Diagnostic performance of Sysmex XN-31 automated hematology analyzer compared to microscopy and PCR for detecting and quantifying malaria parasites in clinical settings of Oromia Regional State, Ethiopia

Diagnostic performance of Sysmex XN-31 automated hematology analyzer compared to microscopy and PCR for detecting and quantifying malaria parasites in clinical settings of Oromia Regional State, Ethiopia | 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 Method Article Diagnostic performance of Sysmex XN-31 automated hematology analyzer compared to microscopy and PCR for detecting and quantifying malaria parasites in clinical settings of Oromia Regional State, Ethiopia Adugna Abera, Adugna Woyessa, Selam Yirga, Yonas Wuletaw, Mahlet Belachew, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9255056/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Malaria remains a significant public health challenge in Ethiopia. To achieve malaria free country with improved clinical care and support, innovative diagnostic tools are needed. The Sysmex XN-31 automated malaria analyzer, which detects malaria-infected red blood cells was assessed in clinical settings in Ethiopia. This study compared the performance of Sysmex XN-31 automated analyzer with microscopy and polymerase chain reaction (PCR) tests. Methods We conducted a facility-based cross-sectional study in two public hospitals from April to September 2024. Febrile patients suspected of malaria who visited the outpatient departments at Shashamene Comprehensive Specialized Hospital and Meki Primary Hospital were enrolled with consent. Finger prick and four milliliters of venous blood were collected in EDTA tubes from each study participant. The diagnostic performance of the Sysmex XN-31 hematology analyzer for malaria parasite detection and quantification was compared to expert microscopist and qPCR. We also evaluated the usability of the Sysmex XN-31 analyzer in near-patient settings at both sites. Statistical analysis was performed using SPSS 27 and Medical Statistical Calculator v23. Sensitivity, specificity, predictive values, and kappa statistics were calculated. Results Of the 400 suspected febrile cases, malaria positivity was 22.2% (n = 89), 21% (n = 84), 19.2% (n = 77), 18.8% (n = 75), and 16.2% (n = 65) by PCR, Sysmex XN-31, expert microscopist, field microscopist, and RDT, respectively. The sensitivity and specificity of Sysmex XN-31 were 93.5% and 96.6%, respectively, compared with the expert microscopist. However, with PCR as the reference, Sysmex XN-31’s sensitivity dropped to 84.3%, with a slight increase in specificity to 97%. The Sysmex-XN-31 exhibited high negative predictive value (NPV) reliability when compared with field and rapid diagnostic test (RDT) results, based on hypothetical community malaria prevalence. We found strong agreement (89.9%) between Sysmex XN-31 and the field microscopist, with a kappa value of 0.68. The usability test of the Sysmex XN-31 by most participants indicated that errors could be resolved easily, interruptions were manageable, and the interpretation of results was straightforward. Conclusions The Sysmex XN-31 automated hematology analyzer offers high sensitivity and specificity compared to expert microscopist and PCR methods, significantly enhancing malaria surveillance and elimination efforts. It can detect and count malaria parasites while also providing a CBC. With a rapid turnaround time of 55 samples in one hour, it is much faster than traditional testing methods. Sysmex XN-31 Malaria Complete Blood Count automated hematology Figures Figure 1 Figure 2 Figure 3 Background Malaria continues to pose significant public health challenges in tropical and subtropical regions, with the highest burden recorded in five countries according to WHO 2024, including Nigeria, Democratic Republic of the Congo, Uganda, Ethiopia, and Mozambique ( 1 ). Ethiopia has played a crucial role in significantly reducing malaria-related morbidity and mortality since the introduction of artemisinin-based combination therapy (ACT), indoor residual spraying (IRS), and the widespread distribution of long-lasting insecticidal nets (LLINs) in 2005 ( 2 ). However, malaria incidence has been steadily increasing since 2019 ( 3 ). Effective case management is essential for reducing severe cases and fatalities associated with malaria. It relies on accurate diagnosis and prompt treatment. Additionally, the administration of anti-gametocidal medications and radical therapy, which help prevent the spread of the infection, requires precise diagnosis and species identification. In Ethiopia, health extension workers are expected to use malaria rapid diagnostic tests (RDTs) to diagnose more than 70% of malaria cases, particularly in remote areas where most cases are believed to occur ( 4 ). There is a significant challenge with the rapid diagnosis of malaria due to the occurrence of false negatives. A large-scale survey conducted in 2020/2021 across the Afar, Amhara, Benishangul Gumuz, Gambella, and Oromia regions revealed a false-negative rate of 6.67%, which was attributed to deletions in the hrp2/3 gene ( 5 ). This study indicated that the population-level prevalence of RDT false negatives due to hrp2/3 gene deletions exceeds the World Health Organization's suggested threshold of 5% for transitioning to alternative diagnostic methods ( 6 ). Routine diagnostic methods like microscopy and rapid diagnostic tests (RDTs) often have limitations with accuracy, especially when dealing with low-parasite density cases. As medical diagnostics continue to advance, new techniques for identifying and quantifying malaria parasites are being developed consistently. Automating the malaria detection process could significantly enhance both speed and accuracy. Recently, the Sysmex XN-31, a novel automated hematology analyzer, has demonstrated the ability to rapidly and accurately diagnose malaria with high sensitivity and specificity ( 7 – 12 ). This sensitive diagnostic tool has the potential to bridge the capacity gap in healthcare facilities that experience a high volume of patients. This study evaluated the diagnostic accuracy and operational feasibility of the Sysmex XN-31 automated hematology analyzer for the detection and quantification of Plasmodium species compared to microscopy and PCR in two public hospitals in Ethiopia. Methods Study design and setting A facility-based cross-sectional study was conducted from April to September 2024 in two public hospitals located in the East Shoa and West Arsi zones of Oromia Regional State (Fig. 1 ). Study sites were selected based on their proximity to Addis Ababa and the presence of both Plasmodium species, P. falciparum and P. vivax . Shashamene Comprehensive Specialized Hospital is located in Shashemane town, West Arsi Zone, 250 km from Addis Ababa, at 7º 12' North latitude and 38º 36' East longitude, with an elevation of 2010 meters above sea level. Meki Primary Hospital, situated in the Great Rift Valley, is located in Meki town, East Shewa Zone, Oromia Region, at 8°9′N 38°49′E and an elevation of 1636 meters above sea level. Meki serves as the administrative center of Dugda district. Although both P. falciparum and P. vivax malaria have been reported, district data indicate that P. vivax is the predominant species in both areas. The region experiences major malaria transmission from September to mid-December, following the main rainy season (June to August), and minor transmission from April to May, after the short rainy season (February to March). Sample size The sample size was calculated based on sensitivity using Buderer’s formula ( 13 ) as follows: Z1-α/2 (standard normal deviate corresponding to the specified size of the critical region (α) = 1.96 SN (anticipated sensitivity), we anticipate 98% sensitivity of the XN-31 machine = 0.98 Prevalence= Prevalence of malaria in the nearby district ( 14 ) was 25% = 0.25 L (absolute precision desired on either side of sensitivity) = 0.03 ~ n = 400 Therefore, the computed minimum sample size is 334 with ~ 20% contingencies. Sampling procedure and study population Purposive sampling was used to recruit study participants. Those clinically suspected for malaria and fulfill the inclusion and exclusion criteria from each site were included as part of study population. Participants aged five years and older were included in this study. Clinicians in charge of the outpatient departments explained the study to the patients and the parents or guardians of the children. Those who met the enrollment criteria were included in the study. Children aged 12 to 17 years provided assent, and consent was obtained from their parents or guardians. For participants over 18 years of age, consent was sought directly from them. The process of patient recruitment and flow is illustrated in Fig. 2 . Sample collection A finger prick blood was obtained from each study participant for RDT, thick and thin blood film preparation. At the same time four ml of whole blood was collected in ethylene diamine tetra-acetic acid (EDTA) tubes for later testing by Sysmex XN-31, RDT, and qPCR. Giemsa staining and Microscopic Examination After the blood film was well dried, the thin film was first fixed with methanol and then both thick and thin films were stained with 10% Giemsa stain. Next, the film was dried again and examined under the oil immersion objective. To determine malaria positivity or negativity, at least 200 high-power fields of the thick film were examined following the WHO guidelines ( 15 ). Parasite counts were performed on slides that tested positive for P. falciparum , counting asexual parasites and gametocytes from the stained blood smears. All slides were kept for later confirmation by an expert microscopist. Malaria detection using Sysmex XN-31 automated hematology analyzer Whole blood samples in EDTA tubes were used for the Sysmex XN-31 analyzer (Sysmex corporation, www.sysmex.co.jp , Japan). The samples were processed within 24 hours of collection. A separate room was designated for the Sysmex XN-31 machine. At the reception, the collected samples were double-blinded, and only designated sample runners transported them to the Sysmex XN-31 reading room. After receiving proper training, all samples were analyzed using the sampler mounted on the Sysmex XN-31 analyzer to minimize mixing errors. The QC was performed every day before starting daily activity using control materials supplied by the manufacturer using X-bar Control method. Rapid diagnostic test Rapid diagnostic testing (RDT) was performed in accordance with the manufacturer’s instructions using the Abbott Bioline Malaria Ag P.f/P.v test (Abbot, US). This assay detects the HRP-2 antigen of P. falciparum and the Plasmodium lactate dehydrogenase (pLDH) of P. vivax. For each test, five microliters of EDTA-anticoagulated blood were introduced into the sample port. Subsequently, three drops of assay diluent were added, and the test was allowed to develop. Results were interpreted 15 minutes after the addition of the assay diluent. Parasite detection by PCR Blood samples collected in EDTA were used for genomic DNA extraction, following the protocols outlined in the manufacturers' DNA extraction kits (AJ Innuscreen GmbH, Berlin, Germany). Real-time polymerase chain reaction (qPCR) was performed to amplify the small subunit of ribosomal RNA for the genus-level detection of Plasmodium , P. vivax specific 18SrRNA for P. vivax and the var gene's acidic terminal sequence for P. falciparum detection. To control for the quality of the DNA extraction and qPCR amplification, the human RNase P gene was used as an internal control. TaqMan fluorescence-based DNA amplification and detection was executed using