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Accordingly, studies are needed to select the best differential diagnostic value of CBC parameters. Determining the value of CBC parameters as a differential diagnostic tool for the detection of bacterial pneumonia among suspected patients was the primary objective of this study.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, period and setting\u003c/h2\u003e \u003cp\u003eAn institutionally based cross-sectional study was conducted in patients with pneumonia at the University of Gondar Comprehensive Specialized Hospital, from June to October, 2024. The hospital is one the biggest teaching and referral hospitals located in northwest Ethiopia. It provides both elective and follow-up health care services through its various units including internal medicine, surgery, emergency, pediatrics and obstetrics and gynecology. Most of the pneumonia patients are admitted and diagnosed in pediatric, internal medicine, emergency, and gynecology wards [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003eAll adult patients admitted with pneumonia (bacterial and non-bacterial pneumonia) and who were available during study period were our source populations and study populations, respectively.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion criteria\u003c/h3\u003e\n\u003cp\u003eAll patients who were initially diagnosed with pneumonia using sign and symptom plus chest x-ray and later isolated pathogenic bacteria using sputum culture were included as bacterial pneumonia. Meanwhile, negative for pathogenic bacteria in sputum culture were negative for pathogenic bacteria in sputum culture were included as non-bacterial pneumonia.\u003c/p\u003e \u003cp\u003eParticipants who were pregnant or had hematological disorders, chronic infections, malignancy, rheumatoid arthritis, hepatic diseases, human immunodeficiency virus (HIV), hypertension, asthma, kidney disease, heart failure, smokers, TB and malaria patients, and patients who received steroids, anticoagulants, anti-inflammatory drugs were excluded from the study. Participants who were unable to provide sputum sample were exclude from the study.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eIn this study, CBC parameters \u003cb\u003e(\u003c/b\u003eWBC, RBC, PLT, ANC, ALC, AMC, ABC, AEC, RDW, MCV, MCH, MCHC, MPV, PDW, NLR, MLR, and PLR) were taken as the dependent variable, whereas socio-demographic (sex, age, and residence) and clinical characteristics (type of pneumonia (CAP or HAP), symptoms (presence of symptoms such as, fever, cough, vomiting, dyspnea, productive cough, and chest pain), length of hospitalization, and recurrent pulmonary infection) of adult patients were included as independent variables.\u003c/p\u003e\n\u003ch3\u003eSample size determination and sampling techniques\u003c/h3\u003e\n\u003cp\u003eThe sample size was determined using G*Power software (version 3.1) by taking the following assumptions: effect size: 0.5 (medium effect size), α error probability: 0.05, study power (1-β probability): 0.95, sample allocation ratios (bacterial pneumonia to non-bacterial pneumonia ratio): 1 to 2, and two-tailed level of significance. Accordingly, when these values were input into the G*Power software, the sample size was 236 (79 bacterial pneumonia and 157 non-bacterial pneumonia patients). In the present study, only 234 samples (81 bacterial pneumonia and 153 non-bacterial pneumonia patients) were enrolled with a study power of 0.96. There were no significant differences in power assumptions between the actual and theoretical assumptions. Moreover, all study participants were fulfilled the eligibility criteria and using a systematic random sampling technique.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eOperational definitions\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003ePneumonia\u003c/strong\u003e \u003cp\u003ea condition characterized by clinical features (fever, fast breathing, fast pulse, cough, dyspnea, sputum production, and pleuritic chest pain) and which can also have positive results according to chest x-rays [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBacterial pneumonia\u003c/strong\u003e \u003cp\u003eis type of pneumonia that initially diagnosed with sign and symptom plus chest x-ray and later pathogenic bacteria identified using sputum culture [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNon-bacterial pneumonia\u003c/strong\u003e \u003cp\u003eis a type of pneumonia that may involve be fungal, viral, or parasitic, and atypical pneumonia and anaerobic bacteria; with a positive result on a chest X-ray and a negative result on bacterial sputum culture [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic performance of each parameter was defined as perfect, excellent, good, fair, Poor (weak) and failed if AUC\u0026thinsp;=\u0026thinsp;1, 0.9\u0026thinsp;\u0026le;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;1, 0.8\u0026thinsp;\u0026le;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.9, 0.7\u0026thinsp;\u0026le;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.8, 0.6\u0026thinsp;\u0026le;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7 and 0.5\u0026thinsp;\u0026le;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.6 respectively [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and laboratory methods\u003c/h3\u003e\n\u003cp\u003eA semi-structured questionnaire and a data collection sheet were used to obtain socio-demographic, clinical, and laboratory data. The data was collected by professionals such as nurses and laboratory technologists. Purulent sputum and 5mL of the venous blood sample were taken under aseptic conditions into in dry, sterile, leak-proof, translucent, and screw-capped plastic containers and di-potassium ethylene diaminetetraacetic acid test tube from each patient for bacterial identifications and complete blood counts.\u003c/p\u003e \u003cp\u003eSmears of sputum samples were prepared and subjected to Gram staining. It allowed for the rapid differentiation of Gram-positive and Gram-negative bacteria, providing valuable information for the diagnosis and treatment of respiratory infections. After gram staining, all sputum sample at least 25 polymorphonuclear leukocytes and fewer than 10 epithelial cells observed microscopically were culture for identification of pathogenic bacteria. The purulent part of the accepted sputum sample was inoculated onto blood agar plates (BAP), MacConkey agar (MAC), mannitol salt agar (MSA), and chocolate agar plates (CHO) (all sourced from Oxoid, Hampshire Company, UK) with a sterile wire loop.\u003c/p\u003e \u003cp\u003ePure colonies were sub-cultured on nutrient agar plates (NAP) (Oxoid, Hampshire, UK). The bacterial species were subsequently identified by colony morphology, Gram stain, and hemolytic reactions to BAP. The identification of bacteria at the species level was performed via biochemical tests such as catalase, coagulase, optochin (5 \u0026micro;g), and bacitracin (0.04 U or 10 U) test for Gram-positive identification and satellite test for BAP and NAP (indole production, urease, citrate utilization, lysine decarboxylation, carbohydrate fermentation, gas production, hydrogen peroxide production, oxidase and motility tests for Gram-negative bacteria).\u003c/p\u003e \u003cp\u003eThe automated Mindray BC-5150 hematology analyzer system was used for complete blood count. This advanced analyzer employs techniques such as electrical impedance, laser scatter technology, and colorimetric analysis to detect and quantify various blood components. During the analysis, a diluted blood sample is subjected to an electrical current, which classifies cells based on their size and conductivity, allowing for the differentiation of RBCs, WBCs, and PLTs. Laser scatter technology further identifies and classifies different types of WBCs according to their size, granularity, and structural complexity. Additionally, the analyzer utilizes a cyanide-free colorimetric method to accurately measure Hb concentration in the blood [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The blood collection and laboratory analysis were carried out by qualified personnel through strict following of study protocols and procedures to assure the quality. Additionally, on the spot, the investigators closely followed the data collection process to ensure data quality.\u003c/p\u003e \u003cp\u003eScreening tests for HIV, hepatitis B virus, hepatitis C virus and pregnancy were conducted using the STAT-PAK, HBsAg, HCV antibody and HCG test kits, respectively. The reliability of the test results has been ensured with known positive and negative samples for each test category. The presence of a control line adjacent to the sample line was duly verified. Malaria infection in participants was assessed by examining 3% Giemsa-stained blood films. Giemsa quality was checked with 1\u0026thinsp;+\u0026thinsp;and 2\u0026thinsp;+\u0026thinsp;plasmodium-positive samples. Participants who were negative for all these tests were included in the study.\u003c/p\u003e\n\u003ch3\u003eData management and quality control\u003c/h3\u003e\n\u003cp\u003eAll relevant Socio-demographic and clinical data were collected by a well-trained clinical nurse, while blood samples were collected by an experienced laboratory technologist, sample collection, processing, and laboratory testing were performed in accordance with the prepared standard operating procedures. Quality control tests were implemented following standard operating procedures. Adherence to specified conditions, such as temperature, time, guidelines for sample-reagent mixing was strictly followed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSputum quality\u003c/strong\u003e \u003cp\u003eThe quality of the collected sputum sample was assessed via Bartlett\u0026rsquo;s scores method by considering the score of pus cells and squamous epithelial cells, and performing macroscopic observation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGram stain quality\u003c/strong\u003e \u003cp\u003eGram stained control slides were verified before patient smears were examined and reported. Manufacturer or user prepared QC slides were used. If your own QC slides are prepared, 18- to 24-hour cultures of known gram-positive and gram-negative organisms are used.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSputum culture quality\u003c/strong\u003e \u003cp\u003e The culture media were prepared according to the manufacturer\u0026rsquo;s instructions. Before fresh culture media, 5% of the prepared batches were incubated at 35\u0026ndash;37\u0026deg;C for 24 hours to confirm their sterility. Standard reference bacterial strains of \u003cem\u003eS. aureus\u003c/em\u003e (American Type Culture Collection (ATCC)-13812), \u003cem\u003eP. aeruginosa\u003c/em\u003e (ATCC (12934), \u003cem\u003eE. coli\u003c/em\u003e (ATCC 25922), \u003cem\u003eH. influenzae\u003c/em\u003e (ATCC-49766), \u003cem\u003eS. pyogenes\u003c/em\u003e (ATCC 12696), and \u003cem\u003eS. pneumoniae\u003c/em\u003e (ATCC 12977) were used as control strains to assess the ability of the prepared media to support the growth of bacteria.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and interpretations\u003c/h2\u003e \u003cp\u003eTo enter and analyze data, Epidata version 3.1 and SPSS version 25.0 software were utilized. The categorical variables were reported using precise values and percentages, while continuous variables were reported using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and median (25th-75th percentile). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD was used for normally distributed data, while median (25th-75th percentile) was used for skewed data. The Kolmogorov-Smirnov test was conducted to check the normality of data. The Mann-Whitney U test was used when the numerical variables did not have shown a normal distribution to the means of bacterial pneumonia and non- bacterial pneumonia. The logistic regression was used to assess the diagnostic ability of CBC parameters with clinical variables. The chi-square test was used to assess any associations between categorical variables. To ascertain the discrimination ability (specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV)) of blood parameters for bacterial pneumonia from non-bacterial pneumonia, receiver operator characteristic (ROC) curve analysis was performed. Additionally, the Youden Index was used to calculate a cut-off value for optimizing the sensitivity and specificity of variables. After ROC curve analysis, blood parameters score area under the curve (AUC) greater than 0.7 was selected as the best diagnostic marker. The accepted threshold for statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests. To present data, text, tables, and figures were employed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003eThe ethical committee of the School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences University of Gondar approved the study protocol, issuing it the number (\u003cem\u003eRef. No: SBMLS/766/20\u003c/em\u003e24). Subsequently, permission for data collection was obtained from the hospital medical director. The objectives, possible benefits, and risks were explained to the parents or legal guardians of each neonate and informed consent was obtained. Any personal identifiers were not used to keep the confidentiality of the collected data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic and clinical characteristics of the study participants\u003c/h2\u003e \u003cp\u003eAmong the pneumonia patients included in this study, 81 had bacterial pneumonia, and 153 had non-bacterial pneumonia. More than half (51.7%) of the study participants were females. Most of the study participants (23.1%) were in the age group of 28\u0026ndash;37 years, followed by 18\u0026ndash;27 years (20.5%). Moreover, most (59%) of the pneumonia patients were rural residents. The mean hospitalization length was 2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89 days, and most (77.8%) bacterial pneumonia patients acquired the disease from hospital. Recurrent pulmonary infections and cough were present in 100% of the study participants. Dyspnea and vomiting were significantly different between the bacterial pneumonia and non-bacterial pneumonia patients. The most common findings observed in bacterial pneumonia patients' initial physical examinations were tachycardia (25.9%), chest retraction (16%), and chest pain (32.1%). About 33.3% of bacterial pneumonia patients had fever (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and clinical characteristics of the study participants at UoGCSH, Northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePneumonia cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacterial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-bacterial pneumonia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77(50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121(51.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u0026ndash;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(17.