the Applied Biosystem QuantStudio 5 Real-time PCR system (Thermo Fisher, USA). Each experiment included positive controls using 3D7 DNA, and negative controls using nuclease-free water. Data management and Analysis Data related to sociodemographic, RDT and Microscopy results were captured using data collection form developed by experts using Kobo Collect digital data collection platform. The raw comma-separated values (CSV) data generated from the QuantStudio 5 Real-time PCR system and Sysmex XN-31 analyzer were archived according to the training provided to the operators. Data from each week and each session were saved on the provided USB stick. All the datasets exported were cleaned and merged into one dataset before analysis. Data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 ( 16 ) and Medical Statistical Calculator, Version 23 ( 17 ) The primary outcome measure for this analysis was the performance of the Sysmex XN-31 automated hematology analyzer for detection and quantification of malaria in a clinical setting. We calculated sensitivity and specificity, along with their 95% confidence intervals, as well as positive and negative predictive values against the gold standard expert microscopy and reference qPCR methods. Given that small variations in the prevalence of malaria can significantly impact on the interpretation of predictive values ( 18 ), positive predictive values (PPVs) and negative predictive values (NPVs) were also estimated taking into consideration of a population prevalence of 5%, 25%, and 50% malaria infection. Agreement between different assays was determined by Kappa statistics. Kappa result was interpreted as follows: values ≤ 0 as indicating no agreement and 0.01–0.20 as slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. Expert microscope readings served as the gold standard, while PCR was used as the reference standard. Furthermore, the Sysmex XN-31 automated hematology analyzer's capability to quantify malaria parasites and stages was assessed against expert microscopic readings. Participants from two health facilities, representing various educational backgrounds and fields of study, were assessed for usability factors using a structured questionnaire. Operators assigned for Sysmex XN-31 received training on how to use and operate the Sysmex XN-31 machine. This study complied with Standards for Reporting of Diagnostic Accuracy guidelines (STARD) to improve the quality of reporting ( 19 ). Results Characteristics of Study Participants A total of 400 malaria suspected patients were included from two hospitals, with 200 participants from each location. The sociodemographic and clinical characteristics of the participants are presented in Table 1 below. More than half of the respondents were female (53%). Additionally, the majority of participants, 127 (31.8%), were under 10 years old. The majority of the study participants had symptoms of muscle pains, sweats, and chills, while 167 (42%) had fever. Table 1 Sociodemographic and clinical characteristics of the study participants from Shashemene and Meki hospitals, Oromia, Ethiopia, 2024 (n = 400). Characteristics Categories n (%) 95% CI (Lower, upper) Sex Female 212 (53.0) (48.0, 57.9) Male 188 (47.0) (42.0, 51.9) Age group, years =50 31 (7.8) (4.9, 10.0) Sweats No 25 (6.3) (3.9, 8.6) Yes 374 (93.7) (91.4, 96) Headache No 48 (12.0) (8.8, 15.2) Yes 351 (88.0) (84.8, 91.2) Muscle pains No 26 (6.5) (4.0, 8.9) Yes 373 (93.5) (91.0, 95.9) Nausea No 88 (22.0) (18.0, 26.0) Yes 311 (78.0) (73.9, 82.0) Vomiting No 116 (29.0) (24.6, 33.5) Yes 283 (71.0) (66.5, 75.4) A temperature > = 37.5℃ No 232 (58.0) (53.3, 63.0) Yes 167 (42.0) (37.0, 46.7) Chills No 5 (1.3) (0.2, 2.3) Yes 394 (98.7) (97.7, 99.8) Prevalence of anemia and thrombocytopenia We computed for the presence of anemia and thrombocytopenia among the study participants based on WHO criteria. Majority of the participants are healthy, but approximately 25–30% of the total participants across all age groups suffer from some form of anemia with both male and female approximately affected in equal proportion. Severe anemia is consistent across genders, affecting roughly 1 in 10 individuals (10.6% of males; 9.4% of females). Thrombocytopenia was more prevalent in males than females (36.6% vs 22.6%) (Table S1). Plasmodium parasite detection by different methods The positivity rate was highest with PCR (22.2%), followed closely by the Sysmex XN-31 (21%). Minor discrepancies were observed in species identification between the Sysmex XN-31 and both field and expert microscopist readings, while PCR and RDT results were comparable. A notable variance in mixed infection detection was observed: field microscopists reported a mixed infection rate of 1.5%, which was double that of expert microscopists (0.8%), indicating a potential for over-identification of mixed species in field settings. The Sysmex XN-31 detected a higher rate of mixed infections (2.8%), whereas PCR detected none, suggesting possible over-identification by the Sysmex XN-31. Overall, the Sysmex XN-31 outperformed both expert and field microscopist in detection rates, identifying 7 to 9 additional cases, respectively (Table 2 ). There was substantial agreement (89.8%) between field microscopist and the Sysmex XN-31 in detecting plasmodium parasites, with a Kappa value of 0.68. The agreement between the Sysmex XN-31 and RDT was also nearly perfect at 91.2% in detecting plasmodium parasites, and between Sysmex and PCR, it was 93.8% (Table S2 and Fig S1). Table 2 Distribution of Plasmodium -positive cases and species types by various methods in Shashemene and Meki hospitals, Oromia, Ethiopia, 2024 (n = 400). Diagnostic methods Characteristics N (%) 95% CI Sysmex XN-31 Negative 316 (79.0) (75.0, 83.0) Positive 84 (21.0) (17.0, 25.0) Expert Microscopist Negative 323 (80.8) (76.9, 84.6) Positive 77 (19.2) (15.4, 23.0) PCR Negative 311 (77.8) (73.7, 81.8) Positive 89 (22.2) (18.2, 26.3) RDT Negative 335 (83.8) (80.0, 87.4) Positive 65 (16.2) (12.6, 19.9) Field Microscopist Negative 325 (81.2) (77.4, 85.0) Positive 75 (18.8) (14.9, 22.6) Result by species type Expert Microscopist Mixed 3 (0.8) (0.0, 1.6) Negative 323 (80.8) (76.9, 84.6) P. falciparum 25 (6.2) (3.9, 8.6) P. vivax 49 (12.2) (9.0, 15.5) PCR Negative 311 (77.8) (73.7, 81.8) P. falciparum 31 (7.7) (5.0, 10.4) P. vivax 58 (14.5) (11.0, 18.0) RDT Mixed 1 (0.3) (0.0, 0.7) Negative 335 (83.7) (80.0, 87.4) P. falciparum 31 (7.8) (5.0, 10.4) P. vivax 33 (8.2) (5.6, 11.0) Field Microscopist Mixed 6 (1.5) (0.3, 2.7) Negative 325 (81.2) (77.4, 85.0) P. falciparum 24 (6.0) (3.7, 8.3) P. vivax 45 (11.3) (8.2, 14.4) Sysmex XN-31 Error 2 (0.5) (0.0, 1.2) Mixed 11 (2.8) (1.2, 4.4) Negative 314 (78.5) (74.5, 82.5) P. falciparum 30 (7.5) (4.9, 10.0) P. vivax 43 (10.7) (7.7, 13.8) Diagnostic Accuracy of Sysmex XN-31 The Sysmex XN-31 when compared to expert microscopist exhibited the highest sensitivity (93.5%) and strong specificity (96.6%), outperforming even PCR in this specific comparison. Using PCR as a reference, the Sysmex XN-31 maintained the highest sensitivity (84.3%) among all methods, surpassing expert microscopist by 4.5%. Across both reference and gold standards, RDTs yielded the lowest sensitivity (64% and 72.7% against PCR and expert microscopist, respectively), while field microscopist demonstrated a significant drop in sensitivity (66.3%) when compared against PCR (Table 3 ). Table 3 Sensitivity, specificity, PPV and NPV of each diagnostic method using the results of the expert microscopist as a gold standard and PCR as reference. Performance of a diagnostic test Expert microscopist as a standard PCR as a reference Sysmex XN-31 Sensitivity 93.5 (88.0, 99.0) 84.3 (80.7, 87.8) Specificity 96.6 (94.2, 98.4) 97.0(95.3, 98.7) PPV 85.7 (78.2, 93.2) 89.3(86.2, 92.3) NPV 98.6 (97.0, 99.8) 95.6(93.5, 97.5) PCR Sensitivity 92.2 (86.2, 98.2) N/A Specificity 94.9 (91.9, 96.9) N/A PPV 79.8 (71.4, 88.1) N/A NPV 98.3 (96.5, 99.6) N/A Expert Microscopist Sensitivity N/A 79.8(75.8, 83.7) Specificity N/A 98 (96.6, 99.4) PPV N/A 92.2(89.3, 94.7) NPV N/A 94.4(92.0, 96.5) RDT Sensitivity 72.7 (62.8, 82.7) 64.0 (59.3, 68.7) Specificity 97.5 (95.4, 99) 97.4 (95.7, 98.9) PPV 86.2 (77.8, 94.6) 87.7 (84.3, 90.7) NPV 94.3 (91.0, 96.0) 90.5(87.3, 93.2) Field Microscopist Sensitivity 77.9 (68.7, 87.2) 66.3 (61.6, 70.9) Specificity 95.8 (93.0, 97.7) 94.9 (92.6, 96.9) PPV 80.0 (70.9, 89.0) 78.7(74.5, 82.5) NPV 95.3 (92.4, 97.2) 90.8(87.9, 93.6) The Sysmex XN-31 demonstrated a high reliability of NPV, up to 98.6%, in identifying individuals who do not have the condition compared to field microscopy and RDT. Furthermore, the NPV showed that the adjusted hypothetical community prevalence changed minimally and the PPV increased substantially as the hypothetical community prevalence increased. While field microscopy and RDT showed significant decreases as the hypothetical community prevalence increased, they showed less reliability at this time (Table 4 ). Table 4 Hypothetical malaria prevalence at different prevalence rates for Sysmex XN-31 analyzer, RDT and field microscopist against expert microscopist and qPCR. Malaria detection by Sysmex XN-31 at different hypothetical prevalence rate against expert microscopist as a gold standard Metrics Prevalence (%) PPV (%) NPV (%) Adjusted PPV (%) Adjusted NPV (%) Sysmex XN-31 5% 85.9 98.7 57.3 99.7 25% 85.9 98.7 89.5 98.2 50% 85.9 98.7 96.2 94.9 RDT 5% 86.2 93.7 57.9 98.5 25% 86.2 93.7 89.7 91.5 50% 86.2 93.7 96.3 78.1 Field Microscopist 5% 80.0 94.8 46.9 98.8 25% 80.0 94.8 84.8 92.8 50% 80.0 94.8 94.4 81.2 Malaria detection by Sysmex XN-31 at different hypothetical prevalence rate against qPCR as a reference Sysmex XN-31 5% 85.9 98.7 57.3 99.7 25% 85.9 98.7 89.5 98.2 50% 85.9 98.7 96.2 94.9 RDT 5% 86.2 93.7 46.6 99.6 25% 86.2 93.7 84.6 97.3 50% 86.2 93.7 94.3 92.4 Field Microscopist 5% 80.0 94.8 57.9 98.5 25% 80.0 94.8 89.7 91.5 50% 80.0 94.8 96.3 78.1 Parasite quantifications Parasite counts from the Sysmex XN-31 and expert microscopist for P. falciparum showed clear differences in both density and statistical correlation. The Sysmex XN-31 gave a much higher average density (24,446.34) than expert microscopist (9,847.8). This result suggests that the Sysmex XN-31 may overcount or use a different sensitivity threshold than human experts. Because the density measurements are so different, these two diagnostic methods should not be used interchangeably in clinical or research settings without further calibration. The Spearman correlation coefficient was 0.214 with a p-value of 0.184, indicating only a weak positive relationship (Fig. 3 ). Test Usability of the XN-31 under investigations Description of study Participants To assess the usability of the test in a near-patient environment, a total of 30 operators participated in the study, of whom 24 were males. The lowest educational qualification among the participants was a diploma, while the highest was a Master of Science degree. Details of the participants are presented in supplement (Table S3). Usability testing of the Sysmex XN-31 showed that laboratory staff were satisfied with the device and found it efficient to use. The analyzer worked well in clinical settings, even for users with different levels of training. The study included 30 participants split into two groups: 12 people (40%) received full usability training, while 18 people (60%) had only a basic orientation. Most participants said the device did not require advanced skills. They found interpreting results straightforward, and any errors were easy to fix. Both daily and monthly maintenance tasks were rated as easy. Reagent management, storage, and quality control were considered manageable, and the laboratory