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u0026ndash;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(9.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u0026ndash;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(10.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u0026ndash;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138(59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65(42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96(41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLength of hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 days (CAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 days (HAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164(70.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77(32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103(67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157(67.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(30.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142(60.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(90.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202(86.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of headache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133(56.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShowing Sore throat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41(17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130(85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193(82.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of Chest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113(73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168(71.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChest retraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(85.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199(85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of Tachycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116(75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176(75.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CAP: community acquired pneumonia, HAP: hospital acquired pneumonia N: frequency\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison of complete blood count parameters among bacterial pneumonia and non-bacterial pneumonia patients\u003c/h2\u003e \u003cp\u003eIn the present study, CBC parameters such as WBC, RBC, Hb, HCT, ANC, PLT, and NLR were significantly different between bacterial pneumonia patients and non-bacterial pneumonia patients. Hematocrit, Hb, WBC, and NLR scores had a significantly low median rank difference (MRD) between the groups. On the other hand, the PLT, AEC, and MLR had high MRD between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CBC parameters between bacterial pneumonia patients and non-bacterial pneumonia patients at UoGCSH, Northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCBC parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacterial pneumonia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-bacterial pneumonia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMann-Whitney U test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP \u0026ndash;value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (*10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3(3.5,5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0(2.7,5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-54.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.8(10.5,15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9(7.0,14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.6(29.6,45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.3(21.5,43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-22.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.9(79.9,93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.6(82.5,93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH (pg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.5(28.6,32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.5(28.9,32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-28.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.9(32.8,36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2(32.2,35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-117.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDWcv (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.5(42.2,50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0(42.0,52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.43(5.4,14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0(3.3,7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.5(4.0,12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2(1.9,6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-51.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0(0.7,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9(0.6,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-58.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5(0.3,0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4(0.2,0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-58.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAEC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04(0.01,0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05(0.02,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-62.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01(0.0,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.0,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-59.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213(141.5,285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155(99,236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.6(8.7,10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(8.4,10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-50.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDW (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(15.5,16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(15.6,16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-43.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.9(3.1,13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4(1.9,6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-48.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5(0.2,0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4(0.25,0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-58.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170.3(114.6,304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.3(95.4,259.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: ABC; absolute basophil count, AEC: absolute eosinophil count, ALC: absolute lymphocyte count, AMC: absolute monocyte count, ANC: absolute neutrophil count, HCT: hematocrit, Hb: hemoglobin, IQR : interquartile range, MCH: mean corpuscular hemoglobin, MCHC: mean corpuscular hemoglobin concentration, MCV: mean corpuscular volume, MRD: median rank difference, MLR: monocyte to lymphocyte ratio, MPV: mean platelet volume, NLR: neutrophil to lymphocyte ratio, PDW: platelet distribution width, PLR: platelet to lymphocyte ratio, PLT: platelet, RBC: red blood cell, RDW: red cell distribution width, and WBC: white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe discriminative values of complete blood count parameters for differentiating pneumonia\u003c/h2\u003e \u003cp\u003eA ROC analysis was performed to assess the discriminative ability of CBC parameters in differentiating pneumonia. Accordingly, total WBC (at cut-of point\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10.14 x 10\u003csup\u003e9\u003c/sup\u003e/L) and ANC (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;5.36 x 10\u003csup\u003e9\u003c/sup\u003e/L) with AUC of 0.75 and 0.74, respectively were found to be acceptable differential diagnostic parameters. In addition, poor discriminative ability was observed for RBC (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;4.69 x 10\u003csup\u003e9\u003c/sup\u003e/L), AMC (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.79 x 10\u003csup\u003e9\u003c/sup\u003e/L), PLT (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;129 x 10\u003csup\u003e9\u003c/sup\u003e/L), NLR (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;4.83), and MLR (at cut-off points\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.47 ) with the AUC values of 0 .60, 0.64, 0.61, 0.67, and 0.60, respectively. On the other hand, remaining CBC parameters were found to have poor discriminative ability for differentiating bacterial pneumonia from non-bacterial pneumonia.\u003c/p\u003e \u003cp\u003eThe total WBC and ANC counts had a sensitivity and specificity of 56.7% and 69.1% vs 88.0% and 69.9%, respectively. The positive predictive value (PPV) and negative predictive value (NPV) of the total WBC and ANC were 71.9% and 79.4% vs 46.7% and 81%, respectively. Additionally, the sensitivities of RBC, AMC, PLT, NLR, and MLR to differentiate pneumonia was 56.7%, 35.8%, 48.1%, 65.4%, and 53.1%, respectively. The specificities of RBC, AMC, PLT, NLR, and MLR for differentiating pneumonia were 70.6, 94.1, 35.9, 67.5, and 68.6, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminative values of CBC parameters for differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSe (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSp (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.75(.68, 82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (*10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.60(.52, .72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.59(.51, .66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.58(.50, .65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;27.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.74(.67, .81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.64(.56, .72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAEC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.58(.34, .49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.61(.53, .68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.67(.60, .74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.60(.51, .67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, AUC: area under the curve, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, Se: sensitivity, Sp: specificity, and WBC: white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLogistic regression analysis to determine the combined effect of complete blood count parameters with other clinical factors in differentiating pneumonia\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the current study, the discriminative ability of CBC parameters was assessed in addition to clinical parameters, and significant discriminative value was observed. A statistically significant difference in RBC, Hb, HCT, WBC, ANC, AMC, NLR, and MLR was detected when these parameters combined with clinical factors such as dyspnea and vomiting to differentiate bacterial pneumonia from non-bacterial pneumonia. According to logistic regression analysis, combining of WBC and ANC counts with dyspnea, vomiting, and both dyspnea and vomiting increased the differentiation ability of patients by approximately 3-fold. Similarly, the ability of vomiting, dyspnea and both (vomiting plus dyspnea) combined with RBC, Hb, HCT, AEC, AMC, PLT, NLR, and MLR to differentiate bacterial pneumonia from non-bacterial pneumonia increases the discriminative power by more than twofold (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis to determine the discriminative effect of CBC parameters combined with other clinical factors for differentiating pneumonia at UoGCSH, Northwest Ethiopia, in 2024 (N\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlus-vomiting and dyspnea\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.95(1.27\u0026ndash;6.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87(1.55\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16(1.9\u0026ndash;8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (*10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.54(1.16\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71(1.5\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5(0.9\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7(1.3\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7(1.6\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6(1.1\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6(1.2\u0026ndash;5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8(1.7\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6(1.05\u0026ndash;6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2(1.4\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8(1.5\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3(1.2\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4(1.06\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7(1.6\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5(0.9\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAEC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5(1.2\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8(1.6\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5(1.04\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5(1.2\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0(1.7\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6(1.04-63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7(1.3\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9(1.6\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7(1.07\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5(1.2\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8(1.6\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5(1.02\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, AUC: area under the curve, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, Se: sensitivity, Sp: Specificity and WBC: white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe combined discriminative values of complete blood count parameters with other clinical factors in differentiating pneumonia.