setup was suitable. The patient barcode system was also described as user-friendly (Table 5 ). Table 5 Results of usability testing of the Sysmex XN-31between trained and non-trained staff Trained on Sysmex-XN-31 Yes = 12 No = 18 Usability questions Strongly disagree n (%) Disagree n (%) Agree n (%) Strongly agree n (%) Strongly disagree n (%) Disagree n (%) Agree n (%) Strongly agree n (%) 1.Adequate training/orientation provided before operational of analyzer 0(0) 0(0) 6(50) 6(50) 0(0) 4(22.2) 6(33.3) 8(44) 2. Does not require high level of skill 0(0) 4(33) 5(41.7) 3( 25 ) 0(0) 5(27.8) 12(66.7) 1(5.6) 3. Easy start up analyzer 0(0) 1(8.3) 5(41.7) 6(50) 0(0) 2( 11 ) 10(55.6) 6(33) 4. Daily maintenance Easy to execute 0(0) 1(8.3) 5(41.7) 6(50) 0(0) 2( 11 ) 9(50) 7(38.9) 5. Monthly maintenance Easy to execute 0(0) 0(0) 8(66.7) 4(33.3) 0(0) 3(16.7) 9(50) 6(33) 6. QC management Easy to execute 0(0) 0(0) 6(50) 6(50) 0(0) 4( 22 ) 9(50) 5(27.8) 7. Reagent management is easy 0(0) 0(0) 6(50) 6(50) 0(0) 1(5.6) 13(72) 4( 22 ) 8. Lab infrastructure suitable for XN-31 0(0) 0(0) 8(66.7) 4(33.3) 0(0) 0(0) 13(72) 5(27.8) 9. Barcode easy to use 0(0) 1(8.3) 6(50) 5(41.7) 0(0) 2( 11 ) 11(61) 5(27.8) 10. Barcode patient tracking is easy 0(0) 1(8.3) 6(50) 5(41.7) 0(0) 4( 22 ) 8(44) 6(33) 11. Reagent storage manageable 0(0) 0(0) 7(58.3) 5(41.7) 0(0) 0(0) 13(72) 5(27.8) 12. Reagent life span acceptable 0(0) 0(0) 8(66.7) 4(33.3) 0(0) 0(0) 14(77.8) 4( 22 ) 13. Analyzer easy to use 0(0) 0(0) 7(58.3) 5(41.7) 0(0) 2( 11 ) 11(61) 5(27.8) 14. TAT was fast 0(0) 0(0) 7(58.3) 5(41.7) 0(0) 0(0) 13(72) 5(27.8) 15.Error easily resolved 0(0) 1(8.3) 8(66.7) 3( 25 ) 0(0) 3(16.7) 12(66.7) 3(16.7) 16. Interruption of results was easy 0(0) 1(8.3) 7(58.3) 4(33.3) 0(0) 3(16.7) 9(50) 6(33) Discussion This study generated evidence on the performance of Sysmex XN-31 hematology analyzer in detecting infected RBCs by malaria parasites and quantifying them compared against expert microscopy, the gold standard and qPCR as reference test in Ethiopia for the first time. The analyzer demonstrated a high sensitivity of 93.5%, specificity of 96.6%, a positive predictive value (PPV) of 85.7%, and a negative predictive value (NPV) of 98.6% against the gold standard expert microscopy. These results indicate that the Sysmex XN-31 analyzer provides relatively accurate and reliable alternative to traditional diagnostic methods, offering a robust tool for malaria detection in regions with high malaria burden. In comparison to field microscopy, which has long been the gold standard for malaria diagnosis, the Sysmex XN-31 malaria analyzer outperformed in sensitivity (93.5% vs. 77.9%) than other test methods and showed comparable specificity (96.6% vs. 95.8%). These findings align with previous studies that highlighted the limitations of microscopy, such as the need for skilled personnel, the subjective nature of parasite detection, and its reduced sensitivity at low parasite densities ( 20 ). While microscopy remains widely used due to its availability and affordability, its lower sensitivity and difficulty in detecting submicroscopic infections make it less suitable for comprehensive malaria surveillance, especially in elimination targeted endemic regions with mixed species infections ( 21 ). The Sysmex XN-31 analyzer's higher sensitivity offers the advantage of detecting malaria cases that might otherwise be missed by microscopy, improving diagnostic accuracy in settings where asymptomatic or low-density malaria infections are prevalent. Compared to PCR, the reference standard for malaria diagnosis, the Sysmex XN-31 analyzer demonstrated a higher sensitivity of 84.3% than other diagnostic methods, with a specificity of 97%, NPV of 95.6%, and PPV of 89.3%. The higher sensitivity of Sysmex XN-31 suggests that it may be more effective at detecting lower parasitemia levels, which is particularly important for epidemiological surveillance and the monitoring of submicroscopic infections ( 22 ). Although PCR has the advantage of providing molecular-level diagnosis, its laboratory requirements, cost, and time constraints make it less suitable for routine use in field settings, particularly in resource-limited areas ( 23 ). The present study demonstrated that Sysmex XN-31 analyzer indicted higher PPV compared to field microscopy, with rates of 89.3% versus 78.7%. This indicates that the Sysmex XN-31 is more reliable in identifying true positive cases. This finding will have several implications. Primarily by significantly decreasing the chances of false-positive results unnecessary treatment can be avoided. This in turn avoids the consequences on the patient’s health, reduces the healthcare cost as well as potential drug resistance. This reliability is especially important in clinical decision-making and is particularly valuable in low-prevalence, or hypo-endemic areas, where diagnosing malaria can be particularly challenging due to the reduced population prevalence affecting the PPV of tests ( 24 ). When comparing the Sysmex XN-31 analyzer to malaria rapid diagnostic tests (RDTs), the analyzer demonstrated a higher sensitivity (93.5% vs. 72.7%) and a comparable specificity (96.6% vs. 97.5%). RDTs have the advantage of being simple, fast, and easy to use in resource-limited settings, making them highly accessible in endemic areas ( 25 ). However, their lower sensitivity, especially in detecting low-density infections, and potential for false-negative results limit their effectiveness in diagnosing asymptomatic malaria ( 26 ). The higher sensitivity of the Sysmex XN-31 analyzer, in contrast, reduces the likelihood of false negatives and improves the detection of cases in the early stages of infection. Additionally, while RDTs are useful for on-site diagnosis, they are limited by their inability to quantify parasitemia or differentiate between malaria species, both of which are strengths of the Sysmex XN-31 analyzer. The Sysmex XN-31 analyzer offers several advantages over routine diagnostic methods. First, its high sensitivity and specificity, as well as the ability to quantify parasitemia, make it a valuable tool for detecting low-density and submicroscopic infections, which may be missed by field microscopy and RDTs ( 27 ). Second, the Sysmex XN-31 analyzer is automated, can analyze 55 patients within one hour, reducing the reliance on skilled personnel and minimizing human error, which are significant limitations in microscopy-based diagnosis ( 28 ). Third, its ability to provide rapid and accurate results in a standard laboratory setting offers an efficient alternative to PCR, which is resource-intensive and requires specialized laboratory infrastructure. Furthermore, the Sysmex XN-31 analyzer's capacity to address challenges related to gene deletion, such as the detection of P. falciparum with deletions in the histidine-rich protein 2 (HRP2) gene, is a notable advantage over RDTs. Gene deletions in the HRP2 gene are a known cause of false-negative RDT results, as many RDTs rely on HRP2 for detection ( 29 ). In Ethiopia, a multisite gene deletion study found a prevalence of 9.7% ( 5 ), exceeding the WHO threshold and indicating a strong evolutionary pressure on malaria diagnostics. Consequently, the Ministry of Health is transitioning from hrp2-based rapid diagnostic tests (RDTs) to non-hrp2-based RDTs. The Sysmex XN-31 analyzer, which uses a different method to detect malaria parasites, is less likely to be affected by such deletions, making it a more reliable diagnostic tool in regions where HRP2 deletions are prevalent. This advantage could be crucial for improving malaria surveillance and ensuring more accurate diagnoses in areas with evolving parasite gene deletion problems. Overall, training on the Sysmex-XN-31 generally enhances user perceptions across various aspects of usability. Trained users consistently rate the analyzer more favorably compared to untrained users, indicating that the training has a positive impact on user experience. Areas like QC management, reagent management, and ease of use show particularly strong improvements due to training. These insights can guide targeted improvements in training programs and highlight areas where additional support may further enhance user satisfaction and efficiency. In summary, the Sysmex XN-31 automated hematology analyzer offers promising performance in malaria detection, with a higher sensitivity compared to PCR, field microscopy, and RDTs. Its automated nature, coupled with its ability to quantify parasitemia and detect submicroscopic infections, provides significant advantages over traditional diagnostic methods. Furthermore, its resilience to HRP2 gene deletions presents an important benefit in regions where these deletions are common. Given its high sensitivity and specificity, obtaining the results as part of routine CBC with short TAT and less need for malaria expert microscopists, the Sysmex XN-31 analyzer could play a key role in enhancing malaria diagnosis and surveillance in endemic areas, ultimately contributing to more effective malaria control and elimination efforts. The Sysmex XN-31 could also be important in a blood bank setting where malaria screening and anemia testing are necessary for every donor. However, there are some practical limitations when using Sysmex XN-31. The first is its limited widespread adoption due to the initial investment of the device, the use of a constant electricity supply, lack of portability, and the requirement of venous blood samples. Further studies at different geographical sites should explore the potential use of Sysmex XN-31 automated analyzers for enhanced malaria surveillance and control as well as for the use of monitoring therapeutic efficacy study in Ethiopia. Declarations Ethics approval and consent to participate The study received ethical approval from the Ethical Review Board of the Scientific and Ethical Review Office of EPHI (EPHI-IRB-542-2023). Participants who tested positive were treated according to the national treatment guidelines. Additionally, the results from the Sysmex XN-31 were not used to inform clinical management decisions for individual patients. Consent for publication Not applicable. Competing interests This study was partially supported by a reagent grant and Sysmex XN-31 Machine from Sysmex South Africa (PTY) Limited. Funding This work was partially supported by the Sysmex South Africa (PTY) Limited and Pyramid Pharma PLC in Addis Ababa, Ethiopia by providing Sysmex XN-31 reagents and Sysmex XN-31 automated analyzer. However, these companies did not have any role in the design or execution of the study or in the analysis and interpretation of the data. The full independence and integrity of our research outcomes was highly ensured. Author Contribution AdA, AdW, AT and GT conceived and designed the experiments, YW, MB, and HD performed the experiments, AdA and SY analysed the data, AdA, GT, AdW, SY, and DW wrote the paper, AT, BG, AA, DM, GTad, AG, GA, HS, MH, GT and GTo reviewed the paper. All authors read and approved the final manuscript. Acknowledgement The research team would like to thank Oromia Health Bureau for its full support and all study participants at Shashamane and Meki hospitals; all staff in Shashamane and Meki hospitals for their support throughout the study period; Adama Regional Lab staff for providing expert microscopy reading. Gratitude is expressed to the health facilities involved and the Ethiopian Ministry of Health for their support and cooperation. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References World malaria. report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization; 2024. Taffese HS, Hemming-Schroeder E, Koepfli C, et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infect Dis Poverty. 