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA clinical model including vomiting, dyspnea and both (vomiting and dyspnea) with WBC counts increased the AUC values from 0.75 to 0.78, 0.79, and 0.77, respectively. The sensitivity and specificity of WBC plus vomiting, plus dyspnea and plus (both vomiting and dyspnea) for differentiating bacterial pneumonia from non-bacterial pneumonia were 85.0%, 78.4%, and 85.6% vs 61.7%, 71.4%, and 62.7%, respectively. The inclusion of vomiting, dyspnea and (both vomiting and dyspnea) in the clinical model increased the AUC (sensitivity, specificity) value to 0.77 (93.5%, 51.9%), 0.78 (90.2%, 56.8%) 0.76 (94.8%, 47.9%), respectively in the case of ANC. Moreover, the diagnostic efficacy, sensitivity and specificity of RBC, Hb, HCT, AMC, AEC, PLT, NLR and MLR were significantly improved by including the clinical factors vomiting, dyspnea and both (vomiting and dyspnea) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminative values of CBC parameters with other clinical factors in differentiating bacterial pneumonia from non-bacterial pneumonia at UoGCSH, Northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSe (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSp (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWBC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRBC (*10\u003csup\u003e12\u003c/sup\u003e / L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHb (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eANC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAMC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAEC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePLT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlus-dyspnea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AEC: absolute eosinophil count, AMC: absolute monocyte count, ANC: absolute neutrophil count, HCT: hematocrit, Hb: hemoglobin, MLR: monocyte to lymphocyte ratio, NLR: neutrophil to lymphocyte ratio, PLT: platelet, RBC: red blood cell, and WBC: white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe pathophysiological state of pneumonia can alter the normal hematopoietic activity of blood cells as documented in the literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is associated with either the induction or dampening of the production, differentiation, and function of blood cells [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite advancements in diagnostic methods, pneumonia remains a significant cause of complications and fatalities, emphasizing the need for simple, quick, and affordable differential diagnostic method. The use of CBC parameters for the differential diagnosis of pneumonia is important. This is because, testing those parameters is simple and quick, and they are easily affordable in all healthcare facilities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].The main objective of the current study was to assess the differential diagnostic value of blood parameters in patients with bacterial pneumonia from non-bacterial pneumonia.\u003c/p\u003e \u003cp\u003eAccording to the current study, WBC, HCT, RBC, Hb, ANC, AMC, PLT, MLR, and NLR values were significantly greater in patients with bacterial pneumonia than in non-bacterial pneumonia. However, AEC levels were lower in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by studies conducted in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Turkey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and Greece [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The possible reason for this phenomenon is that these blood parameters play important roles in systemic inflammation and infection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study revealed an increased WBC count among patients with bacterial pneumonia compared with non-bacterial pneumonia. This finding was supported by studies conducted in Egypt [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], Turkey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], India [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], Romania [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and USA [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. A possible explanation for the high WBC count in bacterial pneumonia could be related to the destruction of lung tissue by bacteria and the subsequent induction of inflammatory responses that induce the release of cytokines or signaling molecules. The release of inflammatory cytokines promotes the production, chemotaxis and maturation of neutrophils, leading to leukocytosis in bacterial pneumonia patients than non-bacterial pneumonia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, presence of high physiological stress of tissue in bacterial further contribute to elevated in WBC counts with compared to others [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Whereas in non-bacterial pneumonia had a lower WBC count due to the immune response's nature, where viral infections activate lymphocytes (T and B cells) rather than neutrophils. Additionally, bone marrow (BM) may produce fewer neutrophils during viral infections, focusing instead on lymphocyte production, leading to a lower overall WBC count, ANC, AMC, MLR, and NLR. Consequently, while lymphocytes may increase, this does not significantly elevate the total WBC count if offset by a decrease in other WBC types [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the current study, the NLR and ANC were also significantly increased in bacterial pneumonia patients. This finding was supported by studies conducted in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Greece [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and Mexico [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. A possible explanation for these changes is that cytokines, such as IL-1α, IL-6, IL-7, IL-8, IL-12, and PLT-derived growth factor, initiate a strong immune response to infection, resulting in increased production and release of neutrophils and suppression of lymphocytes in the BM in response to inflammatory cytokines. This may increase the ANC and NLR [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. An elevated NLR often indicates the regulation of neutrophils and the enhancement of the intensity of action in patients [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, the finding of ANC in this study was supported by studies in China [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and India [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The possible mechanism for this phenomenon is that increased neutrophil counts occur due to the rapid mobilization of neutrophils from the BM into the bloodstream, triggered by inflammatory cytokines such as IL-1 and TNF-α in response to bacterial components. These cytokines enhance hematopoiesis and release mature neutrophils to infection sites for phagocytosis, degranulation, and the release reactive oxygen species. While this response is vital for fighting bacteria and promoting healing, excessive neutrophil activity can cause tissue damage and inflammation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the current study, AMC was significantly greater in bacterial pneumonia patients than in non-bacterial pneumonia patients. This finding is supported by studies conducted in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and Turkey [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The possible mechanism for this is that, during a bacterial infection, monocytes become activated, playing a key role in the immune response. They perform bactericidal and phagocytic functions, acting as the first line of defense by engulfing and eliminating pathogens. Additionally, monocytes produce cytokines and present microbial antigens to T cells, initiating and regulating both cellular and humeral immunity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to this study, the value of the MLR was significantly greater in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by a study conducted in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This is driven by increased monocyte recruitment due to inflammatory cytokines (e.g., IL-6 and TNF-α). This increase in monocytes aids in pathogen clearance and tissue repair, whereas lymphocyte levels may decrease due to stress or infection effects [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Moreover, monocytes can transform into various specialized cells that carry out diverse functions during infections. They can increase their