2018;7:103. https://doi.org/10.1186/s40249-018-0487-3 . World Health Organisation. WHO fact sheet on malaria. Available from: http://www.who.int/malaria . 2022. Ethiopian Federal Ministry of Health (FMoH). National malaria elimination roadmap. Addis Ababa: Ethiopian Federal Ministry of Health; 2016. Feleke SM, Reichert EN, Mohammed H, et al. Plasmodium falciparum is evolving to escape malaria rapid diagnostic tests in Ethiopia. Nat Microbiol. 2021;6:1289–99. https://doi.org/10.1038/s41564-021-00962-4 . WHO. Protocol for estimating the prevalence of pfhrp2/pfhrp3 gene deletions among symptomatic falciparum patients with false-negative RDT results. Geneva: World Health Organization; 2018. https://www.who.int/docs/default-source/malaria/mpac-documentation/mpac-oct2017-hrp2-deletion-protocol-session4.pdf?sfvrsn=2c9dfaf4_2 . Singh A, Narang V, Sood N, Garg B, Gupta VK. Malaria Diagnosis Using Automated Analysers: A Boon for Hematopathologists in Endemic Areas. J Clin Diagn Res. 2015;9(10):EC05–8. Briggs C, Da Costa A, Freeman L, Aucamp I, Ngubeni B, Machin SJ. Development of an automated malaria discriminant factor using VCS technology. Am J Clin Pathol. 2006;126(5):691–8. 10.1309/0PL3-C674-M39D-6GEN . Sunilkumar K, Naik P. Usefulness of automated hematology analyzer Sysmex XN 1000 in detection of malaria. Indian J Path Onc. 2016;3:658–61. Mohapatra S, Samantaray JC, Arulselvi S, Panda J, Munot K, Saxena R. Automated detection of malaria with haematology analyzer Sysmex XE-2100. Indian J Med Sci. 2011;65:26–31. McMorrow ML, Aidoo M, Kachur SP. Malaria rapid diagnostic tests in elimination settings-can they find the last parasite? Review. Clin Microbiol Infect. 2011;17:1624–31. Kagaya W, Takehara I, Kurihara K, et al. Potential application of the haematology analyser XN-31 prototype for field malaria surveillance in Kenya. Malar J. 2022;21:252. https://doi.org/10.1186/s12936-022-04259-7 . Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into sample size calculation for sensitivity and specificity. Acad Emerg Med. 1996;3:895–900. Tadesse F, Fogarty AW, Deressa W. Prevalence and associated risk factors of malaria among adults in East Shewa Zone of Oromia Regional State, Ethiopia: a cross-sectional study. BMC Public Health. 2017;18(1):25. 10.1186/s12889-017-4577-0 . WHO. QA manual for malaria microscopy. 2nd Edition. Geneva; 2015. IBM Corp. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp; 2020. Medical Statistical Calculator. Medical Statistical Calculator, Version 23. [Computer software]. Publisher/Developer; 2023. Tenny S, Hoffman MR. Prevalence. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK430867/ Bossuyt P, Reitsma JB, Bruns DE, et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin Chem. 2003;49:7–18. Ngasala B, Bushukatale S. Evaluation of malaria microscopy diagnostic performance at private health facilities in Tanzania. Malar J. 2019;18:375. https://doi.org/10.1186/s12936-019-2998-1 . Santana-Morales MA, Afonso-Lehmann RN, Quispe MA, et al. Microscopy and molecular biology for the diagnosis and evaluation of malaria in a hospital in a rural area of Ethiopia. Malar J. 2012;11:199. 10.1186/1475-2875-11-199 . M'baya B, Mfune T, Samon A, Hwandih T, Münster M. Evaluation of the Sysmex XN-31 automated analyser for blood donor malaria screening at Malawi Blood Transfusion Services. Vox Sang. 2022;117(3):346–53. 10.1111/vox.13208 . Tegegn G, Gnanasekaren N, Gadisa E, et al. Comparative assessment of microscopy, malaria rapid diagnostic test and polymerase chain reaction as malaria diagnostic tools in Adama Woreda, East shoa zone of Ethiopia: a cross-sectional study. BMC Infect Dis. 2024;24:1363. https://doi.org/10.1186/s12879-024-10173-x . Eisenberg M. Accuracy and predictive values in clinical decision making. Rev Clevel Clin J Med. 1995;62(5):311–6. Yalley AK, Ocran J, Cobbinah JE, et al. Advances in Malaria Diagnostic Methods in Resource-Limited Settings: A Systematic Review. Trop Med Infect Dis. 2024;9:190. Maltha J, Gillet P, Jacobs J. Malaria rapid diagnostic tests in endemic settings. Clin Microbiol Infect. 2013;19(5):399–407. 10.1111/1469-0691.12151 . Zuluaga-Idárraga L, Rios A, Sierra-Cifuentes V, et al. Performance of the hematology analyzer XN-31 prototype in the detection of Plasmodium infections in an endemic region of Colombia. Sci Rep. 2021;11:5268. https://doi.org/10.1038/s41598-021-84594-y . Mulatie Z, Kelem A, Chane E, et al. Diagnostic role of Sysmex hematology analyzer in the detection of malaria: A systematic review and meta-analysis. PLoS ONE. 2024;19(9):e0296766. https://doi.org/10.1371/journal.pone.0296766 . Gatton ML, Chaudhry A, Glenn J, et al. Impact of Plasmodium falciparum gene deletions on malaria rapid diagnostic test performance. Malar J. 2020;19:392. https://doi.org/10.1186/s12936-020-03460-w . Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9255056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":614821284,"identity":"ad0e620a-94be-4428-a492-f461c2e381d3","order_by":0,"name":"Adugna 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Geremew","middleName":"","lastName":"Tasew","suffix":""}],"badges":[],"createdAt":"2026-03-28 20:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9255056/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9255056/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106311476,"identity":"4a4b66f8-0300-4a1a-9aea-6b5aeef37397","added_by":"auto","created_at":"2026-04-07 10:31:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy sites. The blue area represents the administrative map of Meki city, while the green area indicates the administrative area of Shashemene city. The locations of hospitals in both administrative areas are marked in H. The map was built using the free and open source QGIS software version 3.36.3 (QGIS Development Team (2024). QGIS Geographic Information System, version 3.36.3. Open-Source Geospatial Foundation Project. https://qgis. org) and shapefiles were obtained from the free and open-source site \u003c/em\u003e\u003ca href=\"https://data.humdata.org/\"\u003e\u003cem\u003ehttps://data.humdata.org\u003c/em\u003e\u003c/a\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9255056/v1/2c969ad334e8dbf7301f9f3d.jpg"},{"id":106311474,"identity":"e6463b90-88b2-4405-8d0d-8498714f79e5","added_by":"auto","created_at":"2026-04-07 10:31:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic diagram showing workflow at both study sites. At each site, capillary blood samples and 3 mL of whole blood in EDTA tubes were collected. \u0026nbsp;The capillary blood was stained on clean and barcoded slides, stained with Giemsa, and examined as part of routine malaria diagnosis at hospital laboratories. The stained slides were then kept for expert microscopists at the Adama Public Health and Referral Laboratory Center for further analysis. Rapid Diagnostic Tests and Sysmex XN-31 analyses were conducted in the same room at both locations to eliminate potential test bias among technologists. Concurrently, leftover whole blood collected in EDTA tubes was transported to the EPHI for qPCR testing.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9255056/v1/e75dfad7a0c611c3166e89e2.jpg"},{"id":106311475,"identity":"316ad5d5-a07f-4d74-92b8-7e75a77f9f37","added_by":"auto","created_at":"2026-04-07 10:31:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScatter diagram. The scatter plot shows the relationship between Sysmex XN-31 generated Parasite Density (x-axis) and Expert Microscopy Parasite Density (y-axis). The data points appear to follow two distinct patterns: A dense cluster of points (shown in green and orange) near the origin, with relatively low values for both measurements. A few scattered points (in orange) extending outward, with some showing higher values on both axes. The plot suggests some disagreement between the two measurement methods at higher parasite density values, with the Expert Microscopy measurements sometimes showing considerably higher values than the Sysmex XN-31 measurements for the same samples.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9255056/v1/b74cd4c40cbeb94abdc801b0.jpg"},{"id":106403732,"identity":"affe9def-1a90-4741-b23a-185dc87ce15c","added_by":"auto","created_at":"2026-04-08 09:14:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9255056/v1/88a147ce-9b50-4438-a3be-d39a4ab0fb40.pdf"},{"id":106311473,"identity":"cfdefbe4-2ab9-4a88-9199-519dd6b06890","added_by":"auto","created_at":"2026-04-07 10:31:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":119118,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9255056/v1/d9fddc57726ce3e63f597df4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic performance of Sysmex XN-31 automated hematology analyzer compared to microscopy and PCR for detecting and quantifying malaria parasites in clinical settings of Oromia Regional State, Ethiopia","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria continues to pose significant public health challenges in tropical and subtropical regions, with the highest burden recorded in five countries according to WHO 2024, including Nigeria, Democratic Republic of the Congo, Uganda, Ethiopia, and Mozambique (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEthiopia has played a crucial role in significantly reducing malaria-related morbidity and mortality since the introduction of artemisinin-based combination therapy (ACT), indoor residual spraying (IRS), and the widespread distribution of long-lasting insecticidal nets (LLINs) in 2005 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, malaria incidence has been steadily increasing since 2019 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffective case management is essential for reducing severe cases and fatalities associated with malaria. It relies on accurate diagnosis and prompt treatment. Additionally, the administration of anti-gametocidal medications and radical therapy, which help prevent the spread of the infection, requires precise diagnosis and species identification. In Ethiopia, health extension workers are expected to use malaria rapid diagnostic tests (RDTs) to diagnose more than 70% of malaria cases, particularly in remote areas where most cases are believed to occur (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a significant challenge with the rapid diagnosis of malaria due to the occurrence of false negatives. A large-scale survey conducted in 2020/2021 across the Afar, Amhara, Benishangul Gumuz, Gambella, and Oromia regions revealed a false-negative rate of 6.67%, which was attributed to deletions in the hrp2/3 gene (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This study indicated that the population-level prevalence of RDT false negatives due to hrp2/3 gene deletions exceeds the World Health Organization's suggested threshold of 5% for transitioning to alternative diagnostic methods (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoutine diagnostic methods like microscopy and rapid diagnostic tests (RDTs) often have limitations with accuracy, especially when dealing with low-parasite density cases. As medical diagnostics continue to advance, new techniques for identifying and quantifying malaria parasites are being developed consistently. Automating the malaria detection process could significantly enhance both speed and accuracy. Recently, the Sysmex XN-31, a novel automated hematology analyzer, has demonstrated the ability to rapidly and accurately diagnose malaria with high sensitivity and specificity (\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This sensitive diagnostic tool has the potential to bridge the capacity gap in healthcare facilities that experience a high volume of patients.