ability to kill microbes by generating TNF-α and nitric oxide synthases [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Further differentiation into either macrophages or dendritic cells aids effective microbial clearance at infected sites. Mobilization of monocytes into the peripheral circulation results in an elevated MLR [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Conversely, a study conducted in Turkey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], reported that the MLR was lower in pneumonia patients than in controls. This change may be due to changes in the sample size and age of the participants. Moreover, it can be affected by various factors, unrelated to pneumonia, such as other chronic infections, inflammatory conditions, or even medication [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, eosinophil counts were found to be significantly lower in bacterial pneumonia patients. This finding was supported by a study conducted in Turkey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The underlying mechanism is that in bacterial pneumonia, during acute stress or infection, eosinophil levels decrease due to a shift in the immune response favoring neutrophils, which are the primary WBCs that fight infection. Pro-inflammatory cytokines such as IL-6 and TNF-α increase neutrophil production while suppressing eosinophils. Additionally, the BM prioritizes neutrophil production, leading to increased apoptosis in eosinophils and consequently reduced eosinophil counts [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Non-bacterial pneumonia (viral or allergic pneumonia), involves distinct immune responses compared to bacterial pneumonia. In allergic pneumonia, eosinophils are often elevated due to cytokines like IL-4, IL-5, and IL-13 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while in viral pneumonia, the immune system activates both innate (IFN-γ) and adaptive responses, including T-lymphocytes, particularly CD8 + cytotoxic T cells, to target infected cells. They drive inflammation, recruit immune cells, and may cause lung tissue damage [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the present study, PLT counts were significantly greater in the bacterial pneumonia group than in the non-bacterial pneumonia group. This finding was supported by studies conducted in China [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e–\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], Turkey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and India [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. A possible explanation is that PLTs are essential inflammatory cells that generate a significant portion of cytokines and can also behave as acute phase reactants. It generates ADP from immune cells resulting from the inflammatory response and tissue injury, which promotes degranulation, PLT activation, and proliferation with subsequent increase in the PLT count [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, PLTs are vital in adaptive immunity and in eliciting an inflammatory response in addition to their primary role in haemeostasis [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Lower PLT counts in non-bacterial pneumonia during viral infections results from several mechanisms: immune-mediated PLT activation occurs via cytokines like IL-6 and interferon, leading to PLT aggregation and consumption at inflammation sites; BM suppression from cytokines such as IFN-α and IFN-γ reduces megakaryocyte activity, decreasing PLT production; PLT sequestration happens in inflamed lung tissue or the spleen due to increased vascular permeability and immune interactions; and immune complex formation from viral or allergic responses activates and promotes the clearance of PLT. Together, these factors contribute to thrombocytopenia in non-bacterial pneumonia [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e–\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to current study, the RBC and Hb levels were lower in bacterial pneumonia patients than in non-bacterial pneumonia patients. This finding was supported by studies in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and Turkey[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The possible reason for this finding may be that RBC parameters are affected by different mechanisms such as direct bacteria-RBC interactions, oxidative stress due to bacterial release, and finally, immune-mediated damage. These results, indicated that increased reactive oxygen species, inflammation and activated immune cells may inadvertently target RBCs, leading to changes in their parameters [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α have been shown to shorten RBC survival. Erythropoietin production and erythroid precursor cell differentiation are suppressed [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In non-bacterial pneumonia, RBC and Hb levels may rise due to hypoxia from impaired oxygen exchange in inflamed lungs, prompting the kidneys to release erythropoietin that stimulates increased RBC production in the BM; this compensatory response is more pronounced in non-bacterial cases due to the diffuse or prolonged lung tissue damage caused by viral or allergic inflammation, unlike the localized damage typically seen in bacterial pneumonia [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, in the present study, the combination of blood parameters (WBC, RBC, Hb, ANC, AMC, NLR, and MLR) with clinical symptoms (dyspnea and vomiting) had statistically significant value for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia. This finding supported by studies conducted in Switzerland [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The reason for this finding was that there is a significant systemic inflammatory response and potential respiratory distress due to infection. Elevated blood parameters suggest that the body is mounting an immune response to combat bacterial pathogens, whereas dyspnea reflects impaired gas exchange and reduced oxygenation resulting from pulmonary inflammation and fluid accumulation in the alveoli [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Vomiting may occur due to the systemic effects of infection, including the release of toxins and inflammatory mediators by pathogens that can stimulate the vomiting center in the brain [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study the ROC curve revealed that the WBC count (at cut-off point \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e≥\u003c/span\u003e 10.14) and ANC (at cut-off points \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e≥\u003c/span\u003e 5.36) with AUC of 0.75 and 0.74, had a sensitivity and specificity of 56.7%, 69.1% vs 88.0%, 69.9%, respectively. These findings indicating that the WBC and ANC count were acceptable differential diagnostic parameters of bacterial pneumonia from non-bacterial pneumonia. This findings was comparable to studies conducted in Norway [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], Hong Kong [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], Romania [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Turkey [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. According to the findings of the current study, it is possible to use WBC and ANC as differential diagnostic markers along with other markers to differentially diagnose bacterial pneumonia from non-bacterial pneumonia. However, the value AUC in ANC is lower than reported other study performed in China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This could be due to the small sample size and methodological variation.\u003c/p\u003e \u003cp\u003eBesides, ROC curve analysis in the current study revealed that the NLR, AMC, PLT, MLR, RBC, HCT, and AEC had AUCs between 0.6 and 0.67. These parameters have a poor ability to discriminate bacterial pneumonia from non-bacterial pneumonia. This finding was comparable to those studies conducted in Turkey [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], Norway [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], Romania [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Hong Kong [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and China [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e–\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. However, the findings of this study regarding the NLR and MLR were lower than those from earlier research in Turkey [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] and China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This difference in results could be attributed to the small sample size and variations in methodology. It also involves patients with liver injury and is affected by pro-inflammatory cytokines released from damaged liver cells [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], which skews immune cell ratios. The body may also initiate compensatory mechanisms to adjust immune cell production in response to liver dysfunction [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the discriminative ability of CBC parameters (WBC and ANC) for diagnosing pneumonia, was assessed with the combination of other clinical parameters (vomiting, dyspnea and both (vomiting and dyspnea)), which had the highest AUCs (0.78, 0.79, and 0.77 vs 0.77, 0.78, and 0.76), respectively. This finding is supported by study conducted in Switzerland [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. These findings indicate that the combination of blood parameters with clinical symptoms has good diagnostic accuracy for the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia.\u003c/p\u003e \u003cp\u003eFurthermore, the current study revealed that RDW, MPV, MCV, PDW, and PLR have no significant role in the differential diagnosis of bacterial pneumonia from non-bacterial pneumonia, unlike other studies conducted in Romania [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], USA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], and Turkey [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The possible reason for this difference in relation to the above studies may arise from differences in uses lower sample sizes, differences in study population demographics and age that influence disease susceptibility, and variations in study design methodology and diagnostic criteria that affect how outcomes are measured and reported.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe current study could provide the value of CBC parameters in combination with other clinical factors, which may help to identify other possible differential diagnostic markers. Besides, screening sputum via Gram staining prior to culture may minimize the inclusion of non-pathogenic cases and helps in the selection of bacterial cases. Nevertheless, this study had some limitations. Being single center study is one of its limitations, because it may minimize its representativeness. Another limitation of this study is not inclusion of healthy control group and unable to differentially diagnosis of non-bacterial pneumonia may also consider as limitation of this study.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and recommendation","content":"\u003cp\u003eAccording to the findings of this study, the WBC, HCT, ANC, AMC, PLT, and NLR values were significantly higher in patients with bacterial pneumonia than in those with non-bacterial pneumonia. The ROC curve analysis revealed that the WBC and ANC values had acceptable discriminative ability to differentiate bacterial pneumonia from non-bacterial pneumonia. However, other parameters had a poor discriminating ability to differentiate bacterial pneumonia from non-bacterial pneumonia. Moreover, the combination of CBC parameters (WBC and ANC) with clinical parameters (vomiting, dyspnea and both (vomiting plus dyspnea)) was increases the discriminative ability of bacterial pneumonia form non-bacterial pneumonia. In general, CBC parameters and combination of those parameters with clinical factors have the potential to differentially diagnosis of bacterial pneumonia form non-bacterial pneumonia. Therefore, regular screening and assessing of CBC parameters in patients with bacterial pneumonia with respect to clinical variables are important for early management and diagnosis. As a result, it is better healthcare provider and policy makers consider the differential diagnostic role of CBC parameters. Besides, it is better the current results be verified by conducting multicenter and longitudinal studies in large number of patients through rigorously analyzing. Finally, the use of a control group, and specific diagnosis of non-bacterial pneumonia could provide valuable insight.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute Basophil Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdenosine Diphosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute Eosinophil Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute lymphocyte count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute:Monocyte Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute Neutrophil Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAP -plate\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Agar plate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBone Marrow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity-Acquired Pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplete Blood Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChocolate agar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-Reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospital-Acquired Pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMacConkey agar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin Concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Corpuscular Volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMonocyte-to-Lymphocyte Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Platelet Volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedian rank Difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil -to-lymphocyte Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative Predictive Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet Distribution Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-to-Lymphocyte Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive Predictive Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed cell Distribution Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver-Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Operating Procedure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTurn Around Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor- alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUoGCSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversity of Gondar Comprehensive Specialized Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to participate and ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the procedures were performed in accordance with the relevant guidelines and regulations. Ethical approval was obtained from the Ethical Review Committee of the School of Biomedical and Laboratory Sciences, College of Medicine and Health Science, the University of Gondar (Ref number SBMLS 667/2024). The objective and purpose of the study were explained to the medical director, and a permission letter was obtained to collect the data. After providing an explanation of the possible benefits and risks, informed written consent was obtained from the parents or legal guardians. The collected data were kept confidential. Any abnormal findings obtained were linked to physicians for appropriate patient management.