\u003c/p\u003e \u003cp\u003eThis study evaluated the diagnostic accuracy and operational feasibility of the Sysmex XN-31 automated hematology analyzer for the detection and quantification of \u003cem\u003ePlasmodium\u003c/em\u003e species compared to microscopy and PCR in two public hospitals in Ethiopia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA facility-based cross-sectional study was conducted from April to September 2024 in two public hospitals located in the East Shoa and West Arsi zones of Oromia Regional State (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Study sites were selected based on their proximity to Addis Ababa and the presence of both \u003cem\u003ePlasmodium\u003c/em\u003e species, \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e. Shashamene Comprehensive Specialized Hospital is located in Shashemane town, West Arsi Zone, 250 km from Addis Ababa, at 7\u0026ordm; 12' North latitude and 38\u0026ordm; 36' East longitude, with an elevation of 2010 meters above sea level. Meki Primary Hospital, situated in the Great Rift Valley, is located in Meki town, East Shewa Zone, Oromia Region, at 8\u0026deg;9\u0026prime;N 38\u0026deg;49\u0026prime;E and an elevation of 1636 meters above sea level. Meki serves as the administrative center of Dugda district. Although both \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e malaria have been reported, district data indicate that \u003cem\u003eP. vivax\u003c/em\u003e is the predominant species in both areas. The region experiences major malaria transmission from September to mid-December, following the main rainy season (June to August), and minor transmission from April to May, after the short rainy season (February to March).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated based on sensitivity using Buderer\u0026rsquo;s formula (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eZ1-α/2 (standard normal deviate corresponding to the specified size of the critical region (α)\u0026thinsp;=\u0026thinsp;1.96\u003c/p\u003e \u003cp\u003eSN (anticipated sensitivity), we anticipate 98% sensitivity of the XN-31 machine\u0026thinsp;=\u0026thinsp;0.98\u003c/p\u003e \u003cp\u003ePrevalence= Prevalence of malaria in the nearby district (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) was 25% = 0.25\u003c/p\u003e \u003cp\u003eL (absolute precision desired on either side of sensitivity)\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;~\u0026thinsp;\u003cb\u003en\u0026thinsp;=\u0026thinsp;400\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTherefore, the computed minimum sample size is 334 with ~\u0026thinsp;20% contingencies.\u003c/p\u003e\n\u003ch3\u003eSampling procedure and study population\u003c/h3\u003e\n\u003cp\u003ePurposive sampling was used to recruit study participants. Those clinically suspected for malaria and fulfill the inclusion and exclusion criteria from each site were included as part of study population. Participants aged five years and older were included in this study. Clinicians in charge of the outpatient departments explained the study to the patients and the parents or guardians of the children. Those who met the enrollment criteria were included in the study. Children aged 12 to 17 years provided assent, and consent was obtained from their parents or guardians. For participants over 18 years of age, consent was sought directly from them. The process of patient recruitment and flow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003e A finger prick blood was obtained from each study participant for RDT, thick and thin blood film preparation. At the same time four ml of whole blood was collected in ethylene diamine tetra-acetic acid (EDTA) tubes for later testing by Sysmex XN-31, RDT, and qPCR.\u003c/p\u003e\n\u003ch3\u003eGiemsa staining and Microscopic Examination\u003c/h3\u003e\n\u003cp\u003eAfter the blood film was well dried, the thin film was first fixed with methanol and then both thick and thin films were stained with 10% Giemsa stain. Next, the film was dried again and examined under the oil immersion objective. To determine malaria positivity or negativity, at least 200 high-power fields of the thick film were examined following the WHO guidelines (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Parasite counts were performed on slides that tested positive for \u003cem\u003eP. falciparum\u003c/em\u003e, counting asexual parasites and gametocytes from the stained blood smears. All slides were kept for later confirmation by an expert microscopist.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMalaria detection using Sysmex XN-31 automated hematology analyzer\u003c/h2\u003e \u003cp\u003eWhole blood samples in EDTA tubes were used for the Sysmex XN-31 analyzer (Sysmex corporation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://qgis\" target=\"_blank\"\u003ewww.sysmex.co.jp\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.sysmex.co.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Japan). The samples were processed within 24 hours of collection. A separate room was designated for the Sysmex XN-31 machine. At the reception, the collected samples were double-blinded, and only designated sample runners transported them to the Sysmex XN-31 reading room. After receiving proper training, all samples were analyzed using the sampler mounted on the Sysmex XN-31 analyzer to minimize mixing errors. The QC was performed every day before starting daily activity using control materials supplied by the manufacturer using X-bar Control method.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRapid diagnostic test\u003c/h3\u003e\n\u003cp\u003eRapid diagnostic testing (RDT) was performed in accordance with the manufacturer\u0026rsquo;s instructions using the Abbott Bioline Malaria Ag P.f/P.v test (Abbot, US). This assay detects the HRP-2 antigen of \u003cem\u003eP. falciparum\u003c/em\u003e and the \u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase (pLDH) of \u003cem\u003eP. vivax.\u003c/em\u003e For each test, five microliters of EDTA-anticoagulated blood were introduced into the sample port. Subsequently, three drops of assay diluent were added, and the test was allowed to develop. Results were interpreted 15 minutes after the addition of the assay diluent.\u003c/p\u003e\n\u003ch3\u003eParasite detection by PCR\u003c/h3\u003e\n\u003cp\u003eBlood samples collected in EDTA were used for genomic DNA extraction, following the protocols outlined in the manufacturers' DNA extraction kits (AJ Innuscreen GmbH, Berlin, Germany). Real-time polymerase chain reaction (qPCR) was performed to amplify the small subunit of ribosomal RNA for the genus-level detection of \u003cem\u003ePlasmodium\u003c/em\u003e, \u003cem\u003eP. vivax\u003c/em\u003e specific 18SrRNA for \u003cem\u003eP. vivax\u003c/em\u003e and the var gene's acidic terminal sequence for \u003cem\u003eP. falciparum\u003c/em\u003e detection.\u003c/p\u003e \u003cp\u003eTo control for the quality of the DNA extraction and qPCR amplification, the human \u003cem\u003eRNase P\u003c/em\u003e gene was used as an internal control. TaqMan fluorescence-based DNA amplification and detection was executed using the Applied Biosystem QuantStudio 5 Real-time PCR system (Thermo Fisher, USA). Each experiment included positive controls using 3D7 DNA, and negative controls using nuclease-free water.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData management and Analysis\u003c/h2\u003e \u003cp\u003eData related to sociodemographic, RDT and Microscopy results were captured using data collection form developed by experts using Kobo Collect digital data collection platform. The raw comma-separated values (CSV) data generated from the QuantStudio 5 Real-time PCR system and Sysmex XN-31 analyzer were archived according to the training provided to the operators. Data from each week and each session were saved on the provided USB stick. All the datasets exported were cleaned and merged into one dataset before analysis. Data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and Medical Statistical Calculator, Version 23 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) The primary outcome measure for this analysis was the performance of the Sysmex XN-31 automated hematology analyzer for detection and quantification of malaria in a clinical setting. We calculated sensitivity and specificity, along with their 95% confidence intervals, as well as positive and negative predictive values against the gold standard expert microscopy and reference qPCR methods. Given that small variations in the prevalence of malaria can significantly impact on the interpretation of predictive values (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), positive predictive values (PPVs) and negative predictive values (NPVs) were also estimated taking into consideration of a population prevalence of 5%, 25%, and 50% malaria infection. Agreement between different assays was determined by Kappa statistics. Kappa result was interpreted as follows: values\u0026thinsp;\u0026le;\u0026thinsp;0 as indicating no agreement and 0.01\u0026ndash;0.20 as slight, 0.21\u0026ndash;0.40 as fair, 0.41\u0026ndash; 0.60 as moderate, 0.61\u0026ndash;0.80 as substantial, and 0.81\u0026ndash;1.00 as almost perfect agreement. Expert microscope readings served as the gold standard, while PCR was used as the reference standard. Furthermore, the Sysmex XN-31 automated hematology analyzer's capability to quantify malaria parasites and stages was assessed against expert microscopic readings.\u003c/p\u003e \u003cp\u003eParticipants from two health facilities, representing various educational backgrounds and fields of study, were assessed for usability factors using a structured questionnaire. Operators assigned for Sysmex XN-31 received training on how to use and operate the Sysmex XN-31 machine. This study complied with Standards for Reporting of Diagnostic Accuracy guidelines (STARD) to improve the quality of reporting (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of Study Participants\u003c/h2\u003e\n \u003cp\u003eA total of 400 malaria suspected patients were included from two hospitals, with 200 participants from each location. The sociodemographic and clinical characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below. More than half of the respondents were female (53%). Additionally, the majority of participants, 127 (31.8%), were under 10 years old. The majority of the study participants had symptoms of muscle pains, sweats, and chills, while 167 (42%) had fever.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic and clinical characteristics of the study participants from Shashemene and Meki hospitals, Oromia, Ethiopia, 2024 (n\u0026thinsp;=\u0026thinsp;400).