\u003c/p\u003e\n\u003ch3\u003eSupporting information\u003c/h3\u003e\n\u003cp\u003eSupporting information is attached separate page.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no founding for this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no competing interests exist.\u003c/p\u003e\n\u003ch3\u003eAcknowledgment\u003c/h3\u003e\n\u003cp\u003eWe would like to express our great gratitude to the study participants for their willingness to participate in this study. We would also like to\u0026nbsp;thank the University of Gondar Comprehensive Specialized Hospital nurse and laboratory staffs for their involvement during the data collection.\u003c/p\u003e\n\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eYalweayker Eyayu, Aregawi Yalew,\u0026nbsp;Yemataw Gelaw,\u0026nbsp;Dereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation\u003c/strong\u003e:\u0026nbsp;Yalweayker Eyayu, Aregawi Yalew,\u0026nbsp;Yemataw Gelaw,\u0026nbsp;Sintayehu Admas, Nigusie Alemu\u003cstrong\u003e, Abebe Birhanu, Mitkie Tigabie, lyusera marilgn,\u0026nbsp;\u003c/strong\u003eDereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis\u003c/strong\u003e:\u0026nbsp;Yalweayker Eyayu, Yemataw Gelaw,\u0026nbsp;Aregawi Yalew, Dereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation\u003c/strong\u003e: Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, \u003cstrong\u003eAbebe Birhanu, Mitkie Tigabie,\u003c/strong\u003e Dereje Mengesha Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e:\u0026nbsp;Yalweayker Eyayu, Yemataw Gelaw,\u0026nbsp;Aregawi Yalew,\u0026nbsp;Sintayehu Admas, Nigusie Alemu\u003cstrong\u003e, Abebe Birhanu, Mitkie Tigabie, lyusera marilgn,\u003c/strong\u003eDereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware:\u0026nbsp;\u003c/strong\u003eYalweayker Eyayu, Yemataw Gelaw, \u0026nbsp;Aregawi Yalew, Dereje Mengesha Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision\u003c/strong\u003e:\u0026nbsp;Yemataw Gelaw,\u0026nbsp;Aregawi Yalew, Dereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation:\u003c/strong\u003e Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, Dereje Mengesha Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Visualization:\u003c/strong\u003e Yalweayker Eyayu, Yemataw Gelaw, Aregawi Yalew, \u003cstrong\u003eAbebe Birhanu, Mitkie Tigabie\u003c/strong\u003e, Dereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting - original draft:\u0026nbsp;\u003c/strong\u003eYalweayker Eyayu, Yemataw Gelaw,\u0026nbsp;Aregawi Yalew, Dereje Mengesha\u0026nbsp;Berta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; review \u0026amp; editing:\u0026nbsp;\u003c/strong\u003eYalweayker Eyayu,Yemataw Gelaw,\u0026nbsp;Aregawi Yalew,\u0026nbsp;Sintayehu Admas, Nigusie Alemu\u003cstrong\u003e, Abebe Birhanu, Mitkie Tigabie, lyusera marilgn,\u0026nbsp;\u003c/strong\u003eDereje Mengesha Berta\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMackenzie, G. 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(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bacterial pneumonia, Specificity, Sensitivity, Complete blood cell count, Northwest Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-7971696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7971696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ecurrently, the differential diagnosis of pneumonia is challenging. As a result, the uses of less challenging and easily affordable methods, such as complete blood cell (CBC) parameters, are important. However, the differential diagnostic role of CBC parameters in pneumonia has not been well studied. Therefore, the current study aimed to assess the differential diagnostic role of CBC parameters in patients with bacterial pneumonia from non-bacterial pneumonia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study with Systematic random sampling was conducted in 234 patients. Socio-demographic and clinical data were collected by trained nurses via a semi-structured questionnaire. In addition, sputum and 5mL of the venous blood sample were collected. X\u003cb\u003e-\u003c/b\u003eray and sputum examinations were performed for initial screening of study participants. The blood was \u003cb\u003es\u003c/b\u003eubsequently analyzed with mindray-5150 hematology analyzer. The data were entered into Epi-data (3.0.4) and analyzed via SPSS (25.0\u003cb\u003e).\u003c/b\u003e The summary statistics were used. A Mann-Whitney U test was used to compare median differences between groups. The area under the curve (AUC) determined via the receiver-operating characteristic (ROC) curve. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistically significance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 234 study participants were included in the present study. The total WBC, ANC, PLT, NLR, AMC, and MLR values were higher and the AEC levels were lower in the bacterial group than in the non-bacterial group. The ROC curve results revealed that WBC and ANC had higher AUC (0.75 (95% CI: 0.68, 0. 82) and0.74 (95% CI: 0.67, 0.81), respectively) values than the other variables did. Moreover, the WBC and ANC count for vomiting, dyspnea, and both, had good discriminative ability for the diagnosis of pneumonia with AUCs of 0.78, 0.79 and 0.77 vs 0.77, 0.78, and 0.76, respectively.\u003c/p\u003e\u003ch2\u003eConclusion and Recommendation:\u003c/h2\u003e \u003cp\u003eBlood parameters notably, WBC and ANC had greater differential diagnostic value for the differentiation of pneumonia. Moreover, the combination of WBC and ANC count with clinical parameters had good discriminative ability for the differential diagnosis of pneumonia. Therefore, regular screening of blood parameters and/or with clinical symptoms in patients with bacterial pneumonia is important for early diagnosis.\u003c/p\u003e","manuscriptTitle":"The differential diagnostic role of complete blood cell count parameters in patients with bacterial pneumonia from non-bacterial pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 01:10:47","doi":"10.21203/rs.3.rs-7971696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-18T03:19:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T13:18:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-05T17:11:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T08:46:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-30T08:42:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b67a055c-ff07-4c61-8d94-f848c0df5ab2","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58541842,"name":"Health sciences/Diseases"},{"id":58541843,"name":"Health sciences/Health care"},{"id":58541844,"name":"Health sciences/Medical research"},{"id":58541845,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-02-18T03:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 01:10:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7971696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7971696","identity":"rs-7971696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}