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95% CI (Lower, upper)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e212 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(48.0, 57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e188 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(42.0, 51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003eAge group, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e127 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(27.2, 36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e61 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(18.9, 27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e92 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(11.7, 18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e59 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(11.3, 18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e30 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(5.0, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026gt;=50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e31 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(4.9, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSweats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e25 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(3.9, 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e374 (93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(91.4, 96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e48 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(8.8, 15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e351 (88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(84.8, 91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMuscle pains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e26 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(4.0, 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e373 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(91.0, 95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNausea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e88 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(18.0, 26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e311 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(73.9, 82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e116 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(24.6, 33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e283 (71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(66.5, 75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eA temperature\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;37.5℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e232 (58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(53.3, 63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e167 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(37.0, 46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(0.2, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e394 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e(97.7, 99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of anemia and thrombocytopenia\u003c/h2\u003e\n \u003cp\u003eWe computed for the presence of anemia and thrombocytopenia among the study participants based on WHO criteria. Majority of the participants are healthy, but approximately 25\u0026ndash;30% of the total participants across all age groups suffer from some form of anemia with both male and female approximately affected in equal proportion. Severe anemia is consistent across genders, affecting roughly 1 in 10 individuals (10.6% of males; 9.4% of females). Thrombocytopenia was more prevalent in males than females (36.6% vs 22.6%) (Table S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePlasmodium parasite detection by different methods\u003c/h2\u003e\n \u003cp\u003eThe positivity rate was highest with PCR (22.2%), followed closely by the Sysmex XN-31 (21%). Minor discrepancies were observed in species identification between the Sysmex XN-31 and both field and expert microscopist readings, while PCR and RDT results were comparable. A notable variance in mixed infection detection was observed: field microscopists reported a mixed infection rate of 1.5%, which was double that of expert microscopists (0.8%), indicating a potential for over-identification of mixed species in field settings. The Sysmex XN-31 detected a higher rate of mixed infections (2.8%), whereas PCR detected none, suggesting possible over-identification by the Sysmex XN-31. Overall, the Sysmex XN-31 outperformed both expert and field microscopist in detection rates, identifying 7 to 9 additional cases, respectively (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThere was substantial agreement (89.8%) between field microscopist and the Sysmex XN-31 in detecting plasmodium parasites, with a Kappa value of 0.68. The agreement between the Sysmex XN-31 and RDT was also nearly perfect at 91.2% in detecting plasmodium parasites, and between Sysmex and PCR, it was 93.8% (Table S2 and Fig S1).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of \u003cem\u003ePlasmodium\u003c/em\u003e-positive cases and species types by various methods in Shashemene and Meki hospitals, Oromia, Ethiopia, 2024 (n\u0026thinsp;=\u0026thinsp;400).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiagnostic methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSysmex XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e316 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(75.0, 83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e84 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(17.0, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExpert Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e323 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(76.9, 84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e77 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(15.4, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e311 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(73.7, 81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e89 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(18.2, 26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e335 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(80.0, 87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e65 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(12.6, 19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eField Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e325 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(77.4, 85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e75 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(14.9, 22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult by species type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExpert Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(0.0, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e323 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(76.9, 84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e25 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(3.9, 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e49 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(9.0, 15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e311 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(73.7, 81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e31 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(5.0, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e58 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(11.0, 18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(0.0, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e335 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(80.0, 87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e31 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(5.0, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e33 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(5.6, 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eField Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(0.3, 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e325 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(77.4, 85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(3.7, 8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(8.2, 14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSysmex XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(0.0, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(1.2, 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e314 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(74.5, 82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e30 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(4.9, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e43 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(7.7, 13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eDiagnostic Accuracy of Sysmex XN-31\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Sysmex XN-31 when compared to expert microscopist exhibited the highest sensitivity (93.5%) and strong specificity (96.6%), outperforming even PCR in this specific comparison. Using PCR as a reference, the Sysmex XN-31 maintained the highest sensitivity (84.3%) among all methods, surpassing expert microscopist by 4.5%. Across both reference and gold standards, RDTs yielded the lowest sensitivity (64% and 72.7% against PCR and expert microscopist, respectively), while field microscopist demonstrated a significant drop in sensitivity (66.3%) when compared against PCR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity, specificity, PPV and NPV of each diagnostic method using the results of the expert microscopist as a gold standard and PCR as reference.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePerformance of a diagnostic test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eExpert microscopist as a standard\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePCR as a reference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSysmex XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e93.5 (88.0, 99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e84.3 (80.7, 87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e96.6 (94.2, 98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e97.0(95.3, 98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e85.7 (78.2, 93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e89.3(86.2, 92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e98.6 (97.0, 99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95.6(93.5, 97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e92.2 (86.2, 98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e94.9 (91.9, 96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e79.8 (71.4, 88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e98.3 (96.5, 99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExpert Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e79.8(75.8, 83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e98 (96.6, 99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e92.2(89.3, 94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e94.4(92.0, 96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72.7 (62.8, 82.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e64.0 (59.3, 68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e97.5 (95.4, 99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e97.4 (95.7, 98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e86.2 (77.8, 94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e87.7 (84.3, 90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e94.3 (91.0, 96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e90.5(87.3, 93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eField Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e77.9 (68.7, 87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e66.3 (61.6, 70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e95.8 (93.0, 97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e94.9 (92.6, 96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e80.0 (70.9, 89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e78.7(74.5, 82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e95.3 (92.4, 97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e90.8(87.9, 93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Sysmex XN-31 demonstrated a high reliability of NPV, up to 98.6%, in identifying individuals who do not have the condition compared to field microscopy and RDT. Furthermore, the NPV showed that the adjusted hypothetical community prevalence changed minimally and the PPV increased substantially as the hypothetical community prevalence increased. While field microscopy and RDT showed significant decreases as the hypothetical community prevalence increased, they showed less reliability at this time (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHypothetical malaria prevalence at different prevalence rates for Sysmex XN-31 analyzer, RDT and field microscopist against expert microscopist and qPCR.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\n \u003cp\u003eMalaria detection by Sysmex XN-31 at different hypothetical prevalence rate against expert microscopist as a gold standard\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMetrics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAdjusted PPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eAdjusted NPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSysmex XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eField Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalaria detection by Sysmex XN-31 at different hypothetical prevalence rate against qPCR as a reference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSysmex XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eField Microscopist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eParasite quantifications\u003c/h2\u003e\n \u003cp\u003eParasite counts from the Sysmex XN-31 and expert microscopist for \u003cem\u003eP. falciparum\u003c/em\u003e showed clear differences in both density and statistical correlation. The Sysmex XN-31 gave a much higher average density (24,446.34) than expert microscopist (9,847.8). This result suggests that the Sysmex XN-31 may overcount or use a different sensitivity threshold than human experts. Because the density measurements are so different, these two diagnostic methods should not be used interchangeably in clinical or research settings without further calibration. The Spearman correlation coefficient was 0.214 with a p-value of 0.184, indicating only a weak positive relationship (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eTest Usability of the XN-31 under investigations\u003c/h2\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003eDescription of study Participants\u003c/h2\u003e\n \u003cp\u003eTo assess the usability of the test in a near-patient environment, a total of 30 operators participated in the study, of whom 24 were males. The lowest educational qualification among the participants was a diploma, while the highest was a Master of Science degree. Details of the participants are presented in supplement (Table S3).\u003c/p\u003e\n \u003cp\u003eUsability testing of the Sysmex XN-31 showed that laboratory staff were satisfied with the device and found it efficient to use. The analyzer worked well in clinical settings, even for users with different levels of training. The study included 30 participants split into two groups: 12 people (40%) received full usability training, while 18 people (60%) had only a basic orientation. Most participants said the device did not require advanced skills. They found interpreting results straightforward, and any errors were easy to fix. Both daily and monthly maintenance tasks were rated as easy. Reagent management, storage, and quality control were considered manageable, and the laboratory setup was suitable. The patient barcode system was also described as user-friendly (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of usability testing of the Sysmex XN-31between trained and non-trained staff\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\n \u003cp\u003eTrained on Sysmex-XN-31\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsability questions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStrongly disagree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDisagree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAgree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eStrongly agree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eStrongly disagree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eDisagree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eAgree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eStrongly agree n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1.Adequate training/orientation provided before operational of analyzer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e8(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2. Does not require high level of skill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3. Easy start up analyzer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4. Daily maintenance Easy to execute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e7(38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5. Monthly maintenance Easy to execute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6. QC management Easy to execute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7. Reagent management is easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13(72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e8. Lab infrastructure suitable for XN-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13(72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e9. Barcode easy to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e11(61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10. Barcode patient tracking is easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e11. Reagent storage manageable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13(72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e12. Reagent life span acceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e14(77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e13. Analyzer easy to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e11(61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e14. TAT was fast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13(72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e15.Error easily resolved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e3(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e16. Interruption of results was easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study generated evidence on the performance of Sysmex XN-31 hematology analyzer in detecting infected RBCs by malaria parasites and quantifying them compared against expert microscopy, the gold standard and qPCR as reference test in Ethiopia for the first time. The analyzer demonstrated a high sensitivity of 93.5%, specificity of 96.6%, a positive predictive value (PPV) of 85.7%, and a negative predictive value (NPV) of 98.6% against the gold standard expert microscopy. These results indicate that the Sysmex XN-31 analyzer provides relatively accurate and reliable alternative to traditional diagnostic methods, offering a robust tool for malaria detection in regions with high malaria burden.\u003c/p\u003e \u003cp\u003eIn comparison to field microscopy, which has long been the gold standard for malaria diagnosis, the Sysmex XN-31 malaria analyzer outperformed in sensitivity (93.5% vs. 77.9%) than other test methods and showed comparable specificity (96.6% vs. 95.8%). These findings align with previous studies that highlighted the limitations of microscopy, such as the need for skilled personnel, the subjective nature of parasite detection, and its reduced sensitivity at low parasite densities (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). While microscopy remains widely used due to its availability and affordability, its lower sensitivity and difficulty in detecting submicroscopic infections make it less suitable for comprehensive malaria surveillance, especially in elimination targeted endemic regions with mixed species infections (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The Sysmex XN-31 analyzer's higher sensitivity offers the advantage of detecting malaria cases that might otherwise be missed by microscopy, improving diagnostic accuracy in settings where asymptomatic or low-density malaria infections are prevalent.\u003c/p\u003e \u003cp\u003eCompared to PCR, the reference standard for malaria diagnosis, the Sysmex XN-31 analyzer demonstrated a higher sensitivity of 84.3% than other diagnostic methods, with a specificity of 97%, NPV of 95.6%, and PPV of 89.3%. The higher sensitivity of Sysmex XN-31 suggests that it may be more effective at detecting lower parasitemia levels, which is particularly important for epidemiological surveillance and the monitoring of submicroscopic infections (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Although PCR has the advantage of providing molecular-level diagnosis, its laboratory requirements, cost, and time constraints make it less suitable for routine use in field settings, particularly in resource-limited areas (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study demonstrated that Sysmex XN-31 analyzer indicted higher PPV compared to field microscopy, with rates of 89.3% versus 78.7%. This indicates that the Sysmex XN-31 is more reliable in identifying true positive cases. This finding will have several implications. Primarily by significantly decreasing the chances of false-positive results unnecessary treatment can be avoided. This in turn avoids the consequences on the patient\u0026rsquo;s health, reduces the healthcare cost as well as potential drug resistance. This reliability is especially important in clinical decision-making and is particularly valuable in low-prevalence, or hypo-endemic areas, where diagnosing malaria can be particularly challenging due to the reduced population prevalence affecting the PPV of tests (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen comparing the Sysmex XN-31 analyzer to malaria rapid diagnostic tests (RDTs), the analyzer demonstrated a higher sensitivity (93.5% vs. 72.7%) and a comparable specificity (96.6% vs. 97.5%). RDTs have the advantage of being simple, fast, and easy to use in resource-limited settings, making them highly accessible in endemic areas (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, their lower sensitivity, especially in detecting low-density infections, and potential for false-negative results limit their effectiveness in diagnosing asymptomatic malaria (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The higher sensitivity of the Sysmex XN-31 analyzer, in contrast, reduces the likelihood of false negatives and improves the detection of cases in the early stages of infection. Additionally, while RDTs are useful for on-site diagnosis, they are limited by their inability to quantify parasitemia or differentiate between malaria species, both of which are strengths of the Sysmex XN-31 analyzer.\u003c/p\u003e \u003cp\u003eThe Sysmex XN-31 analyzer offers several advantages over routine diagnostic methods. First, its high sensitivity and specificity, as well as the ability to quantify parasitemia, make it a valuable tool for detecting low-density and submicroscopic infections, which may be missed by field microscopy and RDTs (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Second, the Sysmex XN-31 analyzer is automated, can analyze 55 patients within one hour, reducing the reliance on skilled personnel and minimizing human error, which are significant limitations in microscopy-based diagnosis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Third, its ability to provide rapid and accurate results in a standard laboratory setting offers an efficient alternative to PCR, which is resource-intensive and requires specialized laboratory infrastructure.\u003c/p\u003e \u003cp\u003eFurthermore, the Sysmex XN-31 analyzer's capacity to address challenges related to gene deletion, such as the detection of \u003cem\u003eP. falciparum\u003c/em\u003e with deletions in the histidine-rich protein 2 (HRP2) gene, is a notable advantage over RDTs. Gene deletions in the HRP2 gene are a known cause of false-negative RDT results, as many RDTs rely on HRP2 for detection (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In Ethiopia, a multisite gene deletion study found a prevalence of 9.7% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), exceeding the WHO threshold and indicating a strong evolutionary pressure on malaria diagnostics. Consequently, the Ministry of Health is transitioning from hrp2-based rapid diagnostic tests (RDTs) to non-hrp2-based RDTs. The Sysmex XN-31 analyzer, which uses a different method to detect malaria parasites, is less likely to be affected by such deletions, making it a more reliable diagnostic tool in regions where HRP2 deletions are prevalent. This advantage could be crucial for improving malaria surveillance and ensuring more accurate diagnoses in areas with evolving parasite gene deletion problems.\u003c/p\u003e \u003cp\u003eOverall, training on the Sysmex-XN-31 generally enhances user perceptions across various aspects of usability. Trained users consistently rate the analyzer more favorably compared to untrained users, indicating that the training has a positive impact on user experience. Areas like QC management, reagent management, and ease of use show particularly strong improvements due to training. These insights can guide targeted improvements in training programs and highlight areas where additional support may further enhance user satisfaction and efficiency.\u003c/p\u003e \u003cp\u003eIn summary, the Sysmex XN-31 automated hematology analyzer offers promising performance in malaria detection, with a higher sensitivity compared to PCR, field microscopy, and RDTs. Its automated nature, coupled with its ability to quantify parasitemia and detect submicroscopic infections, provides significant advantages over traditional diagnostic methods. Furthermore, its resilience to HRP2 gene deletions presents an important benefit in regions where these deletions are common. Given its high sensitivity and specificity, obtaining the results as part of routine CBC with short TAT and less need for malaria expert microscopists, the Sysmex XN-31 analyzer could play a key role in enhancing malaria diagnosis and surveillance in endemic areas, ultimately contributing to more effective malaria control and elimination efforts. The Sysmex XN-31 could also be important in a blood bank setting where malaria screening and anemia testing are necessary for every donor. However, there are some practical limitations when using Sysmex XN-31. The first is its limited widespread adoption due to the initial investment of the device, the use of a constant electricity supply, lack of portability, and the requirement of venous blood samples. Further studies at different geographical sites should explore the potential use of Sysmex XN-31 automated analyzers for enhanced malaria surveillance and control as well as for the use of monitoring therapeutic efficacy study in Ethiopia.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study received ethical approval from the Ethical Review Board of the Scientific and Ethical Review Office of EPHI (EPHI-IRB-542-2023). Participants who tested positive were treated according to the national treatment guidelines. Additionally, the results from the Sysmex XN-31 were not used to inform clinical management decisions for individual patients.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThis study was partially supported by a reagent grant and Sysmex XN-31 Machine from Sysmex South Africa (PTY) Limited.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was partially supported by the Sysmex South Africa (PTY) Limited and Pyramid Pharma PLC in Addis Ababa, Ethiopia by providing Sysmex XN-31 reagents and Sysmex XN-31 automated analyzer. However, these companies did not have any role in the design or execution of the study or in the analysis and interpretation of the data. The full independence and integrity of our research outcomes was highly ensured.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAdA, AdW, AT and GT conceived and designed the experiments, YW, MB, and HD performed the experiments, AdA and SY analysed the data, AdA, GT, AdW, SY, and DW wrote the paper, AT, BG, AA, DM, GTad, AG, GA, HS, MH, GT and GTo reviewed the paper. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The research team would like to thank Oromia Health Bureau for its full support and all study participants at Shashamane and Meki hospitals; all staff in Shashamane and Meki hospitals for their support throughout the study period; Adama Regional Lab staff for providing expert microscopy reading. Gratitude is expressed to the health facilities involved and the Ethiopian Ministry of Health for their support and cooperation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld malaria. report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaffese HS, Hemming-Schroeder E, Koepfli C, et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. 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Malar J. 2019;18:375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-019-2998-1\u003c/span\u003e\u003cspan address=\"10.1186/s12936-019-2998-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantana-Morales MA, Afonso-Lehmann RN, Quispe MA, et al. Microscopy and molecular biology for the diagnosis and evaluation of malaria in a hospital in a rural area of Ethiopia. Malar J. 2012;11:199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1475-2875-11-199\u003c/span\u003e\u003cspan address=\"10.1186/1475-2875-11-199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM'baya B, Mfune T, Samon A, Hwandih T, M\u0026uuml;nster M. 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Clin Microbiol Infect. 2013;19(5):399\u0026ndash;407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1469-0691.12151\u003c/span\u003e\u003cspan address=\"10.1111/1469-0691.12151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuluaga-Id\u0026aacute;rraga L, Rios A, Sierra-Cifuentes V, et al. Performance of the hematology analyzer XN-31 prototype in the detection of Plasmodium infections in an endemic region of Colombia. Sci Rep. 2021;11:5268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-84594-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-84594-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulatie Z, Kelem A, Chane E, et al. Diagnostic role of Sysmex hematology analyzer in the detection of malaria: A systematic review and meta-analysis. PLoS ONE. 2024;19(9):e0296766. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0296766\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0296766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatton ML, Chaudhry A, Glenn J, et al. Impact of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e gene deletions on malaria rapid diagnostic test performance. Malar J. 2020;19:392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-020-03460-w\u003c/span\u003e\u003cspan address=\"10.1186/s12936-020-03460-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sysmex XN-31, Malaria, Complete Blood Count, automated hematology","lastPublishedDoi":"10.21203/rs.3.rs-9255056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9255056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria remains a significant public health challenge in Ethiopia. To achieve malaria free country with improved clinical care and support, innovative diagnostic tools are needed. The Sysmex XN-31 automated malaria analyzer, which detects malaria-infected red blood cells was assessed in clinical settings in Ethiopia. This study compared the performance of Sysmex XN-31 automated analyzer with microscopy and polymerase chain reaction (PCR) tests.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a facility-based cross-sectional study in two public hospitals from April to September 2024. Febrile patients suspected of malaria who visited the outpatient departments at Shashamene Comprehensive Specialized Hospital and Meki Primary Hospital were enrolled with consent. Finger prick and four milliliters of venous blood were collected in EDTA tubes from each study participant. The diagnostic performance of the Sysmex XN-31 hematology analyzer for malaria parasite detection and quantification was compared to expert microscopist and qPCR. We also evaluated the usability of the Sysmex XN-31 analyzer in near-patient settings at both sites. Statistical analysis was performed using SPSS 27 and Medical Statistical Calculator v23. Sensitivity, specificity, predictive values, and kappa statistics were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 400 suspected febrile cases, malaria positivity was 22.2% (n\u0026thinsp;=\u0026thinsp;89), 21% (n\u0026thinsp;=\u0026thinsp;84), 19.2% (n\u0026thinsp;=\u0026thinsp;77), 18.8% (n\u0026thinsp;=\u0026thinsp;75), and 16.2% (n\u0026thinsp;=\u0026thinsp;65) by PCR, Sysmex XN-31, expert microscopist, field microscopist, and RDT, respectively. The sensitivity and specificity of Sysmex XN-31 were 93.5% and 96.6%, respectively, compared with the expert microscopist. However, with PCR as the reference, Sysmex XN-31\u0026rsquo;s sensitivity dropped to 84.3%, with a slight increase in specificity to 97%. The Sysmex-XN-31 exhibited high negative predictive value (NPV) reliability when compared with field and rapid diagnostic test (RDT) results, based on hypothetical community malaria prevalence. We found strong agreement (89.9%) between Sysmex XN-31 and the field microscopist, with a kappa value of 0.68. The usability test of the Sysmex XN-31 by most participants indicated that errors could be resolved easily, interruptions were manageable, and the interpretation of results was straightforward.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe Sysmex XN-31 automated hematology analyzer offers high sensitivity and specificity compared to expert microscopist and PCR methods, significantly enhancing malaria surveillance and elimination efforts. It can detect and count malaria parasites while also providing a CBC. With a rapid turnaround time of 55 samples in one hour, it is much faster than traditional testing methods.\u003c/p\u003e","manuscriptTitle":"Diagnostic performance of Sysmex XN-31 automated hematology analyzer compared to microscopy and PCR for detecting and quantifying malaria parasites in clinical settings of Oromia Regional State, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 10:30:58","doi":"10.21203/rs.3.rs-9255056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T21:48:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29076640171366246405065059699087605021","date":"2026-05-01T11:02:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195739862619416620209533902795182051567","date":"2026-04-27T15:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T09:00:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T18:57:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T18:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-03-28T20:00:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"983220f2-4e99-4fa5-9ee6-d396f7ced8ba","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T21:48:07+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"29076640171366246405065059699087605021","date":"2026-05-01T11:02:03+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T09:09:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 10:30:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9255056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9255056","identity":"rs-9255056","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}